From 6e03a1dcdcb06f7ad4a69be028e136835645ed51 Mon Sep 17 00:00:00 2001 From: Samarty-1 Date: Sun, 27 Sep 2026 00:49:48 +0800 Subject: [PATCH 01/13] Add React dashboard, fix counting accuracy, start mobile app Dashboard (web/, built into src/countbone/api/static): - Upload with preview, live six-stage pipeline tracker with telemetry, results table with export, review drawer with recounts and undo, frame inspector with box overlay and timeline. - API: live progress + inspector.json (api/telemetry.py), /api/catalog, /api/runs/{id}/video, review recounts/reopen with audit trail, correct caching of the built dashboard. Backbone fixes (measured on 10 synthetic shelves: mean error 11.4% -> 0%): - Tracker now applies camera motion from frames the quality gate drops; before, every object in view at a dropped frame was counted twice. - Contour detector pads frames so cartons cut by the frame edge no longer leak their labels as UNKNOWN objects. - Frame timestamps use the container clock (VFR phone video drifted >1 s). - Demo writes browser-playable VP8 WebM. Mobile (mobile/, work in progress): Expo app scaffold and the capture analysis engine (blur calibrated to the backend quality gate within 1%, motion, light, tilt, distance guidance) with unit tests. Screens and camera integration are not built yet. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/ci.yml | 19 + .gitignore | 8 +- README.md | 46 +- docs/ARCHITECTURE.md | 15 +- mobile/.claude/settings.json | 5 + mobile/.gitignore | 41 + mobile/AGENTS.md | 41 + mobile/CLAUDE.md | 1 + mobile/LICENSE | 21 + mobile/app.json | 41 + mobile/assets/android-icon-background.png | Bin 0 -> 17549 bytes mobile/assets/android-icon-foreground.png | Bin 0 -> 78796 bytes mobile/assets/android-icon-monochrome.png | Bin 0 -> 4140 bytes mobile/assets/favicon.png | Bin 0 -> 1129 bytes mobile/assets/icon.png | Bin 0 -> 393493 bytes mobile/assets/splash-icon.png | Bin 0 -> 17547 bytes mobile/package-lock.json | 7850 +++++++++++++++++ mobile/package.json | 44 + mobile/src/analysis/analysis.test.ts | 174 + mobile/src/analysis/guidance.ts | 315 + mobile/src/analysis/metrics.ts | 463 + mobile/tsconfig.json | 12 + pyproject.toml | 6 +- src/countbone/api/app.py | 109 +- .../api/static/assets/index-IhesMQDT.css | 2 + .../api/static/assets/index-ylytWXzx.js | 18 + ...er-cyrillic-ext-wght-normal-BOeWTOD4.woff2 | Bin 0 -> 25960 bytes .../inter-cyrillic-wght-normal-DqGufNeO.woff2 | Bin 0 -> 18748 bytes ...inter-greek-ext-wght-normal-DlzME5K_.woff2 | Bin 0 -> 11232 bytes .../inter-greek-wght-normal-CkhJZR-_.woff2 | Bin 0 -> 18996 bytes ...inter-latin-ext-wght-normal-DO1Apj_S.woff2 | Bin 0 -> 85068 bytes .../inter-latin-wght-normal-Dx4kXJAl.woff2 | Bin 0 -> 48256 bytes ...nter-vietnamese-wght-normal-CBcvBZtf.woff2 | Bin 0 -> 10252 bytes ...s-mono-cyrillic-wght-normal-D73BlboJ.woff2 | Bin 0 -> 12108 bytes ...ains-mono-greek-wght-normal-Bw9x6K1M.woff2 | Bin 0 -> 9004 bytes ...-mono-latin-ext-wght-normal-DBQx-q_a.woff2 | Bin 0 -> 15196 bytes ...ains-mono-latin-wght-normal-B9CIFXIH.woff2 | Bin 0 -> 40404 bytes ...mono-vietnamese-wght-normal-Bt-aOZkq.woff2 | Bin 0 -> 7504 bytes src/countbone/api/static/index.html | 253 +- src/countbone/api/telemetry.py | 217 + src/countbone/cli.py | 18 +- src/countbone/demo.py | 53 +- src/countbone/pipeline.py | 2 + src/countbone/stages/capture.py | 17 +- src/countbone/stages/count.py | 14 +- src/countbone/stages/detect.py | 19 + src/countbone/store/db.py | 49 +- tests/test_api.py | 148 + tests/test_cli.py | 12 + tests/test_counting.py | 17 + tests/test_detect_identify.py | 21 + tests/test_pipeline.py | 47 + tests/test_plugins.py | 5 + web/README.md | 50 + web/index.html | 16 + web/package-lock.json | 2090 +++++ web/package.json | 30 + web/src/App.tsx | 76 + web/src/components/Sidebar.tsx | 144 + web/src/components/ui.tsx | 249 + web/src/features/inspector/FrameInspector.tsx | 550 ++ web/src/features/pipeline/PipelineTracker.tsx | 283 + web/src/features/results/CountsTable.tsx | 302 + web/src/features/results/MetricCards.tsx | 108 + web/src/features/review/ReviewDrawer.tsx | 594 ++ web/src/features/run/RunView.tsx | 249 + web/src/features/upload/UploadPanel.tsx | 295 + web/src/index.css | 94 + web/src/lib/api.ts | 288 + web/src/lib/cn.ts | 3 + web/src/lib/export.ts | 65 + web/src/lib/format.ts | 64 + web/src/lib/queries.ts | 69 + web/src/lib/route.ts | 80 + web/src/lib/status.ts | 122 + web/src/main.tsx | 23 + web/tsconfig.json | 20 + web/vite.config.ts | 22 + 78 files changed, 15727 insertions(+), 282 deletions(-) create mode 100644 mobile/.claude/settings.json create mode 100644 mobile/.gitignore create mode 100644 mobile/AGENTS.md create mode 100644 mobile/CLAUDE.md create mode 100644 mobile/LICENSE create mode 100644 mobile/app.json create mode 100644 mobile/assets/android-icon-background.png create mode 100644 mobile/assets/android-icon-foreground.png create mode 100644 mobile/assets/android-icon-monochrome.png create mode 100644 mobile/assets/favicon.png create mode 100644 mobile/assets/icon.png create mode 100644 mobile/assets/splash-icon.png create mode 100644 mobile/package-lock.json create mode 100644 mobile/package.json create mode 100644 mobile/src/analysis/analysis.test.ts create mode 100644 mobile/src/analysis/guidance.ts create mode 100644 mobile/src/analysis/metrics.ts create mode 100644 mobile/tsconfig.json create mode 100644 src/countbone/api/static/assets/index-IhesMQDT.css create mode 100644 src/countbone/api/static/assets/index-ylytWXzx.js create mode 100644 src/countbone/api/static/assets/inter-cyrillic-ext-wght-normal-BOeWTOD4.woff2 create mode 100644 src/countbone/api/static/assets/inter-cyrillic-wght-normal-DqGufNeO.woff2 create mode 100644 src/countbone/api/static/assets/inter-greek-ext-wght-normal-DlzME5K_.woff2 create mode 100644 src/countbone/api/static/assets/inter-greek-wght-normal-CkhJZR-_.woff2 create mode 100644 src/countbone/api/static/assets/inter-latin-ext-wght-normal-DO1Apj_S.woff2 create mode 100644 src/countbone/api/static/assets/inter-latin-wght-normal-Dx4kXJAl.woff2 create mode 100644 src/countbone/api/static/assets/inter-vietnamese-wght-normal-CBcvBZtf.woff2 create mode 100644 src/countbone/api/static/assets/jetbrains-mono-cyrillic-wght-normal-D73BlboJ.woff2 create mode 100644 src/countbone/api/static/assets/jetbrains-mono-greek-wght-normal-Bw9x6K1M.woff2 create mode 100644 src/countbone/api/static/assets/jetbrains-mono-latin-ext-wght-normal-DBQx-q_a.woff2 create mode 100644 src/countbone/api/static/assets/jetbrains-mono-latin-wght-normal-B9CIFXIH.woff2 create mode 100644 src/countbone/api/static/assets/jetbrains-mono-vietnamese-wght-normal-Bt-aOZkq.woff2 create mode 100644 src/countbone/api/telemetry.py create mode 100644 web/README.md create mode 100644 web/index.html create mode 100644 web/package-lock.json create mode 100644 web/package.json create mode 100644 web/src/App.tsx create mode 100644 web/src/components/Sidebar.tsx create mode 100644 web/src/components/ui.tsx create mode 100644 web/src/features/inspector/FrameInspector.tsx create mode 100644 web/src/features/pipeline/PipelineTracker.tsx create mode 100644 web/src/features/results/CountsTable.tsx create mode 100644 web/src/features/results/MetricCards.tsx create mode 100644 web/src/features/review/ReviewDrawer.tsx create mode 100644 web/src/features/run/RunView.tsx create mode 100644 web/src/features/upload/UploadPanel.tsx create mode 100644 web/src/index.css create mode 100644 web/src/lib/api.ts create mode 100644 web/src/lib/cn.ts create mode 100644 web/src/lib/export.ts create mode 100644 web/src/lib/format.ts create mode 100644 web/src/lib/queries.ts create mode 100644 web/src/lib/route.ts create mode 100644 web/src/lib/status.ts create mode 100644 web/src/main.tsx create mode 100644 web/tsconfig.json create mode 100644 web/vite.config.ts diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 749592c..67b4118 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -36,3 +36,22 @@ jobs: - name: Test run: pytest -q --cov=countbone --cov-report=term-missing + + dashboard: + runs-on: ubuntu-latest + defaults: + run: + working-directory: web + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-node@v4 + with: + node-version: lts/* + cache: npm + cache-dependency-path: web/package-lock.json + + - run: npm ci + + - name: Typecheck and build + run: npm run build diff --git a/.gitignore b/.gitignore index 784800b..71b06ac 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ -__pycache__/ +__pycache__/ *.py[cod] *.egg-info/ build/ @@ -17,9 +17,15 @@ runs/ *.db-shm examples/*.mp4 examples/*.avi +examples/*.webm # editors / OS .vscode/ .idea/ .DS_Store Thumbs.db + +# dashboard build tooling (the built output in src/countbone/api/static is committed) +web/node_modules/ +web/*.tsbuildinfo +web/tsconfig.tsbuildinfo diff --git a/README.md b/README.md index 08b033b..ffbf8ba 100644 --- a/README.md +++ b/README.md @@ -26,23 +26,22 @@ countbone demo `demo` renders a synthetic shelf with known ground truth, counts it, and scores itself: ``` -synthetic shelf: examples/demo_shelf.mp4 (34 units, 144 frames) +synthetic shelf: examples/demo_shelf.webm (34 units, 144 frames) -run run_628c61b93d2b (0.64s) -frames: 27 used, 2 dropped detections: 416 tracks: 33 +run run_ad7e76965deb (1.30s) +frames: 26 used, 3 dropped detections: 388 tracks: 34 SKU count expected var conf truth err --------------------------------------------------------- SKU-BLU 11 11 +0 94% 11 +0 - SKU-GRN 8 8 +0 96% 8 +0 - SKU-RED 9 10 -1 90% 10 -1 - SKU-YEL 5 5 +0 95% 5 +0 - TOTAL 33 + SKU-GRN 8 8 +0 97% 8 +0 + SKU-RED 10 10 +0 95% 10 +0 + SKU-YEL 5 5 +0 97% 5 +0 + TOTAL 34 -confidence: 93% needs review: yes (18 item(s)) - ! 5 object(s) changed identity between frames +confidence: 95% needs review: no (0 item(s)) -absolute count error: 1 of 34 (2.9%) +absolute count error: 0 of 34 (0.0%) ``` Then on your own footage: @@ -52,6 +51,24 @@ countbone run shelf.mp4 --expect SKU-RED=10 --expect SKU-BLU=11 countbone serve # dashboard + API on http://127.0.0.1:8000 ``` +## The dashboard + +`countbone serve` opens a dark, keyboard-driven dashboard at http://127.0.0.1:8000: + +- **New count**: drop an MP4/MOV, preview it, upload with progress. +- **Live pipeline**: the six stages as they run, with throughput, dropped frames, blur and lighting + telemetry from the pipeline itself. +- **Results**: metric cards and a searchable, filterable SKU table. Export CSV/JSON with reviewer + recounts, or download the pipeline's own audited `counts.csv` / `result.json`. +- **Items to check**: every low-confidence detection, shown as the crop the model saw beside the + catalog SKUs ranked by colour distance. Approve, correct, reject or recount with `A`, `1`–`9`, + `R` and `J`/`K`. Every decision, including undo, goes into the audit trail. +- **Frame inspector**: the source video with every detection box overlaid, counted and uncounted, + per sampled frame, with a timeline and per-object track history. + +The source lives in [`web/`](web/README.md). Its build ships inside the Python package, so running +the dashboard needs no Node. + ## What you get | | | @@ -79,7 +96,7 @@ Each stage does one thing and is replaceable. Counting is the hard part, and it is the part most systems get wrong. A camera panning an aisle moves every box at once, so `stages/motion.py` estimates the global camera displacement between frames by phase correlation and the tracker predicts each box forward by it. Without that step, -the demo above counts 56 instead of 34 — one carton becomes several. With it, 33. +the demo above counts 52 instead of 34 — one carton becomes several. With it, 34. Motion is tracked through frames the quality gate drops, too: skipping it split every carton in view at a dropped frame (see `docs/ARCHITECTURE.md`). ## The plugins @@ -160,8 +177,9 @@ cannot take the backbone down mid-count. exist. For real inventory, train a detector and use `detect.backend: yolo`. - **The default identifier matches colour**, not artwork. Real SKUs need a trained classifier, OCR or a barcode fallback — all of which fit behind the `Identifier` protocol or the `on_items` hook. -- **The 2.9% demo error is on synthetic footage.** It is a regression guard for the backbone, not a - claim about a warehouse. Accuracy on real stock depends entirely on the detector you plug in. +- **The exact demo counts are on synthetic footage.** Ten synthetic shelves count exactly and the + test suite guards three of them, but that is a regression guard for the backbone, not a claim + about a warehouse. Accuracy on real stock depends entirely on the detector you plug in. - **The audit pack is tamper-evident, not tamper-proof.** Anyone who can rewrite the pack can rewrite the hashes. Notarising the manifest hash externally is the next step, deliberately out of scope here. @@ -187,7 +205,7 @@ Each phase adds plugins. The backbone does not change. See [docs/ROADMAP.md](doc ```bash pip install -e ".[api,dev]" -pytest # 105 tests +pytest ruff check . ``` diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index 227a022..24065dd 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -75,8 +75,19 @@ The effect on the bundled demo (34 units of ground truth): | | counted | error | | --- | ---: | ---: | -| without motion compensation | 56 | +65% | -| with motion compensation | 33 | −2.9% | +| without motion compensation | 52 | +53% | +| with motion compensation | 34 | 0% | + +Motion must be accumulated through **every** frame, including those the quality gate drops. +An earlier version only applied motion on frames that reached the tracker, so each dropped frame +left every prediction a whole frame of pan behind; at walking speed that split every object in +view into two tracks. On ten synthetic shelves that bug alone produced errors of up to +36% +(mean 11%). Accumulating it (`Tracker.observe_motion`) brought the same ten to exact totals. + +The contour detector also pads each frame with its median tone before finding edges. Without it, +a carton cut off by the frame edge has an open outline, and its label, the next closed shape +inside, was reported as an object of its own: 145 of 145 "unidentified" sightings in one test +video were exactly this. Track identity is a majority vote across sightings, not the first guess, because identification flickers and object permanence does not. diff --git a/mobile/.claude/settings.json b/mobile/.claude/settings.json new file mode 100644 index 0000000..176e6a5 --- /dev/null +++ b/mobile/.claude/settings.json @@ -0,0 +1,5 @@ +{ + "enabledPlugins": { + "expo@claude-plugins-official": true + } +} diff --git a/mobile/.gitignore b/mobile/.gitignore new file mode 100644 index 0000000..d914c32 --- /dev/null +++ b/mobile/.gitignore @@ -0,0 +1,41 @@ +# Learn more https://docs.github.com/en/get-started/getting-started-with-git/ignoring-files + +# dependencies +node_modules/ + +# Expo +.expo/ +dist/ +web-build/ +expo-env.d.ts + +# Native +.kotlin/ +*.orig.* +*.jks +*.p8 +*.p12 +*.key +*.mobileprovision + +# Metro +.metro-health-check* + +# debug +npm-debug.* +yarn-debug.* +yarn-error.* + +# macOS +.DS_Store +*.pem + +# local env files +.env*.local + +# typescript +*.tsbuildinfo + +# generated native folders +/ios +/android diff --git a/mobile/AGENTS.md b/mobile/AGENTS.md new file mode 100644 index 0000000..be05e77 --- /dev/null +++ b/mobile/AGENTS.md @@ -0,0 +1,41 @@ +This is an Expo/React Native mobile application. Prioritize mobile-first patterns, performance, and cross-platform compatibility. + +## Expo has changed — do not trust your training data + +Expo ships breaking changes every SDK release. APIs you remember are likely renamed, moved, or removed. Before writing any code that touches an Expo, EAS, or React Native API: + +1. Read the major version of the `expo` package in `package.json`. +2. Fetch the matching versioned docs: `https://docs.expo.dev/versions/v.0.0/` +3. For anything else, fetch https://docs.expo.dev/llms.txt — an index of all Expo docs with corrections to common LLM misconceptions. Follow its links to the specific page you need; never answer from memory. + +## Commands + +Use `bunx` instead of `npx` if the project uses bun (`bun.lock` present). + +```bash +npx expo install # ALWAYS use instead of npm/yarn/pnpm/bun add — resolves SDK-compatible versions +npx expo start # start the dev server +npx expo lint # lint +npx tsc --noEmit # typecheck +npx expo-doctor # diagnose dependency and config issues +npx expo install --fix # fix incompatible package versions +``` + +Run lint and typecheck before declaring any task done. + +## Navigation & Routing + +- Use **Expo Router** for all navigation. Routes live in `src/app/` — every file there is a screen, `_layout.tsx` files define navigators. Keep non-route code (components, hooks, utils) outside `src/app/`. +- Import `Link`, `router`, and `useLocalSearchParams` from `expo-router`. +- Docs: https://docs.expo.dev/router/introduction.md + +## Building with EAS + +Use EAS to build, sign, and submit the app in the cloud (`eas build`, `eas submit`) and to ship over-the-air updates (`eas update`) — no local Xcode or Android Studio required. Run EAS CLI as `bunx eas-cli ` in Bun projects, or `npx eas-cli@latest ` otherwise; substitute that for bare `eas` in docs examples. +Docs: https://docs.expo.dev/eas/index.md + +## Rules + +- If `ios/` and `android/` directories do not exist, they are generated (Continuous Native Generation). Never create or edit them by hand — configure native behavior in `app.json` and config plugins. +- Expo Go only includes its bundled native modules. After adding a library with native code, the app needs a development build: `npx expo run:ios|android` locally, or `eas build --profile development`. +- Prefer recommended Expo modules over third-party libraries, and check your available skills before adding dependencies. Docs: https://docs.expo.dev/versions/latest/index.md diff --git a/mobile/CLAUDE.md b/mobile/CLAUDE.md new file mode 100644 index 0000000..43c994c --- /dev/null +++ b/mobile/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/mobile/LICENSE b/mobile/LICENSE new file mode 100644 index 0000000..30b20e3 --- /dev/null +++ b/mobile/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2015-present 650 Industries, Inc. (aka Expo) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/mobile/app.json b/mobile/app.json new file mode 100644 index 0000000..25d70db --- /dev/null +++ b/mobile/app.json @@ -0,0 +1,41 @@ +{ + "expo": { + "name": "Countbone", + "slug": "countbone", + "scheme": "countbone", + "version": "0.1.0", + "orientation": "portrait", + "icon": "./assets/icon.png", + "userInterfaceStyle": "dark", + "backgroundColor": "#0b0c0e", + "ios": { + "supportsTablet": true, + "bundleIdentifier": "com.countbone.mobile", + "infoPlist": { + "NSCameraUsageDescription": "Countbone films the shelf you are counting.", + "NSMicrophoneUsageDescription": "Countbone records audio with the shelf video only if you turn it on.", + "NSMotionUsageDescription": "Countbone uses the motion sensors to tell you when to hold the phone level." + } + }, + "android": { + "package": "com.countbone.mobile", + "permissions": ["android.permission.CAMERA", "android.permission.RECORD_AUDIO"], + "adaptiveIcon": { + "backgroundColor": "#0b0c0e", + "foregroundImage": "./assets/android-icon-foreground.png", + "backgroundImage": "./assets/android-icon-background.png", + "monochromeImage": "./assets/android-icon-monochrome.png" + }, + "predictiveBackGestureEnabled": false + }, + "web": { + "favicon": "./assets/favicon.png", + "output": "single", + "backgroundColor": "#0b0c0e" + }, + "plugins": ["expo-router", "expo-status-bar", "expo-font"], + "experiments": { + "typedRoutes": true + } + } +} diff --git a/mobile/assets/android-icon-background.png b/mobile/assets/android-icon-background.png new file mode 100644 index 0000000000000000000000000000000000000000..5ffefc5bb57a3d7b39ec6ff4e96979226522cc49 GIT binary patch literal 17549 zcmaI8cUV)w^FDe)Ahb|KgdkM~q=WRHV518n(mNVNP>?PyK|rj4^d3|Y5d<`VfRrFo 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+ } + } +} diff --git a/mobile/package.json b/mobile/package.json new file mode 100644 index 0000000..2da1245 --- /dev/null +++ b/mobile/package.json @@ -0,0 +1,44 @@ +{ + "name": "countbone-mobile", + "version": "1.0.0", + "main": "expo-router/entry", + "dependencies": { + "@react-native-async-storage/async-storage": "2.2.0", + "expo": "~57.0.25", + "expo-constants": "~57.0.19", + "expo-font": "~57.0.4", + "expo-haptics": "~57.0.3", + "expo-linking": "~57.0.11", + "expo-router": "~57.0.23", + "expo-sensors": "~57.0.3", + "expo-status-bar": "~57.0.1", + "lucide-react-native": "^1.48.0", + "react": "19.2.3", + "react-dom": "19.2.3", + "react-native": "0.86.3", + "react-native-gesture-handler": "~2.32.0", + "react-native-nitro-image": "^0.15.2", + "react-native-nitro-modules": "^0.37.1", + "react-native-reanimated": "4.5.1", + "react-native-safe-area-context": "~5.7.0", + "react-native-screens": "~4.26.0", + "react-native-svg": "15.15.4", + "react-native-vision-camera": "^5.2.3", + "react-native-vision-camera-worklets": "^5.2.3", + "react-native-web": "^0.21.2", + "react-native-worklets": "0.10.1" + }, + "devDependencies": { + "@types/react": "~19.2.2", + "typescript": "~6.0.3" + }, + "scripts": { + "start": "expo start", + "android": "expo start --android", + "ios": "expo start --ios", + "web": "expo start --web", + "typecheck": "tsc --noEmit", + "test": "node --no-warnings --test src/analysis/analysis.test.ts" + }, + "private": true +} diff --git a/mobile/src/analysis/analysis.test.ts b/mobile/src/analysis/analysis.test.ts new file mode 100644 index 0000000..eab3c99 --- /dev/null +++ b/mobile/src/analysis/analysis.test.ts @@ -0,0 +1,174 @@ +/** + * The analysis core is plain TypeScript, so it is tested under Node directly: + * npm test + * Frames here are synthetic with known answers: a shelf pattern we shift by + * a known amount, blur by a known kernel, or darken to a known level. + */ +import { strict as assert } from 'node:assert'; +import { test } from 'node:test'; + +import { GuidanceEngine, THRESHOLDS } from './guidance.ts'; +import { + blurAtBackendScale, + downsample, + edgeBoxes, + estimateShift, + projections, + sampleFrame, + type FrameSample, +} from './metrics.ts'; + +const W = 960; +const H = 540; + +/** A shelf: grey wall, dark-outlined cartons with a pale label, repeating. */ +function shelf(offset = 0, value = 1): Uint8Array { + const img = new Uint8Array(W * H).fill(Math.round(150 * value)); + const pitch = 142; + for (let row = 0; row < 3; row++) { + const y0 = 30 + row * 175; + for (let k = -2; k < 10; k++) { + // Irregular widths so the pattern is not perfectly periodic. + const x0 = k * pitch + ((k * 37) % 23) - offset; + const bw = 96 + ((k * 13) % 9); + for (let y = y0; y < y0 + 130; y++) { + for (let x = Math.max(0, x0); x < Math.min(W, x0 + bw); x++) { + const edge = y < y0 + 3 || y >= y0 + 127 || x < x0 + 3 || x >= x0 + bw - 3; + const label = y > y0 + 24 && y < y0 + 52 && x > x0 + 16 && x < x0 + bw - 16; + img[y * W + x] = Math.round((edge ? 30 : label ? 235 : 60 + ((k * 40 + row * 70) % 120)) * value); + } + } + } + } + return img; +} + +/** Separable box blur, radius r. */ +function blur(img: Uint8Array, r: number): Uint8Array { + const tmp = new Float32Array(W * H); + const out = new Uint8Array(W * H); + for (let y = 0; y < H; y++) { + for (let x = 0; x < W; x++) { + let s = 0; + let n = 0; + for (let d = -r; d <= r; d++) { + const xx = x + d; + if (xx >= 0 && xx < W) (s += img[y * W + xx]!), n++; + } + tmp[y * W + x] = s / n; + } + } + for (let y = 0; y < H; y++) { + for (let x = 0; x < W; x++) { + let s = 0; + let n = 0; + for (let d = -r; d <= r; d++) { + const yy = y + d; + if (yy >= 0 && yy < H) (s += tmp[yy * W + x]!), n++; + } + out[y * W + x] = s / n; + } + } + return out; +} + +test('blur separates sharp from blurred on the backend scale', () => { + const sharp = blurAtBackendScale(shelf(), W, H, W, 1); + const soft = blurAtBackendScale(blur(shelf(), 6), W, H, W, 1); + assert.ok(sharp > THRESHOLDS.blurBad * 4, `sharp ${sharp}`); + assert.ok(soft < THRESHOLDS.blurBad, `blurred ${soft}`); +}); + +test('blur is resolution-independent: a 2x source reads the same', () => { + const small = shelf(); + const big = new Uint8Array(W * 2 * H * 2); + for (let y = 0; y < H * 2; y++) for (let x = 0; x < W * 2; x++) big[y * W * 2 + x] = small[(y >> 1) * W + (x >> 1)]!; + const a = blurAtBackendScale(small, W, H, W, 1); + const b = blurAtBackendScale(big, W * 2, H * 2, W * 2, 1); + assert.ok(Math.abs(a - b) / a < 0.1, `${a} vs ${b}`); +}); + +test('RGBA input gives the same luma metrics as grayscale', () => { + const g = shelf(); + const rgba = new Uint8Array(W * H * 4); + for (let i = 0; i < W * H; i++) rgba.set([g[i]!, g[i]!, g[i]!, 255], i * 4); + const a = sampleFrame(g, W, H, W, 1, 0, false); + const b = sampleFrame(rgba, W, H, W * 4, 4, 0, false); + assert.ok(Math.abs(a.brightness - b.brightness) < 1); + assert.ok(Math.abs(a.blur - b.blur) / a.blur < 0.02); +}); + +test('horizontal shift is recovered, including past one carton pitch of aliasing', () => { + const a = projections(downsample(shelf(0), W, H, W, 1)); + for (const px of [6, 24, 48]) { + const b = projections(downsample(shelf(px), W, H, W, 1)); + // px at 960 is px/6 at the 160 px analysis width; content moves left. + const s = estimateShift(a.cols, b.cols, 40, -px / 6); + assert.ok(Math.abs(s.shift - -px / 6) < 1, `moved ${px}: got ${s.shift}`); + assert.ok(s.confidence > 0.3, `confidence ${s.confidence}`); + } +}); + +test('edge preview outlines cartons and drops their labels', () => { + let full = 0; // cartons wholly inside the frame, from the same layout as shelf() + for (let row = 0; row < 3; row++) { + for (let k = -2; k < 10; k++) { + const x0 = k * 142 + ((k * 37) % 23); + if (x0 > 6 && x0 + 96 + ((k * 13) % 9) < W - 6) full++; + } + } + const boxes = edgeBoxes(downsample(shelf(), W, H, W, 1)); + // A preview at 160 px: most cartons, never more than exist. + assert.ok(boxes.length >= full - 3 && boxes.length <= full, `found ${boxes.length} of ${full}`); + // No box sits inside another (labels are nested inside cartons). + for (const b of boxes) { + const nested = boxes.some((o) => o !== b && b.x >= o.x && b.y >= o.y && b.x + b.w <= o.x + o.w && b.y + b.h <= o.y + o.h); + assert.equal(nested, false); + } +}); + +// -- guidance ------------------------------------------------------------- + +function run(frames: (i: number) => Uint8Array, n: number, fps = 10, opts: { tilt?: number; recording?: boolean } = {}) { + const engine = new GuidanceEngine(); + let g; + for (let i = 0; i < n; i++) { + const s: FrameSample = sampleFrame(frames(i), W, H, W, 1, i / fps, false); + g = engine.update({ sample: s, tiltDeg: opts.tilt ?? null, recording: opts.recording ?? true }); + } + return g!; +} + +test('a steady, sharp, well-lit walk is "Quality good"', () => { + const g = run((i) => shelf(i * 6), 20); // 6 px/frame at 10 fps = 0.06 frame widths/s + assert.equal(g.status, 'good', JSON.stringify(g.cues)); + assert.equal(g.label, 'Quality good'); +}); + +test('walking too fast raises "Too fast"', () => { + const g = run((i) => shelf(i * 90), 12); // 0.94 frame widths/s + assert.equal(g.pace, 'bad'); + assert.ok(g.cues.some((c) => c.id === 'fast'), JSON.stringify(g.cues)); +}); + +test('a blurred but still view blames the lens, after it persists', () => { + const soft = blur(shelf(), 6); + const early = run(() => soft, 5); // 0.4 s: not yet + assert.ok(!early.cues.some((c) => c.id === 'lens')); + const later = run(() => soft, 20); // 1.9 s + assert.ok(later.cues.some((c) => c.id === 'lens'), JSON.stringify(later.cues)); +}); + +test('darkness and tilt produce their cues', () => { + const dark = run(() => shelf(0, 0.25), 8); + assert.ok(dark.cues.some((c) => c.id === 'dark' && c.level === 'bad')); + const tilted = run((i) => shelf(i * 6), 8, 10, { tilt: -30 }); + assert.ok(tilted.cues.some((c) => c.id === 'tilt-up' && c.arrow === 'up')); +}); + +test('walking back over counted stock is flagged', () => { + // 1.5 s rightwards establishes the direction, then back at ~0.19 frame widths/s. + const g = run((i) => shelf(i < 15 ? i * 12 : 180 - (i - 15) * 18), 25); + assert.equal(g.hud.direction, 'right'); + assert.ok(g.cues.some((c) => c.id === 'reverse'), JSON.stringify(g.cues)); +}); diff --git a/mobile/src/analysis/guidance.ts b/mobile/src/analysis/guidance.ts new file mode 100644 index 0000000..14176cf --- /dev/null +++ b/mobile/src/analysis/guidance.ts @@ -0,0 +1,315 @@ +/** + * Live capture guidance: turns per-frame samples into what the operator + * should do right now. + * + * Every threshold is tied to what the countbone backend will do with the + * footage, so a warning on the phone means "this would hurt the count": + * + * - Blur uses the quality gate's own threshold (min_blur 45) on the same + * 960 px-scale measure (see metrics.blurAtBackendScale). + * - Light mirrors the gate's brightness band (35..225) and clipped limit + * (0.28), warning a little before the gate would drop a frame. + * - Pace: the backend samples every 5th frame (6/s at 30 fps) and needs an + * object in >= 2 kept frames; motion blur at a 1/60 s shutter grows by + * ~16 px per frame-width/s at 960 px. Past ~0.8 frame-widths/s objects get + * too few sharp sightings; 0.45 is the comfortable ceiling. + * - Reversing: the tracker forgets an object a few samples after it leaves + * view, so walking back over counted stock counts it again. + */ + +import { estimateScale, estimateShift, type Box, type FrameSample } from './metrics.ts'; + +export type Level = 'good' | 'caution' | 'bad'; +export type Arrow = 'left' | 'right' | 'up' | 'down' | 'in' | 'out'; + +export type CueId = + | 'lens' + | 'fast' + | 'soft' + | 'dark' + | 'glare' + | 'reverse' + | 'tilt-up' + | 'tilt-down' + | 'level' + | 'closer' + | 'back' + | 'aim'; + +export interface Cue { + id: CueId; + level: Exclude; + title: string; + detail: string; + arrow?: Arrow; +} + +export interface Guidance { + status: Level; + /** Short status for the pill: "Quality good", "Too fast", "Blurry", ... */ + label: string; + pace: Level; + cues: Cue[]; + hud: { + fps: number; + blur: number; + /** 0..1 mean luminance. */ + light: number; + /** Frame widths per second, signed (+ = scene moving right = camera panning left). */ + speed: number; + /** Frame heights per second, signed. */ + vertical: number; + /** Camera scan direction once established. */ + direction: 'left' | 'right' | null; + }; + boxes: Box[]; +} + +export const THRESHOLDS = { + blurBad: 45, // quality_gate min_blur + blurCaution: 90, + lensSeconds: 1.2, // blur must persist this long (while slow) to blame the lens + darkBad: 45, + darkCaution: 60, + brightCaution: 210, + clippedBad: 0.2, + paceCaution: 0.45, + paceBad: 0.8, + directionSpeed: 0.08, // below this the operator is effectively still + directionSeconds: 1.0, + reverseSpeed: 0.1, // walking back slowly still re-counts; just above the noise floor + verticalCaution: 0.15, + tiltCaution: 15, // degrees from upright + tiltBad: 25, + distanceBand: 0.12, // |log scale| drift since the recording started + contrastMin: 10, +} as const; + +/** Cues switch on quickly and off slowly, so the banner does not flicker. */ +const ENTER_S = 0.25; +const EXIT_S = 0.6; + +interface CueState { + since: number | null; // when the condition started holding + lastTrue: number; // last time the condition held + active: boolean; + cue: Cue | null; +} + +export interface EngineInput { + sample: FrameSample; + /** Device pitch in degrees from upright (+ = top tilted away), or null if no sensor. */ + tiltDeg: number | null; + recording: boolean; +} + +export class GuidanceEngine { + private prev: FrameSample | null = null; + private vx = 0; + private vy = 0; + private priorDx = 0; + private priorDy = 0; + private fps = 0; + private logScale = 0; + private lowBlurSince: number | null = null; + private dirSign = 0; + private dirSince: number | null = null; + private direction: 'left' | 'right' | null = null; + private cues = new Map(); + private wasRecording = false; + + /** Forget motion history, e.g. when a recording starts or the source changes. */ + reset(): void { + this.prev = null; + this.vx = this.vy = this.priorDx = this.priorDy = 0; + this.logScale = 0; + this.lowBlurSince = null; + this.dirSign = 0; + this.dirSince = null; + this.direction = null; + this.cues.clear(); + } + + update({ sample, tiltDeg, recording }: EngineInput): Guidance { + if (recording && !this.wasRecording) { + // A new take: direction and distance are relative to how it started. + this.direction = null; + this.dirSince = null; + this.logScale = 0; + } + this.wasRecording = recording; + + const t = sample.t; + const prev = this.prev; + if (prev && t > prev.t && prev.thumbWidth === sample.thumbWidth) { + const dt = t - prev.t; + const cols = Float32Array.from(sample.cols); + const rows = Float32Array.from(sample.rows); + const maxX = Math.round(sample.thumbWidth * 0.25); + const maxY = Math.round(sample.thumbHeight * 0.2); + const sx = estimateShift(Float32Array.from(prev.cols), cols, maxX, this.priorDx); + const sy = estimateShift(Float32Array.from(prev.rows), rows, maxY, this.priorDy); + // A featureless view gives no trustworthy motion; hold the last estimate. + const dx = sx.confidence > 0.15 ? sx.shift : this.priorDx; + const dy = sy.confidence > 0.15 ? sy.shift : this.priorDy; + this.priorDx = dx; + this.priorDy = dy; + const a = Math.min(1, dt / 0.3); // ~0.3 s smoothing + this.vx += a * (dx / sample.thumbWidth / dt - this.vx); + this.vy += a * (dy / sample.thumbHeight / dt - this.vy); + this.fps += Math.min(1, dt / 0.5) * (1 / dt - this.fps); + if (recording) { + const s = estimateScale(Float32Array.from(prev.rows), rows); + // Only confident, non-trivial steps accumulate: noise must not drift. + if (Math.abs(s - 1) >= 0.02 && sy.confidence > 0.3) this.logScale += Math.log(s); + } + } + this.prev = sample; + + this.trackDirection(t, recording); + + const speed = Math.abs(this.vx); + const raw = this.rawCues(sample, t, tiltDeg, recording, speed); + const cues = this.debounce(raw, t); + + const pace: Level = + speed >= THRESHOLDS.paceBad ? 'bad' : speed >= THRESHOLDS.paceCaution ? 'caution' : 'good'; + const status: Level = cues.some((c) => c.level === 'bad') + ? 'bad' + : cues.length || pace === 'caution' + ? 'caution' + : 'good'; + return { + status, + label: status === 'good' ? 'Quality good' : (cues[0]?.title ?? (pace === 'caution' ? 'Ease off' : 'Adjust')), + pace, + cues, + hud: { + fps: this.fps, + blur: sample.blur, + light: sample.brightness / 255, + speed: this.vx, + vertical: this.vy, + direction: this.direction, + }, + boxes: sample.boxes, + }; + } + + /** The scan direction is whichever way the operator first walks steadily. */ + private trackDirection(t: number, recording: boolean): void { + if (!recording) return; + const sign = Math.abs(this.vx) >= THRESHOLDS.directionSpeed ? Math.sign(this.vx) : 0; + if (sign !== this.dirSign) { + this.dirSign = sign; + this.dirSince = sign ? t : null; + } + if (!this.direction && this.dirSince != null && t - this.dirSince >= THRESHOLDS.directionSeconds) { + // Content moving left (vx < 0) means the camera is moving right. + this.direction = this.dirSign < 0 ? 'right' : 'left'; + } + } + + private rawCues(s: FrameSample, t: number, tilt: number | null, recording: boolean, speed: number): Cue[] { + const T = THRESHOLDS; + const out: Cue[] = []; + const lowTexture = s.contrast < T.contrastMin; + + if (lowTexture) { + out.push({ id: 'aim', level: 'caution', title: 'Aim at the shelf', detail: 'Nothing to count in view' }); + } + + if (!lowTexture && s.blur < T.blurBad) { + this.lowBlurSince ??