Add delete event, perspective correction OCR, and OCR crop display
- Delete event: POST /event/{id}/delete removes DB row + files
- Perspective correction: minAreaRect on white plate region → getPerspectiveTransform
gives a flat frontal view of the plate (e.g. 'GR B38 WR' instead of trapezoid)
- Tesseract now scales image 3x before OCR and skips EU blue strip (left 11%)
- Saves plate_ocr.jpg (perspective-corrected + enhanced crop actually fed to OCR)
- Event detail shows both raw detection crop and OCR crop side by side
- Manual plate edit form in event detail
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
13e6038069
commit
accbae41ce
+110
-39
@@ -38,6 +38,18 @@ def _nms(boxes: list, iou_threshold: float = 0.3) -> list:
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return result
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def _order_points(pts: np.ndarray) -> np.ndarray:
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"""Order 4 points: top-left, top-right, bottom-right, bottom-left."""
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rect = np.zeros((4, 2), dtype=np.float32)
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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diff = np.diff(pts, axis=1)
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rect[1] = pts[np.argmin(diff)]
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rect[3] = pts[np.argmax(diff)]
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return rect
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class PlateAnalyzer:
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def __init__(self):
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self._yolo = None
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@@ -75,7 +87,6 @@ class PlateAnalyzer:
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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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@@ -97,7 +108,6 @@ class PlateAnalyzer:
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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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@@ -118,7 +128,6 @@ class PlateAnalyzer:
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work = frame[y_start:y_end, :]
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wh = y_end - y_start
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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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@@ -133,49 +142,107 @@ class PlateAnalyzer:
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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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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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def _find_plate_quad(self, frame: np.ndarray, x1: int, y1: int, x2: int, y2: int) -> np.ndarray | None:
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"""Find the 4 corners of the plate using minAreaRect on the white plate region."""
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fh, fw = frame.shape[:2]
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pw, ph = x2 - x1, y2 - y1
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mx = int(pw * 0.6)
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my = int(ph * 1.2)
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rx1 = max(0, x1 - mx)
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ry1 = max(0, y1 - my)
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rx2 = min(fw, x2 + mx)
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ry2 = min(fh, y2 + my)
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region = frame[ry1:ry2, rx1:rx2]
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gray = cv2.cvtColor(region, cv2.COLOR_BGR2GRAY)
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_, thresh = cv2.threshold(gray, 160, 255, cv2.THRESH_BINARY)
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kernel = np.ones((3, 3), np.uint8)
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thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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return None
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# Use the largest bright contour (plate background)
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cnt = max(contours, key=cv2.contourArea)
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if cv2.contourArea(cnt) < pw * ph * 0.3:
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return None
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rect = cv2.minAreaRect(cnt)
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box = cv2.boxPoints(rect).astype(np.float32)
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box[:, 0] += rx1
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box[:, 1] += ry1
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# Validate: the oriented rect should be plate-shaped
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bw = rect[1][0]
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bh = rect[1][1]
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long_side = max(bw, bh)
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short_side = min(bw, bh)
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if short_side < 4 or long_side / short_side < 2.0:
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return None
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return box
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def _perspective_correct(self, frame: np.ndarray, quad: np.ndarray) -> np.ndarray:
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"""Warp the detected plate quad to a frontal rectangle."""
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pts = _order_points(quad)
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# French plate: 520×110mm → 4.73:1
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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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target_w = int(target_h * 4.73)
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dst = np.array([
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[0, 0], [target_w - 1, 0],
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[target_w - 1, target_h - 1], [0, target_h - 1],
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], dtype=np.float32)
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M = cv2.getPerspectiveTransform(pts, dst)
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return cv2.warpPerspective(frame, M, (target_w, target_h))
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def _enhance_crop(self, crop: np.ndarray) -> np.ndarray:
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"""CLAHE contrast + sharpen for better OCR."""
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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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# Skip EU blue strip (left ~11%) which confuses OCR
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eu_skip = max(0, int(crop.shape[1] * 0.11))
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crop = crop[:, eu_skip:]
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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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# Scale to at least 3× for reliable Tesseract recognition
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if gray.shape[0] < 120:
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scale = max(2, 120 // gray.shape[0])
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gray = cv2.resize(gray, (gray.shape[1] * scale, gray.shape[0] * scale),
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interpolation=cv2.INTER_CUBIC)
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wl = "-c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-"
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results = []
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for psm in [8, 7, 11]:
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cfg = f"--psm {psm} --oem 3 {wl}"
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t = pytesseract.image_to_string(gray, config=cfg).strip().upper()
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t = "".join(c for c in t if c.isalnum() or c == "-")
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alnum = "".join(c for c in t if c.isalnum())
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if len(alnum) >= 4:
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results.append(t)
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if results:
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# Pick the result with the most alphanumeric characters
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best = max(results, key=lambda r: len([c for c in r if c.isalnum()]))
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return best, 0.6
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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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@@ -203,35 +270,39 @@ class PlateAnalyzer:
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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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def read_plate(self, frame: np.ndarray) -> tuple[str, float, tuple | None, np.ndarray | None]:
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"""Detect + read plate.
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Returns (plate_text, confidence, bbox_or_None, corrected_crop_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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return "", 0.0, None, None
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x1, y1, x2, y2, plate_conf = plate_boxes[0]
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pad = 8
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cx1 = max(0, int(x1) - pad)
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cy1 = max(0, int(y1) - pad)
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cx2 = min(frame.shape[1], int(x2) + pad)
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cy2 = min(frame.shape[0], int(y2) + pad)
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bbox = (int(x1), int(y1), int(x2), int(y2))
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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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# Try perspective correction first
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quad = self._find_plate_quad(frame, int(x1), int(y1), int(x2), int(y2))
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if quad is not None:
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corrected = self._perspective_correct(frame, quad)
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log.debug("Perspective correction applied")
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else:
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fh, fw = frame.shape[:2]
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pad = 8
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corrected = frame[max(0, int(y1) - pad):min(fh, int(y2) + pad),
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max(0, int(x1) - pad):min(fw, int(x2) + pad)]
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text, ocr_conf = self._ocr_crop(corrected)
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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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return "", 0.0, bbox, corrected
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return text, (plate_conf + ocr_conf) / 2.0, (int(x1), int(y1), int(x2), int(y2))
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return text, (plate_conf + ocr_conf) / 2.0, bbox, corrected
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