Add MP4 upload endpoint for testing pipeline without live camera
POST /upload saves the file, runs full processing (transcode, frame extraction, LPR, color detection) and redirects to the resulting event page. UI: "Tester un clip" button in header reveals a file picker form. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
perco
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
5594010d78
commit
ec214ed7b2
+60
-41
@@ -86,15 +86,13 @@ def _vehicle_color(frame: np.ndarray, plate_bbox: tuple | None) -> tuple[str, st
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px1, py1, px2, py2 = plate_bbox
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pw = px2 - px1
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ph = py2 - py1
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# Vehicle body: above plate, same horizontal span expanded ×3
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crop_x1 = max(0, px1 - pw * 2)
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crop_x2 = min(w, px2 + pw * 2)
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crop_y2 = max(0, py1 - 5)
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crop_y1 = max(0, py1 - ph * 10) # 10× plate height above plate
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crop_y1 = max(0, py1 - ph * 10)
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if crop_y2 > crop_y1 and crop_x2 > crop_x1:
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crop = frame[int(crop_y1):int(crop_y2), int(crop_x1):int(crop_x2)]
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return extract_dominant_color_from_frame(crop)
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# Fallback: center 50% of image (excludes sky at top, road at bottom)
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cy1 = h // 4
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cy2 = 3 * h // 4
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cx1 = w // 4
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@@ -110,58 +108,43 @@ def _frame_score(frame: np.ndarray, plates: list) -> float:
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return cv2.Laplacian(gray, cv2.CV_64F).var() * 0.001
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def _process_event():
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event_id = str(uuid.uuid4())
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event_dir = os.path.join(EVENTS_DIR, event_id)
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os.makedirs(event_dir, exist_ok=True)
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def _process_clip(event_id: str, event_dir: str, clip_path: str, camera_name: str = None):
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"""Process an existing clip: transcode, extract frames, LPR, store in DB."""
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if camera_name is None:
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camera_name = CAMERA_NAME
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url = _rtsp_url()
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clip_path = os.path.join(event_dir, "clip.mp4")
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frames_pattern = os.path.join(event_dir, "frame_%04d.jpg")
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log.info(f"Capturing {CAPTURE_DURATION}s at {CAPTURE_FPS}fps — event {event_id[:8]}")
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# Step 1: capture clip
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ret = subprocess.run([
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"ffmpeg", "-y", "-rtsp_transport", "tcp",
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"-i", url, "-t", str(CAPTURE_DURATION), "-c", "copy", clip_path,
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], capture_output=True, timeout=CAPTURE_DURATION + 10)
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if not os.path.exists(clip_path) or os.path.getsize(clip_path) < 1000:
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log.error(f"ffmpeg capture failed: {ret.stderr[-200:].decode(errors='ignore')}")
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shutil.rmtree(event_dir, ignore_errors=True)
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return
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# Step 2: extract all frames
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subprocess.run([
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"ffmpeg", "-y", "-i", clip_path,
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"-vf", f"fps={CAPTURE_FPS}", "-q:v", "2", frames_pattern,
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], capture_output=True, timeout=30)
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# Step 2b: transcode to H.264 baseline for browser compatibility
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# Transcode to H.264 baseline for browser compatibility
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web_clip = os.path.join(event_dir, "clip_web.mp4")
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subprocess.run([
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"ffmpeg", "-y", "-i", clip_path,
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"-c:v", "libx264", "-profile:v", "baseline", "-level", "3.1",
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"-preset", "fast", "-crf", "28",
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"-vf", "scale=-2:720", # downsample to 720p — enough for review
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"-an", # no audio needed for surveillance
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"-movflags", "+faststart", # moov atom at front for streaming
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"-vf", "scale=-2:720",
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"-an",
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"-movflags", "+faststart",
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web_clip,
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], capture_output=True, timeout=60)
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], capture_output=True, timeout=120)
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if os.path.exists(web_clip):
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os.replace(web_clip, clip_path)
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else:
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log.warning("Transcode failed, keeping raw clip")
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# Extract frames
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subprocess.run([
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"ffmpeg", "-y", "-i", clip_path,
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"-vf", f"fps={CAPTURE_FPS}", "-q:v", "2", frames_pattern,
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], capture_output=True, timeout=60)