= t; + if (speed >= T.paceCaution) { + out.push({ id: 'fast', level: 'bad', title: 'Too fast', detail: 'Motion blur — slow down', arrow: this.slowArrow() }); + } else if (t - this.lowBlurSince >= T.lensSeconds) { + out.push({ id: 'lens', level: 'bad', title: 'Lens blurry', detail: 'Wipe the lens or tap to focus' }); + } + } else { + this.lowBlurSince = null; + if (speed >= T.paceBad) { + out.push({ id: 'fast', level: 'bad', title: 'Too fast', detail: 'Slow down', arrow: this.slowArrow() }); + } else if (!lowTexture && s.blur < T.blurCaution) { + out.push({ id: 'soft', level: 'caution', title: 'Getting soft', detail: 'Hold steadier' }); + } + } + + if (s.brightness < T.darkBad) { + out.push({ id: 'dark', level: 'bad', title: 'Too dark', detail: 'Turn on the torch or find light' }); + } else if (s.brightness < T.darkCaution) { + out.push({ id: 'dark', level: 'caution', title: 'Dim', detail: 'More light will help' }); + } else if (s.clipped > T.clippedBad) { + out.push({ id: 'glare', level: 'bad', title: 'Glare', detail: 'Angle away from the light' }); + } else if (s.brightness > T.brightCaution) { + out.push({ id: 'glare', level: 'caution', title: 'Very bright', detail: 'Watch for glare' }); + } + + if (recording && this.direction) { + const backwards = this.direction === 'right' ? this.vx > T.reverseSpeed : this.vx < -T.reverseSpeed; + if (backwards) { + out.push({ + id: 'reverse', + level: 'bad', + title: 'Wrong way', + detail: 'Going back counts stock twice', + arrow: this.direction, + }); + } + } + + if (tilt != null && Math.abs(tilt) >= T.tiltCaution) { + const level = Math.abs(tilt) >= T.tiltBad ? 'bad' : 'caution'; + out.push( + tilt > 0 + ? { id: 'tilt-down', level, title: 'Tilt down', detail: 'Keep the phone upright', arrow: 'down' } + : { id: 'tilt-up', level, title: 'Tilt up', detail: 'Keep the phone upright', arrow: 'up' }, + ); + } else if (Math.abs(this.vy) >= T.verticalCaution) { + out.push({ + id: 'level', + level: 'caution', + title: 'Hold level', + detail: 'Keep the shelf at one height', + arrow: this.vy > 0 ? 'up' : 'down', + }); + } + + if (recording && this.logScale > T.distanceBand) { + out.push({ id: 'back', level: 'caution', title: 'Step back', detail: 'Keep a steady distance', arrow: 'out' }); + } else if (recording && this.logScale < -T.distanceBand) { + out.push({ id: 'closer', level: 'caution', title: 'Move closer', detail: 'Keep a steady distance', arrow: 'in' }); + } + + return out; + } + + private slowArrow(): Arrow | undefined { + return this.direction ?? undefined; + } + + private debounce(raw: Cue[], t: number): Cue[] { + const now = new Map(raw.map((c) => [c.id, c])); + for (const id of new Set([...this.cues.keys(), ...now.keys()])) { + const st = this.cues.get(id) ?? { since: null, lastTrue: -Infinity, active: false, cue: null }; + const cue = now.get(id); + if (cue) { + st.since ??= t; + st.lastTrue = t; + st.cue = cue; + if (!st.active && t - st.since >= ENTER_S) st.active = true; + // A bad cue is urgent: show it immediately. + if (cue.level === 'bad') st.active = true; + } else { + st.since = null; + if (st.active && t - st.lastTrue >= EXIT_S) st.active = false; + } + this.cues.set(id, st); + } + const rank = { bad: 0, caution: 1 } as const; + return [...this.cues.values()] + .filter((s) => s.active && s.cue) + .map((s) => s.cue!) + .sort((a, b) => rank[a.level] - rank[b.level]); + } +} diff --git a/mobile/src/analysis/metrics.ts b/mobile/src/analysis/metrics.ts new file mode 100644 index 0000000..d91233c --- /dev/null +++ b/mobile/src/analysis/metrics.ts @@ -0,0 +1,463 @@ +/** + * Frame metrics for live capture guidance. + * + * Plain TypeScript over a small grayscale buffer, with no React and no + * platform APIs, so the same code runs in a VisionCamera worklet, in the + * browser preview, and under `node --test`. Every function here is written + * to be worklet-safe: no closures over module state, no classes. + * + * Definitions match the countbone backend (stages/preprocess.py) where one + * exists, so "blurry" on the phone means what the quality gate means by it: + * blur variance of the 3x3 Laplacian (cv2.Laplacian ksize=1) + * bright mean luminance 0-255 + * clipped fraction of pixels <= 4 or >= 251 + * The backend measures at 960 px wide; this runs at ANALYSIS_WIDTH, so the + * thresholds in guidance.ts are calibrated for this width, not copied. + */ + +export const ANALYSIS_WIDTH = 160; + +export interface Luma { + data: Uint8Array; + width: number; + height: number; +} + +export interface Box { + x: number; + y: number; + w: number; + h: number; +} + +/** + * Area-average a luma (1 byte/px) or RGBA (4 bytes/px) buffer down to + * `targetWidth`, keeping aspect. `stride` is bytes per row of the source. + */ +export function downsample( + src: Uint8Array | Uint8ClampedArray, + srcW: number, + srcH: number, + stride: number, + bytesPerPixel: 1 | 4, + targetWidth: number = ANALYSIS_WIDTH, +): Luma { + 'worklet'; + const w = Math.max(1, Math.min(targetWidth, srcW)); + const h = Math.max(1, Math.round((srcH * w) / srcW)); + const out = new Uint8Array(w * h); + const sx = srcW / w; + const sy = srcH / h; + // Sample a fixed grid inside each cell rather than every pixel: an average + // of 4x4 points is plenty for these metrics and ~20x cheaper at 1080p. + const n = 4; + for (let y = 0; y < h; y++) { + for (let x = 0; x < w; x++) { + let sum = 0; + for (let j = 0; j < n; j++) { + const py = Math.min(srcH - 1, Math.floor((y + (j + 0.5) / n) * sy)); + const row = py * stride; + for (let i = 0; i < n; i++) { + const px = Math.min(srcW - 1, Math.floor((x + (i + 0.5) / n) * sx)); + const o = row + px * bytesPerPixel; + sum += + bytesPerPixel === 1 + ? src[o]! + : // Rec.601 luma, the same weights OpenCV's BGR2GRAY uses. + 0.299 * src[o]! + 0.587 * src[o + 1]! + 0.114 * src[o + 2]!; + } + } + out[y * w + x] = sum / (n * n); + } + } + return { data: out, width: w, height: h }; +} + +/** + * The backend resizes frames to 960 px wide (capture.resize_width) before its + * quality gate measures blur. Measuring blur on a 160 px thumbnail does not + * work: the downsample itself removes the fine detail blur destroys, and on + * calibration footage sharp and badly blurred frames overlapped completely. + * So blur is measured at that same 960 px scale (blurAtBackendScale), which + * lets the phone use the gate's own threshold and mean the same thing: + * on calibration frames the two agree within 1%. + */ +export const BACKEND_WIDTH = 960; + +/** + * Laplacian variance of the whole frame as the backend would measure it + * after resizing to BACKEND_WIDTH, without allocating that resized frame. + * + * A centre crop is not enough: when the centre falls between two cartons + * the crop is blank and reads as blurred while the frame is sharp. The + * whole-frame value matches the gate; evaluating it on every other row and + * column (an unbiased sample of the same statistic) keeps it cheap enough + * to run per frame on a phone. + */ +export function blurAtBackendScale( + src: Uint8Array | Uint8ClampedArray, + srcW: number, + srcH: number, + stride: number, + bytesPerPixel: 1 | 4, +): number { + 'worklet'; + const step = srcW / BACKEND_WIDTH; // source px per backend px + const outW = BACKEND_WIDTH; + const outH = Math.round(srcH / step); + const taps = step >= 2 ? 2 : 1; + // Luma at backend pixel (x, y), box-averaged when shrinking (like INTER_AREA). + const at = (x: number, y: number): number => { + 'worklet'; + let s = 0; + for (let j = 0; j < taps; j++) { + const py = Math.min(srcH - 1, Math.floor((y + (j + 0.5) / taps) * step)); + for (let i = 0; i < taps; i++) { + const px = Math.min(srcW - 1, Math.floor((x + (i + 0.5) / taps) * step)); + const o = py * stride + px * bytesPerPixel; + s += bytesPerPixel === 1 ? src[o]! : 0.299 * src[o]! + 0.587 * src[o + 1]! + 0.114 * src[o + 2]!; + } + } + return s / (taps * taps); + }; + let sum = 0; + let sumSq = 0; + let n = 0; + for (let y = 1; y < outH - 1; y += 2) { + for (let x = 1; x < outW - 1; x += 2) { + const v = at(x, y - 1) + at(x, y + 1) + at(x - 1, y) + at(x + 1, y) - 4 * at(x, y); + sum += v; + sumSq += v * v; + n++; + } + } + if (!n) return 0; + const mean = sum / n; + return sumSq / n - mean * mean; +} + +/** Variance of the 3x3 Laplacian. Low means soft focus or motion blur. */ +export function laplacianVariance(l: Luma): number { + 'worklet'; + const { data, width: w, height: h } = l; + let sum = 0; + let sumSq = 0; + let n = 0; + for (let y = 1; y < h - 1; y++) { + for (let x = 1; x < w - 1; x++) { + const i = y * w + x; + const v = data[i - w]! + data[i + w]! + data[i - 1]! + data[i + 1]! - 4 * data[i]!; + sum += v; + sumSq += v * v; + n++; + } + } + if (!n) return 0; + const mean = sum / n; + return sumSq / n - mean * mean; +} + +export function exposure(l: Luma): { brightness: number; clipped: number } { + 'worklet'; + let sum = 0; + let clipped = 0; + const { data } = l; + for (let i = 0; i < data.length; i++) { + const v = data[i]!; + sum += v; + if (v <= 4 || v >= 251) clipped++; + } + return { brightness: sum / data.length, clipped: clipped / data.length }; +} + +/** Mean of each column and each row, mean-removed (robust to exposure changes). */ +export function projections(l: Luma): { cols: Float32Array; rows: Float32Array } { + 'worklet'; + const { data, width: w, height: h } = l; + const cols = new Float32Array(w); + const rows = new Float32Array(h); + for (let y = 0; y < h; y++) { + for (let x = 0; x < w; x++) { + const v = data[y * w + x]!; + cols[x] += v; + rows[y] += v; + } + } + let cm = 0; + for (let x = 0; x < w; x++) cm += (cols[x]! /= h); + cm /= w; + for (let x = 0; x < w; x++) cols[x]! -= cm; + let rm = 0; + for (let y = 0; y < h; y++) rm += (rows[y]! /= w); + rm /= h; + for (let y = 0; y < h; y++) rows[y]! -= rm; + return { cols, rows }; +} + +export interface Shift { + /** Content displacement from `prev` to `curr`, in analysis pixels (+ = right/down). */ + shift: number; + /** + * 0..1: how much better the chosen shift explains the change than a typical + * candidate does. Near 0 on a featureless wall, where no shift is trustworthy. + */ + confidence: number; +} + +/** + * Global 1-D displacement between two projection profiles by minimising the + * mean absolute difference over the overlap. + * + * Shelves are periodic (a row of identical cartons), so a shift one carton + * pitch away can match almost as well as the true one. `prior` is the last + * measured shift: real camera motion is smooth, so candidates far from it + * pay a penalty. This is the same aliasing the backend's phase correlation + * has to survive, solved the cheap way. + */ +export function estimateShift( + prev: Float32Array, + curr: Float32Array, + maxShift: number, + prior = 0, + priorWeight = 0.35, +): Shift { + 'worklet'; + const n = Math.min(prev.length, curr.length); + const minOverlap = Math.max(8, Math.floor(n * 0.4)); + let bestS = 0; + let best = Infinity; + let bestRaw = Infinity; + const costs: number[] = []; + const raws: number[] = []; + let scale = 0; + for (let i = 0; i < n; i++) scale += Math.abs(prev[i]!); + scale = scale / n || 1; + for (let s = -maxShift; s <= maxShift; s++) { + let acc = 0; + let cnt = 0; + for (let i = Math.max(0, -s); i < n && i + s < n; i++) { + acc += Math.abs(prev[i]! - curr[i + s]!); + cnt++; + } + if (cnt < minOverlap) { + costs.push(Infinity); + continue; + } + const raw = acc / cnt / scale; + raws.push(raw); + const cost = raw + (priorWeight * Math.abs(s - prior)) / Math.max(1, maxShift); + costs.push(cost); + if (cost < best) { + best = cost; + bestS = s; + bestRaw = raw; + } + } + // Parabolic refinement to sub-pixel. + const k = bestS + maxShift; + const c0 = costs[k - 1]; + const c1 = costs[k]; + const c2 = costs[k + 1]; + let refined = bestS; + if (c0 != null && c2 != null && c1 != null && Number.isFinite(c0) && Number.isFinite(c2)) { + const denom = c0 - 2 * c1 + c2; + if (denom > 1e-9) refined = bestS + (0.5 * (c0 - c2)) / denom; + } + raws.sort((a, b) => a - b); + const typical = raws[Math.floor(raws.length / 2)] ?? 0; + const confidence = typical > 1e-6 ? Math.max(0, Math.min(1, 1 - bestRaw / typical)) : 0; + return { shift: refined, confidence }; +} + +/** + * Relative zoom between two row profiles: > 1 means the scene got bigger + * (camera moved closer). Row profiles barely change under a horizontal pan, + * which is what isolates distance from walking. + */ +export function estimateScale(prev: Float32Array, curr: Float32Array): number { + 'worklet'; + const n = Math.min(prev.length, curr.length); + const c = (n - 1) / 2; + let bestScale = 1; + let best = Infinity; + for (let k = -6; k <= 6; k++) { + const s = 1 + k * 0.01; + let acc = 0; + let cnt = 0; + for (let i = 0; i < n; i++) { + // Where row i of `prev` lands in `curr` if the scene scaled by s about the centre. + const j = c + (i - c) * s; + const j0 = Math.floor(j); + if (j0 < 0 || j0 + 1 >= n) continue; + const f = j - j0; + const v = curr[j0]! * (1 - f) + curr[j0 + 1]! * f; + acc += Math.abs(prev[i]! - v); + cnt++; + } + if (cnt < n * 0.6) continue; + const cost = acc / cnt; + if (cost < best) { + best = cost; + bestScale = s; + } + } + return bestScale; +} + +/** + * Cheap on-device object outlines for the viewfinder preview: Sobel edges, + * connected components, then drop anything nested inside a bigger outline + * (a carton's label inside the carton). A preview of what the detector will + * see, not the count; the count happens on the server. + */ +export function edgeBoxes(l: Luma, maxBoxes = 40): Box[] { + 'worklet'; + const { data, width: w, height: h } = l; + const mag = new Float32Array(w * h); + let sum = 0; + let sumSq = 0; + for (let y = 1; y < h - 1; y++) { + for (let x = 1; x < w - 1; x++) { + const i = y * w + x; + const gx = + data[i - w + 1]! + 2 * data[i + 1]! + data[i + w + 1]! - data[i - w - 1]! - 2 * data[i - 1]! - data[i + w - 1]!; + const gy = + data[i + w - 1]! + 2 * data[i + w]! + data[i + w + 1]! - data[i - w - 1]! - 2 * data[i - w]! - data[i - w + 1]!; + const m = Math.abs(gx) + Math.abs(gy); + mag[i] = m; + sum += m; + sumSq += m * m; + } + } + const n = (w - 2) * (h - 2); + const mean = sum / n; + const sd = Math.sqrt(Math.max(0, sumSq / n - mean * mean)); + const thr = Math.max(60, mean + 1.5 * sd); + + const label = new Int32Array(w * h).fill(-1); + const stack = new Int32Array(w * h); + const boxes: Box[] = []; + for (let start = 0; start < w * h; start++) { + if (label[start] !== -1 || mag[start]! < thr) continue; + let top = 0; + stack[top++] = start; + label[start] = boxes.length; + let x0 = w; + let y0 = h; + let x1 = 0; + let y1 = 0; + let count = 0; + while (top > 0) { + const p = stack[--top]!; + const px = p % w; + const py = (p - px) / w; + count++; + if (px < x0) x0 = px; + if (px > x1) x1 = px; + if (py < y0) y0 = py; + if (py > y1) y1 = py; + // 8-connected, with a one-pixel bridge so hairline gaps in an outline join up. + for (let dy = -2; dy <= 2; dy++) { + for (let dx = -2; dx <= 2; dx++) { + const qx = px + dx; + const qy = py + dy; + if (qx < 0 || qy < 0 || qx >= w || qy >= h) continue; + const q = qy * w + qx; + if (label[q] === -1 && mag[q]! >= thr) { + label[q] = boxes.length; + stack[top++] = q; + } + } + } + } + const bw = x1 - x0 + 1; + const bh = y1 - y0 + 1; + const aspect = bw / bh; + const areaFrac = (bw * bh) / (w * h); + const touchesEdge = x0 <= 1 || y0 <= 1 || x1 >= w - 2 || y1 >= h - 2; + boxes.push( + count >= 8 && bw >= 5 && bh >= 5 && aspect >= 0.2 && aspect <= 5 && areaFrac <= 0.3 && !touchesEdge + ? { x: x0, y: y0, w: bw, h: bh } + : { x: -1, y: -1, w: 0, h: 0 }, + ); + } + const real = boxes.filter((b) => b.w > 0); + const kept = real.filter( + (b) => + !real.some( + (o) => o !== b && o.w * o.h > b.w * b.h && b.x >= o.x && b.y >= o.y && b.x + b.w <= o.x + o.w && b.y + b.h <= o.y + o.h, + ), + ); + kept.sort((a, b) => b.w * b.h - a.w * a.h); + return kept.slice(0, maxBoxes); +} + +export interface FrameSample { + /** Seconds, from the camera clock. */ + t: number; + srcWidth: number; + srcHeight: number; + /** Laplacian variance at the backend's 960 px scale (same scale as its quality gate). */ + blur: number; + /** Mean luminance 0-255. */ + brightness: number; + /** Fraction of pixels crushed or blown out. */ + clipped: number; + /** Luminance standard deviation of the thumbnail: near 0 on a blank wall. */ + contrast: number; + /** Column / row projection profiles of the thumbnail (for motion). */ + cols: number[]; + rows: number[]; + thumbWidth: number; + thumbHeight: number; + /** Edge-preview outlines, normalised to 0..1 of the frame. */ + boxes: Box[]; +} + +/** + * Everything measured from one frame, in one pass, stateless: the worklet on + * native and the canvas loop on web both call this and hand the result to the + * guidance engine on the JS thread. Motion needs the previous frame, so it is + * not computed here; the profiles it needs travel with the sample. + */ +export function sampleFrame( + src: Uint8Array | Uint8ClampedArray, + srcW: number, + srcH: number, + stride: number, + bytesPerPixel: 1 | 4, + t: number, + withBoxes: boolean, +): FrameSample { + 'worklet'; + const thumb = downsample(src, srcW, srcH, stride, bytesPerPixel); + const { brightness, clipped } = exposure(thumb); + let sq = 0; + for (let i = 0; i < thumb.data.length; i++) { + const d = thumb.data[i]! - brightness; + sq += d * d; + } + const { cols, rows } = projections(thumb); + const boxes = withBoxes + ? edgeBoxes(thumb).map((b) => ({ + x: b.x / thumb.width, + y: b.y / thumb.height, + w: b.w / thumb.width, + h: b.h / thumb.height, + })) + : []; + return { + t, + srcWidth: srcW, + srcHeight: srcH, + blur: blurAtBackendScale(src, srcW, srcH, stride, bytesPerPixel), + brightness, + clipped, + contrast: Math.sqrt(sq / thumb.data.length), + cols: Array.from(cols), + rows: Array.from(rows), + thumbWidth: thumb.width, + thumbHeight: thumb.height, + boxes, + }; +} diff --git a/mobile/tsconfig.json b/mobile/tsconfig.json new file mode 100644 index 0000000..3bada79 --- /dev/null +++ b/mobile/tsconfig.json @@ -0,0 +1,12 @@ +{ + "extends": "expo/tsconfig.base", + "compilerOptions": { + "strict": true, + "noUncheckedIndexedAccess": true, + "allowImportingTsExtensions": true, + "paths": { + "@/*": ["./src/*"] + } + }, + "include": ["**/*.ts", "**/*.tsx", ".expo/types/**/*.ts", "expo-env.d.ts"] +} diff --git a/pyproject.toml b/pyproject.toml index e726d49..73e8a96 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,8 @@ dependencies = [ [project.optional-dependencies] api = ["fastapi>=0.110", "uvicorn>=0.27", "pydantic>=2.5", "python-multipart>=0.0.9"] yolo = ["ultralytics>=8.1"] -dev = ["pytest>=7.4", "pytest-cov>=4.1", "httpx>=0.26", "ruff>=0.4"] +# imageio-ffmpeg: a bundled ffmpeg, so the variable-frame-rate test can build its clip. +dev = ["pytest>=7.4", "pytest-cov>=4.1", "httpx>=0.26", "ruff>=0.4", "imageio-ffmpeg>=0.5"] [project.scripts] countbone = "countbone.cli:main" @@ -42,7 +43,8 @@ Issues = "https://github.com/Samarty-1/countbone/issues" where = ["src"] [tool.setuptools.package-data] -countbone = ["api/static/*"] +# The dashboard is built from web/ into api/static (see web/README.md). +countbone = ["api/static/*", "api/static/assets/*"] [tool.pytest.ini_options] testpaths = ["tests"] diff --git a/src/countbone/api/app.py b/src/countbone/api/app.py index 4ba6c88..fbdd9dd 100644 --- a/src/countbone/api/app.py +++ b/src/countbone/api/app.py @@ -6,7 +6,9 @@ from __future__ import annotations +import colorsys import logging +import re import shutil import threading from concurrent.futures import ThreadPoolExecutor @@ -19,19 +21,60 @@ from fastapi.staticfiles import StaticFiles from pydantic import BaseModel, Field +from ..catalog import SkuEntry from ..config import Config from ..pipeline import Pipeline from ..plugins import base as plugin_base from ..store.db import Store from ..types import new_id +from . import telemetry log = logging.getLogger(__name__) STATIC_DIR = Path(__file__).parent / "static" +# .webm: the demo writes VP8 WebM, and browsers (incl. the mobile app's web +# preview) record WebM. +VIDEO_SUFFIXES = (".mp4", ".mov", ".m4v", ".avi", ".mkv", ".webm") + + +class DashboardFiles(StaticFiles): + """The built dashboard, cached correctly. + + Vite fingerprints everything under assets/, so those never change and can + be cached forever. index.html is the one file that names the current + fingerprints: it must be revalidated, or a browser keeps running the old + dashboard against a new API after an upgrade. + """ + + async def get_response(self, path: str, scope): # type: ignore[override] + response = await super().get_response(path, scope) + if path.startswith("assets/"): + response.headers["Cache-Control"] = "public, max-age=31536000, immutable" + else: + response.headers["Cache-Control"] = "no-cache" + return response + + +def _swatch(entry: SkuEntry) -> str | None: + """A display colour for a catalog entry: its hue band's centre. + + The colour identifier matches on hue, so this is the colour the model + is actually looking for, not decoration. + """ + if entry.achromatic: + return "#9ca3af" + centre = entry.hue_center() + if centre is None: + return None + r, g, b = colorsys.hsv_to_rgb(centre / 180.0, 0.75, 0.9) # OpenCV hue is 0-179 + return f"#{int(r * 255):02x}{int(g * 255):02x}{int(b * 255):02x}" class ReviewDecision(BaseModel): - status: str = Field(description="accepted | rejected | corrected") + status: str = Field(description="accepted | rejected | corrected | pending (reopen)") resolved_sku: str | None = None + resolved_count: int | None = Field( + default=None, ge=0, description="A reviewer's recount, for a whole-SKU review" + ) reviewer: str = "anonymous" @@ -70,6 +113,7 @@ def create_app(config: Config | None = None, store: Store | None = None) -> Fast # Built once: a Pipeline holds no per-run state, and rebuilding it per # request would reload the detector's weights every time. pipeline = Pipeline(cfg, store=db) + pipeline.plugins = telemetry.attach(pipeline.plugins, tracker.set) uploads = Path(cfg.output.dir) / "_uploads" uploads.mkdir(parents=True, exist_ok=True) @@ -117,9 +161,21 @@ def health() -> dict[str, Any]: "detect": cfg.detect.backend, "identify": cfg.identify.backend, "count": cfg.count.strategy, - "plugins": [p.name for p in pipeline.plugins], + "unknown_sku": cfg.identify.unknown_sku, + "plugins": [p.name for p in pipeline.plugins if not isinstance( + p, (telemetry.TelemetryIntake, telemetry.TelemetryTail))], } + @app.get("/api/catalog") + def catalog() -> list[dict[str, Any]]: + return [ + {"sku": e.sku, "label": e.label, "hue": list(e.hue) if e.hue else None, + "achromatic": e.achromatic, "min_saturation": e.min_saturation, + "unit_value": e.unit_value, + "expected": e.expected, "swatch": _swatch(e)} + for e in pipeline.catalog.entries + ] + @app.get("/api/plugins") def plugins() -> list[dict[str, Any]]: enabled = {p.name for p in cfg.plugins if p.enabled} @@ -130,13 +186,25 @@ def plugins() -> list[dict[str, Any]]: ] @app.post("/api/runs/upload", status_code=202) - async def upload_run(file: UploadFile) -> dict[str, Any]: + # Sync on purpose: FastAPI runs it in a worker thread. As `async def`, the + # blocking copy below stalls the event loop, and every progress poll with + # it, for as long as a large aisle video takes to write. + def upload_run(file: UploadFile) -> dict[str, Any]: run_id = new_id("run") - suffix = Path(file.filename or "upload.mp4").suffix or ".mp4" - target = uploads / f"{run_id}{suffix}" + suffix = (Path(file.filename or "upload.mp4").suffix or ".mp4").lower() + if suffix not in VIDEO_SUFFIXES: + raise HTTPException( + 415, f"unsupported file type {suffix}; expected {', '.join(VIDEO_SUFFIXES)}" + ) + # The original name rides in the stored filename, so the run is still + # recognisable after a restart (the tracker is memory-only). Reduced to + # a safe charset: it is user input and becomes part of a path. + stem = re.sub(r"[^A-Za-z0-9._-]+", "_", Path(file.filename or "").stem).strip("._")[:80] + target = uploads / (f"{run_id}__{stem}{suffix}" if stem else f"{run_id}{suffix}") with target.open("wb") as fh: shutil.copyfileobj(file.file, fh) submit(str(target), run_id) + tracker.set(run_id, filename=file.filename) return {"run_id": run_id, "status": "queued", "source": str(target)} @app.post("/api/runs", status_code=202) @@ -173,29 +241,50 @@ def artifact(run_id: str, path: str) -> FileResponse: raise HTTPException(404, "artifact not found") return FileResponse(target) + @app.get("/api/runs/{run_id}/video") + def video(run_id: str) -> FileResponse: + # Only the source of a run that completed, and only if it is a video. + # A run submitted by path records whatever path it was given, and a + # failed one never proves the file was a video; serving either would + # turn this into a read-any-file endpoint. + run = db.get_run(run_id) + source = run["source"] if run else None + if ( + not source + or Path(source).suffix.lower() not in VIDEO_SUFFIXES + or not Path(source).is_file() + ): + raise HTTPException(404, "source video not available") + # FileResponse answers Range requests, which the player needs to seek. + return FileResponse(source) + @app.get("/api/reviews") def reviews(status: str | None = "pending", limit: int = 200) -> list[dict[str, Any]]: return db.reviews(status=status, limit=limit) @app.post("/api/reviews/{review_id}") def resolve(review_id: str, decision: ReviewDecision) -> dict[str, Any]: - if decision.status not in {"accepted", "rejected", "corrected"}: - raise HTTPException(400, "status must be accepted, rejected or corrected") + # "pending" reopens a decision (the dashboard's undo); the audit trail + # keeps both the decision and its reversal. + if decision.status not in {"accepted", "rejected", "corrected", "pending"}: + raise HTTPException(400, "status must be accepted, rejected, corrected or pending") if decision.status == "corrected" and not decision.resolved_sku: raise HTTPException(400, "a corrected review needs resolved_sku") ok = db.resolve_review( - review_id, decision.status, decision.resolved_sku, decision.reviewer + review_id, decision.status, decision.resolved_sku, decision.reviewer, + resolved_count=decision.resolved_count, ) if not ok: raise HTTPException(404, f"unknown review {review_id}") - return {"review_id": review_id, "status": decision.status} + return {"review_id": review_id, "status": decision.status, + "resolved_count": decision.resolved_count} @app.get("/api/skus/{sku}/history") def sku_history(sku: str, limit: int = 100) -> list[dict[str, Any]]: return db.sku_history(sku, limit) if STATIC_DIR.is_dir(): - app.mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="dashboard") + app.mount("/", DashboardFiles(directory=STATIC_DIR, html=True), name="dashboard") else: # pragma: no cover - only if the package was installed without data @app.get("/") def no_dashboard() -> JSONResponse: diff --git a/src/countbone/api/static/assets/index-IhesMQDT.css b/src/countbone/api/static/assets/index-IhesMQDT.css new file mode 100644 index 0000000..1d51c7d --- /dev/null +++ b/src/countbone/api/static/assets/index-IhesMQDT.css @@ -0,0 +1,2 @@ +/*! tailwindcss v4.3.3 | MIT License | https://tailwindcss.com */ +@layer properties{@supports (((-webkit-hyphens:none)) and (not (margin-trim:inline))) or ((-moz-orient:inline) and (not (color:rgb(from red r g b)))){*,:before,:after,::backdrop{--tw-translate-x:0;--tw-translate-y:0;--tw-translate-z:0;--tw-space-y-reverse:0;--tw-border-style:solid;--tw-gradient-position:initial;--tw-gradient-from:#0000;--tw-gradient-via:#0000;--tw-gradient-to:#0000;--tw-gradient-stops:initial;--tw-gradient-via-stops:initial;--tw-gradient-from-position:0%;--tw-gradient-via-position:50%;--tw-gradient-to-position:100%;--tw-leading:initial;--tw-font-weight:initial;--tw-tracking:initial;--tw-shadow:0 0 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this.#n.config.onSettled?.(o,null,this.state.variables,this.state.context,this,n),await this.options.onSettled?.(o,null,e,this.state.context,n),this.#i({type:`success`,data:o}),o}catch(t){try{await this.#n.config.onError?.(t,e,this.state.context,this,n)}catch(e){Promise.reject(e)}try{await this.options.onError?.(t,e,this.state.context,n)}catch(e){Promise.reject(e)}try{await this.#n.config.onSettled?.(void 0,t,this.state.variables,this.state.context,this,n)}catch(e){Promise.reject(e)}try{await this.options.onSettled?.(void 0,t,e,this.state.context,n)}catch(e){Promise.reject(e)}throw this.#i({type:`error`,error:t}),t}finally{this.#r===r&&(this.#r=void 0),this.#n.runNext(this)}}#i(e){let t=t=>{switch(e.type){case`failed`:return{...t,failureCount:e.failureCount,failureReason:e.error};case`pause`:return{...t,isPaused:!0};case`continue`:return{...t,isPaused:!1};case`pending`:return{...t,context:e.context,data:void 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    + + diff --git a/src/countbone/api/telemetry.py b/src/countbone/api/telemetry.py new file mode 100644 index 0000000..2adc9f6 --- /dev/null +++ b/src/countbone/api/telemetry.py @@ -0,0 +1,217 @@ +"""Live progress and the frame inspector, for the dashboard. + +Two plugins rather than one, because a plugin has a single priority and these +need opposite ends of the chain: the intake must see every frame before the +quality gate can drop it, and the tail must see detections, items and tracks +after every other plugin has had its say. + +Both are attached by the API, never named in a config: they exist to feed the +dashboard, and a CLI run has no one to report progress to. +""" + +from __future__ import annotations + +import json +import math +import time +from collections.abc import Callable +from typing import Any + +from ..context import RunContext +from ..plugins.base import Plugin +from ..stages import capture +from ..types import CountResult, Frame, Item, Track + +Report = Callable[..., None] + +# Frames arrive far faster than anyone reads a dashboard; publishing every one +# only buys lock contention with the request threads. +PUBLISH_EVERY_S = 0.25 + + +def _state(ctx: RunContext) -> dict[str, Any]: + return ctx.state["telemetry"] + + +def _publish(ctx: RunContext, report: Report, force: bool = False) -> None: + t = _state(ctx) + now = time.time() + if not force and now - t["published_at"] < PUBLISH_EVERY_S: + return + t["published_at"] = now + elapsed = max(now - t["started_at"], 1e-6) + blur = t["blur"] + bright = t["brightness"] + report( + ctx.run_id, + phase=t["phase"], + telemetry={ + "frames_expected": t["frames_expected"], + "frames_read": t["frames_read"], + "frames_kept": t["frames_kept"], + "frames_dropped": t["frames_read"] - t["frames_kept"], + "detections": t["detections"], + "items": t["items"], + "tracks": t["tracks"], + "fps": round(t["frames_read"] / elapsed, 2), + "elapsed_s": round(elapsed, 2), + "blur": round(blur[-1], 1) if blur else None, + "blur_avg": round(sum(blur) / len(blur), 1) if blur else None, + "brightness": round(bright[-1], 1) if bright else None, + "brightness_avg": round(sum(bright) / len(bright), 1) if bright else None, + }, + ) + + +class TelemetryIntake(Plugin): + """Sees every frame, before anything can drop it.""" + + name = "dashboard_intake" + layer = "capture" + priority = 0 + + def __init__(self, report: Report) -> None: + super().__init__() + self.report = report + + def on_run_start(self, ctx: RunContext) -> None: + cap = ctx.config.capture + try: + probe = capture.probe(ctx.source) + except Exception: # noqa: BLE001 - progress is best effort, the run is not + probe = {} + expected = None + if probe.get("frame_count"): + expected = math.ceil(probe["frame_count"] / max(1, cap.every_n_frames)) + if cap.max_frames: + expected = min(expected, cap.max_frames) + ctx.state["telemetry"] = { + "started_at": time.time(), + "published_at": 0.0, + "phase": "frames", + "frames_expected": expected, + "frames_read": 0, + "frames_kept": 0, + "detections": 0, + "items": 0, + "tracks": 0, + "blur": [], + "brightness": [], + "frames": {}, + "sightings": [], + "frame_size": None, + } + _publish(ctx, self.report, force=True) + + def on_frame(self, ctx: RunContext, frame: Frame) -> Frame: + t = _state(ctx) + t["frames_read"] += 1 + q = frame.meta.get("quality") or {} + if "blur" in q: + t["blur"].append(q["blur"]) + t["brightness"].append(q["brightness"]) + t["frames"][frame.index] = { + "index": frame.index, + "source_index": frame.source_index, + "t": frame.timestamp_s, + "kept": False, + "quality": {k: round(v, 3) for k, v in q.items()}, + } + if t["frame_size"] is None: + t["frame_size"] = list(frame.shape) + _publish(ctx, self.report) + return frame + + +class TelemetryTail(Plugin): + """Sees the final word on every frame, and writes the inspector file. + + Priority 85 puts on_output after the exception report and before the + audit pack, so inspector.json is hashed into the manifest like any other + artifact. + """ + + name = "dashboard_tail" + layer = "output" + priority = 85 + + def __init__(self, report: Report) -> None: + super().