[AZ-178] Implement streaming video detection endpoint

- Added `/detect/video` endpoint for true streaming video detection, allowing inference to start as upload bytes arrive.
- Introduced `run_detect_video_stream` method in the inference module to handle video processing from a file-like object.
- Updated media hashing to include a new function for computing hashes directly from files with minimal I/O.
- Enhanced documentation to reflect changes in video processing and API behavior.

Made-with: Cursor

_docs/02_document/modules/inference.md

@@ -39,6 +39,7 @@ Core inference orchestrator — manages the AI engine lifecycle, preprocesses me
 | `__init__` | `(loader_client)` | public | Initializes state, calls `init_ai()` |
 | `run_detect_image` | `(bytes image_bytes, AIRecognitionConfig ai_config, str media_name, annotation_callback, status_callback=None)` | cpdef | Decodes image from bytes, runs tiling + inference + postprocessing |
 | `run_detect_video` | `(bytes video_bytes, AIRecognitionConfig ai_config, str media_name, str save_path, annotation_callback, status_callback=None)` | cpdef | Processes video from in-memory bytes via PyAV, concurrently writes to save_path |
+| `run_detect_video_stream` | `(object readable, AIRecognitionConfig ai_config, str media_name, annotation_callback, status_callback=None)` | cpdef | Processes video from a file-like readable (e.g. StreamingBuffer) via PyAV — true streaming, no bytes in RAM (AZ-178) |
 | `stop` | `()` | cpdef | Sets stop_signal to True |
 | `init_ai` | `()` | cdef | Engine initialization: tries TensorRT → falls back to ONNX → background TensorRT conversion |
 | `preprocess` | `(frames) -> ndarray` | via engine | OpenCV blobFromImage: resize, normalize to 0..1, swap RGB, stack batch |
@@ -75,6 +76,15 @@ Both `run_detect_image` and `run_detect_video` accept raw bytes instead of file
 6. Annotation validity heuristics (time gap, detection count increase, spatial movement, confidence improvement)
 7. Valid frames get JPEG-encoded image attached
 
+### Streaming Video Processing (`run_detect_video_stream` — AZ-178)
+
+1. Accepts a file-like `readable` object (e.g. `StreamingBuffer`) instead of `bytes`
+2. Opens directly via `av.open(readable)` — PyAV calls `read()`/`seek()` on the object
+3. No writer thread needed — the caller (API layer) manages disk persistence via the same buffer
+4. Reuses `_process_video_pyav` for frame decoding, batch inference, and annotation delivery
+5. For faststart MP4/MKV/WebM: frames are decoded as bytes stream in (~500ms latency)
+6. For standard MP4 (moov at end): PyAV's `seek(0, 2)` blocks until the buffer signals EOF, then decoding starts
+
 ### Ground Sampling Distance (GSD)
 
 `GSD = sensor_width * altitude / (focal_length * image_width)` — meters per pixel, used for physical size filtering of aerial detections.
@@ -86,7 +96,7 @@ Both `run_detect_image` and `run_detect_video` accept raw bytes instead of file
 
 ## Consumers
 
-- `main` — lazy-initializes Inference, calls `run_detect_image`/`run_detect_video`, reads `ai_availability_status` and `is_engine_ready`
+- `main` — lazy-initializes Inference, calls `run_detect_image`/`run_detect_video`/`run_detect_video_stream`, reads `ai_availability_status` and `is_engine_ready`
 
 ## Data Models
 
@@ -107,5 +117,6 @@ None.
 ## Tests
 
 - `tests/test_ai_config_from_dict.py` — tests `ai_config_from_dict` helper
+- `tests/test_az178_streaming_video.py` — tests `run_detect_video_stream` via the `/detect/video` endpoint and `StreamingBuffer`
 - `e2e/tests/test_video.py` — exercises `run_detect_video` via the full API
 - `e2e/tests/test_single_image.py` — exercises `run_detect_image` via the full API