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frame_files = sorted(glob.glob(os.path.join(event_dir, "frame_*.jpg")))
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log.info(f"Extracted {len(frame_files)} frames")
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if not frame_files:
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shutil.rmtree(event_dir, ignore_errors=True)
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return
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return None
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# Step 3: score ALL frames, rename sorted (best = frame_0001)
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# Score ALL frames, rename sorted (best = frame_0001)
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scored: list[tuple[float, np.ndarray, str]] = []
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for path in frame_files:
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frame = cv2.imread(path)
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@@ -173,7 +156,6 @@ def _process_event():
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scored.sort(key=lambda x: -x[0])
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# Rename to sorted order (temp names to avoid collisions)
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for i, (_, _, old_path) in enumerate(scored):
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os.rename(old_path, old_path + ".tmp")
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for i, (_, _, old_path) in enumerate(scored):
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@@ -182,28 +164,65 @@ def _process_event():
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best_frame = scored[0][1] if scored else None
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if best_frame is None:
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shutil.rmtree(event_dir, ignore_errors=True)
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return
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return None
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# Step 4: run LPR on best frame
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# LPR on best frame
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plate, conf, plate_bbox = ("", 0.0, None)
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if _analyzer:
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plate, conf, plate_bbox = _analyzer.read_plate(best_frame)
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log.info(f"LPR: plate={plate!r} conf={conf:.2f} bbox={plate_bbox}")
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# Step 5: save thumbnail (best frame)
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# Save thumbnail
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snapshot_file = f"{event_id}.jpg"
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snapshot_path = os.path.join(SNAPSHOTS_DIR, snapshot_file)
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cv2.imwrite(snapshot_path, best_frame, [cv2.IMWRITE_JPEG_QUALITY, 90])
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# Color: crop vehicle body above plate (avoids sky/road)
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hex_color, color_name = _vehicle_color(best_frame, plate_bbox)
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insert_event(
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event_id, CAMERA_NAME, int(time.time()),
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event_id, camera_name, int(time.time()),
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f"snapshots/{snapshot_file}",
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f"events/{event_id}/clip.mp4",
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plate or None, hex_color, color_name,
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)
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log.info(f"Stored: {event_id[:8]} | plate={plate} | color={color_name} | frames={len(frame_files)}")
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return event_id
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def _process_event():
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event_id = str(uuid.uuid4())
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event_dir = os.path.join(EVENTS_DIR, event_id)
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os.makedirs(event_dir, exist_ok=True)
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url = _rtsp_url()
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clip_path = os.path.join(event_dir, "clip.mp4")
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log.info(f"Capturing {CAPTURE_DURATION}s at {CAPTURE_FPS}fps — event {event_id[:8]}")
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ret = subprocess.run([
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"ffmpeg", "-y", "-rtsp_transport", "tcp",
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"-i", url, "-t", str(CAPTURE_DURATION), "-c", "copy", clip_path,
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], capture_output=True, timeout=CAPTURE_DURATION + 10)
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if not os.path.exists(clip_path) or os.path.getsize(clip_path) < 1000:
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log.error(f"ffmpeg capture failed: {ret.stderr[-200:].decode(errors='ignore')}")
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shutil.rmtree(event_dir, ignore_errors=True)
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return
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_process_clip(event_id, event_dir, clip_path)
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def process_uploaded_clip(src_path: str) -> str:
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"""Create a new event from an uploaded MP4. Returns event_id."""
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event_id = str(uuid.uuid4())
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event_dir = os.path.join(EVENTS_DIR, event_id)
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os.makedirs(event_dir, exist_ok=True)
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clip_path = os.path.join(event_dir, "clip.mp4")
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shutil.copy2(src_path, clip_path)
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log.info(f"Processing uploaded clip — event {event_id[:8]}")
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result = _process_clip(event_id, event_dir, clip_path, camera_name="upload")
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if result is None:
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raise RuntimeError("Processing failed: no frames extracted")
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return event_id
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def run_watcher():
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