__init__() + self.report = report + + def on_detections(self, ctx: RunContext, frame: Frame, detections): + t = _state(ctx) + t["frames_kept"] += 1 + t["detections"] += len(detections) + if frame.index in t["frames"]: + t["frames"][frame.index]["kept"] = True + return detections + + def on_items(self, ctx: RunContext, frame: Frame, items: list[Item]) -> list[Item]: + t = _state(ctx) + t["items"] += len(items) + # References, not copies: the tracker writes track_id onto these same + # objects after this hook, and an Item holds no pixels. + t["sightings"].extend(items) + _publish(ctx, self.report) + return items + + def on_tracks(self, ctx: RunContext, tracks: list[Track]) -> list[Track]: + t = _state(ctx) + t["phase"] = "count" + t["tracks"] = len(tracks) + t["counted_tracks"] = {tr.track_id for tr in tracks} + # A track's SKU is the majority vote of its sightings; that is what + # was counted, so it is what the inspector should label the box with. + t["track_sku"] = {tr.track_id: tr.sku for tr in tracks} + _publish(ctx, self.report, force=True) + return tracks + + def on_counts(self, ctx: RunContext, result: CountResult) -> CountResult: + _state(ctx)["phase"] = "output" + _publish(ctx, self.report, force=True) + return result + + def on_output(self, ctx: RunContext, result: CountResult) -> None: + t = _state(ctx) + counted = t.get("counted_tracks", set()) + track_sku = t.get("track_sku", {}) + boxes = [ + { + "frame": item.frame_index, + "bbox": [round(v, 1) for v in item.detection.bbox], + "sku": item.sku, # this frame's guess + "track_sku": track_sku.get(item.track_id), # what the object was counted as + "label": item.label, + "confidence": round(item.confidence, 4), + "track_id": item.track_id, + "counted": item.track_id is not None and item.track_id in counted, + } + for item in t["sightings"] + ] + doc = { + "run_id": result.run_id, + "frame_size": t["frame_size"], + "source_info": result.meta.get("source_info"), + "frames": sorted(t["frames"].values(), key=lambda f: f["index"]), + "boxes": boxes, + } + path = ctx.artifacts_dir / "inspector.json" + path.write_text(json.dumps(doc, separators=(",", ":")), encoding="utf-8") + result.meta.setdefault("outputs", {})["inspector"] = str(path) + + def on_run_end(self, ctx: RunContext, result: CountResult | None) -> None: + if "telemetry" not in ctx.state: + return + _state(ctx)["phase"] = "done" if result is not None else "failed" + _publish(ctx, self.report, force=True) + # The sightings list is the one large thing held here; let it go now + # rather than whenever the context is collected. + _state(ctx)["sightings"] = [] + + +def attach(plugins: list[Plugin], report: Report) -> list[Plugin]: + """Return the plugin list with both telemetry plugins in priority order.""" + out = [*plugins, TelemetryIntake(report), TelemetryTail(report)] + out.sort(key=lambda p: p.priority) + return out diff --git a/src/countbone/cli.py b/src/countbone/cli.py index c01524b..dc1a305 100644 --- a/src/countbone/cli.py +++ b/src/countbone/cli.py @@ -77,10 +77,24 @@ def _print_result(result: CountResult, truth: dict[str, int] | None = None) -> N print("\nwrote: " + ", ".join(f"{k} -> {v}" for k, v in outputs.items())) +def _pipeline(cfg: Config) -> Pipeline: + """A pipeline that also writes inspector.json, like one run from the API. + + Without it, a video counted from the CLI appears in the dashboard (same + database) but can never be opened in its frame inspector. There is no one + to report live progress to here, so progress goes nowhere. + """ + from .api import telemetry # no web dependencies: safe without the [api] extra + + pipeline = Pipeline(cfg) + pipeline.plugins = telemetry.attach(pipeline.plugins, lambda *_, **__: None) + return pipeline + + # -- commands ------------------------------------------------------------- def cmd_run(args: argparse.Namespace) -> int: cfg = _load_config(args) - result = Pipeline(cfg).run(args.video) + result = _pipeline(cfg).run(args.video) if args.json: print(json.dumps(result.to_dict(), indent=2)) else: @@ -95,7 +109,7 @@ def cmd_demo(args: argparse.Namespace) -> int: scene = make_demo_video(args.video or "examples/demo_shelf.mp4", seed=args.seed) print(f"synthetic shelf: {scene.path} ({scene.total} units, {scene.frames} frames)") cfg.count.expected = dict(scene.truth) - result = Pipeline(cfg).run(scene.path) + result = _pipeline(cfg).run(scene.path) _print_result(result, truth=scene.truth) error = abs(result.total - scene.total) print(f"\nabsolute count error: {error} of {scene.total} " diff --git a/src/countbone/demo.py b/src/countbone/demo.py index 0f90aec..19af93e 100644 --- a/src/countbone/demo.py +++ b/src/countbone/demo.py @@ -7,7 +7,10 @@ from __future__ import annotations +import contextlib +import os import random +import sys from dataclasses import dataclass from pathlib import Path @@ -117,17 +120,55 @@ def make_demo_video( ) +@contextlib.contextmanager +def _native_stderr_silenced(): + """Point file descriptor 2 at the null device for the duration. + + Python-level redirection cannot catch output written by C libraries, so + this swaps the descriptor itself, and always restores it. + """ + try: + sys.stderr.flush() + saved = os.dup(2) + except (OSError, ValueError): # no real stderr (e.g. some IDE consoles) + yield + return + devnull = os.open(os.devnull, os.O_WRONLY) + try: + os.dup2(devnull, 2) + yield + finally: + os.dup2(saved, 2) + os.close(saved) + os.close(devnull) + + def _open_writer(path: Path, fps: int, w: int, h: int): - """mp4 where the codec exists, AVI where it does not. Never fail silently.""" - attempts = [(path.with_suffix(".mp4"), "mp4v"), (path.with_suffix(".avi"), "MJPG")] + """A format a browser can play if this OpenCV build can write one. + + The dashboard's frame inspector plays the source video, and no browser + plays OpenCV's usual mp4v. VP8 WebM is written by the pip wheels and + played by every current browser. (H.264 would be nicer, but the wheels + ship no encoder and probing for one prints codec errors on every run.) + mp4v and MJPG remain as last resorts. Never fail silently. + """ + attempts = [ + (path.with_suffix(".webm"), "VP80"), + (path.with_suffix(".mp4"), "mp4v"), + (path.with_suffix(".avi"), "MJPG"), + ] for candidate, fourcc in attempts: - writer = cv2.VideoWriter( - str(candidate), cv2.VideoWriter_fourcc(*fourcc), fps, (w, h) - ) + # FFmpeg prints a harmless "tag VP80 is not supported ... webm" notice + # from native code while it picks the right tag itself; keep it off + # the user's terminal. Failures still surface through isOpened(). + with _native_stderr_silenced(): + writer = cv2.VideoWriter( + str(candidate), cv2.VideoWriter_fourcc(*fourcc), fps, (w, h) + ) if writer.isOpened(): return writer, candidate writer.release() raise RuntimeError( - "OpenCV could not open any video writer (tried mp4v and MJPG); " + "OpenCV could not open any video writer (tried VP80, mp4v and MJPG); " "install a build of opencv with video support" ) diff --git a/src/countbone/pipeline.py b/src/countbone/pipeline.py index 254208d..5532c9b 100644 --- a/src/countbone/pipeline.py +++ b/src/countbone/pipeline.py @@ -102,6 +102,8 @@ def _run(self, ctx: RunContext, source: str | int) -> CountResult: ) if kept is None: result.frames_dropped += 1 + # The frame is gone, its camera movement is not (see Tracker). + tracker.observe_motion(frame.meta.get("motion")) continue frame = kept result.frames_used += 1 diff --git a/src/countbone/stages/capture.py b/src/countbone/stages/capture.py index ecf024b..a0eb1e1 100644 --- a/src/countbone/stages/capture.py +++ b/src/countbone/stages/capture.py @@ -47,6 +47,21 @@ def probe(source: str | int) -> dict: cap.release() +def _timestamp(cap: cv2.VideoCapture, source_index: int, fps: float) -> float: + """When the frame just read is shown, in seconds from the start. + + Phones record variable frame rate, so index / average-fps drifts: on a + VFR clip it was measured over a second out, which puts the frame + inspector's boxes on the wrong carton. The container's own timestamp is + right for both constant and variable rate. Some backends (cameras) report + 0 for every frame; then the old estimate is all there is. + """ + pos = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000.0 + if pos > 0 or source_index == 0: + return pos + return source_index / fps if fps > 0 else float(source_index) + + def frames(source: str | int, cfg: CaptureConfig) -> Iterator[Frame]: """Yield sampled, optionally downscaled frames. @@ -65,7 +80,7 @@ def frames(source: str | int, cfg: CaptureConfig) -> Iterator[Frame]: if not ok: break source_index += 1 - ts = source_index / fps if fps > 0 else float(source_index) + ts = _timestamp(cap, source_index, fps) if ts < cfg.start_s: continue if cfg.end_s is not None and ts > cfg.end_s: diff --git a/src/countbone/stages/count.py b/src/countbone/stages/count.py index b745f5b..dbd8b9c 100644 --- a/src/countbone/stages/count.py +++ b/src/countbone/stages/count.py @@ -56,8 +56,7 @@ def update( forward by that displacement is what keeps one physical carton as one track instead of one per frame. """ - if motion and motion.get("estimated"): - self._cum = (self._cum[0] + motion["dx"], self._cum[1] + motion["dy"]) + self.observe_motion(motion) live = [ t for t in self.tracks if frame_index - t.last_frame <= self.max_gap + 1 @@ -105,6 +104,17 @@ def update( self.tracks.append(track) self._attach(track, item, frame_index) + def observe_motion(self, motion: dict[str, float] | None) -> None: + """Add one frame's camera displacement to the running total. + + Called for every frame, including ones the quality gate drops: the + camera moved during a dropped frame too. Skip that, and every track's + prediction lags by a whole frame of pan, which at walking speed is + enough to split each object in view into two tracks and count it twice. + """ + if motion and motion.get("estimated"): + self._cum = (self._cum[0] + motion["dx"], self._cum[1] + motion["dy"]) + def _predict( self, track: Track, frame_index: int, motion: dict[str, float] | None ) -> tuple[float, float, float, float]: diff --git a/src/countbone/stages/detect.py b/src/countbone/stages/detect.py index 8e82f9d..00a8bf0 100644 --- a/src/countbone/stages/detect.py +++ b/src/countbone/stages/detect.py @@ -88,9 +88,22 @@ class ContourDetector: def __init__(self, cfg: DetectConfig) -> None: self.cfg = cfg + # Width of the synthetic border added around each frame (see detect()). + PAD = 12 + def detect(self, frame: Frame) -> list[Detection]: image = frame.image gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if image.ndim == 3 else image + # An object cut off by the frame edge has no edge along the cut, so + # its outline never closes; RETR_EXTERNAL then reports whatever is + # *inside* it (a carton's white label) as an object of its own. A + # border in the frame's median tone closes those outlines against the + # cut, where the plain background, being close to the median, stays + # edge-free. Objects touching the border are then dropped below: + # they are partial, and a pan shows each one whole in another frame. + pad = self.PAD + fill = int(np.median(gray)) + gray = cv2.copyMakeBorder(gray, pad, pad, pad, pad, cv2.BORDER_CONSTANT, value=fill) gray = cv2.GaussianBlur(gray, (5, 5), 0) median = float(np.median(gray)) @@ -102,10 +115,16 @@ def detect(self, frame: Frame) -> list[Detection]: contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) detections: list[Detection] = [] + frame_w, frame_h = frame.shape for contour in contours: x, y, w, h = cv2.boundingRect(contour) if w < 8 or h < 8: continue + # Back to frame coordinates; skip anything reaching the frame edge. + x -= pad + y -= pad + if x <= 0 or y <= 0 or x + w >= frame_w or y + h >= frame_h: + continue rect_area = float(w * h) fill = float(cv2.contourArea(contour)) / rect_area if rect_area else 0.0 aspect = w / h if h else 0.0 diff --git a/src/countbone/store/db.py b/src/countbone/store/db.py index 1b4b5bf..2f24f32 100644 --- a/src/countbone/store/db.py +++ b/src/countbone/store/db.py @@ -185,19 +185,58 @@ def add_audit(self, run_id: str, kind: str, payload: Any) -> None: self._conn.commit() def resolve_review( - self, review_id: str, status: str, resolved_sku: str | None, resolved_by: str + self, + review_id: str, + status: str, + resolved_sku: str | None, + resolved_by: str, + resolved_count: int | None = None, ) -> bool: + """Record a reviewer's decision. + + A recount goes into the review's meta rather than over the run's + count: the machine's answer and the human's correction are both + evidence, and the audit trail needs to show which was which. + """ if status not in {"accepted", "rejected", "corrected", "pending"}: raise ValueError(f"invalid review status {status!r}") + now = time.time() with self._lock: - cur = self._conn.execute( + row = self._conn.execute( + "SELECT run_id, sku, meta FROM reviews WHERE review_id = ?", (review_id,) + ).fetchone() + if row is None: + return False + meta = json.loads(row["meta"] or "{}") + if resolved_count is not None: + meta["resolved_count"] = int(resolved_count) + else: + meta.pop("resolved_count", None) + self._conn.execute( """UPDATE reviews - SET status = ?, resolved_sku = ?, resolved_by = ?, resolved_at = ? + SET status = ?, resolved_sku = ?, resolved_by = ?, resolved_at = ?, + meta = ? WHERE review_id = ?""", - (status, resolved_sku, resolved_by, time.time(), review_id), + (status, resolved_sku, resolved_by, now, _json(meta), review_id), + ) + self._conn.execute( + "INSERT INTO audit (run_id, kind, payload, created_at) VALUES (?,?,?,?)", + ( + row["run_id"], + "review_decision", + _json({ + "review_id": review_id, + "sku": row["sku"], + "status": status, + "resolved_sku": resolved_sku, + "resolved_count": resolved_count, + "resolved_by": resolved_by, + }), + now, + ), ) self._conn.commit() - return cur.rowcount > 0 + return True # -- reads ----------------------------------------------------------- def _rows(self, sql: str, params: Iterable[Any] = ()) -> list[dict[str, Any]]: diff --git a/tests/test_api.py b/tests/test_api.py index 8f43c53..0b77a9e 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -2,6 +2,7 @@ from __future__ import annotations +import json import time from pathlib import Path @@ -123,7 +124,154 @@ def test_one_run_cannot_read_another_runs_artifacts(client, config): assert client.get("/api/runs/run_10/artifacts/secret.txt").status_code == 200 +def test_a_run_reports_progress_and_writes_the_inspector(client, demo_scene): + run_id = client.post("/api/runs", json={"path": demo_scene.path}).json()["run_id"] + run = wait_for(client, run_id) + + live = run["live"] + assert live["phase"] == "done" + t = live["telemetry"] + assert t["frames_read"] == run["frames_read"] + assert t["frames_kept"] == run["frames_used"] + assert t["detections"] == run["detections"] + + doc = client.get(f"/api/runs/{run_id}/artifacts/inspector.json").json() + assert len(doc["frames"]) == run["frames_read"] + assert sum(f["kept"] for f in doc["frames"]) == run["frames_used"] + assert doc["boxes"] and all(len(b["bbox"]) == 4 for b in doc["boxes"]) + # Every counted object is exactly one surviving track. + counted = {b["track_id"] for b in doc["boxes"] if b["counted"]} + assert len(counted) == run["tracks"] + # Boxes carry the identity the object was counted as, one per track. + per_track = {} + for b in doc["boxes"]: + if b["counted"]: + per_track.setdefault(b["track_id"], set()).add(b["track_sku"]) + assert all(len(s) == 1 and None not in s for s in per_track.values()) + by_sku = {} + for (sku,) in per_track.values(): + by_sku[sku] = by_sku.get(sku, 0) + 1 + assert by_sku == {c["sku"]: c["count"] for c in run["counts"]} + # Hashed into the audit pack like any other artifact. + manifest = client.get(f"/api/runs/{run_id}/artifacts/audit_pack.json").json() + assert "inspector.json" in manifest["manifest"]["artifacts"] + + +def test_the_source_video_is_streamed_with_range_support(client, demo_scene): + run_id = client.post("/api/runs", json={"path": demo_scene.path}).json()["run_id"] + wait_for(client, run_id) + res = client.get(f"/api/runs/{run_id}/video", headers={"Range": "bytes=0-99"}) + assert res.status_code == 206 + assert len(res.content) == 100 + assert client.get("/api/runs/nope/video").status_code == 404 + + +def test_video_endpoint_never_serves_a_non_video_source(client, tmp_path): + """A run records whatever path it was given; that must not become a file read.""" + from countbone.types import CountResult + + secret = tmp_path / "secret.txt" + secret.write_text("not for you", encoding="utf-8") + client.store.save_run(CountResult(run_id="run_sneaky", source=str(secret))) + assert client.get("/api/runs/run_sneaky/video").status_code == 404 + + # Nor the source of a run that failed (it never reached the store). + failed = client.post("/api/runs", json={"path": str(secret)}).json()["run_id"] + deadline = time.time() + 30 + while client.get(f"/api/runs/{failed}").json().get("pending", {}).get("status") != "failed": + assert time.time() < deadline, "run should have failed" + time.sleep(0.1) + assert client.get(f"/api/runs/{failed}/video").status_code == 404 + + +def test_a_failed_run_reports_failed_phase(client, tmp_path): + bad = tmp_path / "broken.mp4" + bad.write_bytes(b"this is not a video") + run_id = client.post("/api/runs", json={"path": str(bad)}).json()["run_id"] + deadline = time.time() + 30 + while True: + live = client.get(f"/api/runs/{run_id}").json()["pending"] + if live["status"] == "failed": + break + assert time.time() < deadline + time.sleep(0.1) + assert live["error"] + # Nothing half-written reached the store. + assert run_id not in [r["run_id"] for r in client.get("/api/runs").json()["runs"]] + + +def test_a_decision_can_be_reopened(client): + from countbone.types import CountResult, ReviewItem, SkuCount + + client.store.save_run( + CountResult( + run_id="run_undo", + source="x.mp4", + counts=[SkuCount("SKU-A", 3)], + reviews=[ReviewItem("rev_undo", "run_undo", "SKU-A", "unidentified", 0.2, 1)], + ) + ) + client.post("/api/reviews/rev_undo", json={"status": "rejected"}) + client.post("/api/reviews/rev_undo", json={"status": "pending"}) + run = client.get("/api/runs/run_undo").json() + assert run["reviews"][0]["status"] == "pending" + kinds = [a["payload"]["status"] for a in run["audit"] if a["kind"] == "review_decision"] + assert kinds == ["rejected", "pending"] + + +def test_upload_keeps_a_safe_original_name(client, demo_scene): + with open(demo_scene.path, "rb") as fh: + res = client.post( + "/api/runs/upload", files={"file": ("../../Aisle 7 (east).mp4", fh, "video/mp4")} + ) + source = Path(res.json()["source"]) + assert source.name.endswith("__Aisle_7_east.mp4") + assert source.parent.name == "_uploads" # the ../ went nowhere + wait_for(client, res.json()["run_id"]) + + +def test_uploads_must_be_video(client): + res = client.post("/api/runs/upload", files={"file": ("notes.txt", b"hi", "text/plain")}) + assert res.status_code == 415 + + +def test_catalog_gives_each_sku_a_swatch(client): + rows = {r["sku"]: r for r in client.get("/api/catalog").json()} + assert rows["SKU-RED"]["swatch"].startswith("#") + red = rows["SKU-RED"]["swatch"] + assert int(red[1:3], 16) > int(red[3:5], 16) # red hue band renders red + + +def test_a_recount_is_kept_beside_the_machine_count(client): + from countbone.types import CountResult, ReviewItem, SkuCount + + client.store.save_run( + CountResult( + run_id="run_rc", + source="x.mp4", + counts=[SkuCount("SKU-A", 3)], + reviews=[ReviewItem("rev_rc", "run_rc", "SKU-A", "low_sku_confidence", 0.4, -1)], + ) + ) + res = client.post( + "/api/reviews/rev_rc", + json={"status": "corrected", "resolved_sku": "SKU-A", "resolved_count": 5}, + ) + assert res.status_code == 200 + run = client.get("/api/runs/run_rc").json() + assert run["counts"][0]["count"] == 3 # the machine's answer is untouched + review = run["reviews"][0] + assert json.loads(review["meta"])["resolved_count"] == 5 + decisions = [a for a in run["audit"] if a["kind"] == "review_decision"] + assert decisions[0]["payload"]["resolved_count"] == 5 + + bad = client.post("/api/reviews/rev_rc", json={"status": "accepted", "resolved_count": -1}) + assert bad.status_code == 422 + + def test_dashboard_is_served(client): res = client.get("/") assert res.status_code == 200 assert "countbone" in res.text + # index.html names the current asset fingerprints: never serve it stale. + assert res.headers["cache-control"] == "no-cache" diff --git a/tests/test_cli.py b/tests/test_cli.py index 20b7a5e..a24e6ea 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -3,6 +3,7 @@ from __future__ import annotations import json +from pathlib import Path import pytest @@ -29,6 +30,17 @@ def test_run_prints_a_table(demo_scene, config, tmp_path, capsys): assert "wrote:" in out +def test_a_cli_run_can_be_inspected_in_the_dashboard(demo_scene, config, tmp_path, capsys): + """Same database as the dashboard, so it must leave the inspector file too.""" + main(["run", demo_scene.path, "--json", *base_args(config, tmp_path)]) + payload = json.loads(capsys.readouterr().out) + doc = json.loads( + (Path(config.output.dir) / payload["run_id"] / "inspector.json").read_text(encoding="utf-8") + ) + assert len(doc["frames"]) == payload["frames_read"] + assert doc["boxes"] + + def test_run_json_output_is_parseable(demo_scene, config, tmp_path, capsys): main(["run", demo_scene.path, "--json", *base_args(config, tmp_path)]) payload = json.loads(capsys.readouterr().out) diff --git a/tests/test_counting.py b/tests/test_counting.py index b425e67..c394e84 100644 --- a/tests/test_counting.py +++ b/tests/test_counting.py @@ -40,6 +40,23 @@ def test_a_pan_without_motion_compensation_fragments_tracks(): assert len(tracker.tracks) > 1 +def test_a_dropped_frame_still_moves_the_camera(): + """Regression: motion during a frame the quality gate dropped was lost, so + every track lagged a frame of pan and split in two (a double count).""" + tracker = Tracker() + motion = {"dx": -30.0, "dy": 0.0, "response": 0.9, "estimated": True} + x = 200.0 + for f in range(6): + if f: + x -= 30 + if f == 3: # dropped by the quality gate: no items, but the camera moved + tracker.observe_motion(motion) + continue + tracker.update(f, [item(x, frame=f)], motion if f else None) + assert len(tracker.tracks) == 1 + assert tracker.tracks[0].hits == 5 + + def test_motion_compensation_keeps_a_panning_object_as_one_track(): tracker = Tracker() motion = {"dx": -45.0, "dy": 0.0, "response": 0.9, "estimated": True} diff --git a/tests/test_detect_identify.py b/tests/test_detect_identify.py index 29e1eb8..1d88df2 100644 --- a/tests/test_detect_identify.py +++ b/tests/test_detect_identify.py @@ -62,6 +62,27 @@ def test_contour_detector_finds_separated_boxes(): assert all(d.score > 0.5 for d in dets) +def test_a_box_cut_by_the_frame_edge_does_not_leak_its_label(): + """Regression: a carton cut off by the frame edge has an open outline, so + its white label was reported as an object of its own (an UNKNOWN count and + a review item). The partial carton is skipped; the label must be too.""" + image = np.full((480, 640, 3), 170, dtype=np.uint8) + # whole carton with a label, for contrast + cv2.rectangle(image, (100, 120), (200, 300), (40, 40, 205), -1) + cv2.rectangle(image, (100, 120), (200, 300), (30, 30, 30), 3) + cv2.rectangle(image, (116, 150), (184, 180), (235, 235, 235), -1) + # carton running off the right edge, label fully inside the frame + cv2.rectangle(image, (560, 120), (700, 300), (40, 40, 205), -1) + cv2.rectangle(image, (560, 120), (700, 300), (30, 30, 30), 3) + cv2.rectangle(image, (576, 150), (630, 180), (235, 235, 235), -1) + frame = Frame(index=0, source_index=0, timestamp_s=0.0, image=image) + + dets = detect.build(DetectConfig()).detect(frame) + assert len(dets) == 1 # only the whole carton: no partial, no label + x1, y1, x2, y2 = dets[0].bbox + assert x1 <= 100 and y1 <= 120 and x2 >= 200 and y2 >= 300 and x2 < 260 + + def test_unknown_detector_backend_fails_loudly(): with pytest.raises(ValueError, match="unknown detect backend"): detect.build(DetectConfig(backend="magic")) diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index 88cf727..ee6aabf 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -27,6 +27,23 @@ def test_counts_synthetic_shelf_within_tolerance(demo_scene, config): assert {c.sku for c in result.counts} <= set(demo_scene.truth) | {"UNKNOWN"} +@pytest.mark.parametrize("seed", [0, 3, 9]) +def test_other_shelves_count_exactly(seed, config, tmp_path): + """Regression: these seeds over-counted by +9 to +13 (up to 36%) while the + single default seed looked fine, because motion during dropped frames was + lost and every object in view split into two tracks. The number of + physical objects must now be exact. Which SKU each one is may still be off + by one (colour identification, a separate and weaker stage).""" + from countbone.demo import make_demo_video + + scene = make_demo_video(tmp_path / f"shelf_{seed}.mp4", seed=seed) + result = Pipeline(config).run(scene.path) + counted = {c.sku: c.count for c in result.counts} + assert sum(counted.get(s, 0) for s in scene.truth) == scene.total + for sku, truth in scene.truth.items(): + assert abs(counted.get(sku, 0) - truth) <= 1, f"{sku}: {counted.get(sku, 0)} vs {truth}" + + def test_per_sku_counts_are_close(demo_scene, config): result = Pipeline(config).run(demo_scene.path) counted = {c.sku: c.count for c in result.counts} @@ -109,6 +126,36 @@ def test_missing_video_is_a_clear_error(config): Pipeline(config).run("does-not-exist.mp4") +def test_frame_timestamps_follow_the_video_clock(demo_scene, config): + """Constant-rate footage: timestamps are exactly frame / fps.""" + stamps = [(f.source_index, f.timestamp_s) for f in capture.frames(demo_scene.path, config.capture)] + fps = capture.probe(demo_scene.path)["fps"] + assert stamps and all(abs(t - i / fps) < 1e-3 for i, t in stamps) + + +def test_variable_frame_rate_timestamps_do_not_drift(tmp_path, config): + """Regression: index / average-fps drifted over a second on VFR phone + footage, putting the inspector's boxes on the wrong frame.""" + imageio_ffmpeg = pytest.importorskip("imageio_ffmpeg") + import subprocess + + from countbone.demo import make_demo_video + + src = make_demo_video(tmp_path / "cfr.webm", seed=1).path + vfr = tmp_path / "vfr.mkv" + # 30 fps for 60 frames, then 15 fps: frame 60 is at exactly 2.000 s. + subprocess.run( + [imageio_ffmpeg.get_ffmpeg_exe(), "-y", "-loglevel", "error", "-i", src, + "-vf", "setpts='if(lt(N,60),N/(30*TB),(2+(N-60)/15)/TB)'", + "-fps_mode", "vfr", "-c:v", "libvpx", str(vfr)], + check=True, + ) + config.capture.every_n_frames = 1 + stamps = {f.source_index: f.timestamp_s for f in capture.frames(str(vfr), config.capture)} + assert stamps[60] == pytest.approx(2.0, abs=0.02) + assert stamps[90] == pytest.approx(4.0, abs=0.02) + + def test_run_video_helper(demo_scene, tmp_path): cfg = Config() cfg.output.dir = str(tmp_path / "runs") diff --git a/tests/test_plugins.py b/tests/test_plugins.py index a3cbed2..d957b97 100644 --- a/tests/test_plugins.py +++ b/tests/test_plugins.py @@ -177,6 +177,11 @@ def test_tolerance_ignores_skus_with_no_expected_count(): def test_every_review_item_gets_its_own_crop(demo_scene, config): """Regression: identical cartons in one frame overwrote each other's crop, so a reviewer was shown evidence belonging to a different item.""" + # Review every sighting: the demo is now clean enough to raise none by + # itself, and this test is about crops, not about what gets flagged. + for spec in config.plugins: + if spec.name == "review_queue": + spec.options = {**(spec.options or {}), "item_threshold": 1.01} result = Pipeline(config).run(demo_scene.path) crops = [r.crop_path for r in result.reviews if r.crop_path] diff --git a/web/README.md b/web/README.md new file mode 100644 index 0000000..d1694e0 --- /dev/null +++ b/web/README.md @@ -0,0 +1,50 @@ +# countbone dashboard + +React + TypeScript + Tailwind v4, built with Vite into `src/countbone/api/static/`. +`countbone serve` serves that build, so **running the dashboard never needs Node**. +You need Node only to change it. + +```bash +cd web +npm install +npm run dev # http://localhost:5173, proxies /api to countbone serve on :8000 +npm run build # typecheck, then write the build into the Python package +``` + +Run `countbone serve` alongside `npm run dev`. Commit the rebuilt `static/` with any change +to `web/src`, because that build is what ships. + +## Layout + +``` +src/ + lib/ api client and types, queries, routing, status tiers, export + components/ ui primitives (Button, Badge, ConfidenceBar, …) and the sidebar + features/ + upload/ dropzone, preview, upload with progress + pipeline/ live six-stage tracker and telemetry readouts + results/ metric cards, counts table, export + review/ "Items to check" drawer: crop vs candidate SKUs, recounts + inspector/ video player with box overlay and frame timeline + run/ the run page that composes the above +``` + +## Where the data comes from + +Everything shown comes from the pipeline. Nothing is simulated. + +| UI | Source | +| --- | --- | +| Live stage progress, FPS, blur, lighting | `api/telemetry.py`: two plugins the API attaches, published through `GET /api/runs/{id}` while the run is pending | +| Box overlay, frame timeline | `inspector.json`, written per run by the same telemetry and hashed into the audit pack | +| SKU swatches, candidate ranking | `GET /api/catalog`: each SKU's hue band, the same evidence the colour identifier uses | +| Recounts | `POST /api/reviews/{id}` with `resolved_count`. Stored beside the machine count, never over it, and logged to the audit trail | + +## Conventions + +- Colours come only from the tokens in `src/index.css`. Every text/background pair is measured + at 4.5:1 or better, so check the numbers there before adding a colour. +- Status is never shown by colour alone: tiers carry a label and confidence carries a number. +- Every action has a keyboard path. The review drawer is built to be driven with `J`/`K`, `1`–`9`, + `A`, `R` and `Z`. +- Animation respects `prefers-reduced-motion` (see `MotionConfig` in `main.tsx`). diff --git a/web/index.html b/web/index.html new file mode 100644 index 0000000..cf92269 --- /dev/null +++ b/web/index.html @@ -0,0 +1,16 @@ + + + + + + + + + countbone + + +
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"@tailwindcss/vite": "^4.3.3", + "@types/react": "^19.3.0", + "@types/react-dom": "^19.3.0", + "@vitejs/plugin-react": "^6.1.1", + "tailwindcss": "^4.3.3", + "typescript": "^7.0.2", + "vite": "^8.3.1" + } +} diff --git a/web/src/App.tsx b/web/src/App.tsx new file mode 100644 index 0000000..fbbede0 --- /dev/null +++ b/web/src/App.tsx @@ -0,0 +1,76 @@ +import { useEffect, useState } from "react"; +import { AnimatePresence, motion } from "motion/react"; +import { Menu, X } from "lucide-react"; +import { Sidebar } from "@/components/Sidebar"; +import { IconButton } from "@/components/ui"; +import { UploadPanel } from "@/features/upload/UploadPanel"; +import { RunView } from "@/features/run/RunView"; +import { navigate, useRoute } from "@/lib/route"; + +export function App() { + const route = useRoute(); + const [navOpen, setNavOpen] = useState(false); + + // Close the mobile nav whenever the route changes. + useEffect(() => setNavOpen(false), [route]); + + useEffect(() => { + const onKey = (e: KeyboardEvent) => { + const t = e.target as HTMLElement; + if (t.tagName === "INPUT" || t.tagName === "SELECT" || t.tagName === "TEXTAREA") return; + if (e.metaKey || e.ctrlKey || e.altKey || document.querySelector('[role="dialog"]')) return; + if (e.key.toLowerCase() === "n") navigate({ view: "new" }); + }; + window.addEventListener("keydown", onKey); + return () => window.removeEventListener("keydown", onKey); + }, []); + + return ( +
    + + Skip to content + + +
    + +
    + + + {navOpen && ( + <> + setNavOpen(false)} + /> + + + setNavOpen(false)} /> + + + )} + + +
    +
    + setNavOpen(true)} /> + countbone +
    +
    + {route.view === "new" ? : } +
    +
    +
    + ); +} diff --git a/web/src/components/Sidebar.tsx b/web/src/components/Sidebar.tsx new file mode 100644 index 0000000..774c4d3 --- /dev/null +++ b/web/src/components/Sidebar.tsx @@ -0,0 +1,144 @@ +import { AlertTriangle, CheckCircle2, CircleDashed, Loader2, Plus, ScanLine, XCircle } from "lucide-react"; +import type { LiveRun, RunSummary } from "@/lib/api"; +import { cn } from "@/lib/cn"; +import { ago, pct, runTitle } from "@/lib/format"; +import { useHealth, useRuns } from "@/lib/queries"; +import { href, type Route } from "@/lib/route"; +import { Kbd, Skeleton } from "./ui"; + +function Logo() { + return ( + + + + + countbone + + ); +} + +interface Entry { + runId: string; + title: string; + sub: string; + state: "live" | "failed" | "review" | "clean"; +} + +function toEntries(inFlight: LiveRun[], runs: RunSummary[]): Entry[] { + const done = new Set(runs.map((r) => r.run_id)); + const live: Entry[] = inFlight + .filter((r) => !done.has(r.run_id) && r.status !== "done") + .map((r) => ({ + runId: r.run_id, + title: runTitle(r.run_id, r.source, r.filename), + sub: r.status === "failed" ? "Failed" : r.status === "queued" ? "Queued" : "Counting…", + state: r.status === "failed" ? "failed" : "live", + })); + const names = new Map(inFlight.map((r) => [r.run_id, r.filename])); + const finished: Entry[] = runs.map((r) => ({ + runId: r.run_id, + title: runTitle(r.run_id, r.source, names.get(r.run_id)), + sub: `${r.total} units · ${pct(r.overall_confidence)} · ${ago(r.started_at)}`, + state: r.needs_review ? "review" : "clean", + })); + return [...live.reverse(), ...finished]; +} + +const STATE_ICON = { + live: , + failed: , + review: , + clean: , +}; + +export function Sidebar({ route }: { route: Route }) { + const runs = useRuns(); + const health = useHealth(); + const entries = runs.data ? toEntries(runs.data.inFlight, runs.data.runs) : []; + const activeId = route.view === "run" ? route.runId : null; + + return ( + + ); +} diff --git a/web/src/components/ui.tsx b/web/src/components/ui.tsx new file mode 100644 index 0000000..f7ce2bb --- /dev/null +++ b/web/src/components/ui.tsx @@ -0,0 +1,249 @@ +import { forwardRef, type ButtonHTMLAttributes, type ReactNode } from "react"; +import type { LucideIcon } from "lucide-react"; +import { cn } from "@/lib/cn"; +import { pct } from "@/lib/format"; +import { TIER_LABEL, tierOf, type Tier } from "@/lib/status"; + +/* ------------------------------------------------------------------ Button */ + +type Variant = "primary" | "secondary" | "ghost" | "danger" | "success"; +type Size = "sm" | "md"; + +const VARIANTS: Record = { + primary: "bg-accent text-accent-fg hover:brightness-110 font-semibold", + secondary: "bg-raised text-fg border border-line hover:bg-hover hover:border-line-strong", + ghost: "text-muted hover:text-fg hover:bg-hover", + danger: "bg-raised text-bad border border-line hover:border-bad/60 hover:bg-bad/10", + success: "bg-ok text-accent-fg hover:brightness-110 font-semibold", +}; + +const SIZES: Record = { + // 32px visual height on desktop; the hit area is padded out to 44px on touch. + sm: "h-7 px-2.5 text-xs gap-1.5", + md: "h-8 px-3 text-[13px] gap-2", +}; + +export interface ButtonProps extends ButtonHTMLAttributes { + variant?: Variant; + size?: Size; + icon?: LucideIcon; + loading?: boolean; + kbd?: string; +} + +export const Button = forwardRef(function Button( + { variant = "secondary", size = "md", icon: Icon, loading, kbd, className, children, disabled, ...rest }, + ref, +) { + return ( + + ); +}); + +export function IconButton({ + icon: Icon, + label, + className, + active, + ...rest +}: ButtonHTMLAttributes & { icon: LucideIcon; label: string; active?: boolean }) { + return ( + + ); +} + +export const Spinner = ({ className }: { className?: string }) => ( + +); + +/* -------------------------------------------------------------------- Kbd */ + +export const Kbd = ({ children, className }: { children: ReactNode; className?: string }) => ( + + {children} + +); + +/* ------------------------------------------------------------------ Badge */ + +type Tone = "neutral" | "ok" | "warn" | "bad" | "accent" | "info"; +const TONES: Record = { + neutral: "text-muted bg-raised border-line", + ok: "text-ok bg-ok/10 border-ok/25", + warn: "text-warn bg-warn/10 border-warn/25", + bad: "text-bad bg-bad/10 border-bad/25", + accent: "text-accent bg-accent/10 border-accent/25", + info: "text-info bg-info/10 border-info/25", +}; + +export const Badge = ({ + tone = "neutral", + dot, + children, + className, +}: { + tone?: Tone; + dot?: boolean; + children: ReactNode; + className?: string; +}) => ( + + {dot && } + {children} + +); + +export const TIER_TONE: Record = { high: "ok", medium: "info", review: "warn" }; + +export const TierBadge = ({ tier }: { tier: Tier }) => ( + + {TIER_LABEL[tier]} + +); + +/* --------------------------------------------------------- ConfidenceBar */ + +const BAR_FILL: Record = { high: "bg-ok", medium: "bg-info", review: "bg-warn" }; + +/** Always paired with the number: colour alone never carries the meaning. */ +export function ConfidenceBar({ value, className }: { value: number | null; className?: string }) { + const tier = tierOf(value); + const w = Math.max(0, Math.min(1, value ?? 0)) * 100; + return ( +
    +
    +
    +
    + {pct(value)} +
    + ); +} + +/* ----------------------------------------------------------------- Swatch */ + +export function Swatch({ color, size = 20, label }: { color: string | null; size?: number; label?: string }) { + return ( + + ); +} + +/* ------------------------------------------------------------------- Card */ + +export const Card = ({ className, children }: { className?: string; children: ReactNode }) => ( +
    + {children} +
    +); + +export const CardHeader = ({ + title, + icon: Icon, + children, +}: { + title: string; + icon?: LucideIcon; + children?: ReactNode; +}) => ( +
    + {Icon && } +

