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[AZ-175] Media table integration with XxHash64 content hashing and status lifecycle
Made-with: Cursor
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Oleksandr Bezdieniezhnykh
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# Stream-Based run_detect
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**Task**: AZ-173_stream_based_run_detect
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**Name**: Replace path-based run_detect with stream-based API in inference.pyx
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**Description**: Refactor `run_detect` in `inference.pyx` to accept media bytes/stream instead of a config dict with local file paths. Enable simultaneous disk write and frame-by-frame detection for video.
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**Complexity**: 3 points
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**Dependencies**: None (core change, other subtasks depend on this)
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**Component**: Inference
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**Jira**: AZ-173
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**Parent**: AZ-172
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## Problem
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`run_detect` currently takes a `config_dict` containing `paths: list[str]` — local filesystem paths. It iterates over them, guesses media type via `mimetypes.guess_type`, and opens files with `cv2.VideoCapture` or `cv2.imread`. This doesn't work when the caller is on a different device.
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## Current State
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```python
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cpdef run_detect(self, dict config_dict, object annotation_callback, object status_callback=None):
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ai_config = AIRecognitionConfig.from_dict(config_dict)
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for p in ai_config.paths:
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if self.is_video(p): videos.append(p)
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else: images.append(p)
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self._process_images(ai_config, images) # cv2.imread(path)
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for v in videos:
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self._process_video(ai_config, v) # cv2.VideoCapture(path)
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```
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## Target State
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Split into two dedicated methods:
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- `run_detect_video(self, stream, AIRecognitionConfig ai_config, str media_name, str save_path, ...)` — accepts a video stream/bytes, writes to `save_path` while decoding frames for detection
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- `run_detect_image(self, bytes image_bytes, AIRecognitionConfig ai_config, str media_name, ...)` — accepts image bytes, decodes in memory
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Remove:
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- `is_video(self, str filepath)` method
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- `paths` iteration loop in `run_detect`
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- Direct `cv2.VideoCapture(local_path)` and `cv2.imread(local_path)` calls
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## Video Stream Processing — PyAV (preferred)
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Use **PyAV** (`av` package) for byte-stream video decoding. Preferred approach per user decision.
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**Why PyAV**: Decode frames directly from bytes/stream via `av.open(io.BytesIO(data))` without intermediate file I/O. Most flexible for streaming, no concurrent file read/write synchronization needed.
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**Investigation needed during implementation**:
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- Verify PyAV can decode frames from a growing `BytesIO` or chunked stream (not just complete files)
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- Check if `av.open()` supports reading from an async generator or pipe for true streaming
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- Evaluate whether simultaneous disk write can be done with a `tee`-style approach (write bytes to file while feeding to PyAV)
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- Confirm PyAV frame format (RGB/BGR/YUV) compatibility with existing `process_frames` pipeline (expects BGR numpy arrays)
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- Check PyAV version compatibility with the project's Python/Cython setup
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- Fallback: if PyAV streaming is not viable, use write-then-read with `cv2.VideoCapture` on completed file
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## Acceptance Criteria
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- [ ] Video can be processed from bytes/stream without a local file path from the caller
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- [ ] Video is simultaneously written to disk and processed frame-by-frame
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- [ ] Image can be processed from bytes without a local file path
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- [ ] `_process_video_batch` and batch processing logic preserved (only input source changes)
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- [ ] All existing detection logic (tile splitting, validation, tracking) unaffected
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## File Changes
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| File | Action | Description |
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|------|--------|-------------|
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| `src/inference.pyx` | Modified | New stream-based methods, remove path-based `run_detect` |
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| `src/main.py` | Modified | Adapt callers to new method signatures |
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# DB-Driven AI Config
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**Task**: AZ-174_db_driven_ai_config
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**Name**: Fetch AIRecognitionConfig from DB by userId instead of UI-passed config
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**Description**: Replace UI-passed AI configuration with database-driven config fetched by userId from the annotations service.
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**Complexity**: 2 points
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**Dependencies**: Annotations service needs new endpoint `GET /api/users/{userId}/ai-settings`
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**Component**: Configuration
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**Jira**: AZ-174
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**Parent**: AZ-172
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## Problem
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`AIRecognitionConfig` is currently built from a dict passed by the caller (UI). In the distributed architecture, the UI should not own or pass detection configuration — it should be stored server-side per user.
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## Current State
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- `main.py`: `AIConfigDto` Pydantic model with hardcoded defaults, passed as `config_dict`
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- `ai_config.pyx`: `AIRecognitionConfig.from_dict(data)` builds from dict with defaults
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- Camera settings (`altitude`, `focal_length`, `sensor_width`) baked into the config DTO
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- No DB interaction for config
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## Target State
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- Extract userId from JWT (already parsed in `TokenManager._decode_exp`)
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- Call annotations service: `GET /api/users/{userId}/ai-settings`
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- Response contains merged `AIRecognitionSettings` + `CameraSettings` fields
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- Build `AIRecognitionConfig` from the API response
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- Remove `AIConfigDto` from `main.py` (or keep as optional override for testing)
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- Remove `paths` field from `AIRecognitionConfig` entirely
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## DB Tables (from schema)
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**AIRecognitionSettings:**
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- FramePeriodRecognition (default 4)
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- FrameRecognitionSeconds (default 2)
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- ProbabilityThreshold (default 0.25)
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- TrackingDistanceConfidence
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- TrackingProbabilityIncrease
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- TrackingIntersectionThreshold
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- ModelBatchSize
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- BigImageTileOverlapPercent
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**CameraSettings:**
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- Altitude (default 400m)
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- FocalLength (default 24mm)
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- SensorWidth (default 23.5mm)
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**Linking:** These tables currently have no FK to Users. The backend needs either:
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- Add `UserId` FK to both tables, or
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- Create a `UserAIConfig` join table referencing both
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## Backend Dependency
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The annotations C# service needs:
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1. New endpoint: `GET /api/users/{userId}/ai-settings` returning merged config
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2. On user creation: seed default `AIRecognitionSettings` + `CameraSettings` rows
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3. Optional: `PUT /api/users/{userId}/ai-settings` for user to update their config
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## Acceptance Criteria
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- [ ] Detection endpoint extracts userId from JWT
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- [ ] AIRecognitionConfig is fetched from annotations service by userId
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- [ ] Fallback to sensible defaults if service is unreachable
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- [ ] `paths` field removed from `AIRecognitionConfig`
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- [ ] Camera settings come from DB, not request payload
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## File Changes
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| File | Action | Description |
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|------|--------|-------------|
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| `src/main.py` | Modified | Fetch config from annotations service via HTTP |
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| `src/ai_config.pxd` | Modified | Remove `paths` field |
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| `src/ai_config.pyx` | Modified | Remove `paths` from `__init__` and `from_dict` |
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| `src/loader_http_client.pyx` | Modified | Add method to fetch user AI config |
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| `src/loader_http_client.pxd` | Modified | Declare new method |
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