Improve LPR accuracy and add manual plate edit
- Tiled YOLO (3x2, 20% overlap): plate confidence 0.307 -> 0.838 on test frame
- Aspect ratio filter (2.0-6.5:1): rejects non-plate shapes
- Confidence threshold 0.03 -> 0.04: removes fence/noise false positives
- Exclude top 15% (sky) and bottom 8% (timestamp overlay) from detection zone
- Add Tesseract as primary OCR (better for Latin plates), PaddleOCR as fallback
- Enhance plate crop before OCR: CLAHE + sharpening + min 80px upscale
- Save plate_crop.jpg (4x upscaled) to event dir for manual review
- Show plate crop in event detail page
- Add manual plate edit form in event detail (POST /event/{id}/plate)
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
92f435935e
commit
13e6038069
@@ -2,6 +2,7 @@ FROM python:3.11-slim
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RUN apt-get update && apt-get install -y --no-install-recommends \
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libgl1 libglib2.0-0 libgomp1 ffmpeg \
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tesseract-ocr tesseract-ocr-fra tesseract-ocr-eng \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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@@ -107,6 +107,13 @@ def get_event(event_id: str) -> dict | None:
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return dict(row) if row else None
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def update_plate(event_id: str, plate: str):
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conn = get_db()
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conn.execute("UPDATE events SET plate = ? WHERE id = ?", (plate.strip().upper() or None, event_id))
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conn.commit()
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conn.close()
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def get_cameras():
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conn = get_db()
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rows = conn.execute("SELECT DISTINCT camera FROM events ORDER BY camera").fetchall()
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+136
-35
@@ -7,8 +7,35 @@ log = logging.getLogger("lpr")
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MODEL_CACHE = os.environ.get("MODEL_CACHE", "/models")
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# Output format: [N, 7] where each row = [batch_idx, x1, y1, x2, y2, class_id, confidence]
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_YOLO_CONF_THRESHOLD = 0.03
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_YOLO_CONF_THRESHOLD = 0.04
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def _iou(a: tuple, b: tuple) -> float:
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ax1, ay1, ax2, ay2 = a[:4]
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bx1, by1, bx2, by2 = b[:4]
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ix1, iy1 = max(ax1, bx1), max(ay1, by1)
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ix2, iy2 = min(ax2, bx2), min(ay2, by2)
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inter = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
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if inter == 0:
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return 0.0
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union = (ax2 - ax1) * (ay2 - ay1) + (bx2 - bx1) * (by2 - by1) - inter
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return inter / union if union > 0 else 0.0
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def _nms(boxes: list, iou_threshold: float = 0.3) -> list:
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if not boxes:
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return []
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boxes = sorted(boxes, key=lambda b: -b[4])
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suppressed = [False] * len(boxes)
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result = []
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for i, b1 in enumerate(boxes):
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if suppressed[i]:
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continue
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result.append(b1)
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for j in range(i + 1, len(boxes)):
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if not suppressed[j] and _iou(b1, boxes[j]) > iou_threshold:
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suppressed[j] = True
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return result
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class PlateAnalyzer:
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@@ -47,58 +74,123 @@ class PlateAnalyzer:
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except Exception as e:
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log.error(f"LPR model load error: {e}")
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def _yolo_on_tile(self, tile: np.ndarray, offset_x: int, offset_y: int) -> list[tuple]:
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"""Run YOLO on a single tile, return boxes in original frame coordinates."""