    {title}

    +
    {children}
    +
    +); + +/* ------------------------------------------------------------ Empty state */ + +export function Empty({ + icon: Icon, + title, + children, + className, +}: { + icon: LucideIcon; + title: string; + children?: ReactNode; + className?: string; +}) { + return ( +
    +
    + +
    +

    {title}

    + {children &&
    {children}
    } +
    + ); +} + +export const Skeleton = ({ className }: { className?: string }) => ( +
    +); diff --git a/web/src/features/inspector/FrameInspector.tsx b/web/src/features/inspector/FrameInspector.tsx new file mode 100644 index 0000000..83adffb --- /dev/null +++ b/web/src/features/inspector/FrameInspector.tsx @@ -0,0 +1,550 @@ +import { useCallback, useEffect, useMemo, useRef, useState } from "react"; +import { + BoxSelect, + ChevronLeft, + ChevronRight, + CircleSlash, + Eye, + EyeOff, + Film, + Pause, + Play, + ScanEye, + SkipBack, + SkipForward, +} from "lucide-react"; +import { videoUrl, type CatalogEntry, type InspectorBox, type InspectorDoc } from "@/lib/api"; +import { cn } from "@/lib/cn"; +import { num, pct, timecode } from "@/lib/format"; +import { useInspector } from "@/lib/queries"; +import { Badge, Card, CardHeader, Empty, IconButton, Kbd, Skeleton, Swatch } from "@/components/ui"; + +const isTyping = (el: EventTarget | null) => + el instanceof HTMLElement && ["INPUT", "TEXTAREA", "SELECT"].includes(el.tagName); + +/** Space on a focused button already clicks it; handling it too would double-fire. */ +const activatesOnSpace = (el: EventTarget | null) => + el instanceof HTMLElement && (el.tagName === "BUTTON" || el.tagName === "A"); + +/** The identity the object was counted as (majority of its sightings). */ +const idOf = (b: InspectorBox) => b.track_sku ?? b.sku; +/** This frame's colour guess disagreed with the object's counted identity. */ +const flickered = (b: InspectorBox) => b.track_sku != null && b.track_sku !== b.sku; +/** A filter matches the counted identity or this frame's guess (reviews name the latter). */ +const matches = (b: InspectorBox, sku: string) => idOf(b) === sku || b.sku === sku; + +/** Sampled frames are sparse; find the one the player is currently showing. */ +function frameAt(frames: InspectorDoc["frames"], t: number, halfStep: number): number | null { + let lo = 0; + let hi = frames.length - 1; + let best = -1; + while (lo <= hi) { + const mid = (lo + hi) >> 1; + if (frames[mid]!.t <= t + 1e-3) (best = mid), (lo = mid + 1); + else hi = mid - 1; + } + if (best < 0) return null; + // Past the last sample by more than a step: the pipeline never saw this. + return t - frames[best]!.t > halfStep * 2 ? null : best; +} + +function Timeline({ + doc, + boxesByFrame, + current, + onSeek, + skuFilter, +}: { + doc: InspectorDoc; + boxesByFrame: Map; + current: number | null; + onSeek: (i: number) => void; + skuFilter: string | null; +}) { + const max = Math.max(1, ...doc.frames.map((f) => boxesByFrame.get(f.index)?.length ?? 0)); + const ref = useRef(null); + const seekFromPointer = (clientX: number) => { + const r = ref.current!.getBoundingClientRect(); + const i = Math.round(((clientX - r.left) / r.width) * (doc.frames.length - 1)); + onSeek(Math.max(0, Math.min(doc.frames.length - 1, i))); + }; + return ( +
    +
    + Sampled frames · bar height = objects seen + + kept + dropped + +
    +
    { + e.currentTarget.setPointerCapture(e.pointerId); + seekFromPointer(e.clientX); + }} + onPointerMove={(e) => e.buttons === 1 && seekFromPointer(e.clientX)} + className="relative flex h-10 cursor-pointer touch-none items-end gap-px rounded-md bg-bg px-px" + > + {doc.frames.map((f) => { + const boxes = boxesByFrame.get(f.index) ?? []; + const hits = skuFilter ? boxes.filter((b) => matches(b, skuFilter)).length : boxes.length; + // Dropped frames are marked, not emphasised: at full height they drown the data. + const h = f.kept ? Math.max(8, (hits / max) * 100) : 45; + return ( + + ); + })} + {current != null && ( + + )} +
    +
    + ); +} + +export function FrameInspector({ + runId, + catalog, + initialFrame, + skuFilter, + onFrameChange, + onSkuFilter, +}: { + runId: string; + catalog: CatalogEntry[] | undefined; + initialFrame: number | null; + skuFilter: string | null; + onFrameChange: (frame: number) => void; + onSkuFilter: (sku: string | null) => void; +}) { + const inspector = useInspector(runId, true); + const doc = inspector.data; + const videoRef = useRef(null); + const [cur, setCur] = useState(null); + const [playing, setPlaying] = useState(false); + const [overlay, setOverlay] = useState(true); + // Arriving from a review (a SKU and a frame), the flagged sighting is often + // one that was never counted; hiding uncounted boxes would hide the evidence. + const [uncounted, setUncounted] = useState(initialFrame != null && skuFilter != null); + const [selected, setSelected] = useState(null); // index into the frame's boxes + const [videoFailed, setVideoFailed] = useState(false); + + const swatch = useMemo(() => new Map(catalog?.map((c) => [c.sku, c.swatch])), [catalog]); + + const boxesByFrame = useMemo(() => { + const m = new Map(); + doc?.boxes.forEach((b) => { + const list = m.get(b.frame) ?? []; + list.push(b); + m.set(b.frame, list); + }); + return m; + }, [doc]); + + const trackSpan = useMemo(() => { + const m = new Map(); + doc?.boxes.forEach((b) => { + if (b.track_id == null) return; + const s = m.get(b.track_id) ?? { first: b.frame, last: b.frame, hits: 0 }; + s.first = Math.min(s.first, b.frame); + s.last = Math.max(s.last, b.frame); + s.hits++; + m.set(b.track_id, s); + }); + return m; + }, [doc]); + + const frames = doc?.frames ?? []; + const fps = frames.length > 1 ? 1 / Math.max(1e-3, (frames[frames.length - 1]!.t - frames[0]!.t) / (frames.length - 1)) : 30; + const halfStep = 0.5 / fps; + const [W, H] = doc?.frame_size ?? [16, 9]; + const frame = cur != null ? frames[cur] : undefined; + const allBoxes = frame ? (boxesByFrame.get(frame.index) ?? []) : []; + const boxes = allBoxes.filter((b) => uncounted || b.counted); + const sel = selected != null ? boxes[selected] : undefined; + + const seekTo = useCallback( + (i: number) => { + const f = frames[i]; + if (!f) return; + setCur(i); + setSelected(null); + const v = videoRef.current; + // Nudge past the frame boundary so the decoder lands on this frame, not the one before. + if (v && !videoFailed) v.currentTime = f.t + Math.min(0.01, halfStep / 2); + }, + [frames, halfStep, videoFailed], + ); + + // Follow the player. requestVideoFrameCallback tracks presented frames exactly; + // timeupdate (~4 Hz) is the fallback. + useEffect(() => { + const v = videoRef.current; + if (!v || !frames.length) return; + const sync = () => setCur(frameAt(frames, v.currentTime, halfStep)); + if ("requestVideoFrameCallback" in v) { + let id = 0; + const loop = () => { + sync(); + id = v.requestVideoFrameCallback(loop); + }; + id = v.requestVideoFrameCallback(loop); + v.addEventListener("seeked", sync); + return () => { + v.cancelVideoFrameCallback(id); + v.removeEventListener("seeked", sync); + }; + } + const vv = v as HTMLVideoElement; + vv.addEventListener("timeupdate", sync); + vv.addEventListener("seeked", sync); + return () => { + vv.removeEventListener("timeupdate", sync); + vv.removeEventListener("seeked", sync); + }; + }, [frames, halfStep]); + + // Deep link: land on the requested frame once the video can seek. + const landed = useRef(false); + useEffect(() => { + if (landed.current || !frames.length) return; + const i = initialFrame != null ? frames.findIndex((f) => f.index === initialFrame) : 0; + const go = () => { + landed.current = true; + seekTo(Math.max(0, i)); + }; + const v = videoRef.current; + if (videoFailed || (v && v.readyState >= 1)) go(); + else v?.addEventListener("loadedmetadata", go, { once: true }); + }, [frames, initialFrame, seekTo, videoFailed]); + + // Already open and asked for a different frame (e.g. "Show in frame + // inspector" from the drawer): go there. The URL tracks the paused frame, + // so this is a no-op for the inspector's own steps. + useEffect(() => { + if (!landed.current || initialFrame == null || playing) return; + if (frames[cur ?? -1]?.index === initialFrame) return; + const i = frames.findIndex((f) => f.index === initialFrame); + if (i >= 0) { + seekTo(i); + if (skuFilter) setUncounted(true); + } + // Only when the requested frame changes. + }, [initialFrame]); + + // Keep the URL on the paused frame so it can be shared. + useEffect(() => { + if (!playing && frame) onFrameChange(frame.index); + // Only on a new frame or a pause; onFrameChange is a fresh closure each render. + }, [playing, frame?.index]); + + const togglePlay = () => { + const v = videoRef.current; + if (!v || videoFailed) return; + if (v.paused) v.play(); + else v.pause(); + }; + const step = (d: number) => { + videoRef.current?.pause(); + seekTo(Math.max(0, Math.min(frames.length - 1, (cur ?? 0) + d))); + }; + + useEffect(() => { + const onKey = (e: KeyboardEvent) => { + if (isTyping(e.target) || e.metaKey || e.ctrlKey || document.querySelector('[role="dialog"]')) return; + if (e.key === " ") { + if (activatesOnSpace(e.target)) return; + e.preventDefault(); + togglePlay(); + } + else if (e.key === "ArrowLeft") (e.preventDefault(), step(e.shiftKey ? -10 : -1)); + else if (e.key === "ArrowRight") (e.preventDefault(), step(e.shiftKey ? 10 : 1)); + else if (e.key.toLowerCase() === "b") setOverlay((o) => !o); + else if (e.key.toLowerCase() === "u") setUncounted((o) => !o); + }; + window.addEventListener("keydown", onKey); + return () => window.removeEventListener("keydown", onKey); + }); + + if (inspector.isLoading) { + return ( + + + + ); + } + if (inspector.isError || !doc) { + return ( + + + Runs counted before the dashboard's telemetry was added, or from the CLI, don't have{" "} + inspector.json. Re-run the video from the dashboard to inspect it. + + + ); + } + + const skus = [...new Set(doc.boxes.map(idOf))].sort(); + + return ( +
    + + + + setOverlay((o) => !o)} /> + setUncounted((o) => !o)} /> + + + {/* Stage: the SVG shares the video's box and the frame's pixel space. */} +
    +
    + {!videoFailed ? ( +
    +
    + + {/* transport */} +
    + step(-10)} /> + step(-1)} /> + + step(1)} /> + step(10)} /> + + {frame ? ( + <> + {timecode(frame.t)} · frame {frame.index + 1}/{frames.length} + + ) : ( + "between samples" + )} + + + ←→ step B boxes U uncounted + +
    + (videoRef.current?.pause(), seekTo(i))} skuFilter={skuFilter} /> +     + + {/* side panel */} + +
    + ); +} diff --git a/web/src/features/pipeline/PipelineTracker.tsx b/web/src/features/pipeline/PipelineTracker.tsx new file mode 100644 index 0000000..a76b002 --- /dev/null +++ b/web/src/features/pipeline/PipelineTracker.tsx @@ -0,0 +1,283 @@ +import { motion, useReducedMotion } from "motion/react"; +import { + Check, + Database, + Film, + Hash, + type LucideIcon, + ScanSearch, + SlidersHorizontal, + Tags, + X, +} from "lucide-react"; +import type { LiveRun, RunDetail, Telemetry } from "@/lib/api"; +import { cn } from "@/lib/cn"; +import { duration, num, pct } from "@/lib/format"; + +type StepState = "pending" | "active" | "done" | "failed"; + +interface Step { + key: string; + label: string; + icon: LucideIcon; + metric: (t: Telemetry, done: boolean) => string; +} + +/** The backbone, in order. It never changes, so neither does this list. */ +const STEPS: Step[] = [ + { + key: "capture", + label: "Capture", + icon: Film, + metric: (t) => + t.frames_expected ? `${t.frames_read} / ~${t.frames_expected} frames` : `${t.frames_read} frames`, + }, + { + key: "preprocess", + label: "Pre-process", + icon: SlidersHorizontal, + metric: (t) => (t.frames_dropped ? `${t.frames_dropped} dropped` : `${t.frames_kept} kept`), + }, + { key: "detect", label: "Detect", icon: ScanSearch, metric: (t) => `${num(t.detections)} boxes` }, + { key: "identify", label: "Identify", icon: Tags, metric: (t) => `${num(t.items)} matched` }, + { + key: "count", + label: "Count", + icon: Hash, + metric: (t, done) => (done || t.tracks ? `${t.tracks} objects` : "waiting"), + }, + { key: "output", label: "Output", icon: Database, metric: (_, done) => (done ? "JSON · CSV · SQLite" : "waiting") }, +]; + +/** + * Stages 1-4 run per frame, interleaved, so while frames are flowing all four + * are genuinely active at once. Count and Output run once, after the last + * frame. The tracker says exactly that rather than faking a sequence. + */ +function stepStates(live: LiveRun | null, t: Telemetry | null): StepState[] { + if (!live) return STEPS.map(() => "done"); + if (live.status === "queued") return STEPS.map(() => "pending"); + if (live.status === "done" || live.phase === "done") return STEPS.map(() => "done"); + + if (live.status === "failed") { + // Mark done whatever produced evidence; the first stage without any failed. + const reached = [ + (t?.frames_read ?? 0) > 0, + (t?.frames_read ?? 0) > 0, + (t?.frames_kept ?? 0) > 0, + (t?.items ?? 0) > 0, + live.phase === "output", + false, + ]; + const firstMissing = reached.indexOf(false); + return STEPS.map((_, i) => (i < firstMissing ? "done" : i === firstMissing ? "failed" : "pending")); + } + + switch (live.phase) { + case "count": + return ["done", "done", "done", "done", "active", "pending"]; + case "output": + return ["done", "done", "done", "done", "done", "active"]; + default: + return ["active", "active", "active", "active", "pending", "pending"]; + } +} + +/** For a finished run the server no longer remembers live telemetry for. */ +function telemetryFromRun(run: RunDetail): Telemetry { + return { + frames_expected: null, + frames_read: run.frames_read, + frames_kept: run.frames_used, + frames_dropped: run.frames_dropped, + detections: run.detections, + items: run.detections, + tracks: run.tracks, + fps: run.duration_s ? run.frames_read / run.duration_s : 0, + elapsed_s: run.duration_s ?? 0, + blur: null, + blur_avg: null, + brightness: null, + brightness_avg: null, + }; +} + +function StepNode({ step, state, metric, index }: { step: Step; state: StepState; metric: string; index: number }) { + const reduce = useReducedMotion(); + const Icon = state === "done" ? Check : state === "failed" ? X : step.icon; + return ( +
    +
    + {state === "active" && !reduce && ( + + )} + + + +
    +
    +

    + {index + 1} + {step.label} + + {state === "active" ? "(in progress)" : state === "done" ? "(complete)" : state === "failed" ? "(failed)" : "(waiting)"} + +

    +

    {metric}

    +
    +
    + ); +} + +function Connector({ state }: { state: "idle" | "flow" | "done" }) { + const reduce = useReducedMotion(); + return ( +
    + {state === "done" &&
    } + {state === "flow" && !reduce && ( + + )} + {state === "flow" && reduce &&
    } +
    + ); +} + +function Readout({ label, value, sub, bar }: { label: string; value: string; sub?: string; bar?: number | null }) { + return ( +
    +
    {label}
    +
    {value}
    + {bar != null && ( +
    +
    +
    + )} + {sub &&
    {sub}
    } +
    + ); +} + +export function PipelineTracker({ live, run }: { live: LiveRun | null; run?: RunDetail }) { + const t = live?.telemetry ?? (run ? telemetryFromRun(run) : null); + const states = stepStates(run && !live ? null : live, t); + const finished = states.every((s) => s === "done"); + const running = live?.status === "running"; + const progress = + t?.frames_expected && live?.phase === "frames" ? Math.min(1, t.frames_read / t.frames_expected) : null; + + const headline = + live?.status === "queued" + ? "Queued — waiting for the worker" + : live?.status === "failed" + ? "Run failed" + : finished + ? "Pipeline complete" + : !live?.phase + ? "Starting — probing and hashing the source" + : live.phase === "frames" + ? "Reading frames" + : live.phase === "count" + ? "Deduplicating across frames" + : "Writing outputs"; + + return ( +
    +
    +

    + {headline} +

    + {progress != null && ( + {pct(progress)} + )} + + {t ? `${duration(t.elapsed_s)} elapsed` : ""} + +
    + + {progress != null && ( +
    + +
    + )} + +
      + {STEPS.map((step, i) => ( +
    1. + + {i < STEPS.length - 1 && ( + + )} +
      2. + ))} +
    + + {live?.status === "failed" && live.error && ( +

    + {live.error} +

    + )} + + {t && ( +
    + + + + + + +
    + )} +
    + ); +} diff --git a/web/src/features/results/CountsTable.tsx b/web/src/features/results/CountsTable.tsx new file mode 100644 index 0000000..0784e7e --- /dev/null +++ b/web/src/features/results/CountsTable.tsx @@ -0,0 +1,302 @@ +import { useEffect, useMemo, useRef, useState } from "react"; +import { + ArrowDown, + ArrowUp, + ChevronsUpDown, + Download, + FileJson, + FileSpreadsheet, + ScanEye, + Search, + Table2, +} from "lucide-react"; +import type { RunDetail } from "@/lib/api"; +import { artifactUrl } from "@/lib/api"; +import { cn } from "@/lib/cn"; +import { exportCsv, exportJson } from "@/lib/export"; +import { signed } from "@/lib/format"; +import { TIER_LABEL, type SkuRow, type Tier } from "@/lib/status"; +import { Badge, Button, Card, CardHeader, ConfidenceBar, Empty, Swatch, TierBadge } from "@/components/ui"; + +type SortKey = "sku" | "count" | "confidence" | "variance" | "pending"; +type Filter = "all" | Tier; + +const FILTERS: Filter[] = ["all", "high", "medium", "review"]; + +function ExportMenu({ run, rows }: { run: RunDetail; rows: SkuRow[] }) { + const [open, setOpen] = useState(false); + const ref = useRef(null); + const outputs = run.meta.outputs ?? {}; + const artifact = (p: string | undefined) => + p ? artifactUrl(run.run_id, p.split(/[\\/]/).pop()!) : null; + const machineCsv = artifact(outputs.csv); + const machineJson = artifact(outputs.json); + + const item = + "flex w-full cursor-pointer items-center gap-2.5 rounded px-2.5 py-2 text-left text-[13px] text-fg hover:bg-hover pointer-coarse:py-3"; + + // Close on any press outside. Not onBlur: Safari doesn't focus buttons on + // click, so a blur-based popover never closes there. + useEffect(() => { + if (!open) return; + const onDown = (e: PointerEvent) => !ref.current?.contains(e.target as Node) && setOpen(false); + const onKey = (e: KeyboardEvent) => e.key === "Escape" && setOpen(false); + document.addEventListener("pointerdown", onDown); + document.addEventListener("keydown", onKey); + return () => { + document.removeEventListener("pointerdown", onDown); + document.removeEventListener("keydown", onKey); + }; + }, [open]); + + return ( +
    + + {open && ( +
    +

    Current view · {rows.length} rows, with recounts

    + + + {(machineCsv || machineJson) && ( + <> +
    +

    Pipeline output · audited, unedited

    + {machineCsv && ( + + counts.csv + + )} + {machineJson && ( + + result.json + + )} + + )} +
    + )} +
    + ); +} + +function SortHeader({ + label, + k, + sort, + onSort, + align = "left", +}: { + label: string; + k: SortKey; + sort: { key: SortKey; dir: 1 | -1 }; + onSort: (k: SortKey) => void; + align?: "left" | "right"; +}) { + const active = sort.key === k; + const Icon = !active ? ChevronsUpDown : sort.dir === 1 ? ArrowUp : ArrowDown; + return ( + + + + ); +} + +export function CountsTable({ + run, + rows, + onInspectSku, + onReviewSku, +}: { + run: RunDetail; + rows: SkuRow[]; + onInspectSku: (sku: string) => void; + onReviewSku: (sku: string) => void; +}) { + const [query, setQuery] = useState(""); + const [filter, setFilter] = useState("all"); + const [sort, setSort] = useState<{ key: SortKey; dir: 1 | -1 }>({ key: "count", dir: -1 }); + + const counts = useMemo(() => { + const c: Record = { all: rows.length, high: 0, medium: 0, review: 0 }; + rows.forEach((r) => c[r.tier]++); + return c; + }, [rows]); + + const visible = useMemo(() => { + const q = query.trim().toLowerCase(); + return rows + .filter((r) => filter === "all" || r.tier === filter) + .filter((r) => !q || r.sku.toLowerCase().includes(q) || (r.label ?? "").toLowerCase().includes(q)) + .sort((a, b) => { + const av = a[sort.key] ?? -Infinity; + const bv = b[sort.key] ?? -Infinity; + return (av < bv ? -1 : av > bv ? 1 : 0) * sort.dir; + }); + }, [rows, query, filter, sort]); + + const hasExpected = rows.some((r) => r.expected != null); + const hasRecount = rows.some((r) => r.reviewedCount != null); + const onSort = (key: SortKey) => + setSort((s) => (s.key === key ? { key, dir: (s.dir * -1) as 1 | -1 } : { key, dir: key === "sku" ? 1 : -1 })); + + return ( + + +
    + + setQuery(e.target.value)} + placeholder="Search SKU or name" + aria-label="Search SKUs" + className="h-7 w-44 rounded-md border border-line bg-bg pr-2 pl-8 text-xs text-fg placeholder:text-subtle focus:border-accent/60 pointer-coarse:h-11 sm:w-56" + /> +
    + +     + +
    + {FILTERS.map((f) => ( + + ))} +
    + + {visible.length === 0 ? ( + + {rows.length ? "Try a different search or filter." : "The pipeline finished without counting any objects."} + + ) : ( +
    + {/* relative: makes this the containing block for the sr-only header, + which otherwise escapes the scroller and widens the whole page. */} + + + + + + {hasExpected && } + {hasExpected && } + + + + + + + + {visible.map((r) => ( + + + + {hasExpected && } + {hasExpected && ( + + )} + + + + + + ))} + +
    ExpectedStatusActions
    +
    + +
    +

    {r.sku}

    +

    {r.label}

    +
    +
    +     		+ {r.reviewedCount != null && r.reviewedCount !== r.count ? ( + + {r.count} + {r.reviewedCount} + + ) : ( + r.count + )} + {r.expected ?? "—"} + {(() => { + // After a recount, the variance that matters is the recount's. + const v = r.reviewedVariance ?? r.variance; + const tone = v == null ? "text-subtle" : v === 0 ? "text-ok" : "text-warn"; + return r.reviewedVariance != null && r.reviewedVariance !== r.variance ? ( + + {signed(r.variance)} + {signed(v)} + + ) : ( + {signed(v)} + ); + })()} + + {r.pending ? ( + + ) : ( + 0 + )} + + +
    +
    + )} + {hasRecount && ( +

    + Recount + Blue figures are reviewer recounts. The struck-through machine count stays in the audit trail. +

    + )} +     + ); +} diff --git a/web/src/features/results/MetricCards.tsx b/web/src/features/results/MetricCards.tsx new file mode 100644 index 0000000..565d89c --- /dev/null +++ b/web/src/features/results/MetricCards.tsx @@ -0,0 +1,108 @@ +import { AlertTriangle, Boxes, Gauge, type LucideIcon, Package } from "lucide-react"; +import { motion } from "motion/react"; +import type { RunDetail } from "@/lib/api"; +import { cn } from "@/lib/cn"; +import { pct } from "@/lib/format"; +import { tierOf, type SkuRow } from "@/lib/status"; + +interface Metric { + label: string; + value: string; + sub: string; + icon: LucideIcon; + tone?: "warn" | "ok" | "info"; + action?: { label: string; onClick: () => void }; +} + +export function MetricCards({ + run, + rows, + unknownSku, + onOpenReview, +}: { + run: RunDetail; + rows: SkuRow[]; + unknownSku: string; + onOpenReview: () => void; +}) { + const recounted = rows.some((r) => r.reviewedCount != null); + const reviewedTotal = rows.reduce((n, r) => n + (r.reviewedCount ?? r.count), 0); + const skus = run.counts.filter((c) => c.count > 0 && c.sku !== unknownSku).length; + const unknown = run.counts.find((c) => c.sku === unknownSku)?.count ?? 0; + const pending = run.reviews.filter((r) => r.status === "pending").length; + const conf = tierOf(run.overall_confidence); + + const metrics: Metric[] = [ + { + label: "SKUs detected", + value: String(skus), + sub: unknown ? `+ ${unknown} unidentified` : `of ${run.counts.length} in result`, + icon: Boxes, + }, + { + label: "Units counted", + value: run.total.toLocaleString(), + sub: + recounted && reviewedTotal !== run.total + ? `${reviewedTotal.toLocaleString()} after recounts` + : `${run.tracks} tracks · ${run.detections.toLocaleString()} sightings`, + icon: Package, + }, + { + label: "Overall confidence", + value: pct(run.overall_confidence), + sub: `${run.frames_used}/${run.frames_read} frames usable`, + icon: Gauge, + tone: conf === "high" ? "ok" : conf === "medium" ? "info" : "warn", + }, + { + label: "Items to check", + value: String(pending), + sub: pending ? "low-confidence detections" : "queue is clear", + icon: AlertTriangle, + tone: pending ? "warn" : "ok", + action: pending ? { label: "Review", onClick: onOpenReview } : undefined, + }, + ]; + + return ( +
    + {metrics.map((m, i) => ( + +
    + + {m.label} + {m.action && ( + + )} +
    +

    + {m.value} +

    +

    {m.sub}

    +     + ))} +
    + ); +} diff --git a/web/src/features/review/ReviewDrawer.tsx b/web/src/features/review/ReviewDrawer.tsx new file mode 100644 index 0000000..df2c36a --- /dev/null +++ b/web/src/features/review/ReviewDrawer.tsx @@ -0,0 +1,594 @@ +import { useCallback, useEffect, useMemo, useRef, useState } from "react"; +import { AnimatePresence, motion } from "motion/react"; +import { + Check, + CheckCheck, + ChevronDown, + ChevronUp, + ClipboardCheck, + Crosshair, + Minus, + Plus, + Replace, + Undo2, + X, +} from "lucide-react"; +import { artifactUrl, type CatalogEntry, type Review, type RunDetail } from "@/lib/api"; +import { cn } from "@/lib/cn"; +import { pct, REASONS } from "@/lib/format"; +import { useResolveReview } from "@/lib/queries"; +import { hsvToHex, rankCandidates } from "@/lib/status"; +import { Badge, Button, Empty, IconButton, Kbd, Swatch } from "@/components/ui"; + +/** Text entry only: a focused radio (a candidate SKU) must not swallow the shortcuts. */ +const isTyping = (el: EventTarget | null) => + (el instanceof HTMLInputElement && !["radio", "checkbox", "button"].includes(el.type)) || + (el instanceof HTMLElement && (el.tagName === "TEXTAREA" || el.isContentEditable)); + +/* --------------------------------------------------------------- list row */ + +function QueueRow({ review, active, onSelect }: { review: Review; active: boolean; onSelect: () => void }) { + const ref = useRef(null); + useEffect(() => { + if (active) ref.current?.scrollIntoView({ block: "nearest" }); + }, [active]); + return ( + + ); +} + +/* ------------------------------------------------------ item-scope detail */ + +function ItemDetail({ + review, + catalog, + unknownSku, + choice, + setChoice, + onInspect, +}: { + review: Review; + catalog: CatalogEntry[]; + unknownSku: string; + choice: string | null; + setChoice: (sku: string) => void; + onInspect: () => void; +}) { + const { hue, sat, val } = review.meta; + const measured = hue != null && sat != null && val != null ? hsvToHex(hue, sat, val) : null; + const candidates = useMemo(() => rankCandidates(catalog, hue, sat), [catalog, hue, sat]); + const washedOut = candidates.length > 0 && candidates.every((c) => c.distanceDeg == null) && hue != null; + const [w, h] = review.bbox ? [review.bbox[2] - review.bbox[0], review.bbox[3] - review.bbox[1]] : [0, 0]; + + return ( +
    + {/* What the camera saw */} +
    +
    + Camera crop + frame {review.frame_index} +
    +
    + {review.crop_path ? ( + {`Crop + ) : ( +

    No crop saved

    + )} +
    +
    +
    Measured colour
    +
    + {measured ? ( + <> + + {washedOut ? "near-grey" : `${Math.round(hue! * 2)}°`} + + ) : ( + "—" + )} +
    +
    Saturation
    +
    {sat != null ? pct(sat / 255) : "—"}
    +
    Box size
    +
    {w ? `${Math.round(w)}×${Math.round(h)} px` : "—"}
    +
    + +
    + + {/* What it could be */} +
    + Choose the correct SKU +
    + Candidate SKUs + {washedOut ? "no colour evidence" : "by colour distance"} +
    +
      + {candidates.map((c, i) => { + const picked = c.entry.sku === review.sku; + const selected = choice === c.entry.sku; + return ( +
    • + +
      • + ); + })} +
    + {washedOut ? ( +

    + This patch is almost colourless, so colour can't say which SKU it is. Often it's a shelf label or + packaging detail rather than stock. Reject it if so. +

    + ) : review.sku === unknownSku ? ( +

    + The model matched no SKU. Pick one, or reject if this isn't stock. +

    + ) : null} +
    +
    + ); +} + +/* ------------------------------------------------------- sku-scope detail */ + +function SkuDetail({ + review, + catalog, + recount, + setRecount, + onInspect, +}: { + review: Review; + catalog: CatalogEntry[]; + recount: number; + setRecount: (n: number) => void; + onInspect: () => void; +}) { + const entry = catalog.find((c) => c.sku === review.sku); + const machine = review.meta.count ?? 0; + const diff = recount - machine; + return ( +
    +
    + +
    +

    {review.sku}

    +

    {entry?.label}

    +
    +
    +

    + The pipeline flagged this SKU's total for a human check. Recount it on the shelf or walk through it in the + frame inspector, then confirm the machine count or save your recount. +

    +
    + {[ + ["Machine count", String(machine)], + ["Expected", review.meta.expected != null ? String(review.meta.expected) : "—"], + ["Confidence", pct(review.confidence)], + ].map(([k, v]) => ( +
    +
    {k}
    +
    {v}
    +
    + ))} +
    + +
    + setRecount(Math.max(0, recount - 1))} className="border border-line" /> + setRecount(Math.max(0, Math.floor(Number(e.target.value) || 0)))} + className="h-8 w-20 rounded-md border border-line bg-surface text-center font-mono text-base text-fg tabular focus:border-accent/60 pointer-coarse:h-11" + /> + setRecount(recount + 1)} className="border border-line" /> + {diff !== 0 && ( + 0 ? "text-ok" : "text-warn")}> + {diff > 0 ? `+${diff}` : diff} vs machine + + )} +
    + +
    + ); +} + +/* ----------------------------------------------------------------- drawer */ + +export function ReviewDrawer({ + open, + run, + catalog, + unknownSku, + skuFilter, + onClearFilter, + onClose, + onInspect, +}: { + open: boolean; + run: RunDetail; + catalog: CatalogEntry[]; + unknownSku: string; + skuFilter: string | null; + onClearFilter: () => void; + onClose: () => void; + onInspect: (review: Review) => void; +}) { + const resolve = useResolveReview(run.run_id); + const panelRef = useRef(null); + const returnFocus = useRef(null); + // Hidden optimistically so the queue advances instantly; the refetch confirms. + const [done, setDone] = useState>(new Map()); + const [lastUndo, setLastUndo] = useState(null); + + const queue = useMemo( + () => + run.reviews + .filter((r) => r.status === "pending" && !done.has(r.review_id)) + .filter((r) => !skuFilter || r.sku === skuFilter) + // Whole-SKU reviews first: one decision there can settle many items. + .sort((a, b) => Number(b.meta.scope === "sku") - Number(a.meta.scope === "sku") || a.confidence - b.confidence), + [run.reviews, done, skuFilter], + ); + const total = run.reviews.filter((r) => !skuFilter || r.sku === skuFilter).length; + const resolvedCount = total - queue.length; + + const [activeId, setActiveId] = useState(null); + const active = queue.find((r) => r.review_id === activeId) ?? queue[0] ?? null; + const activeIndex = active ? queue.indexOf(active) : -1; + + const [choice, setChoice] = useState(null); + const [recount, setRecount] = useState(0); + useEffect(() => { + if (!active) return; + setChoice(active.sku !== unknownSku ? active.sku : null); + setRecount(active.meta.count ?? 0); + // Reset only when the item changes, not on every refetch of the same one. + }, [active?.review_id]); + + const move = useCallback( + (delta: number) => { + if (!queue.length) return; + const next = queue[(Math.max(0, activeIndex) + delta + queue.length) % queue.length]; + setActiveId(next!.review_id); + }, + [queue, activeIndex], + ); + + const decide = useCallback( + (status: "accepted" | "rejected" | "corrected", extra: { resolved_sku?: string; resolved_count?: number } = {}) => { + if (!active) return; + const next = queue[activeIndex + 1] ?? queue[activeIndex - 1] ?? null; + setDone((m) => new Map(m).set(active.review_id, status)); + setLastUndo(active); + setActiveId(next?.review_id ?? null); + resolve.mutate( + { reviewId: active.review_id, status, ...extra }, + { + onError: () => + setDone((m) => { + const n = new Map(m); + n.delete(active.review_id); + return n; + }), + }, + ); + }, + [active, activeIndex, queue, resolve], + ); + + const isSku = active?.meta.scope === "sku"; + const changed = !!active && !isSku && choice != null && choice !== active.sku; + const recounted = !!active && isSku && recount !== (active.meta.count ?? 0); + + const primary = () => { + if (!active) return; + if (isSku) { + return recounted ? decide("corrected", { resolved_sku: active.sku, resolved_count: recount }) : decide("accepted"); + } + if (changed) return decide("corrected", { resolved_sku: choice! }); + if (active.sku === unknownSku) return; // nothing to approve: pick a SKU or reject + decide("accepted"); + }; + + const undo = () => { + if (!lastUndo) return; + resolve.mutate({ reviewId: lastUndo.review_id, status: "pending" }); + setDone((m) => { + const n = new Map(m); + n.delete(lastUndo.review_id); + return n; + }); + setActiveId(lastUndo.review_id); + setLastUndo(null); + }; + + // Focus in on open, back to the trigger on close. + useEffect(() => { + if (open) { + returnFocus.current = document.activeElement as HTMLElement; + requestAnimationFrame(() => panelRef.current?.focus()); + } else { + returnFocus.current?.focus?.(); + } + }, [open]); + + useEffect(() => { + if (!open) return; + const onKey = (e: KeyboardEvent) => { + if (e.key === "Escape") return onClose(); + if (e.key === "Tab" && panelRef.current) { + // Keep focus inside the dialog. + const f = panelRef.current.querySelectorAll( + 'button:not([disabled]),input:not([disabled]):not(.sr-only),[href],[tabindex]:not([tabindex="-1"])', + ); + const first = f[0]; + const last = f[f.length - 1]; + if (e.shiftKey && document.activeElement === first) (e.preventDefault(), last?.focus()); + else if (!e.shiftKey && document.activeElement === last) (e.preventDefault(), first?.focus()); + return; + } + if (isTyping(e.target) || e.metaKey || e.ctrlKey || e.altKey) return; + const k = e.key.toLowerCase(); + if (k === "j" || e.key === "ArrowDown") (e.preventDefault(), move(1)); + else if (k === "k" || e.key === "ArrowUp") (e.preventDefault(), move(-1)); + // Not Enter: on a focused button it would fire this and the click. + else if (k === "a") (e.preventDefault(), primary()); + else if (k === "r" && active) (e.preventDefault(), decide("rejected")); + else if (k === "z") (e.preventDefault(), undo()); + else if (/^[1-9]$/.test(e.key) && active && !isSku) { + const c = rankCandidates(catalog, active.meta.hue, active.meta.sat)[Number(e.key) - 1]; + if (c) setChoice(c.entry.sku); + } + }; + window.addEventListener("keydown", onKey); + return () => window.removeEventListener("keydown", onKey); + }); + + return ( + + {open && ( + <> + + + {/* header */} +
    +

    Items to check

    + + {resolvedCount}/{total} resolved + + {skuFilter && ( + + )} +
    + {lastUndo && ( + + )} + +
    +
    +
    + +
    + + {!active ? ( + + {total ? "Every flagged item has a decision. They're recorded in the run's audit trail." : "Nothing was flagged in this run."} + + ) : ( +
    + {/* queue */} + + + {/* detail */} +
    +
    + {REASONS[active.reason] ?? active.reason} + conf {pct(active.confidence)} + + {activeIndex + 1} of {queue.length} + + move(-1)} /> + move(1)} /> +
    + + + + {isSku ? ( + onInspect(active)} + /> + ) : ( + onInspect(active)} + /> + )} + + + + {/* actions */} +
    + + + {resolve.isError && Couldn't save — try again} + {isSku ? ( + + ) : changed ? ( + + ) : ( + active.sku === unknownSku ? ( + Pick a SKU 1–9 or reject + ) : ( + + ) + )} +
    +
    +
    + )} + +

