percoandClaude Sonnet 4.6
13e6038069
- 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>
238 lines
9.1 KiB
Python
238 lines
9.1 KiB
Python
import os
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import cv2
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import numpy as np
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import logging
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log = logging.getLogger("lpr")
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MODEL_CACHE = os.environ.get("MODEL_CACHE", "/models")
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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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def __init__(self):
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self._yolo = None
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self._rec = None
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self._keys: list[str] = []
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self._load()
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def _load(self):
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try:
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import onnxruntime as ort
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providers = ["CPUExecutionProvider"]
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yolo_path = os.path.join(MODEL_CACHE, "yolov9_license_plate", "yolov9-256-license-plates.onnx")
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rec_path = os.path.join(MODEL_CACHE, "paddleocr-onnx", "recognition_v4.onnx")
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keys_path = os.path.join(MODEL_CACHE, "paddleocr-onnx", "ppocr_keys_v1.txt")
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if os.path.exists(yolo_path):
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self._yolo = ort.InferenceSession(yolo_path, providers=providers)
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log.info("YOLOv9 plate detector loaded")
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else:
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log.warning(f"YOLOv9 model not found at {yolo_path}")
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if os.path.exists(rec_path):
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self._rec = ort.InferenceSession(rec_path, providers=providers)
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log.info("PaddleOCR recognition model loaded")
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if os.path.exists(keys_path):
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with open(keys_path) as f:
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self._keys = f.read().splitlines()
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log.info(f"Loaded {len(self._keys)} OCR characters")
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except ImportError:
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log.warning("onnxruntime not installed — LPR disabled")
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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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"""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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all_boxes = []
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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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# 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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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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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]
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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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if idx != prev_idx and idx != 0 and idx <= len(self._keys):
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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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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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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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crop = frame[cy1:cy2, cx1:cx2]
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text, ocr_conf = self._ocr_crop(crop)
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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 text, (plate_conf + ocr_conf) / 2.0, (int(x1), int(y1), int(x2), int(y2))
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