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th, tw = tile.shape[:2]
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inp = cv2.resize(tile, (256, 256))
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inp = cv2.cvtColor(inp, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
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inp = inp.transpose(2, 0, 1)[np.newaxis]
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out = self._yolo.run(None, {"images": inp})[0]
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boxes = []
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for row in out:
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if len(row) < 7:
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continue
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_, x1, y1, x2, y2, _, conf = row
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if conf < _YOLO_CONF_THRESHOLD:
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continue
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x1 = offset_x + max(0.0, float(x1) / 256.0 * tw)
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y1 = offset_y + max(0.0, float(y1) / 256.0 * th)
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x2 = offset_x + min(float(tw), float(x2) / 256.0 * tw)
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y2 = offset_y + min(float(th), float(y2) / 256.0 * th)
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bw, bh = x2 - x1, y2 - y1
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if bw < 8 or bh < 4:
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continue
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# Plates are always wider than tall — French plates ~4.7:1
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ratio = bw / bh
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if ratio < 2.0 or ratio > 6.5:
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continue
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boxes.append((x1, y1, x2, y2, float(conf)))
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return boxes
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def detect_plates(self, frame: np.ndarray) -> list[tuple]:
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"""Returns [(x1, y1, x2, y2, conf), ...] in original frame coordinates."""
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"""Tiled YOLO detection — returns [(x1,y1,x2,y2,conf),...] in original coords."""
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if self._yolo is None:
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return []
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h, w = frame.shape[:2]
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inp = cv2.resize(frame, (256, 256))
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inp = cv2.cvtColor(inp, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
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inp = inp.transpose(2, 0, 1)[np.newaxis]
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all_boxes = []
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out = self._yolo.run(None, {"images": inp})[0] # [N, 7]
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# Ignore top 15% (sky/trees) and bottom 8% (timestamp overlay)
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y_start = int(h * 0.15)
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y_end = int(h * 0.92)
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work = frame[y_start:y_end, :]
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wh = y_end - y_start
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boxes = []
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for row in out:
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# row: [batch_idx, x1, y1, x2, y2, class_id, confidence]
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if len(row) >= 7:
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_, x1, y1, x2, y2, _, conf = row
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if conf < _YOLO_CONF_THRESHOLD:
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continue
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# Scale from 256x256 to original
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x1 = max(0.0, float(x1) / 256.0 * w)
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y1 = max(0.0, float(y1) / 256.0 * h)
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x2 = min(float(w), float(x2) / 256.0 * w)
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y2 = min(float(h), float(y2) / 256.0 * h)
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if x2 > x1 + 4 and y2 > y1 + 4:
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boxes.append((x1, y1, x2, y2, float(conf)))
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# 3×2 tiles with 20% overlap so plates near tile edges are caught
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cols, rows = 3, 2
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overlap = 0.2
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tw = int(w / (cols - overlap * (cols - 1)))
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th = int(wh / (rows - overlap * (rows - 1)))
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step_x = int(tw * (1 - overlap))
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step_y = int(th * (1 - overlap))
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return sorted(boxes, key=lambda b: -b[4])
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for row in range(rows):
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for col in range(cols):
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tx1 = col * step_x
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ty1 = row * step_y
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tx2 = min(w, tx1 + tw)
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ty2 = min(wh, ty1 + th)
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tile = work[ty1:ty2, tx1:tx2]
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# offset_y accounts for the cropped top strip
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all_boxes.extend(self._yolo_on_tile(tile, tx1, ty1 + y_start))
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def _ocr_crop(self, crop: np.ndarray) -> tuple[str, float]:
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"""Run PaddleOCR recognition on a crop. Returns (text, confidence)."""
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if self._rec is None or not self._keys or crop.size == 0:
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return _nms(all_boxes, iou_threshold=0.3)
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def _enhance_crop(self, crop: np.ndarray) -> np.ndarray:
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"""Upscale + CLAHE + sharpen a plate crop for better OCR."""
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h, w = crop.shape[:2]
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# Upscale so the plate is at least 80px tall
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target_h = 80
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if h < target_h:
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scale = target_h / h
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crop = cv2.resize(crop, (max(10, int(w * scale)), target_h), interpolation=cv2.INTER_CUBIC)
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# CLAHE contrast enhancement
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lab = cv2.cvtColor(crop, cv2.COLOR_BGR2LAB)
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l, a, b = cv2.split(lab)
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clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(4, 4))
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l = clahe.apply(l)
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crop = cv2.cvtColor(cv2.merge([l, a, b]), cv2.COLOR_LAB2BGR)
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# Mild sharpening
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kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=np.float32)
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return cv2.filter2D(crop, -1, kernel)
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def _ocr_tesseract(self, crop: np.ndarray) -> tuple[str, float]:
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"""Tesseract OCR tuned for license plates."""