    + J K move + 1–9 pick SKU + A approve + R reject + Z undo + Esc close +

    +     + + )} +     + ); +} diff --git a/web/src/features/run/RunView.tsx b/web/src/features/run/RunView.tsx new file mode 100644 index 0000000..1f6d4bf --- /dev/null +++ b/web/src/features/run/RunView.tsx @@ -0,0 +1,249 @@ +import { useMemo, useState } from "react"; +import { AnimatePresence, motion } from "motion/react"; +import { AlertTriangle, ChevronDown, ClipboardCheck, FileQuestion, Fingerprint, ScanEye, Table2 } from "lucide-react"; +import { ApiError, isPending, type RunDetail } from "@/lib/api"; +import { cn } from "@/lib/cn"; +import { duration, runTitle, shortId } from "@/lib/format"; +import { useCatalog, useHealth, useRun, useRuns } from "@/lib/queries"; +import { navigate, patchRun, type Route } from "@/lib/route"; +import { buildRows } from "@/lib/status"; +import { Button, Empty, Skeleton } from "@/components/ui"; +import { PipelineTracker } from "@/features/pipeline/PipelineTracker"; +import { MetricCards } from "@/features/results/MetricCards"; +import { CountsTable } from "@/features/results/CountsTable"; +import { ReviewDrawer } from "@/features/review/ReviewDrawer"; +import { FrameInspector } from "@/features/inspector/FrameInspector"; + +type RunRoute = Extract; + +function Warnings({ warnings }: { warnings: string[] }) { + if (!warnings.length) return null; + return ( +
    +

    + {warnings.length === 1 ? "1 warning" : `${warnings.length} warnings`} from the pipeline +

    +
      + {warnings.map((w) => ( +
    • {w}
      • + ))} +
    +
    + ); +} + +function Provenance({ run }: { run: RunDetail }) { + const [open, setOpen] = useState(false); + return ( +
    + + + {open && ( + +
    + +
    +
    source
    +
    {run.source}
    +
    config
    +
    {run.config_fingerprint ?? "—"}
    + {run.meta.audit && ( + <> +
    manifest sha256
    +
    {run.meta.audit.manifest_sha256}
    +
    source sha256
    +
    {run.meta.audit.source_sha256 ?? "—"}
    + + )} +
    +
    +     + )} +     +
    + ); +} + +function Tabs({ route, pending }: { route: RunRoute; pending: number }) { + const tabs = [ + { key: "results" as const, label: "Results", icon: Table2 }, + { key: "inspect" as const, label: "Frame inspector", icon: ScanEye }, + ]; + return ( + + ); +} + +export function RunView({ route }: { route: RunRoute }) { + const q = useRun(route.runId); + const catalog = useCatalog(); + const health = useHealth(); + const runs = useRuns(); + const unknownSku = health.data?.unknown_sku ?? "UNKNOWN"; + + const detail = q.data && !isPending(q.data) ? q.data : null; + const rows = useMemo( + () => (detail ? buildRows(detail.counts, detail.reviews, catalog.data, unknownSku) : []), + [detail, catalog.data, unknownSku], + ); + + if (q.isLoading) { + return ( +
    + +
    + {[0, 1, 2, 3].map((i) => ( + + ))} +
    + +
    + ); + } + + if (q.isError || !q.data) { + const notFound = q.error instanceof ApiError && q.error.status === 404; + return ( + + {notFound ? "It may have been counted against a different database." : String(q.error)} +
    + +
    +     + ); + } + + const filename = runs.data?.inFlight.find((r) => r.run_id === route.runId)?.filename; + + if (isPending(q.data)) { + const live = q.data.pending; + return ( +
    +
    +

    {runTitle(route.runId, live.source, live.filename)}

    +

    {route.runId}

    +
    + + {live.status !== "failed" && ( +

    + Results appear here when the run is committed: counts, the review queue and the audit pack are written + together, so you never see a half-finished run. +

    + )} +
    + ); + } + + const run = q.data; + const pending = run.reviews.filter((r) => r.status === "pending").length; + + return ( +
    +
    +
    +
    +

    {runTitle(run.run_id, run.source, filename)}

    +

    + {shortId(run.run_id)} + {new Date(run.started_at * 1000).toLocaleString()} + {duration(run.duration_s)} to count +

    +
    +
    + +
    +
    +
    + +
    +
    + +
    + {route.tab === "results" ? ( + <> + + patchRun(route, { review: true })} /> + patchRun(route, { tab: "inspect", sku, frame: null })} + onReviewSku={(sku) => patchRun(route, { review: true, sku })} + /> + + + ) : ( + patchRun(route, { frame }, { replace: true })} + onSkuFilter={(sku) => patchRun(route, { sku }, { replace: true })} + /> + )} +
    + + patchRun(route, { sku: null }, { replace: true })} + onClose={() => patchRun(route, { review: false, sku: route.tab === "inspect" ? route.sku : null })} + // Whole-SKU reviews have no frame (-1): open at the start, filtered to the SKU. + onInspect={(r) => + patchRun(route, { review: false, tab: "inspect", frame: r.frame_index >= 0 ? r.frame_index : null, sku: r.sku }) + } + /> +
    + ); +} diff --git a/web/src/features/upload/UploadPanel.tsx b/web/src/features/upload/UploadPanel.tsx new file mode 100644 index 0000000..db66161 --- /dev/null +++ b/web/src/features/upload/UploadPanel.tsx @@ -0,0 +1,295 @@ +import { useCallback, useEffect, useId, useRef, useState } from "react"; +import { AnimatePresence, motion } from "motion/react"; +import { AlertCircle, FileVideo, FolderInput, Play, Upload, X } from "lucide-react"; +import { useQueryClient } from "@tanstack/react-query"; +import { api, ApiError } from "@/lib/api"; +import { cn } from "@/lib/cn"; +import { bytes, duration } from "@/lib/format"; +import { keys } from "@/lib/queries"; +import { navigate } from "@/lib/route"; +import { Button, Card, CardHeader, IconButton } from "@/components/ui"; + +const ACCEPT = ["video/mp4", "video/quicktime"]; +const ACCEPT_EXT = /\.(mp4|mov|m4v)$/i; + +interface Picked { + file: File; + url: string; + meta: { duration: number; width: number; height: number } | null; + /** Set when the browser cannot decode it (e.g. HEVC .mov on some browsers). */ + previewFailed: boolean; +} + +export function UploadPanel() { + const qc = useQueryClient(); + const inputId = useId(); + const inputRef = useRef(null); + const abortRef = useRef(null); + const [picked, setPicked] = useState(null); + const [dragging, setDragging] = useState(false); + const [error, setError] = useState(null); + const [progress, setProgress] = useState(null); + const [serverPath, setServerPath] = useState(""); + const [showPath, setShowPath] = useState(false); + + // Object URLs pin the whole file in memory until revoked. Keyed on the URL, + // not the object: adding metadata to `picked` must not revoke a live preview. + const pickedUrl = picked?.url; + useEffect(() => () => void (pickedUrl && URL.revokeObjectURL(pickedUrl)), [pickedUrl]); + + const pick = useCallback((file: File | undefined) => { + setError(null); + if (!file) return; + if (!ACCEPT.includes(file.type) && !ACCEPT_EXT.test(file.name)) { + setError(`${file.name} isn't an MP4 or MOV video.`); + return; + } + setPicked({ file, url: URL.createObjectURL(file), meta: null, previewFailed: false }); + }, []); + + const start = async () => { + if (!picked) return; + setError(null); + setProgress(0); + abortRef.current = new AbortController(); + try { + const { run_id } = await api.upload(picked.file, setProgress, abortRef.current.signal); + qc.invalidateQueries({ queryKey: keys.runs }); + navigate({ view: "run", runId: run_id, tab: "results", frame: null, review: false, sku: null }); + } catch (e) { + setError(e instanceof ApiError ? e.message : "Upload failed"); + setProgress(null); + } + }; + + const startFromPath = async () => { + setError(null); + try { + const { run_id } = await api.startFromPath(serverPath.trim()); + qc.invalidateQueries({ queryKey: keys.runs }); + navigate({ view: "run", runId: run_id, tab: "results", frame: null, review: false, sku: null }); + } catch (e) { + setError(e instanceof ApiError ? e.message : "Could not start the run"); + } + }; + + const uploading = progress !== null; + + return ( +
    +
    +

    New count

    +

    + Walk the aisle at a steady pace with the shelf filling the frame. Every item is counted once, however + many frames it appears in. +

    +
    + + + + + + +
    + + {!picked ? ( + { + e.preventDefault(); + setDragging(true); + }} + onDragLeave={() => setDragging(false)} + onDrop={(e) => { + e.preventDefault(); + setDragging(false); + pick(e.dataTransfer.files[0]); + }} + className={cn( + "flex min-h-64 cursor-pointer flex-col items-center justify-center gap-3 rounded-lg border border-dashed", + "px-6 text-center transition-colors duration-150", + dragging + ? "border-accent bg-accent/8 text-fg" + : "border-line-strong bg-bg/50 text-muted hover:border-accent/50 hover:bg-accent/4", + )} + > + + + + + Drop a video here or{" "} + browse + + MP4 or MOV · one aisle per video works best + pick(e.target.files?.[0])} + /> + + ) : ( + +
    + {picked.previewFailed ? ( +
    + This browser can't preview this file (often HEVC-encoded .mov). It can still be counted: + the server decodes it with OpenCV. +
    + ) : ( +
    + +
    +
    + +

    {picked.file.name}

    + {!uploading && ( + setPicked(null)} /> + )} +
    +
    +
    Size
    +
    {bytes(picked.file.size)}
    +
    Length
    +
    {duration(picked.meta?.duration)}
    +
    Resolution
    +
    + {picked.meta ? `${picked.meta.width}×${picked.meta.height}` : "—"} +
    +
    + +
    + {uploading && ( +
    +
    + {progress! < 1 ? "Uploading" : "Queuing…"} + {Math.round(progress! * 100)}% +
    +
    + +
    +
    + )} + {uploading ? ( + + ) : ( + + )} +
    +
    +     + )} +     + + + {showPath && ( + { + e.preventDefault(); + if (serverPath.trim()) startFromPath(); + }} + > + +
    + setServerPath(e.target.value)} + placeholder="examples/demo_shelf.mp4" + className="h-8 min-w-0 flex-1 rounded-md border border-line bg-bg px-2.5 font-mono text-xs text-fg placeholder:text-subtle focus:border-accent/60 pointer-coarse:h-11" + /> + +
    +     + )} +     + + + {error && ( + + + {error} + + )} + +
    +     + +
      + {[ + ["Steady pace", "Motion blur is the main reason frames get dropped by the quality gate."], + ["Even light", "Very dark or blown-out frames are dropped and reported, not guessed at."], + ["Check the queue", "Anything the model isn't sure about lands in Items to check, with the crop it saw."], + ].map(([t, d], i) => ( +
    1. + + {i + 1} + {t} + + {d} +
      2. + ))} +
    +
    + ); +} diff --git a/web/src/index.css b/web/src/index.css new file mode 100644 index 0000000..474b6d1 --- /dev/null +++ b/web/src/index.css @@ -0,0 +1,94 @@ +@import "tailwindcss"; +@import "@fontsource-variable/inter"; +@import "@fontsource-variable/jetbrains-mono"; + +/* + * Design tokens. Every colour in a component comes from here, never a raw hex. + * Dark is primary: stockrooms are dim and screens are glanced at, so text sits + * at 7:1 or better and status colours are bright enough to read at arm's length. + * WCAG contrast, measured on bg / surface / raised: + * fg 16.9 / 16.0 / 15.0 · muted 7.5 / 7.2 / 6.7 · subtle 5.1 / 4.8 / 4.5 + * accent 6.2 / 5.9 / 5.5 · ok 10.4 · warn 10.9 · bad 6.8 · info 7.8 (on bg) + * Everything clears AA (4.5:1). `subtle` on raised is the floor: don't darken it. + */ +@theme { + --font-sans: "Inter Variable", ui-sans-serif, system-ui, sans-serif; + --font-mono: "JetBrains Mono Variable", ui-monospace, "Cascadia Code", Consolas, monospace; + + --color-bg: #0b0c0e; + --color-surface: #111316; + --color-raised: #171a1e; + --color-hover: #1d2126; + --color-line: #23272e; + --color-line-strong: #323841; + + --color-fg: #edeef0; + --color-muted: #9ba1ab; + --color-subtle: #7c828c; + + --color-accent: #5b8cff; + --color-accent-fg: #0b0c0e; + --color-ok: #3dd68c; + --color-warn: #ffb224; + --color-bad: #ff6369; + --color-info: #52a8ff; + + --radius-card: 10px; + --shadow-drawer: -24px 0 48px -12px rgb(0 0 0 / 0.6); + + --animate-shimmer: shimmer 1.6s linear infinite; + @keyframes shimmer { + from { background-position: -200% 0; } + to { background-position: 200% 0; } + } +} + +@layer base { + html { + color-scheme: dark; + -webkit-font-smoothing: antialiased; + font-feature-settings: "cv11", "ss01"; + } + body { + @apply bg-bg text-fg font-sans text-[13px] leading-5; + } + /* One focus treatment everywhere, only for keyboard users. */ + :focus-visible { + outline: 2px solid var(--color-accent); + outline-offset: 2px; + border-radius: 4px; + } + ::selection { + background: color-mix(in srgb, var(--color-accent) 35%, transparent); + } + * { + scrollbar-width: thin; + scrollbar-color: var(--color-line-strong) transparent; + } +} + +@utility tabular { + font-variant-numeric: tabular-nums; +} + +@utility skeleton { + background: linear-gradient( + 90deg, + var(--color-raised) 25%, + var(--color-hover) 50%, + var(--color-raised) 75% + ); + background-size: 200% 100%; + animation: var(--animate-shimmer); + border-radius: 6px; +} + +@media (prefers-reduced-motion: reduce) { + *, + *::before, + *::after { + animation-duration: 0.01ms !important; + animation-iteration-count: 1 !important; + transition-duration: 0.01ms !important; + } +} diff --git a/web/src/lib/api.ts b/web/src/lib/api.ts new file mode 100644 index 0000000..5a582f8 --- /dev/null +++ b/web/src/lib/api.ts @@ -0,0 +1,288 @@ +/** + * The HTTP surface, typed. Shapes mirror countbone/api/app.py and store/db.py. + * + * The store returns some columns as JSON text (review bbox/meta, run meta in + * listings); they are parsed here, once, so no component ever sees a string + * where it expects an object. + */ + +export type Phase = "frames" | "count" | "output" | "done" | "failed"; +export type LiveStatus = "queued" | "running" | "done" | "failed"; + +export interface Telemetry { + frames_expected: number | null; + frames_read: number; + frames_kept: number; + frames_dropped: number; + detections: number; + items: number; + tracks: number; + fps: number; + elapsed_s: number; + blur: number | null; + blur_avg: number | null; + brightness: number | null; + brightness_avg: number | null; +} + +export interface LiveRun { + run_id: string; + status: LiveStatus; + source: string; + filename?: string; + error: string | null; + phase?: Phase; + telemetry?: Telemetry; + total?: number; + needs_review?: boolean; + confidence?: number; +} + +export interface RunSummary { + run_id: string; + source: string; + started_at: number; + finished_at: number | null; + duration_s: number | null; + frames_read: number; + frames_used: number; + frames_dropped: number; + detections: number; + tracks: number; + total: number; + overall_confidence: number; + needs_review: boolean; +} + +export interface SkuCountRow { + sku: string; + label: string | null; + count: number; + expected: number | null; + variance: number | null; + confidence: number | null; +} + +export type ReviewStatus = "pending" | "accepted" | "rejected" | "corrected"; + +export interface ReviewMeta { + scope?: "sku" | "item"; + count?: number; + expected?: number | null; + resolved_count?: number; + hue?: number; + sat?: number; + val?: number; + id_source?: string; +} + +export interface Review { + review_id: string; + run_id: string; + sku: string; + reason: string; + confidence: number; + frame_index: number; + bbox: [number, number, number, number] | null; + crop_path: string | null; + status: ReviewStatus; + resolved_sku: string | null; + resolved_by: string | null; + meta: ReviewMeta; +} + +export interface RunMeta { + warnings?: string[]; + outputs?: Record; + audit?: { manifest_sha256: string; source_sha256: string | null }; + capture_quality?: { + frames_seen: number; + frames_dropped: number; + drop_rate: number; + reasons: Record; + }; + source_info?: { fps: number; width: number; height: number; duration_s: number | null }; +} + +export interface RunDetail extends RunSummary { + config_fingerprint: string | null; + meta: RunMeta; + counts: SkuCountRow[]; + reviews: Review[]; + live: LiveRun | null; +} + +export type RunResponse = RunDetail | { run_id: string; pending: LiveRun }; + +export interface CatalogEntry { + sku: string; + label: string; + hue: [number, number] | null; + achromatic: boolean; + min_saturation: number; + unit_value: number; + expected: number | null; + swatch: string | null; +} + +export interface Health { + status: string; + detect: string; + identify: string; + count: string; + unknown_sku: string; + plugins: string[]; +} + +export interface InspectorFrame { + index: number; + source_index: number; + t: number; + kept: boolean; + quality: { blur?: number; brightness?: number; contrast?: number; clipped_frac?: number }; +} + +export interface InspectorBox { + frame: number; + bbox: [number, number, number, number]; + /** This frame's colour guess. */ + sku: string; + /** What the object was counted as: the majority of its sightings. Absent in older runs. */ + track_sku?: string | null; + label: string; + confidence: number; + track_id: number | null; + counted: boolean; +} + +export interface InspectorDoc { + run_id: string; + frame_size: [number, number] | null; + frames: InspectorFrame[]; + boxes: InspectorBox[]; +} + +export class ApiError extends Error { + constructor( + public status: number, + message: string, + ) { + super(message); + } +} + +async function request(path: string, init?: RequestInit): Promise { + const res = await fetch(path, init); + if (!res.ok) { + let message = res.statusText; + try { + const body = await res.json(); + message = typeof body.detail === "string" ? body.detail : JSON.stringify(body.detail); + } catch { + /* not JSON: keep the status text */ + } + throw new ApiError(res.status, message); + } + return res.json() as Promise; +} + +const parse = (value: unknown, fallback: T): T => { + if (typeof value !== "string") return (value as T) ?? fallback; + try { + return JSON.parse(value) as T; + } catch { + return fallback; + } +}; + +function normaliseReview(raw: Record): Review { + return { + ...(raw as unknown as Review), + bbox: parse(raw.bbox, null), + meta: parse(raw.meta, {}), + }; +} + +function normaliseSummary(raw: Record): RunSummary { + return { ...(raw as unknown as RunSummary), needs_review: Boolean(raw.needs_review) }; +} + +export const isPending = (r: RunResponse): r is { run_id: string; pending: LiveRun } => + "pending" in r; + +/** Artifacts are stored with OS separators; URLs want forward slashes. */ +export const artifactUrl = (runId: string, path: string) => + `/api/runs/${encodeURIComponent(runId)}/artifacts/${path.replaceAll("\\", "/")}`; + +export const videoUrl = (runId: string) => `/api/runs/${encodeURIComponent(runId)}/video`; + +export const api = { + health: () => request("/api/health"), + catalog: () => request("/api/catalog"), + + runs: async () => { + const body = await request<{ in_flight: LiveRun[]; runs: Record[] }>( + "/api/runs", + ); + return { inFlight: body.in_flight, runs: body.runs.map(normaliseSummary) }; + }, + + run: async (runId: string): Promise => { + const body = await request>(`/api/runs/${encodeURIComponent(runId)}`); + if ("pending" in body) return body as unknown as RunResponse; + return { + ...(normaliseSummary(body) as RunDetail), + meta: parse(body.meta, {}), + config_fingerprint: (body.config_fingerprint as string) ?? null, + counts: body.counts as SkuCountRow[], + reviews: (body.reviews as Record[]).map(normaliseReview), + live: (body.live as LiveRun) ?? null, + }; + }, + + inspector: (runId: string) => request(artifactUrl(runId, "inspector.json")), + + startFromPath: (path: string) => + request<{ run_id: string }>("/api/runs", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ path }), + }), + + /** + * XHR rather than fetch: fetch still cannot report upload progress, and a + * multi-hundred-megabyte aisle walk with no progress bar looks like a hang. + */ + upload: (file: File, onProgress: (fraction: number) => void, signal?: AbortSignal) => + new Promise<{ run_id: string }>((resolve, reject) => { + const xhr = new XMLHttpRequest(); + const form = new FormData(); + form.append("file", file); + xhr.open("POST", "/api/runs/upload"); + xhr.responseType = "json"; + xhr.upload.onprogress = (e) => e.lengthComputable && onProgress(e.loaded / e.total); + xhr.onload = () => { + if (xhr.status >= 200 && xhr.status < 300) resolve(xhr.response); + else reject(new ApiError(xhr.status, xhr.response?.detail ?? "Upload failed")); + }; + xhr.onerror = () => reject(new ApiError(0, "Network error during upload")); + xhr.onabort = () => reject(new ApiError(0, "Upload cancelled")); + signal?.addEventListener("abort", () => xhr.abort()); + xhr.send(form); + }), + + resolveReview: ( + reviewId: string, + decision: { + /** "pending" reopens a decided review (undo). */ + status: ReviewStatus; + resolved_sku?: string | null; + resolved_count?: number | null; + }, + ) => + request<{ review_id: string }>(`/api/reviews/${encodeURIComponent(reviewId)}`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ reviewer: "dashboard", ...decision }), + }), +}; diff --git a/web/src/lib/cn.ts b/web/src/lib/cn.ts new file mode 100644 index 0000000..f918ebd --- /dev/null +++ b/web/src/lib/cn.ts @@ -0,0 +1,3 @@ +/** Join class names, dropping falsy ones. Small enough not to need clsx. */ +export const cn = (...parts: (string | false | null | undefined)[]) => + parts.filter(Boolean).join(" "); diff --git a/web/src/lib/export.ts b/web/src/lib/export.ts new file mode 100644 index 0000000..0f2c756 --- /dev/null +++ b/web/src/lib/export.ts @@ -0,0 +1,65 @@ +import type { SkuRow } from "./status"; + +function download(name: string, body: string, type: string) { + const url = URL.createObjectURL(new Blob([body], { type })); + const a = Object.assign(document.createElement("a"), { href: url, download: name }); + a.click(); + setTimeout(() => URL.revokeObjectURL(url), 1000); +} + +const csvCell = (v: unknown) => { + const s = v == null ? "" : String(v); + return /[",\n\r]/.test(s) ? `"${s.replaceAll('"', '""')}"` : s; +}; + +const COLUMNS = [ + "sku", + "label", + "count", + "reviewed_count", + "expected", + "variance", + "reviewed_variance", + "confidence", + "status", + "pending_reviews", +] as const; + +const record = (runId: string, r: SkuRow) => ({ + run_id: runId, + sku: r.sku, + label: r.label ?? "", + count: r.count, + reviewed_count: r.reviewedCount, + expected: r.expected, + variance: r.variance, + reviewed_variance: r.reviewedVariance, + confidence: r.confidence == null ? null : Number(r.confidence.toFixed(4)), + status: r.tier, + pending_reviews: r.pending, +}); + +/** + * The rows as currently filtered, with reviewer recounts alongside the + * machine count. The pipeline's own counts.csv / result.json stay the + * untouched record; these exports are the working view. + */ +export function exportCsv(runId: string, rows: SkuRow[]) { + const lines = [ + ["run_id", ...COLUMNS].join(","), + ...rows.map((r) => { + const rec = record(runId, r); + return [rec.run_id, ...COLUMNS.map((c) => rec[c])].map(csvCell).join(","); + }), + ]; + download(`${runId}-counts.csv`, lines.join("\r\n"), "text/csv;charset=utf-8"); +} + +export function exportJson(runId: string, rows: SkuRow[]) { + const body = JSON.stringify( + { run_id: runId, exported_at: new Date().toISOString(), counts: rows.map((r) => record(runId, r)) }, + null, + 2, + ); + download(`${runId}-counts.json`, body, "application/json"); +} diff --git a/web/src/lib/format.ts b/web/src/lib/format.ts new file mode 100644 index 0000000..5e40ec8 --- /dev/null +++ b/web/src/lib/format.ts @@ -0,0 +1,64 @@ +export const pct = (v: number | null | undefined, digits = 0) => + v == null ? "—" : `${(v * 100).toFixed(digits)}%`; + +export const num = (v: number | null | undefined) => + v == null ? "—" : v.toLocaleString(undefined, { maximumFractionDigits: 1 }); + +export const signed = (v: number | null | undefined) => + v == null ? "—" : v > 0 ? `+${v}` : `${v}`; + +export const bytes = (n: number) => { + const units = ["B", "KB", "MB", "GB"]; + let i = 0; + while (n >= 1024 && i < units.length - 1) { + n /= 1024; + i++; + } + return `${n.toFixed(i ? 1 : 0)} ${units[i]}`; +}; + +export const duration = (s: number | null | undefined) => { + if (s == null || !Number.isFinite(s)) return "—"; + if (s < 60) return `${s.toFixed(s < 10 ? 1 : 0)}s`; + const m = Math.floor(s / 60); + return `${m}m ${Math.round(s % 60)}s`; +}; + +export const timecode = (s: number) => { + const m = Math.floor(s / 60); + const sec = s % 60; + return `${m}:${sec.toFixed(2).padStart(5, "0")}`; +}; + +const rtf = new Intl.RelativeTimeFormat(undefined, { numeric: "auto" }); +export const ago = (epochS: number) => { + const diff = epochS - Date.now() / 1000; + const abs = Math.abs(diff); + if (abs < 60) return "just now"; + if (abs < 3600) return rtf.format(Math.round(diff / 60), "minute"); + if (abs < 86400) return rtf.format(Math.round(diff / 3600), "hour"); + return rtf.format(Math.round(diff / 86400), "day"); +}; + +export const shortId = (runId: string) => runId.replace(/^run_/, "").slice(0, 8); + +export const fileName = (path: string) => path.split(/[\\/]/).pop() ?? path; + +/** Human text for the review queue's machine reasons. */ +export const REASONS: Record = { + low_sku_confidence: "Whole SKU uncertain", + low_item_confidence: "Low-confidence sighting", + unidentified: "Not matched to any SKU", +}; + +/** + * Uploads are stored as __., so the original name + * survives a server restart; the live tracker's copy is exact but memory-only. + */ +export function runTitle(runId: string, source: string, filename?: string) { + if (filename) return filename; + const f = fileName(source); + const upload = /^run_[0-9a-f]+__(.+)$/.exec(f); + if (upload) return upload[1]!; + return f.startsWith("run_") ? `Upload ${shortId(runId)}` : f; +} diff --git a/web/src/lib/queries.ts b/web/src/lib/queries.ts new file mode 100644 index 0000000..4bb935d --- /dev/null +++ b/web/src/lib/queries.ts @@ -0,0 +1,69 @@ +import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query"; +import { api, isPending, type RunResponse } from "./api"; + +export const keys = { + health: ["health"] as const, + catalog: ["catalog"] as const, + runs: ["runs"] as const, + run: (id: string) => ["run", id] as const, + inspector: (id: string) => ["inspector", id] as const, +}; + +export const useHealth = () => + useQuery({ queryKey: keys.health, queryFn: api.health, staleTime: 60_000, retry: 1 }); + +export const useCatalog = () => + useQuery({ queryKey: keys.catalog, queryFn: api.catalog, staleTime: Infinity }); + +export const useRuns = () => + useQuery({ + queryKey: keys.runs, + queryFn: api.runs, + // Fast while something is counting, relaxed otherwise. + refetchInterval: (q) => + q.state.data?.inFlight.some((r) => r.status === "queued" || r.status === "running") + ? 1000 + : 8000, + // People start a count and switch tabs; the sidebar should be current + // when they come back. Hidden tabs are throttled by the browser anyway. + refetchIntervalInBackground: true, + refetchOnWindowFocus: true, + }); + +/** A run; polls quickly while it is still in the pipeline, then stops. */ +export const useRun = (runId: string | null) => + useQuery({ + queryKey: keys.run(runId ?? ""), + queryFn: () => api.run(runId!), + enabled: !!runId, + refetchInterval: (q) => { + const d = q.state.data as RunResponse | undefined; + if (!d || !isPending(d)) return false; + return d.pending.status === "failed" ? false : 500; + }, + refetchIntervalInBackground: true, + }); + +export const useInspector = (runId: string, enabled: boolean) => + useQuery({ + queryKey: keys.inspector(runId), + queryFn: () => api.inspector(runId), + enabled, + staleTime: Infinity, + retry: false, + }); + +export function useResolveReview(runId: string) { + const qc = useQueryClient(); + return useMutation({ + // One queue for every decision: "reject then undo" fired quickly must + // reach the server in that order, or the undo lands first and is lost. + scope: { id: `reviews:${runId}` }, + mutationFn: (v: Parameters[1] & { reviewId: string }) => + api.resolveReview(v.reviewId, v), + onSettled: () => { + qc.invalidateQueries({ queryKey: keys.run(runId) }); + qc.invalidateQueries({ queryKey: keys.runs }); + }, + }); +} diff --git a/web/src/lib/route.ts b/web/src/lib/route.ts new file mode 100644 index 0000000..5f5a8cb --- /dev/null +++ b/web/src/lib/route.ts @@ -0,0 +1,80 @@ +import { useSyncExternalStore } from "react"; + +/** + * Hash routing, because the dashboard is served as static files by FastAPI: + * a path route would 404 on refresh, a hash route never reaches the server. + * + * #/new upload a video + * #/runs/ results + * #/runs//inspect?f=12 frame inspector, at sampled frame 12 + * ...?review=1 with the review drawer open + */ +export type Route = + | { view: "new" } + | { view: "run"; runId: string; tab: "results" | "inspect"; frame: number | null; review: boolean; sku: string | null }; + +function parse(hash: string): Route { + const [path = "", query = ""] = hash.replace(/^#/, "").split("?"); + const params = new URLSearchParams(query); + const parts = path.split("/").filter(Boolean); + if (parts[0] === "runs" && parts[1]) { + const f = params.get("f"); + return { + view: "run", + runId: decodeURIComponent(parts[1]), + tab: parts[2] === "inspect" ? "inspect" : "results", + frame: f != null && f !== "" ? Number(f) : null, + review: params.get("review") === "1", + sku: params.get("sku"), + }; + } + return { view: "new" }; +} + +export function href(route: Route): string { + if (route.view === "new") return "#/new"; + const params = new URLSearchParams(); + if (route.frame != null) params.set("f", String(route.frame)); + if (route.sku) params.set("sku", route.sku); + if (route.review) params.set("review", "1"); + const q = params.toString(); + const tab = route.tab === "inspect" ? "/inspect" : ""; + return `#/runs/${encodeURIComponent(route.runId)}${tab}${q ? `?${q}` : ""}`; +} + +const subscribe = (cb: () => void) => { + window.addEventListener("hashchange", cb); + return () => window.removeEventListener("hashchange", cb); +}; + +let cachedHash: string | null = null; +let cachedRoute: Route = { view: "new" }; +const snapshot = () => { + if (window.location.hash !== cachedHash) { + cachedHash = window.location.hash; + cachedRoute = parse(cachedHash); + } + return cachedRoute; +}; + +export const useRoute = () => useSyncExternalStore(subscribe, snapshot); + +export function navigate(route: Route, opts: { replace?: boolean } = {}) { + const next = href(route); + if (opts.replace) { + history.replaceState(null, "", next); + window.dispatchEvent(new HashChangeEvent("hashchange")); + } else { + window.location.hash = next; + } +} + +/** Patch the current run route (e.g. open the drawer, move the frame). */ +export function patchRun( + current: Route, + patch: Partial>, + opts?: { replace?: boolean }, +) { + if (current.view !== "run") return; + navigate({ ...current, ...patch }, opts); +} diff --git a/web/src/lib/status.ts b/web/src/lib/status.ts new file mode 100644 index 0000000..154e8c2 --- /dev/null +++ b/web/src/lib/status.ts @@ -0,0 +1,122 @@ +import type { CatalogEntry, Review, SkuCountRow } from "./api"; + +export type Tier = "high" | "medium" | "review"; + +/** + * Display bands for confidence. 0.60 matches review_queue's *default* + * sku_threshold; the actual threshold is configurable, so buildRows lets an + * open whole-SKU review from the pipeline override these. + */ +export const REVIEW_BELOW = 0.6; +export const HIGH_FROM = 0.85; + +export function tierOf(confidence: number | null | undefined): Tier { + const c = confidence ?? 0; + if (c < REVIEW_BELOW) return "review"; + if (c < HIGH_FROM) return "medium"; + return "high"; +} + +export const TIER_LABEL: Record = { + high: "High", + medium: "Medium", + review: "Needs review", +}; + +export interface SkuRow extends SkuCountRow { + tier: Tier; + pending: number; + reviewedCount: number | null; + /** Variance against the recount, when there is one and an expected count. */ + reviewedVariance: number | null; + swatch: string | null; +} + +/** Join counts with the catalog and the review queue into one table row. */ +export function buildRows( + counts: SkuCountRow[], + reviews: Review[], + catalog: CatalogEntry[] | undefined, + unknownSku: string, +): SkuRow[] { + const bySku = new Map(catalog?.map((c) => [c.sku, c])); + return counts.map((c) => { + const mine = reviews.filter((r) => r.sku === c.sku); + // A recount lives on the whole-SKU review, resolved by a person. + const recount = mine.find( + (r) => r.meta.scope === "sku" && r.status !== "pending" && r.meta.resolved_count != null, + ); + const reviewedCount = recount?.meta.resolved_count ?? null; + return { + ...c, + reviewedCount, + reviewedVariance: reviewedCount != null && c.expected != null ? reviewedCount - c.expected : null, + // The pipeline's own verdict wins over the UI's default thresholds: + // review_queue's sku_threshold is configurable, and when it raised a + // whole-SKU review that is still open, this SKU needs review, full stop. + // An object nobody could name always does too, however sure the box is. + tier: + c.sku === unknownSku || mine.some((r) => r.meta.scope === "sku" && r.status === "pending") + ? "review" + : tierOf(c.confidence), + pending: mine.filter((r) => r.status === "pending").length, + swatch: bySku.get(c.sku)?.swatch ?? null, + }; + }); +} + +/** The colour the colour-identifier measured, from OpenCV HSV (H 0-179, S/V 0-255). */ +export function hsvToHex(h: number, s: number, v: number): string { + const hh = (h * 2) / 60; + const ss = s / 255; + const vv = v / 255; + const c = vv * ss; + const x = c * (1 - Math.abs((hh % 2) - 1)); + const [r, g, b] = + hh < 1 ? [c, x, 0] : hh < 2 ? [x, c, 0] : hh < 3 ? [0, c, x] : hh < 4 ? [0, x, c] : hh < 5 ? [x, 0, c] : [c, 0, x]; + const m = vv - c; + return `#${[r, g, b].map((n) => Math.round((n + m) * 255).toString(16).padStart(2, "0")).join("")}`; +} + +export function hueCentre(entry: CatalogEntry): number | null { + if (!entry.hue) return null; + const [lo, hi] = entry.hue; + const span = lo <= hi ? hi - lo : 180 - lo + hi; + return (lo + span / 2) % 180; +} + +/** Shortest distance round the OpenCV hue wheel, in its native 0-179 units. */ +export function hueDistance(a: number, b: number): number { + const d = Math.abs(a - b) % 180; + return Math.min(d, 180 - d); +} + +export interface Candidate { + entry: CatalogEntry; + /** Degrees on a 360° wheel, which is what people expect to read. */ + distanceDeg: number | null; + inBand: boolean; +} + +/** + * Rank catalog SKUs by how close their hue band is to the colour the + * identifier measured on this crop. That is the model's own evidence, so the + * ranking shows the reviewer why it chose what it chose, and what came next. + */ +export function rankCandidates( + catalog: CatalogEntry[], + hue: number | undefined, + sat: number | undefined, +): Candidate[] { + const out = catalog.map((entry) => { + const centre = hueCentre(entry); + // Below the catalog's saturation floor the identifier ignores hue, and + // so must we: hue of a near-grey patch is noise, not evidence. + const washedOut = sat != null && sat < entry.min_saturation; + if (hue == null || centre == null || washedOut) return { entry, distanceDeg: null, inBand: false }; + const [lo, hi] = entry.hue!; + const inBand = lo <= hi ? hue >= lo && hue <= hi : hue >= lo || hue <= hi; + return { entry, distanceDeg: hueDistance(hue, centre) * 2, inBand }; + }); + return out.sort((a, b) => (a.distanceDeg ?? 999) - (b.distanceDeg ?? 999)); +} diff --git a/web/src/main.tsx b/web/src/main.tsx new file mode 100644 index 0000000..b1b92e5 --- /dev/null +++ b/web/src/main.tsx @@ -0,0 +1,23 @@ +import { StrictMode } from "react"; +import { createRoot } from "react-dom/client"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import { MotionConfig } from "motion/react"; +import { App } from "./App"; +import "./index.css"; + +const queryClient = new QueryClient({ + defaultOptions: { + queries: { refetchOnWindowFocus: false, retry: (n, e) => n < 2 && !(e as { status?: number }).status }, + }, +}); + +createRoot(document.getElementById("root")!).render( + + + {/* Honour the OS reduced-motion setting for every animation. */} + + + + + , +); diff --git a/web/tsconfig.json b/web/tsconfig.json new file mode 100644 index 0000000..85cfd42 --- /dev/null +++ b/web/tsconfig.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "target": "ES2022", + "lib": ["ES2023", "DOM", "DOM.Iterable"], + "module": "ESNext", + "moduleResolution": "bundler", + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedIndexedAccess": true, + "isolatedModules": true, + "skipLibCheck": true, + "noEmit": true, + "types": ["vite/client"], + "paths": { "@/*": ["./src/*"] } + }, + "include": ["src"] +} diff --git a/web/vite.config.ts b/web/vite.config.ts new file mode 100644 index 0000000..fa87238 --- /dev/null +++ b/web/vite.config.ts @@ -0,0 +1,22 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; +import tailwindcss from "@tailwindcss/vite"; +import { fileURLToPath } from "node:url"; + +// The build lands inside the Python package, so `countbone serve` ships the +// dashboard with no Node at runtime. In dev, Vite proxies the API instead. +export default defineConfig({ + plugins: [react(), tailwindcss()], + resolve: { + alias: { "@": fileURLToPath(new URL("./src", import.meta.url)) }, + }, + build: { + outDir: fileURLToPath(new URL("../src/countbone/api/static", import.meta.url)), + emptyOutDir: true, + assetsDir: "assets", + }, + server: { + port: 5173, + proxy: { "/api": "http://127.0.0.1:8000" }, + }, +}); From cbdb6227266c0c8c8170c95f160bd4d802441490 Mon Sep 17 00:00:00 2001 From: Samarty-1 Date: Sun, 27 Sep 2026 00:53:31 +0800 Subject: [PATCH 02/13] Add opt-in CORS for the mobile web preview; mobile theme tokens - countbone serve --allow-origin ORIGIN (repeatable, off by default) so a browser app on another origin (the Expo web preview) can call the API. Tested: only named origins get CORS headers. - mobile/src/theme.ts: the dashboard's measured colour tokens. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01Cuy339178aznLgLdpqqD4V --- mobile/src/theme.ts | 42 ++++++++++++++++++++++++++++++++++++++++ src/countbone/api/app.py | 19 +++++++++++++++++- src/countbone/cli.py | 9 ++++++++- tests/test_api.py | 13 +++++++++++++ 4 files changed, 81 insertions(+), 2 deletions(-) create mode 100644 mobile/src/theme.ts diff --git a/mobile/src/theme.ts b/mobile/src/theme.ts new file mode 100644 index 0000000..398088a --- /dev/null +++ b/mobile/src/theme.ts @@ -0,0 +1,42 @@ +import { Platform } from 'react-native'; + +import type { Level } from '@/analysis/guidance.ts'; + +/** + * The dashboard's tokens (web/src/index.css), so phone and desktop read as + * one product. Contrast on `bg` was measured there: fg 16.9, muted 7.5, + * subtle 5.1, ok 10.4, warn 10.9, bad 6.8 : 1. Over live video, text also + * sits on a scrim (`overlay`) so bright shelves cannot wash it out. + */ +export const color = { + bg: '#0b0c0e', + surface: '#111316', + raised: '#171a1e', + hover: '#1d2126', + line: '#23272e', + lineStrong: '#323841', + fg: '#edeef0', + muted: '#9ba1ab', + subtle: '#7c828c', + accent: '#5b8cff', + accentFg: '#0b0c0e', + ok: '#3dd68c', + warn: '#ffb224', + bad: '#ff6369', + info: '#52a8ff', + overlay: 'rgba(11,12,14,0.72)', + overlayStrong: 'rgba(11,12,14,0.88)', +} as const; + +export const levelColor: Record = { good: color.ok, caution: color.warn, bad: color.bad }; + +export const space = { xs: 4, sm: 8, md: 12, lg: 16, xl: 24, xxl: 32 } as const; +export const radius = { sm: 6, md: 10, lg: 14, pill: 999 } as const; + +/** Apple HIG / Material minimums; gloved warehouse hands get more. */ +export const touch = { min: 48, primary: 84 } as const; + +export const font = { + mono: Platform.select({ ios: 'Menlo', android: 'monospace', default: 'ui-monospace, Menlo, Consolas, monospace' }), + size: { xs: 11, sm: 13, md: 15, lg: 17, xl: 22, xxl: 32 }, +} as const; diff --git a/src/countbone/api/app.py b/src/countbone/api/app.py index fbdd9dd..0c94c35 100644 --- a/src/countbone/api/app.py +++ b/src/countbone/api/app.py @@ -11,6 +11,7 @@ import re import shutil import threading +from collections.abc import Sequence from concurrent.futures import ThreadPoolExecutor from contextlib import asynccontextmanager from pathlib import Path @@ -102,7 +103,11 @@ def all(self) -> list[dict[str, Any]]: return [dict(v) for v in self._state.values()] -def create_app(config: Config | None = None, store: Store | None = None) -> FastAPI: +def create_app( + config: Config | None = None, + store: Store | None = None, + allow_origins: Sequence[str] = (), +) -> FastAPI: cfg = config or Config() db = store or Store(cfg.output.sqlite or "countbone.db") tracker = RunTracker() @@ -131,6 +136,18 @@ async def lifespan(_: FastAPI): app.state.config = cfg app.state.store = db app.state.tracker = tracker + if allow_origins: + # Only for browser apps on another origin (the mobile app's web + # preview). Native apps and the bundled dashboard never need it, so it + # is off unless origins are named; "*" is deliberately not special. + from fastapi.middleware.cors import CORSMiddleware + + app.add_middleware( + CORSMiddleware, + allow_origins=list(allow_origins), + allow_methods=["GET", "POST"], + allow_headers=["Content-Type"], + ) # -- helpers --------------------------------------------------------- def submit(source: str, run_id: str) -> None: diff --git a/src/countbone/cli.py b/src/countbone/cli.py index dc1a305..a4664c5 100644 --- a/src/countbone/cli.py +++ b/src/countbone/cli.py @@ -123,7 +123,8 @@ def cmd_serve(args: argparse.Namespace) -> int: from .api.app import create_app cfg = _load_config(args) - uvicorn.run(create_app(cfg), host=args.host, port=args.port, log_level="info") + app = create_app(cfg, allow_origins=args.allow_origin or ()) + uvicorn.run(app, host=args.host, port=args.port, log_level="info") return 0 @@ -210,6 +211,12 @@ def common(p: argparse.ArgumentParser) -> None: serve = sub.add_parser("serve", help="run the dashboard and API") serve.add_argument("--host", default="127.0.0.1") serve.add_argument("--port", type=int, default=8000) + serve.add_argument( + "--allow-origin", action="append", metavar="ORIGIN", + help="let a browser app on this origin call the API, e.g. the mobile app's " + "web preview at http://localhost:8081 (repeatable; none by default). " + "Native apps need no origin.", + ) common(serve) serve.set_defaults(func=cmd_serve) diff --git a/tests/test_api.py b/tests/test_api.py index 0b77a9e..581a079 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -269,6 +269,19 @@ def test_a_recount_is_kept_beside_the_machine_count(client): assert bad.status_code == 422 +def test_cors_is_off_unless_origins_are_named(client, config, tmp_path): + origin = {"Origin": "http://localhost:8081"} + assert "access-control-allow-origin" not in client.get("/api/health", headers=origin).headers + + store = Store(tmp_path / "cors.db") + with TestClient(create_app(config, store=store, allow_origins=["http://localhost:8081"])) as c: + allowed = c.get("/api/health", headers=origin).headers + other = c.get("/api/health", headers={"Origin": "http://evil.example"}).headers + store.close() + assert allowed["access-control-allow-origin"] == "http://localhost:8081" + assert "access-control-allow-origin" not in other + + def test_dashboard_is_served(client): res = client.get("/") assert res.status_code == 200 From f533b3646d707735816adb7dfd260274cffe184e Mon Sep 17 00:00:00 2001 From: Samarty-1 Date: Sun, 27 Sep 2026 14:18:42 +0800 Subject: [PATCH 03/13] =?UTF-8?q?=EF=BB=BFReview=20and=20roadmap;=20fix=20?= =?UTF-8?q?mobile=20typecheck?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit docs/REVIEW_AND_ROADMAP.md records a full health check of backend, dashboard and mobile app, a root-caused accuracy risk (fast pans at the default stride undercount silently at high confidence), a prioritised feature list and an ecosystem proposal. Nothing in the roadmap is built. Mobile: tsc --noEmit now passes (non-null assertions under noUncheckedIndexedAccess; tests excluded from the app typecheck since they run under Node's own runner). Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01Cuy339178aznLgLdpqqD4V --- docs/REVIEW_AND_ROADMAP.md | 186 +++++++++++++++++++++++++++++++++ mobile/src/analysis/metrics.ts | 4 +- mobile/tsconfig.json | 5 +- 3 files changed, 192 insertions(+), 3 deletions(-) create mode 100644 docs/REVIEW_AND_ROADMAP.md diff --git a/docs/REVIEW_AND_ROADMAP.md b/docs/REVIEW_AND_ROADMAP.md new file mode 100644 index 0000000..976dffc --- /dev/null +++ b/docs/REVIEW_AND_ROADMAP.md @@ -0,0 +1,186 @@ +# Countbone: health check, feature roadmap, ecosystem + +*Review of branch `dashboard-and-mobile`, 2026-09-27. Nothing in sections 3 and 4 +has been built; they are proposals awaiting a decision.* + +--- + +## 1. Health check: what was run, what came back + +| Part | Check | Result | +|---|---|---| +| Backend | `pytest` (123 tests), `ruff check` | all pass, lint clean | +| Backend | live `countbone serve --allow-origin …`, HTTP upload → poll → artifacts | works: seed 11 counted 37/37, CORS header correct, inspector.json served | +| Backbone accuracy | `countbone demo` on 7 seeds never used in tests (3, 11, 21, 31, 42, 55, 77) | 6 exact, **seed 21 off by 3 of 32** (see 2.1) | +| Dashboard | `npm run build` (tsc + vite) | builds, 148 kB gzip JS | +| Mobile | `tsc --noEmit` | **was failing (4 errors), fixed in this commit** | +| Mobile | analysis engine tests (10) | all pass | +| Mobile | `expo-doctor` | 21/21 checks pass | +| Mobile | `expo export --platform web` | bundles, but **there are no screens yet** (see 2.2) | + +## 2. Findings, in order of how much they matter + +### 2.1 A fast walk undercounts silently, with high confidence +Seed 21 counted 29 of 32 at **95% confidence, needs review: no**. Root cause +isolated by re-running at different frame strides: + +| every_n_frames | tracks | error | +|---|---|---| +| 1 | 32 | 0 | +| 2 | 32 | 0 | +| 3 | 32 | 0 | +| 5 (default) | 29 | −3 | + +The tracker links objects across frames by box overlap. The default stride of 5, +plus 5 of 29 sampled frames rejected by the quality gate, left gaps where items +moved more than about a box width between usable frames. Merged detections were +ruled out: no counted track has an abnormal box area. + +Why this matters: confidence only measures colour identification, not tracking +continuity, so the system reports a wrong answer as a certain one. For a +stock-count product, that is the worst failure mode. + +Two amplifiers: +- The `tolerance` plugin is not in `DEFAULT_PLUGINS`, so a count that misses + every expected SKU still says "needs review: no". +- The test seeds all pan slowly, so CI cannot see this. + +Proposed fix (not applied): +- Measure per-gap motion ÷ median box width. +- Adaptively re-sample the skipped frames in that window when the ratio exceeds about 0.5. +- Fold a "tracking continuity" term into confidence. +- Enable tolerance whenever expected counts exist. +- Add a fast-pan seed to the tests. + +The mobile app's "Slow Down" cue is the capture-side half of this fix. + +### 2.2 The mobile app is an engine without a body +Done and tested: +- The analysis engine (blur calibrated to the backend within 1%, exposure, + motion/shift, scale, edge boxes, debounced guidance cues). +- Theme tokens. +- Permissions. + +Not built: +- Every screen: capture, HUD, record button, processing/summary, swipe review, history, settings. +- The camera feeds (VisionCamera native, getUserMedia web). +- The API client. + +The app launches to Expo Router's "unmatched route" page. Native builds need +EAS or a Mac or Android SDK, and have not been run anywhere. + +### 2.3 No authentication, anywhere +Anyone who can reach the port can: +- Upload. +- Resolve or undo reviews. The `reviewer` field is free text and defaults to "anonymous", so the audit trail is not attributable. +- Submit a server-side path to `/api/runs`. + +This is fine on localhost and not sellable. Minimum: API keys per site, and a +signed-in reviewer identity written into `review_decision` audit rows. + +### 2.4 Works on synthetic footage; unproven on real shelves +Every accuracy number comes from generated video: flat, coloured, uncluttered +cartons. Real aisles bring: +- Look-alike packaging. The colour identifier can separate maybe 6–8 SKUs by hue, and a real store has thousands. +- Shrink-wrap glare. +- Items stacked behind items. Video only sees facings. +- Partial occlusion by price rails. + +The architecture (plugins, audit pack, review loop) will carry over. The +contour detector and colour identifier will not. A `yolo` detector backend +exists, but no learned SKU identifier does. + +### 2.5 Operational limits (fine now, fix before multi-site) +- The in-memory `RunTracker`: queued runs are lost on restart. +- One worker thread. +- SQLite. +- Local-disk video. +- Progress by polling. + +None of these are bugs at pilot scale. + +## 3. Features: add, replace, cut (proposals only) + +**P0: required before a paying pilot** +1. **Tracking-continuity confidence and adaptive stride** (2.1). Small change, removes the worst failure. +2. **Learned SKU identity** *(replace the colour identifier)*: an image-embedding model with few-shot enrolment ("photograph 5 facings of a new SKU"), plus reading barcodes and shelf labels when visible. +3. **Auth and attributable reviews** (2.3). +4. **Finish the mobile app** (2.2). +5. **Tolerance on by default** when expected counts exist. + +**P1: what makes customers stay** +6. **Location anchoring**: a QR or label per bay/rack scanned at the start of a walk, so counts attach to a location, not just a video. +7. **Overlap de-duplication**: the same bay walked twice, or two phones in adjacent aisles, must not double count. +8. **Offline-first mobile**: queue recordings and resume uploads (tus protocol), since warehouses have Wi-Fi dead zones. +9. **Recount tasks**: a variance creates a task, assigned to a person, with a due date and a closure proof. +10. **ERP/WMS connectors**: pull expected quantities and push approved adjustments. Start with CSV/SFTP and webhooks, then NetSuite, SAP, Odoo, Shopify. +11. **Per-location trend and shrink history** (the SKU history endpoint already exists). + +**P2: scale** +12. Postgres, a job queue (arq or Celery), object storage for video, and Server-Sent Events instead of polling *(replace)*. +13. On-device counting for no-connectivity sites. The mobile engine already runs frame analysis in a worklet. +14. Stacked-depth estimation (facings × depth from a side pass). This is a research problem, so keep it out of promises. + +**Cut or de-emphasise:** the "lux" readout on the mobile HUD. Phones expose +no calibrated lux sensor to apps on iOS, so it is an estimate. Label it +"light" rather than claiming a unit. + +## 4. Ecosystem: products that make customers buy more than one thing + +Market context: +- Retail inventory records are wrong most of the time: studies put 60–65% of SKU records inaccurate. +- Correcting them produced 4–8% sales lifts. +- Incumbents split by capture method: + - Drones for high-bay warehouses (Corvus, Gather AI). + - Fixed shelf cameras and robots for stores (Focal Systems, Simbe, Trax). + - Phone scanning (Scandit MatrixScan Count, ShelfView). + - Dock cameras for receiving (Vimaan). + +Countbone's opening: **phone video, no hardware install**. It is cheap to start, +which suits mid-market warehouses, 3PLs and multi-site retailers that cannot +justify drones or shelf cameras. + +The ecosystem should follow the stock's journey. Each product reuses the same +video → count → review → audit backbone with a different "expected" source. + +``` + RECEIVE ──────────► STORE ──────────► SELL / SHIP + Countbone Receive Countbone Count Countbone Shelf + (count vs ASN/PO) (cycle counts) (gaps, planogram) + \ | / + └──── Countbone Reconcile ──────┘ + (variance desk, ERP sync, approvals) + | + Countbone Evidence + (audit packs → vendor claims, insurance, auditors) +``` + +| Product | What it does | Why a Count customer buys it | Reuse of today's code | +|---|---|---|---| +| **Receive** | Film a pallet or truck unload; count vs the ASN/PO; flag shorts with photo evidence while the driver is still at the dock | Most inventory errors start at receiving; stops them before they reach the shelf | High: the pipeline as is; expected comes from the ASN instead of the catalog | +| **Shelf** | From the same walk video: out-of-stock gaps, facings, planogram compliance | Zero extra labour: the walk they already do for Count | Medium: new analytics plugin on existing detections | +| **Reconcile** | Variance inbox, approval rules by value (tolerance bands already exist), push adjustments to ERP | Becomes the system finance signs off on, which is the lock-in | High: review queue, tolerance, audit trail | +| **Evidence** | Tamper-evident audit packs (already hashed) turned into vendor chargeback claims, insurance and loss files, external-auditor year-end packs | Turns counting from a cost into recovered money; easy ROI story | High: `audit_pack` plugin already hashes artifacts | +| **Enrol** (Catalog Studio) | Photograph a SKU a few times to teach the identifier | Needed for P0 item 2 anyway; sell it as self-serve onboarding | New | +| **Count-as-a-Service** | Your crews walk their site with the app monthly | Services revenue; also your data engine for training models on real footage | Uses the mobile app | + +**Recommended sequencing:** +1. **Count + Reconcile** first. Reconcile is where the money decision lives, and it builds on code that exists. +2. **Receive** second. Same pipeline, a buyer persona the customer already has (the receiving lead), and the fastest ROI. +3. **Evidence** as the premium tier. +4. **Shelf** only for retail customers. + +**Pricing shape to test:** per site per month for Count; Reconcile bundled into +a higher tier; Receive per dock door; Evidence as a percentage of recovered +claims or a flat premium add-on. + +## Sources +- [Corvus One inventory drones (DroneDJ, 2026)](https://dronedj.com/2026/03/03/corvus-one-inventory-warehouse-drone/) +- [Gather AI drone vision](https://gather.ai/platform/vision/drone) +- [Warehouse drone market outlook (CXTMS)](https://cxtms.com/blog/warehouse-drones-autonomous-inventory-scanning-7-billion-market-2026) +- [Focal Systems retail OS (IBM)](https://www.ibm.com/new/product-blog/focal-systems-boosting-store-performance-with-an-ai-retail-operating-system-and-real-time-data) +- [Planogram compliance landscape (CB Insights)](https://www.cbinsights.com/esp/consumer-&-retail/in-store-tech/planogram-compliance-&-inventory-visibility) +- [Scandit store operations / MatrixScan Count](https://www.scandit.com/industries/retail/store-operations/) +- [Vimaan inbound pallet receiving](https://vimaan.ai/inbound-pallet-receiving-automation/) +- [Dock door verification (iFactory)](https://ifactoryapp.com/ai-vision-camera/ai-vision-dock-door-monitoring-inbound-outbound-verification) +- [Inventory accuracy and phantom inventory (RELEX)](https://www.relexsolutions.com/resources/how-to-optimize-cycle-counting-and-banish-phantom-inventory/) diff --git a/mobile/src/analysis/metrics.ts b/mobile/src/analysis/metrics.ts index d91233c..1b63ca8 100644 --- a/mobile/src/analysis/metrics.ts +++ b/mobile/src/analysis/metrics.ts @@ -179,8 +179,8 @@ export function projections(l: Luma): { cols: Float32Array; rows: Float32Array } for (let y = 0; y < h; y++) { for (let x = 0; x < w; x++) { const v = data[y * w + x]!; - cols[x] += v; - rows[y] += v; + cols[x]! += v; + rows[y]! += v; } } let cm = 0; diff --git a/mobile/tsconfig.json b/mobile/tsconfig.json index 3bada79..97f3fa2 100644 --- a/mobile/tsconfig.json +++ b/mobile/tsconfig.json @@ -8,5 +8,8 @@ "@/*": ["./src/*"] } }, - "include": ["**/*.ts", "**/*.tsx", ".expo/types/**/*.ts", "expo-env.d.ts"] + "include": ["**/*.ts", "**/*.tsx", ".expo/types/**/*.ts", "expo-env.d.ts"], + // Tests run under Node's own runner (npm test), which strips types; the app + // itself has no Node types, so the typecheck covers app code only. + "exclude": ["node_modules", "**/*.test.ts"] } From da83ceb1d3a14b4abf1c160fb4547e7844534184 Mon Sep 17 00:00:00 2001 From: Samarty-1 Date: Sun, 27 Sep 2026 14:58:05 +0800 Subject: [PATCH 04/13] Adaptive sampling, continuity-aware confidence, photo-enrolled identity Backbone accuracy: - Closed-loop sampler: back-fills skipped frames when the scene moved more than 0.6 object widths, retries the next frame after a quality-gate drop, and adapts stride to camera speed. 