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try:
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import pytesseract
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gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
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# Try both single-word and single-line modes, take the longer result
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cfg = "--psm 8 --oem 3 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-"
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text8 = pytesseract.image_to_string(gray, config=cfg).strip().replace(" ", "").upper()
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cfg7 = "--psm 7 --oem 3 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-"
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text7 = pytesseract.image_to_string(gray, config=cfg7).strip().replace(" ", "").upper()
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text = text8 if len(text8) >= len(text7) else text7
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alnum = "".join(c for c in text if c.isalnum())
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if len(alnum) >= 4:
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return text, 0.6 # Tesseract doesn't give per-char conf easily; use fixed score
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except Exception as e:
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log.debug(f"Tesseract error: {e}")
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return "", 0.0
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def _ocr_paddle(self, crop: np.ndarray) -> tuple[str, float]:
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"""PaddleOCR recognition fallback."""
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if self._rec is None or not self._keys or crop.size == 0:
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return "", 0.0
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h, w = crop.shape[:2]
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if h == 0 or w == 0:
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return "", 0.0
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inp_h = 48
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inp_w = max(10, int(inp_h * w / h))
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resized = cv2.resize(crop, (inp_w, inp_h))
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rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB).astype(np.float32)
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normalized = (rgb / 127.5) - 1.0
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inp = normalized.transpose(2, 0, 1)[np.newaxis]
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out = self._rec.run(None, {"x": inp})[0][0] # [seq, 6625]
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out = self._rec.run(None, {"x": inp})[0][0]
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chars: list[str] = []
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confs: list[float] = []
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prev_idx = 0
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for step in out:
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idx = int(np.argmax(step))
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conf = float(step[idx])
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@@ -106,14 +198,24 @@ class PlateAnalyzer:
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chars.append(self._keys[idx - 1])
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confs.append(conf)
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prev_idx = idx
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text = "".join(chars)
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avg_conf = float(np.mean(confs)) if confs else 0.0
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return text, avg_conf
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def _ocr_crop(self, crop: np.ndarray) -> tuple[str, float]:
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"""Run OCR on a plate crop. Tesseract first, PaddleOCR as fallback."""
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if crop.size == 0:
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return "", 0.0
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enhanced = self._enhance_crop(crop)
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# Tesseract is better for Latin/French plates
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text, conf = self._ocr_tesseract(enhanced)
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if text:
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return text, conf
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# Fallback to PaddleOCR
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return self._ocr_paddle(enhanced)
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def read_plate(self, frame: np.ndarray) -> tuple[str, float, tuple | None]:
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"""Detect plate, read text. Returns (plate_text, confidence, bbox_or_None).
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bbox = (x1, y1, x2, y2) in pixel coords."""
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"""Detect plate, read text. Returns (plate_text, confidence, bbox_or_None)."""
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plate_boxes = self.detect_plates(frame)
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if not plate_boxes:
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return "", 0.0, None
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@@ -128,7 +230,6 @@ class PlateAnalyzer:
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crop = frame[cy1:cy2, cx1:cx2]
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text, ocr_conf = self._ocr_crop(crop)
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# Filter noise: plate must have at least 4 alphanumeric chars
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alnum = "".join(c for c in text if c.isalnum())
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if len(alnum) < 4:
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return "", 0.0, (int(x1), int(y1), int(x2), int(y2))
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+14
-1
@@ -5,7 +5,7 @@ import threading
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import tempfile
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import asyncio
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from contextlib import asynccontextmanager
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from fastapi import FastAPI, Request, Query, HTTPException, UploadFile, File
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from fastapi import FastAPI, Request, Query, HTTPException, UploadFile, File, Form
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from fastapi.responses import HTMLResponse, RedirectResponse
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from fastapi.staticfiles import StaticFiles
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from fastapi.templating import Jinja2Templates
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@@ -103,12 +103,16 @@ async def event_detail(request: Request, event_id: str):
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size = os.path.getsize(clip_abs)
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clip_size = f"{size // 1024 // 1024}MB" if size > 1024 * 1024 else f"{size // 1024}KB"
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plate_crop_abs = os.path.join(event_dir, "plate_crop.jpg")
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plate_crop = f"events/{event_id}/plate_crop.jpg" if os.path.exists(plate_crop_abs) else None
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return templates.TemplateResponse("event_detail.html", {
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"request": request,
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"ev": ev,
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"frames": frames,
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"has_clip": has_clip,
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"clip_size": clip_size,
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"plate_crop": plate_crop,
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"capture_duration": CAPTURE_DURATION,
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"capture_fps": CAPTURE_FPS,
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"capture_total": CAPTURE_DURATION * CAPTURE_FPS,
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@@ -136,6 +140,15 @@ async def api_events(
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return {"events": events, "total": total}
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@app.post("/event/{event_id}/plate")
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async def update_plate(event_id: str, plate: str = Form("")):
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ev = database.get_event(event_id)
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if not ev:
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raise HTTPException(status_code=404, detail="Événement introuvable")
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database.update_plate(event_id, plate)
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return RedirectResponse(f"/event/{event_id}", status_code=303)
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@app.post("/upload")
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async def upload_clip(file: UploadFile = File(...)):
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if not file.filename.lower().endswith(".mp4"):
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@@ -6,3 +6,4 @@ requests==2.32.3
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opencv-python-headless==4.10.0.84
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numpy==1.26.4
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onnxruntime==1.20.0
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pytesseract==0.3.13
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@@ -45,13 +45,26 @@
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<div class="card p-4 space-y-4">
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<div>
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<label>Plaque</label>
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<div class="mt-1">
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<div class="mt-1 mb-2">
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{% if ev.plate %}
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<span class="plate text-xl font-bold px-4 py-2 rounded" style="background:#1e3a5f;color:#60a5fa;border:1px solid #2563eb;">{{ ev.plate }}</span>
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{% else %}
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<span class="text-slate-500 italic">Non lue</span>
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<span class="text-slate-500 italic text-sm">Non lue automatiquement</span>
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{% endif %}
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</div>
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{% if plate_crop %}
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<div class="mb-2">
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<label>Crop détecté</label>
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<img src="/{{ plate_crop }}" class="mt-1 rounded border border-slate-600 w-full" style="image-rendering:pixelated;" title="Zoom plate crop">
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</div>
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{% endif %}
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<form action="/event/{{ ev.id }}/plate" method="post" class="flex gap-2 mt-1">
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<input type="text" name="plate" value="{{ ev.plate or '' }}"
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placeholder="Saisir plaque…"
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class="flex-1 text-sm font-mono uppercase"
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style="background:#0f172a;border:1px solid #475569;color:#e2e8f0;border-radius:6px;padding:5px 8px;">
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<button type="submit" class="text-xs px-3 py-1 rounded" style="background:#1e3a5f;color:#60a5fa;border:1px solid #2563eb;">✓</button>
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</form>
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</div>
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<div>
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@@ -172,6 +172,20 @@ def _process_clip(event_id: str, event_dir: str, clip_path: str, camera_name: st
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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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# Save plate crop for manual review (even when OCR fails)
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if plate_bbox:
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px1, py1, px2, py2 = plate_bbox
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fh, fw = best_frame.shape[:2]
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pad = max(8, int((py2 - py1) * 0.5))
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cx1, cy1 = max(0, px1 - pad), max(0, py1 - pad)
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cx2, cy2 = min(fw, px2 + pad), min(fh, py2 + pad)
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plate_crop = best_frame[cy1:cy2, cx1:cx2]
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if plate_crop.size > 0:
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# Save at 4× upscale for readability
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ph, pw = plate_crop.shape[:2]
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big = cv2.resize(plate_crop, (pw * 4, ph * 4), interpolation=cv2.INTER_CUBIC)
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cv2.imwrite(os.path.join(event_dir, "plate_crop.jpg"), big, [cv2.IMWRITE_JPEG_QUALITY, 95])
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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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