30/30 synthetic shelves exact (was 29/30). - Tracking continuity (max shift between kept frames, in object widths) now caps confidence and flags broken gaps, so a wrong count cannot report 95%. - max_frames reaching its cap is flagged, not silent; cap raised to 5000. - Expected SKUs never seen get a zero row, so an empty shelf is a variance. - Tolerance on by default and flags every miss; per-run expected counts. - Review queue asks once per object, carries track ids, raises possible_missed_item for confident single sightings, and skips objects settled by their own vote. Identity: - appearance.py: OpenCV/NumPy product embedding (colour layout, orientation histograms), nearest-exemplar index with per-SKU self-calibrated open-set thresholds from held-out augmented probes. - identify: 'auto' (default) uses photos once any exist, else colour; barcodes override when legible. - On a look-alike range (four red cartons differing only in artwork): colour 448 per-SKU errors over 10 shelves, 5 photos per SKU: 0 errors, 10/10 exact; an unenrolled look-alike is flagged unknown, not misfiled. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01Cuy339178aznLgLdpqqD4V --- src/countbone/appearance.py | 336 ++++++++++++++++++++++++++ src/countbone/catalog.py | 26 ++ src/countbone/config.py | 15 +- src/countbone/demo.py | 145 +++++++++-- src/countbone/pipeline.py | 147 ++++++++++- src/countbone/plugins/base.py | 4 + src/countbone/plugins/confidence.py | 19 +- src/countbone/plugins/review_queue.py | 131 +++++++++- src/countbone/plugins/tolerance.py | 8 +- src/countbone/stages/capture.py | 206 ++++++++++++++-- src/countbone/stages/count.py | 73 +++++- src/countbone/stages/identify.py | 100 ++++++++ tests/test_plugins.py | 24 +- 13 files changed, 1164 insertions(+), 70 deletions(-) create mode 100644 src/countbone/appearance.py diff --git a/src/countbone/appearance.py b/src/countbone/appearance.py new file mode 100644 index 0000000..1ac6e42 --- /dev/null +++ b/src/countbone/appearance.py @@ -0,0 +1,336 @@ +"""Appearance embeddings: teach the system a product from a handful of photos. + +The colour identifier can separate as many SKUs as there are distinct hues, +which on a real shelf is a handful. Products are told apart by their artwork: +where the colours are, and the shapes of logos, text and stripes. This module +turns a product image into a vector that captures both, and a small index +that matches new sightings against enrolled examples. + +Design choices, for a pilot rather than a benchmark: + +* No deep-learning runtime. Everything is OpenCV + NumPy, runs on a laptop + CPU at thousands of crops a second, and is deterministic. The embedder is a + single function (`embed`) so a learned model (ONNX) can replace it later + without touching the index, the catalog or the API. +* Few-shot by nearest exemplar. Five photos per SKU are stored as-is (plus a + few augmented copies); a sighting is scored against its closest exemplar, + blended with the SKU's centroid. Adding a photo takes effect immediately, + with no training step, which is what makes review corrections usable as + new examples. +* Open set. A sighting unlike every enrolled SKU is reported as unknown + rather than forced onto the nearest one; a shelf always holds something + nobody enrolled. +""" + +from __future__ import annotations + +import zlib +from dataclasses import dataclass + +import cv2 +import numpy as np + +SIZE = 64 +EMBED_VERSION = 1 # bump when `embed` changes: stored vectors must be recomputed +_GRID = 4 # orientation cells +_LAYOUT_GRID = 8 # colour-layout cells: where colour sits is what tells look-alikes apart +_ORIENT_BINS = 8 + + +def _prepare(bgr: np.ndarray, inset: float = 0.06) -> np.ndarray: + h, w = bgr.shape[:2] + dy, dx = int(h * inset), int(w * inset) + core = bgr[dy : h - dy or h, dx : w - dx or w] if h > 8 and w > 8 else bgr + small = cv2.resize(core, (SIZE, SIZE), interpolation=cv2.INTER_AREA) + # A mild blur narrows the gap between crisp enrolment photos and + # motion-softened video crops more than it costs detail. + return cv2.GaussianBlur(small, (3, 3), 0) + + +def _l2(v: np.ndarray) -> np.ndarray: + n = float(np.linalg.norm(v)) + return v / n if n > 1e-9 else v + + +def _colour_hist(hsv: np.ndarray) -> np.ndarray: + h, s, v = hsv[:, :, 0], hsv[:, :, 1], hsv[:, :, 2] + chroma = s >= 40 + hs, _, _ = np.histogram2d( + h[chroma].ravel(), np.minimum(s[chroma].ravel(), 255), + bins=(18, 3), range=((0, 180), (40, 256)), + ) + grey, _ = np.histogram(v[~chroma].ravel(), bins=8, range=(0, 256)) + hist = np.concatenate([hs.ravel(), grey]).astype(np.float32) + hist /= max(hist.sum(), 1.0) + return np.sqrt(hist) # Hellinger: big bins stop drowning small ones + + +def _layout(lab: np.ndarray) -> np.ndarray: + g = _LAYOUT_GRID + cells = lab.reshape(g, SIZE // g, g, SIZE // g, 3).mean(axis=(1, 3)) + feats = cells.reshape(-1, 3).astype(np.float32) + feats[:, 0] = (feats[:, 0] - 128.0) / 128.0 * 0.5 # lightness matters less than colour + feats[:, 1:] = (feats[:, 1:] - 128.0) / 64.0 + return feats.ravel() + + +def _orientations(gray: np.ndarray) -> np.ndarray: + gx = cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3) + gy = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3) + mag, ang = cv2.cartToPolar(gx, gy) + ang = np.mod(ang, np.pi) # unsigned: an edge is an edge whichever side is darker + bins = np.minimum((ang / np.pi * _ORIENT_BINS).astype(np.int32), _ORIENT_BINS - 1) + step = SIZE // _GRID + out = np.zeros((_GRID, _GRID, _ORIENT_BINS), np.float32) + for gy_ in range(_GRID): + for gx_ in range(_GRID): + sl = (slice(gy_ * step, (gy_ + 1) * step), slice(gx_ * step, (gx_ + 1) * step)) + out[gy_, gx_] = np.bincount( + bins[sl].ravel(), weights=mag[sl].ravel(), minlength=_ORIENT_BINS + ) + # Normalise per cell so contrast changes do not matter, keep an overall + # edge-energy term so a plain box and a busy label still differ. + energy = out.sum(axis=2, keepdims=True) + cells = out / np.maximum(energy, 1e-6) + density = np.log1p(energy / (step * step)) / 5.0 + return np.concatenate([np.sqrt(cells).ravel(), density.ravel()]) + + +def embed(bgr: np.ndarray) -> np.ndarray: + """A unit vector describing how a product looks. Cosine = similarity.""" + if bgr is None or bgr.size == 0 or bgr.ndim != 3: + raise ValueError("embed needs a colour image") + img = _prepare(bgr) + hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB).astype(np.float32) + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).astype(np.float32) + parts = [ + # Weights measured on a look-alike range (same red packaging, different + # artwork): the colour histogram alone cannot tell those apart and, at + # full weight, made unenrolled products look enrolled. + (_colour_hist(hsv), 0.3), + (_layout(lab), 2.0), + (_orientations(gray), 1.0), + ] + return _l2(np.concatenate([_l2(p) * w for p, w in parts]).astype(np.float32)) + + +def product_crop(photo: np.ndarray) -> np.ndarray: + """The product in an enrolment photo, without the table it stood on. + + Video crops are tight boxes from the detector; a phone photo has a + margin of whatever was behind the product. Embedding that margin teaches + the index the background. The product is taken to be the largest strong + rectangle-ish outline covering a fair share of the photo; if none is + found the photo is assumed to be framed on the product already. + """ + h, w = photo.shape[:2] + gray = cv2.cvtColor(photo, cv2.COLOR_BGR2GRAY) + gray = cv2.GaussianBlur(gray, (5, 5), 0) + edges = cv2.Canny(gray, 40, 120) + edges = cv2.dilate(edges, np.ones((5, 5), np.uint8), iterations=2) + contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + # An outline broken by glare or a label comes out as several contours; + # the product is the union of the substantial ones. Anything touching + # the photo's edge is background running out of frame. + boxes = [] + for c in contours: + x, y, bw, bh = cv2.boundingRect(c) + if bw * bh < 0.01 * w * h: + continue + if x <= 2 or y <= 2 or x + bw >= w - 2 or y + bh >= h - 2: + continue + boxes.append((x, y, x + bw, y + bh)) + if not boxes: + return photo + x1 = min(b[0] for b in boxes) + y1 = min(b[1] for b in boxes) + x2 = max(b[2] for b in boxes) + y2 = max(b[3] for b in boxes) + frac = (x2 - x1) * (y2 - y1) / float(w * h) + if not 0.2 <= frac <= 0.97: + return photo + # The dilation grew the outline outward; take back roughly what it added. + t = 4 + return photo[y1 + t : y2 - t, x1 + t : x2 - t] + + +def augment(bgr: np.ndarray, rng: np.random.Generator, n: int = 3) -> list[np.ndarray]: + """Plausible variations of one enrolment photo. + + Five phone photos are not five viewpoints of a shelf: video crops are + smaller, softer, differently lit and slightly off-centre. Stretching each + photo across those conditions is what lets five photos generalise. + """ + out = [] + h, w = bgr.shape[:2] + for _ in range(n): + img = bgr.astype(np.float32) + img = img * rng.uniform(0.75, 1.2) + rng.uniform(-18, 18) + img = np.clip(img, 0, 255).astype(np.uint8) + # a slightly loose or tight crop + m = rng.uniform(-0.06, 0.08) + dx, dy = int(w * m), int(h * m) + if dx > 0 and dy > 0: + img = img[dy : h - dy, dx : w - dx] + elif dx < 0 or dy < 0: + img = cv2.copyMakeBorder(img, -min(dy, 0), -min(dy, 0), -min(dx, 0), -min(dx, 0), + cv2.BORDER_REPLICATE) + # video-like softness and scale + scale = rng.uniform(0.25, 0.6) + small = cv2.resize(img, (max(8, int(img.shape[1] * scale)), max(8, int(img.shape[0] * scale))), + interpolation=cv2.INTER_AREA) + k = int(rng.choice([1, 3, 5])) + if k > 1: + small = cv2.GaussianBlur(small, (k, k), 0) + out.append(small) + return out + + +@dataclass +class Match: + sku: str + score: float # cosine similarity, blended nearest-exemplar and centroid + + +@dataclass +class Exemplar: + """One stored vector: an enrolment photo, or an augmented copy of one.""" + + sku: str + vector: np.ndarray + group: str # the photo it came from; copies of one photo share it + kind: str = "train" # train: matched against | probe: only used to calibrate + + +def vectors_for_photo(photo: np.ndarray, group: str, sku: str, + rng: np.random.Generator | None = None) -> list[Exemplar]: + """Everything one enrolment photo contributes: itself, 3 training copies + under video-like conditions, and 4 held-out probes for calibration.""" + # Seeded from the photo's id, not hash(): str hashes change per process. + rng = rng or np.random.default_rng(zlib.crc32(group.encode())) + product = product_crop(photo) + out = [Exemplar(sku, embed(product), group, "train")] + out += [Exemplar(sku, embed(a), group, "train") for a in augment(product, rng, 3)] + out += [Exemplar(sku, embed(a), group, "probe") for a in augment(product, rng, 4)] + return out + + +DEFAULT_ACCEPT = 0.93 # used until a catalog has enough photos to calibrate +DEFAULT_MARGIN = 0.02 + + +class ExemplarIndex: + """Nearest-exemplar matching against enrolled products, self-calibrating. + + The similarity a genuine sighting reaches depends on the products (a + range of look-alikes scores everything high), so no constant threshold + is right for every customer. The index measures its own: each held-out + probe is scored against everything except the photo it came from, which + is how a real sighting (a view no photo captured exactly) is scored. + `accept` is set just under the low tail of those scores, so about 98% of + genuine sightings clear it and anything less alike is called unknown. + """ + + def __init__(self, exemplars: list[Exemplar] | dict[str, np.ndarray]) -> None: + if isinstance(exemplars, dict): # plain {sku: vectors}: one group per vector + exemplars = [ + Exemplar(sku, v, f"{sku}:{i}") + for sku, vecs in exemplars.items() + for i, v in enumerate(np.asarray(vecs, np.float32)) + ] + train = [e for e in exemplars if e.kind == "train"] + self.skus: list[str] = sorted({e.sku for e in train}) + pos = {s: i for i, s in enumerate(self.skus)} + if train: + self.matrix = np.vstack([e.vector for e in train]).astype(np.float32) + self.owner = np.asarray([pos[e.sku] for e in train], np.int32) + self.groups = np.asarray([e.group for e in train]) + self.centroids = np.vstack([ + _l2(self.matrix[self.owner == i].mean(axis=0)) for i in range(len(self.skus)) + ]) + else: + self.matrix = np.zeros((0, 1), np.float32) + self.owner = np.zeros(0, np.int32) + self.groups = np.zeros(0, dtype=object) + self.centroids = np.zeros((0, 1), np.float32) + self.accept = DEFAULT_ACCEPT + self.margin = DEFAULT_MARGIN + self.accept_by_sku: dict[str, float] = {} + self.calibration: dict[str, float | int | str] = {"source": "default"} + self._calibrate([e for e in exemplars if e.kind == "probe"]) + + def __len__(self) -> int: + return len(self.skus) + + def _scores(self, vec: np.ndarray, exclude_group: str | None = None) -> np.ndarray: + sims = self.matrix @ vec + if exclude_group is not None: + sims = np.where(self.groups == exclude_group, -1.0, sims) + best = np.full(len(self.skus), -1.0, np.float32) + np.maximum.at(best, self.owner, sims) + central = self.centroids @ vec + return 0.7 * best + 0.3 * central + + def match(self, vec: np.ndarray, k: int = 3) -> list[Match]: + """Best-first SKUs for one embedding.""" + if not self.skus: + return [] + scores = self._scores(vec) + order = np.argsort(-scores)[:k] + return [Match(self.skus[i], float(scores[i])) for i in order] + + def _calibrate(self, probes: list[Exemplar], max_probes: int = 2000) -> None: + usable = [p for p in probes if p.sku in self.skus] + # A SKU needs a second photo for its probes to be scored without + # their own photo; below a handful of such probes, keep the default. + own: list[float] = [] + by_sku: dict[str, list[float]] = {} + for p in usable[:max_probes]: + i = self.skus.index(p.sku) + if not np.any((self.groups != p.group) & (self.owner == i)): + continue + score = float(self._scores(p.vector, exclude_group=p.group)[i]) + own.append(score) + by_sku.setdefault(p.sku, []).append(score) + if len(own) < 8: + return + low = float(np.percentile(own, 2)) + self.accept = float(np.clip(low - 0.005, 0.5, 0.99)) + # The spread of genuine scores sets how big a lead counts as decisive. + self.margin = float(np.clip((np.percentile(own, 50) - low) / 2, 0.005, 0.1)) + # Per SKU, too: a plain box matches its own photos very closely, so + # an unenrolled look-alike that merely resembles it must clear that + # SKU's own bar, not the loosest bar in the catalog. Measured on a + # look-alike range, this took unenrolled-product rejection from 0% to + # over 90% of sightings, with no wrong acceptances. + for sku, scores in by_sku.items(): + if len(scores) >= 6: + self.accept_by_sku[sku] = float( + np.clip(np.percentile(scores, 5) - 0.005, 0.5, 0.995) + ) + self.calibration = {"source": "probes", "probes": len(own), + "accept": round(self.accept, 4), "margin": round(self.margin, 4), + "per_sku": {k: round(v, 4) for k, v in self.accept_by_sku.items()}} + + def accept_for(self, sku: str) -> float: + return self.accept_by_sku.get(sku, self.accept) + + +def confidence(matches: list[Match], accept: float, margin: float) -> float: + """How sure a match is: absolute similarity and the lead over the runner-up. + + Both matter: a sighting can look a lot like two SKUs (high score, no + lead), or be closest to one while resembling nothing much (a lead, low + score). Either way a person should look. + """ + if not matches: + return 0.0 + top = matches[0].score + if top < accept: + # Unlike anything enrolled: at most a weak hint, never a decision. + return float(0.3 * np.clip(top / max(accept, 1e-6), 0.0, 1.0)) + second = matches[1].score if len(matches) > 1 else accept - margin + absolute = np.clip((top - accept) / max(1.0 - accept, 1e-6), 0.0, 1.0) + lead = np.clip((top - second) / max(margin, 1e-6), 0.0, 1.0) + return float(np.clip(0.35 + 0.35 * absolute + 0.30 * lead, 0.0, 1.0)) diff --git a/src/countbone/catalog.py b/src/countbone/catalog.py index edc8647..55acbab 100644 --- a/src/countbone/catalog.py +++ b/src/countbone/catalog.py @@ -23,6 +23,8 @@ class SkuEntry: classes: list[str] = field(default_factory=list) # detector class names that map here unit_value: float = 0.0 # used by the tolerance plugin expected: int | None = None + barcodes: list[str] = field(default_factory=list) # EAN/UPC printed on the product + source: str = "config" # config | studio (made in Catalog Studio) def matches_hue(self, hue: float) -> bool: if self.hue is None: @@ -63,6 +65,29 @@ def hue_center_distance(self, hue: float) -> float: @dataclass class Catalog: entries: list[SkuEntry] = field(default_factory=list) + # Enrolled photos, as appearance vectors (see appearance.py). Empty for a + # catalog that identifies by colour or detector class only. + exemplars: list[Any] = field(default_factory=list) + _index: Any = field(default=None, repr=False, compare=False) + + def index(self): + """The appearance index over enrolled photos, built once per catalog.""" + if self._index is None: + from .appearance import ExemplarIndex + + self._index = ExemplarIndex(self.exemplars) + return self._index + + def by_barcode(self, code: str) -> SkuEntry | None: + return next((e for e in self.entries if code in e.barcodes), None) + + def merged(self, extra: list[SkuEntry]) -> Catalog: + """This catalog with `extra` entries added; an extra entry with the + same SKU replaces the configured one (the studio is the newer edit).""" + by_sku = {e.sku: e for e in self.entries} + for e in extra: + by_sku[e.sku] = e + return Catalog(list(by_sku.values()), list(self.exemplars)) @classmethod def load(cls, path: str | Path | None) -> Catalog: @@ -86,6 +111,7 @@ def from_dict(cls, raw: dict[str, Any]) -> Catalog: classes=list(item.get("classes", []) or []), unit_value=float(item.get("unit_value", 0.0)), expected=item.get("expected"), + barcodes=[str(b) for b in item.get("barcodes", []) or []], ) ) return cls(entries) diff --git a/src/countbone/config.py b/src/countbone/config.py index 9b033cb..79796ce 100644 --- a/src/countbone/config.py +++ b/src/countbone/config.py @@ -18,11 +18,16 @@ @dataclass class CaptureConfig: - every_n_frames: int = 5 # sample rate through the source video - max_frames: int | None = 400 # hard cap so a long video cannot stall a run + every_n_frames: int = 5 # the widest stride; adaptive sampling only ever narrows it + # Hard cap so a runaway source cannot stall a run. Reaching it is flagged + # for review (the rest of the video went uncounted), never silent. + max_frames: int | None = 5000 start_s: float = 0.0 end_s: float | None = None resize_width: int | None = 960 # None keeps native resolution + adaptive: bool = True # closed-loop sampling, see stages.capture.Sampler + target_shift_frac: float = 0.25 # aim for this much scene movement per sample, in object widths + max_shift_frac: float = 0.6 # back-fill skipped frames when a gap moves further than this @dataclass @@ -46,7 +51,8 @@ class DetectConfig: @dataclass class IdentifyConfig: - backend: str = "color" # color | classmap | fixture + backend: str = "auto" # auto (appearance once photos exist, else color) + # | appearance | color | classmap | fixture catalog: str | None = None # path to a SKU catalog YAML min_confidence: float = 0.2 unknown_sku: str = "UNKNOWN" @@ -81,6 +87,9 @@ class PluginSpec: PluginSpec("quality_gate"), PluginSpec("multiframe"), PluginSpec("confidence"), + # On by default: it only acts when a count has an expected number, and a + # count that misses its expected number must never say "no review needed". + PluginSpec("tolerance"), PluginSpec("review_queue"), PluginSpec("exception_report"), PluginSpec("audit_pack"), diff --git a/src/countbone/demo.py b/src/countbone/demo.py index 19af93e..2237e7d 100644 --- a/src/countbone/demo.py +++ b/src/countbone/demo.py @@ -26,6 +26,35 @@ } +# A range the colour identifier cannot separate: four red cartons told apart +# only by their artwork, plus one blue. This is what a real shelf looks like to +# a hue histogram (one brand, one colour, many products). +LOOKALIKE_SKUS: dict[str, dict] = { + "RED-PLAIN": {"label": "Red carton, plain", "color": (40, 40, 205), "art": "plain"}, + "RED-STRIPE": {"label": "Red carton, stripes", "color": (40, 40, 205), "art": "stripes"}, + "RED-DOT": {"label": "Red carton, black dot", "color": (40, 40, 205), "art": "dot"}, + "RED-BAND": {"label": "Red carton, dark band", "color": (40, 40, 205), "art": "band"}, + "BLU-PLAIN": {"label": "Blue carton", "color": (200, 110, 40), "art": "plain"}, +} + + +def draw_carton(board: np.ndarray, x1: int, y1: int, x2: int, y2: int, + color: tuple[int, int, int], art: str = "plain") -> None: + """One carton: body, outline, a white label patch, and its artwork.""" + cv2.rectangle(board, (x1, y1), (x2, y2), color, -1) + cv2.rectangle(board, (x1, y1), (x2, y2), (30, 30, 30), 3) + cv2.rectangle(board, (x1 + 16, y1 + 24), (x2 - 16, y1 + 52), (235, 235, 235), -1) + w, h = x2 - x1, y2 - y1 + if art == "stripes": + for k in range(3): + sx = x1 + int(w * (0.22 + 0.25 * k)) + cv2.rectangle(board, (sx, y1 + 62), (sx + max(4, w // 12), y2 - 8), (235, 235, 235), -1) + elif art == "dot": + cv2.circle(board, (x1 + w // 2, y1 + int(h * 0.68)), max(6, int(w * 0.24)), (25, 25, 25), -1) + elif art == "band": + cv2.rectangle(board, (x1 + 4, y1 + int(h * 0.62)), (x2 - 4, y1 + int(h * 0.78)), (30, 30, 90), -1) + + @dataclass class DemoScene: path: str @@ -33,14 +62,82 @@ class DemoScene: frames: int fps: int shelf_size: tuple[int, int] + # Every carton on the board, for tests that check where things are + # (shelf gaps, cross-video merging): (sku, x1, y1, x2, y2) in board pixels. + layout: list[tuple[str, int, int, int, int]] | None = None + # Empty slots between cartons, same coordinates: (row_top, x1, x2). + empty_slots: list[tuple[int, int, int]] | None = None + view_x: tuple[int, int] = (0, 0) # the board range the pan covered @property def total(self) -> int: return sum(self.truth.values()) -def _shelf(width: int, height: int, rng: random.Random) -> tuple[np.ndarray, dict[str, int]]: - """Paint a shelf wall with evenly spaced cartons.""" +def _lookalike_shelf(width: int, height: int, rng: random.Random): + board = np.full((height, width, 3), 168, dtype=np.uint8) + noise = np.random.default_rng(rng.randint(0, 2**31)).normal(0, 6, board.shape) + board = np.clip(board.astype(np.float64) + noise, 0, 255).astype(np.uint8) + for y in (height // 3, 2 * height // 3): + cv2.rectangle(board, (0, y - 6), (width, y + 6), (120, 120, 120), -1) + truth: dict[str, int] = {} + layout, empty = [], [] + box_w, box_h, gap_x, gap_y = 96, 132, 46, 40 + tops = [20, height // 3 + 20, 2 * height // 3 + 20] + x = 40 + while x + box_w < width - 40: + for top in tops: + if rng.random() < 0.15: + empty.append((top, x, x + box_w)) + continue + sku = rng.choice(list(LOOKALIKE_SKUS)) + spec = LOOKALIKE_SKUS[sku] + j = rng.randint(-6, 6) + x1, y1, x2, y2 = x + j, top, x + j + box_w, top + box_h - gap_y // 2 + draw_carton(board, x1, y1, x2, y2, spec["color"], spec["art"]) + truth[sku] = truth.get(sku, 0) + 1 + layout.append((sku, x1, y1, x2, y2)) + x += box_w + gap_x + return board, truth, layout, empty + + +def product_photos(sku: str, n: int = 5, seed: int = 0) -> list[np.ndarray]: + """What a person enrolling a look-alike SKU would photograph with a phone. + + Close up, a bit rotated, on a random background, with phone-camera + exposure and softness. Deliberately unlike the shelf video, so enrolment + is tested across the domain gap it faces in practice. + """ + spec = LOOKALIKE_SKUS.get(sku) or {"color": SKU_COLORS[sku], "art": "plain"} + rng = np.random.default_rng(seed) + photos = [] + for _ in range(n): + canvas = np.full((460, 380, 3), rng.integers(60, 220, 3), dtype=np.uint8) + canvas = np.clip(canvas + rng.normal(0, 8, canvas.shape), 0, 255).astype(np.uint8) + # The same carton as on the shelf, photographed closer: drawn at shelf + # scale, then enlarged, so its proportions are the product's own. + small = np.full((120, 104, 3), 168, np.uint8) + draw_carton(small, 4, 4, 100, 116, spec["color"], spec["art"]) + art = cv2.resize(small, (250, 340), interpolation=cv2.INTER_CUBIC) + angle = rng.uniform(-6, 6) + m = cv2.getRotationMatrix2D((125, 170), angle, rng.uniform(0.95, 1.05)) + art = cv2.warpAffine(art, m, (250, 340), borderMode=cv2.BORDER_REPLICATE) + ox, oy = int(rng.integers(40, 90)), int(rng.integers(40, 90)) + canvas[oy : oy + 340, ox : ox + 250] = art + gain = rng.uniform(0.8, 1.2) + canvas = np.clip(canvas.astype(np.float32) * gain, 0, 255).astype(np.uint8) + photo = canvas[oy - 12 : oy + 352, ox - 12 : ox + 262] # the user framed it roughly + photos.append(cv2.GaussianBlur(photo, (3, 3), 0)) + return photos + + +def _shelf(width: int, height: int, rng: random.Random): + """Paint a shelf wall with evenly spaced cartons. + + Returns the board, the truth per SKU, every carton's box and every empty + slot. (The random sequence is unchanged from the first version, so a seed + still renders the same shelf.) + """ board = np.full((height, width, 3), 168, dtype=np.uint8) noise = np.random.default_rng(rng.randint(0, 2**31)).normal(0, 6, board.shape) board = np.clip(board.astype(np.float64) + noise, 0, 255).astype(np.uint8) @@ -50,6 +147,8 @@ def _shelf(width: int, height: int, rng: random.Random) -> tuple[np.ndarray, dic cv2.rectangle(board, (0, y - 6), (width, y + 6), (120, 120, 120), -1) truth: dict[str, int] = {} + layout: list[tuple[str, int, int, int, int]] = [] + empty: list[tuple[int, int, int]] = [] box_w, box_h = 96, 132 gap_x, gap_y = 46, 40 top_offsets = [20, height // 3 + 20, 2 * height // 3 + 20] @@ -57,21 +156,18 @@ def _shelf(width: int, height: int, rng: random.Random) -> tuple[np.ndarray, dic while x + box_w < width - 40: for top in top_offsets: if rng.random() < 0.18: # a few empty slots keeps it honest + empty.append((top, x, x + box_w)) continue sku = rng.choice(list(SKU_COLORS)) color = SKU_COLORS[sku] jitter = rng.randint(-6, 6) x1, y1 = x + jitter, top x2, y2 = x1 + box_w, y1 + box_h - gap_y // 2 - cv2.rectangle(board, (x1, y1), (x2, y2), color, -1) - cv2.rectangle(board, (x1, y1), (x2, y2), (30, 30, 30), 3) - # a label patch, so the box is not a flat colour field - cv2.rectangle( - board, (x1 + 16, y1 + 24), (x2 - 16, y1 + 52), (235, 235, 235), -1 - ) + draw_carton(board, x1, y1, x2, y2, color) truth[sku] = truth.get(sku, 0) + 1 + layout.append((sku, x1, y1, x2, y2)) x += box_w + gap_x - return board, truth + return board, truth, layout, empty def make_demo_video( @@ -81,29 +177,47 @@ def make_demo_video( seconds: float = 6.0, view: tuple[int, int] = (960, 540), bad_frame_rate: float = 0.08, + palette: str = "primary", + window: tuple[float, float] = (0.0, 1.0), + shelf_views: float = 2.2, ) -> DemoScene: """Render a pan across a synthetic shelf. Returns the ground truth with it. `bad_frame_rate` blurs a fraction of frames, so the quality gate has - something real to reject. + something real to reject. `palette="lookalike"` paints products that differ + only in artwork. `window` pans across only part of the shelf (as fractions + of the full pan): two overlapping windows of the same seed are two videos + of one shelf, filmed by two people or twice. Shorter `seconds` pan faster. """ rng = random.Random(seed) view_w, view_h = view - shelf_w, shelf_h = int(view_w * 2.2), view_h - board, truth = _shelf(shelf_w, shelf_h, rng) + shelf_w, shelf_h = int(view_w * shelf_views), view_h + if palette == "primary": + board, _, layout, empty = _shelf(shelf_w, shelf_h, rng) + elif palette == "lookalike": + board, _, layout, empty = _lookalike_shelf(shelf_w, shelf_h, rng) + else: + raise ValueError(f"unknown palette {palette!r}; choose primary or lookalike") out_path = Path(path) out_path.parent.mkdir(parents=True, exist_ok=True) frames = int(fps * seconds) + travel = shelf_w - view_w + start, end = int(window[0] * travel), int(window[1] * travel) + # What this video can see: cartons wholly inside the covered range count. + seen_lo, seen_hi = start, end + view_w + truth: dict[str, int] = {} + for sku, x1, _, x2, _ in layout: + if x1 >= seen_lo and x2 <= seen_hi: + truth[sku] = truth.get(sku, 0) + 1 writer, out_path = _open_writer(out_path, fps, view_w, view_h) try: - travel = shelf_w - view_w for i in range(frames): # ease-in-out pan: a human hand does not move linearly t = i / max(frames - 1, 1) eased = 3 * t**2 - 2 * t**3 - x = int(eased * travel) + x = start + int(eased * (end - start)) frame = board[0:view_h, x : x + view_w].copy() if rng.random() < bad_frame_rate: frame = cv2.GaussianBlur(frame, (21, 21), 0) @@ -117,6 +231,9 @@ def make_demo_video( frames=frames, fps=fps, shelf_size=(shelf_w, shelf_h), + layout=[c for c in layout if c[1] >= seen_lo and c[3] <= seen_hi], + empty_slots=empty, + view_x=(seen_lo, seen_hi), ) diff --git a/src/countbone/pipeline.py b/src/countbone/pipeline.py index 5532c9b..f0c86ba 100644 --- a/src/countbone/pipeline.py +++ b/src/countbone/pipeline.py @@ -10,9 +10,13 @@ from __future__ import annotations import logging +import math +import statistics import time -from collections import defaultdict +from collections import defaultdict, deque +from collections.abc import Callable from pathlib import Path +from typing import Any from .catalog import Catalog from .config import Config @@ -24,6 +28,63 @@ log = logging.getLogger(__name__) +CatalogProvider = Callable[[], Catalog] + + +class Continuity: + """How well the tracker could follow the scene between usable frames. + + Confidence used to measure only how sure identification was, so a count + that lost objects between frames was reported as certain. This measures + the thing that actually breaks tracking: how far the scene moved between + two consecutive *kept* frames, in object widths. Dropped frames widen that + gap; the adaptive sampler narrows it; this records what was achieved. + """ + + RISKY = 0.5 # beyond this, association starts to rely on motion compensation + BROKEN = 1.0 # beyond this, an object can pass between kept frames unseen + + def __init__(self) -> None: + self.ratios: list[float] = [] + self.unknown_gaps = 0 + self._last_cum: tuple[float, float] | None = None + self._cum = (0.0, 0.0) + self._unknown = False + + def observe(self, motion_: dict[str, float] | None) -> None: + if motion_ and motion_.get("estimated"): + self._cum = (self._cum[0] + motion_["dx"], self._cum[1] + motion_["dy"]) + elif self._last_cum is not None: + self._unknown = True + + def kept(self, box_px: float | None) -> None: + if self._last_cum is not None and box_px: + if self._unknown: + self.unknown_gaps += 1 + else: + moved = math.hypot(self._cum[0] - self._last_cum[0], self._cum[1] - self._last_cum[1]) + self.ratios.append(moved / box_px) + self._last_cum = self._cum + self._unknown = False + + def summary(self) -> dict[str, Any]: + worst = max(self.ratios, default=0.0) + risky = sum(r > self.RISKY for r in self.ratios) + broken = sum(r > self.BROKEN for r in self.ratios) + # 1.0 while every gap is comfortably followable, falling to 0 as the + # worst gap approaches three object widths. + score = 1.0 if worst <= self.RISKY else max(0.0, 1.0 - (worst - self.RISKY) / 2.5) + if self.unknown_gaps: + score = min(score, 0.8) + return { + "gaps": len(self.ratios), + "max_shift_objects": round(worst, 3), + "risky_gaps": int(risky), + "broken_gaps": int(broken), + "unknown_gaps": self.unknown_gaps, + "score": round(score, 4), + } + class Pipeline: """Runs one video to one result. @@ -32,9 +93,17 @@ class Pipeline: RunContext, not on self. """ - def __init__(self, config: Config | None = None, store: Store | None = None) -> None: + def __init__( + self, + config: Config | None = None, + store: Store | None = None, + catalog_provider: CatalogProvider | None = None, + ) -> None: self.config = config or Config() - self.catalog = Catalog.load(self.config.identify.catalog) + self._catalog_provider = catalog_provider + self.catalog = catalog_provider() if catalog_provider else Catalog.load( + self.config.identify.catalog + ) self.detector = detect.build(self.config.detect) self.identifier = identify.build(self.config.identify, self.catalog) self.plugins = plugin_base.build(self.config.plugins) @@ -44,16 +113,39 @@ def __init__(self, config: Config | None = None, store: Store | None = None) -> log.info( "pipeline ready: detect=%s identify=%s count=%s plugins=[%s]", self.config.detect.backend, - self.config.identify.backend, + self.identifier.name, self.config.count.strategy, ", ".join(p.name for p in self.plugins), ) + def refresh_catalog(self) -> None: + """Pick up catalog edits (new SKUs, enrolled photos) before a run.""" + if self._catalog_provider is None: + return + self.catalog = self._catalog_provider() + self.identifier = identify.build(self.config.identify, self.catalog) + # -- public API ------------------------------------------------------ - def run(self, source: str | int, run_id: str | None = None) -> CountResult: + def run( + self, + source: str | int, + run_id: str | None = None, + expected: dict[str, int] | None = None, + context: dict[str, Any] | None = None, + ) -> CountResult: + """Count one video. + + `expected` replaces the configured expectations for this run (a + location's book stock, a purchase order). `context` describes why the + run exists (location, kind, who filmed it); plugins read and may fill + it, and it is kept on the result. + """ + self.refresh_catalog() ctx = RunContext(config=self.config, source=str(source), store=self.store) if run_id: ctx.run_id = run_id + ctx.state["run_context"] = dict(context or {}) + ctx.state["expected"] = dict(expected) if expected is not None else None result: CountResult | None = None try: result = self._run(ctx, source) @@ -83,18 +175,25 @@ def _run(self, ctx: RunContext, source: str | int) -> CountResult: iou_threshold=cfg.count.track_iou, max_gap=cfg.count.track_max_gap ) camera = motion.MotionEstimator() + sampler = capture.Sampler(cfg.capture) + continuity = Continuity() + widths: deque[float] = deque(maxlen=400) # recent object widths, for scale per_frame: dict[int, list[Item]] = defaultdict(list) # Only the frame-tally strategies need every sighting kept; tracking # does not, and on a long run that is a lot of objects held for nothing. needs_per_frame = cfg.count.strategy in ("peak_frame", "median_frame") - for frame in capture.frames(source, cfg.capture): # 1. Capture + def box_px() -> float | None: + return statistics.median(widths) if widths else None + + for frame in capture.frames(source, cfg.capture, sampler): # 1. Capture result.frames_read += 1 frame = preprocess.apply(frame, cfg.preprocess) # 2. Pre-process # Estimated before the quality gate can drop the frame, so a # dropped frame still contributes its share of camera movement. frame.meta["motion"] = camera.update(frame.image) + continuity.observe(frame.meta["motion"]) # Capture-layer plugins may reject a frame outright. kept = plugin_base.fire( @@ -104,6 +203,7 @@ def _run(self, ctx: RunContext, source: str | int) -> CountResult: result.frames_dropped += 1 # The frame is gone, its camera movement is not (see Tracker). tracker.observe_motion(frame.meta.get("motion")) + sampler.feedback(kept=False) continue frame = kept result.frames_used += 1 @@ -113,6 +213,7 @@ def _run(self, ctx: RunContext, source: str | int) -> CountResult: self.plugins, "on_detections", ctx, frame, detections, transform=True ) result.detections += len(detections) + widths.extend(d.bbox[2] - d.bbox[0] for d in detections) items = self.identifier.identify(frame, detections) # 4. Identify items = plugin_base.fire( @@ -122,20 +223,52 @@ def _run(self, ctx: RunContext, source: str | int) -> CountResult: if needs_per_frame: per_frame[frame.index] = items tracker.update(frame.index, items, frame.meta.get("motion")) # 5. Count + # Track ids are known now and the pixels are still in hand. + plugin_base.fire(self.plugins, "on_frame_tracked", ctx, frame, items) + continuity.kept(box_px()) + sampler.feedback(kept=True, box_px=box_px()) + + result.meta["sampling"] = {**sampler.stats, "base_step": sampler.base, + "adaptive": sampler.adaptive} + result.meta["continuity"] = continuity.summary() + ctx.state["continuity"] = result.meta["continuity"] + if sampler.truncated: + ctx.warn( + f"video longer than the {cfg.capture.max_frames}-frame cap: the end of " + "it was not counted" + ) + result.needs_review = True + broken = result.meta["continuity"]["broken_gaps"] + if broken: + ctx.warn( + f"camera moved more than an object's width between usable frames " + f"{broken} time(s); items may have been missed (film more slowly)" + ) + result.needs_review = True tracks = plugin_base.fire( self.plugins, "on_tracks", ctx, tracker.tracks, transform=True ) - result = count.finalise(result, tracks, dict(per_frame), cfg.count, self.catalog) + result = count.finalise( + result, tracks, dict(per_frame), cfg.count, self.catalog, + expected=ctx.state.get("expected"), + ) + result.meta["tracks_world"] = [ + {"track_id": t.track_id, "sku": t.sku, "hits": t.hits, **w} + for t in tracks + if t.hits >= cfg.count.min_hits and (w := count.track_world(t)) is not None + ] result = plugin_base.fire( self.plugins, "on_counts", ctx, result, transform=True ) + result.meta["context"] = ctx.state.get("run_context", {}) result.warnings = list(dict.fromkeys(result.warnings + ctx.warnings)) result.finished_at = time.time() result.meta["config_fingerprint"] = cfg.fingerprint() result.meta["plugins"] = [p.name for p in self.plugins] result.meta["artifacts_dir"] = str(ctx.artifacts_dir) + result.meta["identifier"] = self.identifier.name written = output.emit(ctx, result, cfg.output) # 6. Output result.meta["outputs"] = written diff --git a/src/countbone/plugins/base.py b/src/countbone/plugins/base.py index aca41c1..42af036 100644 --- a/src/countbone/plugins/base.py +++ b/src/countbone/plugins/base.py @@ -10,6 +10,8 @@ on_frame(ctx, frame) -> Frame | None capture layer; None drops the frame on_detections(ctx, f, d) -> list[Detection] on_items(ctx, f, items) -> list[Item] identification layer + on_frame_tracked(ctx, f, items) -> None after the tracker assigned track ids, + while the frame's pixels are in hand on_tracks(ctx, tracks) -> list[Track] after counting has grouped sightings on_counts(ctx, result) -> CountResult count layer on_output(ctx, result) -> None output layer, side effects only @@ -61,6 +63,8 @@ def on_items( ) -> list[Item]: return items + def on_frame_tracked(self, ctx: RunContext, frame: Frame, items: list[Item]) -> None: ... + def on_tracks(self, ctx: RunContext, tracks: list[Track]) -> list[Track]: return tracks diff --git a/src/countbone/plugins/confidence.py b/src/countbone/plugins/confidence.py index 6a6f9a7..68f6b93 100644 --- a/src/countbone/plugins/confidence.py +++ b/src/countbone/plugins/confidence.py @@ -79,11 +79,11 @@ def _frame_quality(frame: Frame) -> float: # -- per SKU and per run --------------------------------------------- def on_counts(self, ctx: RunContext, result: CountResult) -> CountResult: + capture_term = self._capture_term(ctx) for sku_count in result.counts: # The count stage already scored track quality; fold in how well # the whole capture went so a good count off bad footage is not # reported as certain. - capture_term = self._capture_term(ctx) blended = 0.75 * sku_count.confidence + 0.25 * capture_term sku_count.confidence = float(np.clip(blended, 0.0, 1.0)) sku_count.evidence["below_threshold"] = ( @@ -111,8 +111,15 @@ def _capture_term(ctx: RunContext) -> float: stats = ctx.state.get("quality") or {} scores = stats.get("scores") or [] if not scores: - return 0.7 - sharpness = min(1.0, float(np.mean(scores)) / 150.0) - seen = max(stats.get("seen", 1), 1) - usable = 1.0 - stats.get("dropped", 0) / seen - return float(np.clip(0.6 * sharpness + 0.4 * usable, 0.0, 1.0)) + base = 0.7 + else: + sharpness = min(1.0, float(np.mean(scores)) / 150.0) + seen = max(stats.get("seen", 1), 1) + usable = 1.0 - stats.get("dropped", 0) / seen + base = float(np.clip(0.6 * sharpness + 0.4 * usable, 0.0, 1.0)) + # Tracking continuity caps the whole term: sharp frames are worth + # little if the camera outran the tracker between them. + continuity = (ctx.state.get("continuity") or {}).get("score") + if continuity is not None: + base = min(base, 0.5 + 0.5 * float(continuity)) * (0.6 + 0.4 * float(continuity)) + return float(np.clip(base, 0.0, 1.0)) diff --git a/src/countbone/plugins/review_queue.py b/src/countbone/plugins/review_queue.py index ec65500..c20520b 100644 --- a/src/countbone/plugins/review_queue.py +++ b/src/countbone/plugins/review_queue.py @@ -3,6 +3,18 @@ The queue is the honest half of the product. Every item it raises carries the crop the model saw, so the reviewer decides from evidence rather than from trust. + +Three kinds of question are raised: + + low_sku_confidence a whole SKU's number is doubtful (one task, not N) + low_item_confidence one object's identity is doubtful + unidentified an object nobody could name + possible_missed_item an object seen clearly but only once, which the + counter discards as noise; usually a real carton at + the edge of the walk, occasionally a reflection + +Item-level questions are asked once per tracked object, not once per frame, +and carry the track id, so a decision can change the count (ops.final). """ from __future__ import annotations @@ -31,8 +43,14 @@ def configure( max_items: int = 40, save_crops: bool = True, review_unknown: bool = True, + review_single_sightings: bool = True, + single_min_confidence: float = 0.6, + settled_agreement: float = 0.7, **_: Any, ) -> None: + # An object seen 3+ times whose sightings agree at least this much is + # settled by its own vote; its odd doubtful frame raises no question. + self.settled_agreement = float(settled_agreement) self.sku_threshold = float(sku_threshold) self.item_threshold = float(item_threshold) self.max_items = int(max_items) @@ -41,6 +59,8 @@ def configure( self.candidate_cap = max(self.max_items * 2, self.max_items + 20) self.save_crops = bool(save_crops) self.review_unknown = bool(review_unknown) + self.review_single_sightings = bool(review_single_sightings) + self.single_min_confidence = float(single_min_confidence) def on_items(self, ctx: RunContext, frame, items): """Collect low-confidence sightings as they go past. @@ -63,12 +83,59 @@ def on_items(self, ctx: RunContext, frame, items): "index_in_frame": index, "bbox": item.detection.bbox, "crop": self._crop(frame, item) if self.save_crops else None, - "meta": {"id_source": item.id_source, **item.meta}, + # The tracker writes track_id onto this same object later. + "item": item, + "meta": {"id_source": item.id_source, **_public_meta(item.meta)}, } ) self._trim(pending) return items + def on_frame_tracked(self, ctx: RunContext, frame, items) -> None: + """Remember the one sighting of objects seen once so far. + + Most tracks get a second sighting a frame later and are forgotten + here; what is left at the end is exactly the set of objects the + counter will discard for being seen only once. + """ + if not self.review_single_sightings: + return + hits: dict[int, int] = ctx.setdefault("review_track_hits", dict) + singles: dict[int, dict[str, Any]] = ctx.setdefault("review_singles", dict) + for index, item in enumerate(items): + tid = item.track_id + if tid is None: + continue + hits[tid] = hits.get(tid, 0) + 1 + if hits[tid] == 1 and item.confidence >= self.single_min_confidence: + singles[tid] = { + "sku": item.sku, + "reason": "possible_missed_item", + "confidence": item.confidence, + "frame_index": frame.index, + "index_in_frame": index, + "bbox": item.detection.bbox, + "crop": self._crop(frame, item) if self.save_crops else None, + "item": item, + "meta": {"id_source": item.id_source, **_public_meta(item.meta)}, + } + if len(singles) > self.candidate_cap: + # Oldest first: a long-open track is likely to be seen again. + del singles[next(iter(singles))] + elif hits[tid] >= 2: + singles.pop(tid, None) + + def on_tracks(self, ctx: RunContext, tracks): + ctx.state["review_track_index"] = {t.track_id: (t.sku, t.hits) for t in tracks} + # How firmly each object's sightings agree on what it is. One doubtful + # frame of an object seen ten times, nine of them clearly, is settled + # by the vote; asking a person about it is noise. + ctx.state["review_track_agreement"] = { + t.track_id: (sum(i.sku == t.sku for i in t.items) / len(t.items)) if t.items else 0.0 + for t in tracks + } + return tracks + def _trim(self, pending: list[dict[str, Any]]) -> None: """Keep only the least confident candidates. @@ -82,10 +149,40 @@ def _trim(self, pending: list[dict[str, Any]]) -> None: pending.sort(key=lambda c: c["confidence"]) del pending[self.max_items :] + def _one_per_track(self, candidates: list[dict[str, Any]], index) -> list[dict[str, Any]]: + """Ask about each object once: its least confident sighting.""" + seen: set[int] = set() + out = [] + for cand in sorted(candidates, key=lambda c: c["confidence"]): + tid = cand["item"].track_id + if tid is not None: + if tid in seen: + continue + seen.add(tid) + out.append(cand) + return out + def on_counts(self, ctx: RunContext, result: CountResult) -> CountResult: - candidates = sorted( - ctx.state.get("review_candidates", []), key=lambda c: c["confidence"] + index: dict[int, tuple[str, int]] = ctx.state.get("review_track_index", {}) + agreement: dict[int, float] = ctx.state.get("review_track_agreement", {}) + min_hits = ctx.config.count.min_hits + unknown_sku = ctx.config.identify.unknown_sku + + def settled(cand: dict[str, Any]) -> bool: + tid = cand["item"].track_id + if tid is None or tid not in index: + return False + sku, hits = index[tid] + return (sku != unknown_sku and hits >= max(min_hits, 3) + and agreement.get(tid, 0.0) >= self.settled_agreement) + + candidates = self._one_per_track( + [c for c in ctx.state.get("review_candidates", []) if not settled(c)], index ) + singles = [ + c for tid, c in ctx.state.get("review_singles", {}).items() + if index.get(tid, ("", 0))[1] < min_hits + ] reviews: list[ReviewItem] = [] # A whole SKU whose count is doubtful is one review task, not N. @@ -108,18 +205,32 @@ def on_counts(self, ctx: RunContext, result: CountResult) -> CountResult: ) ) - for cand in candidates[: self.max_items]: + # Unsure identities first (they can move units between SKUs), then + # possible misses, highest-confidence first (most likely real). + ranked = candidates[: self.max_items] + room = max(0, self.max_items - len(ranked)) + ranked += sorted(singles, key=lambda c: -c["confidence"])[:room] + for cand in ranked: + tid = cand["item"].track_id + track_sku, hits = index.get(tid, (None, 0)) if tid is not None else (None, 0) reviews.append( ReviewItem( review_id=new_id("rev"), run_id=result.run_id, - sku=cand["sku"], + sku=track_sku or cand["sku"], reason=cand["reason"], confidence=cand["confidence"], frame_index=cand["frame_index"], bbox=cand["bbox"], crop_path=self._write_crop(ctx, cand), - meta={**cand["meta"], "scope": "item"}, + meta={ + **cand["meta"], + "scope": "item", + "track_id": tid, + "track_sku": track_sku, + "counted": hits >= min_hits, + "sighting_sku": cand["sku"], + }, ) ) @@ -129,7 +240,8 @@ def on_counts(self, ctx: RunContext, result: CountResult) -> CountResult: result.meta["review_queue"] = { "raised": len(reviews), "candidates_seen": len(candidates), - "truncated": max(0, len(candidates) - self.max_items), + "possible_misses": len(singles), + "truncated": max(0, len(candidates) + len(singles) - self.max_items), } return result @@ -161,3 +273,8 @@ def _write_crop(self, ctx: RunContext, cand: dict[str, Any]) -> str | None: path = crops_dir / name cv2.imwrite(str(path), image) return str(path.relative_to(ctx.artifacts_dir)) + + +def _public_meta(meta: dict[str, Any]) -> dict[str, Any]: + """Item metadata worth showing a reviewer (not internal bookkeeping).""" + return {k: v for k, v in meta.items() if k not in ("world", "embedding", "low_confidence")} diff --git a/src/countbone/plugins/tolerance.py b/src/countbone/plugins/tolerance.py index 05e022d..5a59856 100644 --- a/src/countbone/plugins/tolerance.py +++ b/src/countbone/plugins/tolerance.py @@ -14,9 +14,11 @@ from .base import Plugin, register DEFAULT_BANDS = [ - # up to unit_value, allowed absolute units, allowed fraction of expected - {"max_unit_value": 10.0, "abs": 2, "pct": 0.03}, - {"max_unit_value": 100.0, "abs": 1, "pct": 0.01}, + # Out of the box every miss is flagged: a count that disagrees with the + # expected number is exactly what a customer bought this to find. Loosen + # per value band in config once the business has decided what is noise, + # e.g. {"max_unit_value": 10.0, "abs": 2, "pct": 0.03} for cheap cartons. + # (What gets auto-approved is a separate decision: see ops.reconcile.) {"max_unit_value": None, "abs": 0, "pct": 0.0}, ] diff --git a/src/countbone/stages/capture.py b/src/countbone/stages/capture.py index a0eb1e1..fb8a265 100644 --- a/src/countbone/stages/capture.py +++ b/src/countbone/stages/capture.py @@ -7,6 +7,7 @@ from pathlib import Path import cv2 +import numpy as np from ..config import CaptureConfig from ..types import Frame @@ -62,17 +63,166 @@ def _timestamp(cap: cv2.VideoCapture, source_index: int, fps: float) -> float: return source_index / fps if fps > 0 else float(source_index) -def frames(source: str | int, cfg: CaptureConfig) -> Iterator[Frame]: +def _resize(image: np.ndarray, width: int | None) -> np.ndarray: + if width and image.shape[1] > width: + scale = width / image.shape[1] + return cv2.resize( + image, + (width, max(1, int(round(image.shape[0] * scale)))), + interpolation=cv2.INTER_AREA, + ) + return image + + +PROBE_WIDTH = 160 # the look-ahead shift check runs on a thumbnail + + +def _thumb(image: np.ndarray) -> np.ndarray: + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if image.ndim == 3 else image + scale = PROBE_WIDTH / gray.shape[1] + small = cv2.resize( + gray, (PROBE_WIDTH, max(1, int(round(gray.shape[0] * scale)))), + interpolation=cv2.INTER_AREA, + ) + return small.astype(np.float32) + + +class Sampler: + """Decides which source frames become pipeline frames, and adapts. + + A fixed stride is blind to the two things that break counting: the + camera moving further between samples than the tracker can follow, and + the quality gate rejecting a sample, which silently doubles the gap to + the next one. At a fixed stride of 5 both cost real cartons (an item at + the aisle's edge seen in one usable frame is discarded as noise). + + So sampling is closed-loop: + + * look-ahead: before a sample is emitted, a thumbnail phase correlation + measures how far the scene moved since the last emitted frame. If that + is more than `max_shift_frac` of an object's width, the skipped frames + in between are emitted first (they are still in memory), so no gap the + tracker has to bridge is ever that large; + * the stride shrinks toward whatever keeps the shift near `target_shift_frac` + of an object width, and grows back to `every_n_frames` when the camera + slows; + * after the pipeline drops a frame, the very next source frame is taken, + instead of waiting a whole stride. + + Decoding already reads every frame, so this costs thumbnails, not I/O. + """ + + def __init__(self, cfg: CaptureConfig) -> None: + self.base = max(1, int(cfg.every_n_frames)) + self.adaptive = bool(cfg.adaptive) + self.target_shift_frac = float(cfg.target_shift_frac) + self.max_shift_frac = float(cfg.max_shift_frac) + self.step = self.base + self.retry = False + self.box_px: float | None = None # a typical object width, fed back by the pipeline + self.frame_width: int | None = None + self.truncated = False + self.stats = {"emitted": 0, "backfilled": 0, "retries": 0, "min_step": self.base} + self._speed: float | None = None # px per source frame, smoothed + self._window: np.ndarray | None = None + + # -- feedback from the pipeline -------------------------------------- + def feedback(self, kept: bool, box_px: float | None = None) -> None: + if box_px and box_px > 0: + self.box_px = float(box_px) + if not self.adaptive: + return + if not kept: + self.retry = True + self.stats["retries"] += 1 + else: + self.retry = False + + # -- decisions ------------------------------------------------------- + def _object_px(self) -> float: + if self.box_px: + return self.box_px + # Before the first detection: assume objects about a tenth of the frame. + return 0.1 * (self.frame_width or PROBE_WIDTH * 6) + + def due(self, gap: int) -> bool: + """Is the frame `gap` source frames after the last emitted one due?""" + if not self.adaptive: + return gap >= self.base + return self.retry or gap >= self.step + + def shift(self, prev: np.ndarray, curr: np.ndarray, gap: int) -> float | None: + """Scene shift between two thumbnails, in full-resolution pixels. + + None when it cannot be trusted: a weak correlation, or an answer the + recent speed says is implausible (a repeating shelf can alias a big + move into a small one). + """ + if prev.shape != curr.shape: + return None + if self._window is None or self._window.shape != curr.shape: + self._window = cv2.createHanningWindow((curr.shape[1], curr.shape[0]), cv2.CV_32F) + (dx, dy), response = cv2.phaseCorrelate(prev, curr, self._window) + if response < 0.1: + return None + scale = (self.frame_width or PROBE_WIDTH) / PROBE_WIDTH + moved = float(np.hypot(dx, dy)) * scale + if self._speed is not None and gap > 0: + predicted = self._speed * gap + if predicted > 0.5 * self._object_px() and moved < 0.4 * predicted: + return None + return moved + + def observe(self, moved: float | None, gap: int) -> None: + """Adapt the stride from the measured speed.""" + if not self.adaptive or gap <= 0: + return + if moved is None: + self.step = 1 + else: + speed = moved / gap + self._speed = speed if self._speed is None else 0.5 * self._speed + 0.5 * speed + target = self.target_shift_frac * self._object_px() + ideal = int(target / max(self._speed, 1e-6)) + self.step = int(min(self.base, max(1, ideal))) + self.stats["min_step"] = min(self.stats["min_step"], self.step) + + def too_far(self, moved: float | None) -> bool: + return moved is None or moved > self.max_shift_frac * self._object_px() + + +def frames( + source: str | int, cfg: CaptureConfig, sampler: Sampler | None = None +) -> Iterator[Frame]: """Yield sampled, optionally downscaled frames. Sampling is by frame index rather than by time so that a variable-rate - file still gives an even spread of the shelf. + file still gives an even spread of the shelf. Pass a Sampler (and feed it + back) for closed-loop sampling; without one the stride is fixed. """ cap = open_source(source) fps = cap.get(cv2.CAP_PROP_FPS) or 0.0 - step = max(1, int(cfg.every_n_frames)) + sampler = sampler or Sampler(CaptureConfig(**{**cfg.__dict__, "adaptive": False})) emitted = 0 source_index = -1 + last_index: int | None = None + last_thumb: np.ndarray | None = None + # Frames skipped since the last emitted one, kept so a gap that turns out + # too wide can be filled in after the fact. Bounded by the stride. + skipped: list[tuple[int, float, np.ndarray]] = [] + + def make(idx: int, ts: float, image: np.ndarray, gap: int, backfill: bool) -> Frame: + nonlocal emitted + frame = Frame( + index=emitted, + source_index=idx, + timestamp_s=round(ts, 4), + image=image, + meta={"fps": round(fps, 3), "gap": gap, "backfill": backfill}, + ) + emitted += 1 + sampler.stats["emitted"] = emitted + return frame try: while True: @@ -85,25 +235,37 @@ def frames(source: str | int, cfg: CaptureConfig) -> Iterator[Frame]: continue if cfg.end_s is not None and ts > cfg.end_s: break - if source_index % step: + image = _resize(image, cfg.resize_width) + if sampler.frame_width is None: + sampler.frame_width = image.shape[1] + + gap = source_index - last_index if last_index is not None else 0 + if last_index is not None and not sampler.due(gap): + skipped.append((source_index, ts, image)) + # Only frames since the last emission are useful for backfill. + if len(skipped) > sampler.base: + skipped.pop(0) continue - if cfg.resize_width and image.shape[1] > cfg.resize_width: - scale = cfg.resize_width / image.shape[1] - image = cv2.resize( - image, - (cfg.resize_width, max(1, int(round(image.shape[0] * scale)))), - interpolation=cv2.INTER_AREA, - ) - yield Frame( - index=emitted, - source_index=source_index, - timestamp_s=round(ts, 4), - image=image, - meta={"fps": round(fps, 3)}, - ) - emitted += 1 - if cfg.max_frames and emitted >= cfg.max_frames: - log.info("capture stopped at max_frames=%d", cfg.max_frames) - break + + thumb = _thumb(image) if sampler.adaptive else None + to_emit: list[tuple[int, float, np.ndarray, bool]] = [] + if last_thumb is not None and thumb is not None: + moved = sampler.shift(last_thumb, thumb, gap) + if skipped and sampler.too_far(moved): + to_emit = [(i, t, img, True) for i, t, img in skipped] + sampler.stats["backfilled"] += len(skipped) + sampler.observe(moved, gap) + to_emit.append((source_index, ts, image, False)) + skipped = [] + + for idx, t, img, backfill in to_emit: + frame_gap = idx - last_index if last_index is not None else 0 + yield make(idx, t, img, frame_gap, backfill) + last_index = idx + if cfg.max_frames and emitted >= cfg.max_frames: + sampler.truncated = True + log.warning("capture stopped at max_frames=%d", cfg.max_frames) + return + last_thumb = thumb finally: cap.release() diff --git a/src/countbone/stages/count.py b/src/countbone/stages/count.py index dbd8b9c..8745206 100644 --- a/src/countbone/stages/count.py +++ b/src/countbone/stages/count.py @@ -127,6 +127,16 @@ def _predict( def _attach(self, track: Track, item: Item, frame_index: int) -> None: item.track_id = track.track_id + # Where the object sits on the shelf, in the first frame's pixel + # coordinates: its image position minus how far the scene has slid. + # Stable across the whole walk, so cross-video merging and shelf-gap + # checks can reason about the shelf rather than the screen. + cx, cy = item.detection.centroid + x1, y1, x2, y2 = item.detection.bbox + item.meta["world"] = ( + round(cx - self._cum[0], 1), round(cy - self._cum[1], 1), + round(x2 - x1, 1), round(y2 - y1, 1), + ) track.items.append(item) track.last_frame = frame_index # A track's SKU is the majority vote of its sightings, not the first @@ -136,6 +146,17 @@ def _attach(self, track: Track, item: Item, frame_index: int) -> None: self._last_cum[track.track_id] = self._cum +def track_world(track: Track) -> dict[str, float] | None: + """A track's shelf position and size: the median over its sightings.""" + pts = [i.meta["world"] for i in track.items if "world" in i.meta] + if not pts: + return None + arr = np.asarray(pts, dtype=np.float64) + x, y, w, h = np.median(arr, axis=0) + return {"x": round(float(x), 1), "y": round(float(y), 1), + "w": round(float(w), 1), "h": round(float(h), 1)} + + def _track_confidence(track: Track, min_hits: int) -> float: """How much we believe this track is one real object.""" if not track.items: @@ -151,8 +172,23 @@ def _track_confidence(track: Track, min_hits: int) -> float: )) +def expected_for(sku: str, cfg: CountConfig, catalog: Catalog, + override: dict[str, int] | None) -> int | None: + """The number this SKU should have come to. + + A per-run expectation (a location's book quantity, a purchase order's + lines) replaces the global ones outright: a bay's stock has nothing to + do with the catalog-wide default. + """ + if override is not None: + return override.get(sku) + entry = catalog.by_sku(sku) + return cfg.expected.get(sku, entry.expected if entry else None) + + def count_from_tracks( - tracks: list[Track], cfg: CountConfig, catalog: Catalog + tracks: list[Track], cfg: CountConfig, catalog: Catalog, + expected: dict[str, int] | None = None, ) -> list[SkuCount]: grouped: dict[str, list[Track]] = defaultdict(list) for track in tracks: @@ -169,7 +205,7 @@ def count_from_tracks( count=len(sku_tracks), label=entry.label if entry else sku, confidence=float(np.mean(confidences)) if confidences else 0.0, - expected=cfg.expected.get(sku, entry.expected if entry else None), + expected=expected_for(sku, cfg, catalog, expected), evidence={ "strategy": "tracking", "tracks": [t.track_id for t in sku_tracks], @@ -182,7 +218,8 @@ def count_from_tracks( def count_per_frame( - per_frame: dict[int, list[Item]], cfg: CountConfig, catalog: Catalog, how: str + per_frame: dict[int, list[Item]], cfg: CountConfig, catalog: Catalog, how: str, + expected: dict[str, int] | None = None, ) -> list[SkuCount]: """peak_frame / median_frame: aggregate the per-frame tallies.""" tallies: dict[str, list[int]] = defaultdict(list) @@ -213,7 +250,7 @@ def count_per_frame( count=value, label=entry.label if entry else sku, confidence=float(np.clip(0.7 * base + 0.3 * stability, 0.0, 1.0)), - expected=cfg.expected.get(sku, entry.expected if entry else None), + expected=expected_for(sku, cfg, catalog, expected), evidence={ "strategy": how, "per_frame_min": min(series), @@ -231,16 +268,40 @@ def finalise( per_frame: dict[int, list[Item]], cfg: CountConfig, catalog: Catalog, + expected: dict[str, int] | None = None, ) -> CountResult: if cfg.strategy == "tracking": - result.counts = count_from_tracks(tracks, cfg, catalog) + result.counts = count_from_tracks(tracks, cfg, catalog, expected) elif cfg.strategy in ("peak_frame", "median_frame"): - result.counts = count_per_frame(per_frame, cfg, catalog, cfg.strategy) + result.counts = count_per_frame(per_frame, cfg, catalog, cfg.strategy, expected) else: raise ValueError( f"unknown count strategy {cfg.strategy!r}; " "choose from tracking, peak_frame, median_frame" ) + # An expected SKU that was never seen is the worst miss there is (the + # shelf is empty), and without a row it would have no variance to flag. + seen = {c.sku for c in result.counts} + wanted = expected if expected is not None else { + **catalog.expected_counts(), **cfg.expected + } + for sku, qty in sorted(wanted.items()): + if sku in seen or qty is None: + continue + entry = catalog.by_sku(sku) + result.counts.append( + SkuCount( + sku=sku, + count=0, + label=entry.label if entry else sku, + # Nothing was seen, so there is no identification to doubt; + # the variance, not the confidence, is what flags this row. + confidence=1.0, + expected=int(qty), + evidence={"strategy": "not_seen"}, + ) + ) + result.counts.sort(key=lambda c: c.sku) result.tracks = len(tracks) if result.counts: total = sum(c.count for c in result.counts) or 1 diff --git a/src/countbone/stages/identify.py b/src/countbone/stages/identify.py index 7305dde..ece607c 100644 --- a/src/countbone/stages/identify.py +++ b/src/countbone/stages/identify.py @@ -149,7 +149,107 @@ def identify(self, frame: Frame, detections: list[Detection]) -> list[Item]: ] +class BarcodeReader: + """Read an EAN/UPC off a sighting when one is legible. + + A readable barcode is the strongest identity evidence there is, but most + video crops are too small or soft to decode, so this is tried only on + large crops and only when the catalog lists barcodes at all. + """ + + MIN_WIDTH = 90 # px; below this a 1D code cannot resolve at video quality + + def __init__(self, catalog: Catalog) -> None: + self.catalog = catalog + self.enabled = any(e.barcodes for e in catalog.entries) + self._detector = cv2.barcode.BarcodeDetector() if self.enabled else None + + def read(self, patch: np.ndarray): + if not self.enabled or patch.shape[1] < self.MIN_WIDTH: + return None + try: + text, _, _ = self._detector.detectAndDecode(patch) + except cv2.error: + return None + return self.catalog.by_barcode(text) if text else None + + +class AppearanceIdentifier: + """Identify by artwork, from photos enrolled in Catalog Studio. + + Each sighting is embedded and matched against the enrolled examples + (see appearance.py); a readable barcode overrides the match. SKUs that + were never photographed are still recognised by colour, so a catalog + can move from colour bands to photos one product at a time. + """ + + name = "appearance" + + def __init__(self, cfg: IdentifyConfig, catalog: Catalog) -> None: + from .. import appearance + + self.cfg = cfg + self.catalog = catalog + self.index = catalog.index() + self._embed = appearance.embed + self._confidence = appearance.confidence + self.barcodes = BarcodeReader(catalog) + enrolled = set(self.index.skus) + # Colour covers only products nobody has photographed: colour-matching + # an enrolled look-alike would undo what the photos taught. + self.colour = ColorIdentifier( + cfg, Catalog([e for e in catalog.entries if e.sku not in enrolled]) + ) + + def identify(self, frame: Frame, detections: list[Detection]) -> list[Item]: + items: list[Item] = [] + for det in detections: + patch = crop(frame, det) + entry = self.barcodes.read(patch) + if entry is not None: + items.append(Item(det, entry.sku, entry.label, 0.98, "barcode", + meta={"candidates": [{"sku": entry.sku, "score": 1.0}]})) + continue + if patch.size < 16 * 3: + items.append(Item(det, self.cfg.unknown_sku, "Unidentified", 0.0, "fallback")) + continue + matches = self.index.match(self._embed(patch)) if len(self.index) else [] + accept = self.index.accept_for(matches[0].sku) if matches else self.index.accept + conf = self._confidence(matches, accept, self.index.margin) + candidates = [{"sku": m.sku, "score": round(m.score, 4)} for m in matches] + if matches and matches[0].score >= accept: + entry = self.catalog.by_sku(matches[0].sku) + items.append(Item(det, matches[0].sku, entry.label if entry else matches[0].sku, + conf, "appearance", meta={"candidates": candidates})) + continue + # Not like any photo: perhaps a product that is only colour-banded. + fallback = self.colour.identify(frame, [det])[0] + if fallback.sku != self.cfg.unknown_sku: + fallback.meta["candidates"] = candidates + items.append(fallback) + continue + items.append(Item(det, self.cfg.unknown_sku, "Unidentified", conf, "fallback", + meta={"candidates": candidates, **fallback.meta})) + return items + + +class AutoIdentifier: + """The default: appearance once any product has photos, colour until then. + + Chosen per run (the pipeline rebuilds its identifier when the catalog + changes), so enrolling the first photos switches a deployment over + without a config edit. + """ + + def __new__(cls, cfg: IdentifyConfig, catalog: Catalog): # type: ignore[misc] + if catalog.exemplars: + return AppearanceIdentifier(cfg, catalog) + return ColorIdentifier(cfg, catalog) + + BACKENDS = { + "auto": AutoIdentifier, + "appearance": AppearanceIdentifier, "color": ColorIdentifier, "classmap": ClassMapIdentifier, "fixture": FixtureIdentifier, diff --git a/tests/test_plugins.py b/tests/test_plugins.py index d957b97..f151e68 100644 --- a/tests/test_plugins.py +++ b/tests/test_plugins.py @@ -141,13 +141,32 @@ def test_quality_gate_warns_when_most_footage_is_unusable(): # -- analytics layer ------------------------------------------------------- -def test_tolerance_bands_scale_with_unit_value(): +def test_tolerance_flags_every_miss_by_default(): rules = ToleranceRules() + assert rules.band_for(2.0)["abs"] == 0 + assert rules.band_for(5000.0)["abs"] == 0 + + +def test_tolerance_bands_scale_with_unit_value(): + rules = ToleranceRules(bands=[ + {"max_unit_value": 10.0, "abs": 2, "pct": 0.03}, + {"max_unit_value": 100.0, "abs": 1, "pct": 0.01}, + {"max_unit_value": None, "abs": 0, "pct": 0.0}, + ]) assert rules.band_for(2.0)["abs"] == 2 # cheap stock, slack allowed assert rules.band_for(50.0)["abs"] == 1 assert rules.band_for(5000.0)["abs"] == 0 # expensive stock, none allowed +def test_a_cheap_one_unit_miss_is_flagged_by_default(): + from countbone.catalog import Catalog, SkuEntry + + ctx = RunContext(config=Config(), source="x") + ctx.state["catalog"] = Catalog([SkuEntry("SKU-C", "Carton", unit_value=2.0)]) + result = CountResult(run_id="r", source="x", counts=[SkuCount("SKU-C", count=9, expected=10)]) + assert ToleranceRules().on_counts(ctx, result).needs_review + + def test_tolerance_flags_a_breach_and_prices_it(): from countbone.catalog import Catalog, SkuEntry @@ -181,7 +200,8 @@ def test_every_review_item_gets_its_own_crop(demo_scene, config): # itself, and this test is about crops, not about what gets flagged. for spec in config.plugins: if spec.name == "review_queue": - spec.options = {**(spec.options or {}), "item_threshold": 1.01} + spec.options = {**(spec.options or {}), "item_threshold": 1.01, + "settled_agreement": 1.01} result = Pipeline(config).run(demo_scene.path) crops = [r.crop_path for r in result.reviews if r.crop_path] From f8680e08e6bf6ad8518fa896e2ce63314485243e Mon Sep 17 00:00:00 2001 From: Samarty-1 Date: Sun, 27 Sep 2026 15:55:09 +0800 Subject: [PATCH 05/13] Platform: accounts, locations, reconcile, receive, evidence, studio, service Accounts: scrypt passwords, hashed session tokens (HttpOnly cookie for the dashboard with a CSRF header rule, Bearer for the phone), API keys for machines, counter/manager/admin roles, login throttling, first-run setup gated by a console code, and every decision recorded under the signed-in user on a hash-chained audit trail. Store: numbered migrations (a v1 database upgrades in place), durable job queue resumed after a restart, resumable chunked uploads keyed by the phone's own recording id. Operations (countbone.ops): - final counts: reviewer decisions applied per tracked object - locations with printable QR labels, read back from the video (location_tag) to file a run or flag one filed under the wrong bay - walks: several videos of one place merged by shelf-coordinate alignment with symmetric conflict checks (no false merges on 12 disjoint pairs) - reconcile: adjustments, recount tasks, value-based approval rules, posting to Shopify / NetSuite / SAP / signed webhook, CSV export - receive: count a delivery against its PO; shortages draft a claim - evidence: Ed25519-signed claim packs with custody trail and verify.py - shelf_check: empty facings with photos, planogram compliance - contact_sheet: a picture of every counted object - catalog studio: enrol by photo, quality report, learn from reviews - service jobs, sites, COCO export of consented footage Also fixes the colour identifier misreading red as green when hue straddles 0/179 (circular mean), and keeps colour from vouching for an unphotographed product that shares a photographed product's colour. 186 tests (was 125). Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01Cuy339178aznLgLdpqqD4V --- pyproject.toml | 18 +- src/countbone/api/app.py | 338 ++++------- src/countbone/api/auth.py | 99 ++++ src/countbone/api/jobs.py | 139 +++++ src/countbone/api/routes_admin.py | 214 +++++++ src/countbone/api/routes_auth.py | 146 +++++ src/countbone/api/routes_ops.py | 589 +++++++++++++++++++ src/countbone/api/routes_runs.py | 452 +++++++++++++++ src/countbone/cli.py | 73 ++- src/countbone/config.py | 3 + src/countbone/demo.py | 25 + src/countbone/integrations/__init__.py | 48 ++ src/countbone/integrations/base.py | 110 ++++ src/countbone/integrations/csvio.py | 147 +++++ src/countbone/integrations/netsuite.py | 204 +++++++ src/countbone/integrations/sap.py | 196 +++++++ src/countbone/integrations/shopify.py | 158 +++++ src/countbone/integrations/webhook.py | 67 +++ src/countbone/ops/__init__.py | 41 ++ src/countbone/ops/catalog.py | 217 +++++++ src/countbone/ops/dataset.py | 102 ++++ src/countbone/ops/evidence.py | 359 ++++++++++++ src/countbone/ops/final.py | 116 ++++ src/countbone/ops/labels.py | 54 ++ src/countbone/ops/merge.py | 245 ++++++++ src/countbone/ops/postrun.py | 49 ++ src/countbone/ops/receive.py | 149 +++++ src/countbone/ops/reconcile.py | 369 ++++++++++++ src/countbone/ops/tasks.py | 113 ++++ src/countbone/pipeline.py | 1 + src/countbone/plugins/base.py | 3 + src/countbone/plugins/contact_sheet.py | 107 ++++ src/countbone/plugins/location_tag.py | 122 ++++ src/countbone/plugins/shelf_check.py | 197 +++++++ src/countbone/security.py | 231 ++++++++ src/countbone/stages/capture.py | 19 + src/countbone/stages/count.py | 5 + src/countbone/stages/identify.py | 56 +- src/countbone/store/db.py | 312 +++++----- src/countbone/store/repos.py | 760 +++++++++++++++++++++++++ src/countbone/store/schema.py | 378 ++++++++++++ tests/test_accuracy_features.py | 294 ++++++++++ tests/test_api.py | 21 +- tests/test_integrations.py | 212 +++++++ tests/test_platform.py | 489 ++++++++++++++++ 45 files changed, 7690 insertions(+), 357 deletions(-) create mode 100644 src/countbone/api/auth.py create mode 100644 src/countbone/api/jobs.py create mode 100644 src/countbone/api/routes_admin.py create mode 100644 src/countbone/api/routes_auth.py create mode 100644 src/countbone/api/routes_ops.py create mode 100644 src/countbone/api/routes_runs.py create mode 100644 src/countbone/integrations/__init__.py create mode 100644 src/countbone/integrations/base.py create mode 100644 src/countbone/integrations/csvio.py create mode 100644 src/countbone/integrations/netsuite.py create mode 100644 src/countbone/integrations/sap.py create mode 100644 src/countbone/integrations/shopify.py create mode 100644 src/countbone/integrations/webhook.py create mode 100644 src/countbone/ops/__init__.py create mode 100644 src/countbone/ops/catalog.py create mode 100644 src/countbone/ops/dataset.py create mode 100644 src/countbone/ops/evidence.py create mode 100644 src/countbone/ops/final.py create mode 100644 src/countbone/ops/labels.py create mode 100644 src/countbone/ops/merge.py create mode 100644 src/countbone/ops/postrun.py create mode 100644 src/countbone/ops/receive.py create mode 100644 src/countbone/ops/reconcile.py create mode 100644 src/countbone/ops/tasks.py create mode 100644 src/countbone/plugins/contact_sheet.py create mode 100644 src/countbone/plugins/location_tag.py create mode 100644 src/countbone/plugins/shelf_check.py create mode 100644 src/countbone/security.py create mode 100644 src/countbone/store/repos.py create mode 100644 src/countbone/store/schema.py create mode 100644 tests/test_accuracy_features.py create mode 100644 tests/test_integrations.py create mode 100644 tests/test_platform.py diff --git a/pyproject.toml b/pyproject.toml index 73e8a96..8e14e97 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "countbone" -version = "0.1.0" +version = "0.2.0" description = "A video-to-count pipeline for stock counting. The backbone is the product; everything else is a plugin." readme = "README.md" license = { text = "MIT" } @@ -27,10 +27,17 @@ dependencies = [ ] [project.optional-dependencies] -api = ["fastapi>=0.110", "uvicorn>=0.27", "pydantic>=2.5", "python-multipart>=0.0.9"] +api = [ + "fastapi>=0.110", "uvicorn>=0.27", "pydantic>=2.5", "python-multipart>=0.0.9", + "httpx>=0.26", # integrations (Shopify, NetSuite, SAP, webhooks) + "cryptography>=42", # sealed integration secrets, signed evidence packs + "segno>=1.6", # printable QR labels for bays +] yolo = ["ultralytics>=8.1"] # imageio-ffmpeg: a bundled ffmpeg, so the variable-frame-rate test can build its clip. -dev = ["pytest>=7.4", "pytest-cov>=4.1", "httpx>=0.26", "ruff>=0.4", "imageio-ffmpeg>=0.5"] +# oauthlib: the NetSuite OAuth 1.0a signature is checked against it in the tests. +dev = ["pytest>=7.4", "pytest-cov>=4.1", "httpx>=0.26", "ruff>=0.4", "imageio-ffmpeg>=0.5", + "oauthlib>=3.2"] [project.scripts] countbone = "countbone.cli:main" @@ -58,3 +65,8 @@ target-version = "py310" [tool.ruff.lint] select = ["E", "F", "I", "UP", "B", "C4"] ignore = ["E501"] + +[tool.ruff.lint.flake8-bugbear] +# FastAPI's dependency markers are meant to sit in argument defaults. +extend-immutable-calls = ["fastapi.Depends", "fastapi.File", "fastapi.Form", "fastapi.Query", + "countbone.api.auth.require"] diff --git a/src/countbone/api/app.py b/src/countbone/api/app.py index 0c94c35..7c6f5e4 100644 --- a/src/countbone/api/app.py +++ b/src/countbone/api/app.py @@ -1,34 +1,34 @@ """HTTP API and dashboard. -Deliberately thin: it submits videos to the pipeline, reads the store, and -records review decisions. No counting logic lives here. +Deliberately thin: routes check who is asking and hand off to the pipeline +(counting), the store (reading) and countbone.ops (business rules). No +counting logic and no business rules live in the routes. """ from __future__ import annotations -import colorsys import logging -import re -import shutil +import secrets import threading from collections.abc import Sequence -from concurrent.futures import ThreadPoolExecutor from contextlib import asynccontextmanager +from dataclasses import dataclass, field from pathlib import Path from typing import Any -from fastapi import FastAPI, HTTPException, UploadFile -from fastapi.responses import FileResponse, JSONResponse +from fastapi import FastAPI, HTTPException, Request +from fastapi.responses import JSONResponse from fastapi.staticfiles import StaticFiles -from pydantic import BaseModel, Field -from ..catalog import SkuEntry +from ..catalog import Catalog from ..config import Config +from ..ops import Forbidden, OpsError, Services +from ..ops import catalog as catalog_ops from ..pipeline import Pipeline -from ..plugins import base as plugin_base +from ..security import Keyring, LoginThrottle from ..store.db import Store -from ..types import new_id from . import telemetry +from .jobs import JobRunner, RunTracker log = logging.getLogger(__name__) STATIC_DIR = Path(__file__).parent / "static" @@ -55,87 +55,120 @@ async def get_response(self, path: str, scope): # type: ignore[override] return response -def _swatch(entry: SkuEntry) -> str | None: - """A display colour for a catalog entry: its hue band's centre. +class CatalogCache: + """The live catalog, rebuilt only when products or photos change. - The colour identifier matches on hue, so this is the colour the model - is actually looking for, not decoration. + Loading every enrolled vector and recalibrating the index is cheap for a + pilot and not for a thousand products, so it is keyed on a fingerprint + of the catalog tables and reused while that is unchanged. """ - if entry.achromatic: - return "#9ca3af" - centre = entry.hue_center() - if centre is None: - return None - r, g, b = colorsys.hsv_to_rgb(centre / 180.0, 0.75, 0.9) # OpenCV hue is 0-179 - return f"#{int(r * 255):02x}{int(g * 255):02x}{int(b * 255):02x}" - - -class ReviewDecision(BaseModel): - status: str = Field(description="accepted | rejected | corrected | pending (reopen)") - resolved_sku: str | None = None - resolved_count: int | None = Field( - default=None, ge=0, description="A reviewer's recount, for a whole-SKU review" - ) - reviewer: str = "anonymous" - - -class RunRequest(BaseModel): - path: str = Field(description="Server-side path to a video file") - - -class RunTracker: - """Which runs are in flight. Small enough to keep in memory.""" - def __init__(self) -> None: + def __init__(self, base: Catalog, services: Services) -> None: + self.base = base + self.services = services self._lock = threading.Lock() - self._state: dict[str, dict[str, Any]] = {} - - def set(self, run_id: str, **fields: Any) -> None: - with self._lock: - self._state.setdefault(run_id, {"run_id": run_id}).update(fields) - - def get(self, run_id: str) -> dict[str, Any] | None: - with self._lock: - return dict(self._state[run_id]) if run_id in self._state else None - - def all(self) -> list[dict[str, Any]]: + self._key: tuple | None = None + self._catalog: Catalog | None = None + + def _fingerprint(self) -> tuple: + store = self.services.store + v = store._one("SELECT COUNT(*) AS n, MAX(id) AS m FROM sku_vectors") or {} + s = store._one("SELECT COUNT(*) AS n, COALESCE(SUM(LENGTH(label) + unit_value * 100 " + "+ archived * 7 + LENGTH(COALESCE(barcodes, ''))), 0) AS h FROM skus") or {} + return (v.get("n"), v.get("m"), s.get("n"), s.get("h")) + + def __call__(self) -> Catalog: + key = self._fingerprint() with self._lock: - return [dict(v) for v in self._state.values()] + if self._catalog is None or key != self._key: + self._catalog = catalog_ops.load_catalog(self.base, self.services.store) + self._key = key + return self._catalog + + +@dataclass +class ApiContext: + cfg: Config + store: Store + services: Services + pipeline: Pipeline + runner: JobRunner + tracker: RunTracker + keyring: Keyring + throttle: LoginThrottle + uploads_dir: Path + auth_enabled: bool + setup_token: str | None = None + extra: dict[str, Any] = field(default_factory=dict) def create_app( config: Config | None = None, store: Store | None = None, allow_origins: Sequence[str] = (), + auth: bool = True, + data_dir: str | Path | None = None, + http_client: Any = None, + resume_jobs: bool = True, ) -> FastAPI: cfg = config or Config() db = store or Store(cfg.output.sqlite or "countbone.db") + output_dir = Path(cfg.output.dir) + data = Path(data_dir) if data_dir else ( + Path(db.path).resolve().parent / "countbone-data" if db.path != ":memory:" + else output_dir / "_data") + data.mkdir(parents=True, exist_ok=True) + keyring = Keyring(data / "keys") + services = Services(store=db, keyring=keyring, data_dir=data, output_dir=output_dir, + http_client=http_client, + extra={"unknown_sku": cfg.identify.unknown_sku, + "resize_width": cfg.capture.resize_width}) + base_catalog = Catalog.load(cfg.identify.catalog) + services.catalog = CatalogCache(base_catalog, services) tracker = RunTracker() - # One worker: counting is CPU-bound, and a queue is easier to reason about - # than contention between two runs writing artifacts at once. One worker - # also means the single Pipeline below is never entered concurrently. - pool = ThreadPoolExecutor(max_workers=1, thread_name_prefix="countbone") # Built once: a Pipeline holds no per-run state, and rebuilding it per # request would reload the detector's weights every time. - pipeline = Pipeline(cfg, store=db) + pipeline = Pipeline(cfg, store=db, catalog_provider=services.catalog) pipeline.plugins = telemetry.attach(pipeline.plugins, tracker.set) - uploads = Path(cfg.output.dir) / "_uploads" + runner = JobRunner(pipeline, services, tracker) + uploads = output_dir / "_uploads" uploads.mkdir(parents=True, exist_ok=True) + setup_token = None + if auth and db.count_users() == 0: + # First run: whoever holds this (printed on the server's console) may + # create the first admin. Without it, anyone who reached the server + # before its owner could claim it. + setup_token = secrets.token_urlsafe(9) + log.warning("countbone first-run setup code: %s (enter it in the dashboard)", setup_token) + print(f"\n countbone: no accounts yet. Open the dashboard and use setup code {setup_token}\n", + flush=True) + + ctx = ApiContext(cfg=cfg, store=db, services=services, pipeline=pipeline, runner=runner, + tracker=tracker, keyring=keyring, throttle=LoginThrottle(), + uploads_dir=uploads, auth_enabled=auth, setup_token=setup_token) + @asynccontextmanager async def lifespan(_: FastAPI): + if resume_jobs: + resumed = runner.resume() + if resumed: + log.info("resumed %d queued run(s) from before the restart", resumed) + db.purge_expired_sessions() yield - pool.shutdown(wait=False, cancel_futures=True) + runner.shutdown() app = FastAPI( lifespan=lifespan, title="countbone", - version="0.1.0", + version="0.2.0", description="Video in, counts out. Everything else is a plugin.", ) app.state.config = cfg app.state.store = db app.state.tracker = tracker + app.state.ctx = ctx + app.state.auth_enabled = auth if allow_origins: # Only for browser apps on another origin (the mobile app's web # preview). Native apps and the bundled dashboard never need it, so it @@ -145,160 +178,29 @@ async def lifespan(_: FastAPI): app.add_middleware( CORSMiddleware, allow_origins=list(allow_origins), - allow_methods=["GET", "POST"], - allow_headers=["Content-Type"], + allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE"], + allow_headers=["Content-Type", "Authorization", "Upload-Offset", "X-Countbone"], + expose_headers=["Upload-Offset"], ) - # -- helpers --------------------------------------------------------- - def submit(source: str, run_id: str) -> None: - tracker.set(run_id, status="queued", source=source, error=None) - - def work() -> None: - tracker.set(run_id, status="running") - try: - result = pipeline.run(source, run_id=run_id) - tracker.set( - run_id, - status="done", - total=result.total, - needs_review=result.needs_review, - confidence=round(result.overall_confidence, 4), - ) - except Exception as exc: # noqa: BLE001 - surfaced to the client - log.exception("run %s failed", run_id) - tracker.set(run_id, status="failed", error=str(exc)) - - pool.submit(work) - - # -- routes ---------------------------------------------------------- - @app.get("/api/health") - def health() -> dict[str, Any]: - return { - "status": "ok", - "detect": cfg.detect.backend, - "identify": cfg.identify.backend, - "count": cfg.count.strategy, - "unknown_sku": cfg.identify.unknown_sku, - "plugins": [p.name for p in pipeline.plugins if not isinstance( - p, (telemetry.TelemetryIntake, telemetry.TelemetryTail))], - } - - @app.get("/api/catalog") - def catalog() -> list[dict[str, Any]]: - return [ - {"sku": e.sku, "label": e.label, "hue": list(e.hue) if e.hue else None, - "achromatic": e.achromatic, "min_saturation": e.min_saturation, - "unit_value": e.unit_value, - "expected": e.expected, "swatch": _swatch(e)} - for e in pipeline.catalog.entries - ] - - @app.get("/api/plugins") - def plugins() -> list[dict[str, Any]]: - enabled = {p.name for p in cfg.plugins if p.enabled} - return [ - {"name": name, "layer": cls.layer, "priority": cls.priority, - "enabled": name in enabled, "doc": (cls.__doc__ or "").strip().split("\n")[0]} - for name, cls in sorted(plugin_base.available().items()) - ] - - @app.post("/api/runs/upload", status_code=202) - # Sync on purpose: FastAPI runs it in a worker thread. As `async def`, the - # blocking copy below stalls the event loop, and every progress poll with - # it, for as long as a large aisle video takes to write. - def upload_run(file: UploadFile) -> dict[str, Any]: - run_id = new_id("run") - suffix = (Path(file.filename or "upload.mp4").suffix or ".mp4").lower() - if suffix not in VIDEO_SUFFIXES: - raise HTTPException( - 415, f"unsupported file type {suffix}; expected {', '.join(VIDEO_SUFFIXES)}" - ) - # The original name rides in the stored filename, so the run is still - # recognisable after a restart (the tracker is memory-only). Reduced to - # a safe charset: it is user input and becomes part of a path. - stem = re.sub(r"[^A-Za-z0-9._-]+", "_", Path(file.filename or "").stem).strip("._")[:80] - target = uploads / (f"{run_id}__{stem}{suffix}" if stem else f"{run_id}{suffix}") - with target.open("wb") as fh: - shutil.copyfileobj(file.file, fh) - submit(str(target), run_id) - tracker.set(run_id, filename=file.filename) - return {"run_id": run_id, "status": "queued", "source": str(target)} - - @app.post("/api/runs", status_code=202) - def start_run(req: RunRequest) -> dict[str, Any]: - if not Path(req.path).exists(): - raise HTTPException(404, f"no such video: {req.path}") - run_id = new_id("run") - submit(req.path, run_id) - return {"run_id": run_id, "status": "queued", "source": req.path} - - @app.get("/api/runs") - def list_runs(limit: int = 50) -> dict[str, Any]: - return {"in_flight": tracker.all(), "runs": db.list_runs(limit)} - - @app.get("/api/runs/{run_id}") - def get_run(run_id: str) -> dict[str, Any]: - run = db.get_run(run_id) - if run is None: - live = tracker.get(run_id) - if live: - return {"run_id": run_id, "pending": live} - raise HTTPException(404, f"unknown run {run_id}") - run["live"] = tracker.get(run_id) - run["audit"] = db.audit_trail(run_id) - return run - - @app.get("/api/runs/{run_id}/artifacts/{path:path}") - def artifact(run_id: str, path: str) -> FileResponse: - root = (Path(cfg.output.dir) / run_id).resolve() - target = (root / path).resolve() - # is_relative_to, not startswith: "runs/run_1" is a string prefix of - # "runs/run_10", so a prefix check would let one run read another's. - if not target.is_relative_to(root) or not target.is_file(): - raise HTTPException(404, "artifact not found") - return FileResponse(target) - - @app.get("/api/runs/{run_id}/video") - def video(run_id: str) -> FileResponse: - # Only the source of a run that completed, and only if it is a video. - # A run submitted by path records whatever path it was given, and a - # failed one never proves the file was a video; serving either would - # turn this into a read-any-file endpoint. - run = db.get_run(run_id) - source = run["source"] if run else None - if ( - not source - or Path(source).suffix.lower() not in VIDEO_SUFFIXES - or not Path(source).is_file() - ): - raise HTTPException(404, "source video not available") - # FileResponse answers Range requests, which the player needs to seek. - return FileResponse(source) - - @app.get("/api/reviews") - def reviews(status: str | None = "pending", limit: int = 200) -> list[dict[str, Any]]: - return db.reviews(status=status, limit=limit) - - @app.post("/api/reviews/{review_id}") - def resolve(review_id: str, decision: ReviewDecision) -> dict[str, Any]: - # "pending" reopens a decision (the dashboard's undo); the audit trail - # keeps both the decision and its reversal. - if decision.status not in {"accepted", "rejected", "corrected", "pending"}: - raise HTTPException(400, "status must be accepted, rejected, corrected or pending") - if decision.status == "corrected" and not decision.resolved_sku: - raise HTTPException(400, "a corrected review needs resolved_sku") - ok = db.resolve_review( - review_id, decision.status, decision.resolved_sku, decision.reviewer, - resolved_count=decision.resolved_count, - ) - if not ok: - raise HTTPException(404, f"unknown review {review_id}") - return {"review_id": review_id, "status": decision.status, - "resolved_count": decision.resolved_count} + @app.exception_handler(Forbidden) + async def _forbidden(_: Request, exc: Forbidden): + return JSONResponse({"detail": str(exc)}, status_code=403) + + @app.exception_handler(OpsError) + async def _ops_error(_: Request, exc: OpsError): + return JSONResponse({"detail": str(exc)}, status_code=400) - @app.get("/api/skus/{sku}/history") - def sku_history(sku: str, limit: int = 100) -> list[dict[str, Any]]: - return db.sku_history(sku, limit) + from . import routes_admin, routes_auth, routes_ops, routes_runs + + app.include_router(routes_auth.router(ctx)) + app.include_router(routes_runs.router(ctx)) + app.include_router(routes_ops.router(ctx)) + app.include_router(routes_admin.router(ctx)) + + @app.get("/api/{rest:path}", include_in_schema=False) + def _api_404(rest: str): + raise HTTPException(404, f"no API route /api/{rest}") if STATIC_DIR.is_dir(): app.mount("/", DashboardFiles(directory=STATIC_DIR, html=True), name="dashboard") @@ -310,4 +212,14 @@ def no_dashboard() -> JSONResponse: return app -app = create_app() +def _default_app() -> FastAPI: # pragma: no cover - for `uvicorn countbone.api.app:app` + return create_app() + + +def __getattr__(name: str): # pragma: no cover + # `app` is created on first access, not at import: importing this module + # (the CLI, the tests) must not open countbone.db in the working directory. + if name == "app": + globals()["app"] = _default_app() + return globals()["app"] + raise AttributeError(name) diff --git a/src/countbone/api/auth.py b/src/countbone/api/auth.py new file mode 100644 index 0000000..9761f25 --- /dev/null +++ b/src/countbone/api/auth.py @@ -0,0 +1,99 @@ +"""Who is asking, and are they allowed. + +Three ways in, one principal out: + + * the dashboard: an HttpOnly session cookie (so