feat: update with claude code
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@@ -7,244 +7,93 @@ import logging
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from pathlib import Path
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from dataclasses import dataclass
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def _uniform_car_plate(img: cv.typing.MatLike) -> cv.typing.MatLike:
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"""
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Uniform the image size to 512x512 while maintaining aspect ratio, padding with black.
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:param img: The image in BGR format to be uniformed.
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:return: The uniformed image in BGR format.
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"""
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UNI_HW: int = 512
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# Calculate the new width and height for given image
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h, w = img.shape[:2]
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scale = min(UNI_HW / w, UNI_HW / h)
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new_w = int(w * scale)
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new_h = int(h * scale)
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# Resize the image
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resized_img = cv.resize(img, (new_w, new_h))
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# Create a black canvas of size UNI_HW x UNI_HW
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padded_img = np.zeros((UNI_HW, UNI_HW, 3), dtype=np.uint8)
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# Calculate position to paste the resized image (centered)
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y_offset = (UNI_HW - new_h) // 2
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x_offset = (UNI_HW - new_w) // 2
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# Paste the resized image onto the canvas
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padded_img[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = resized_img
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# Return the padded image
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return padded_img
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def extract_car_plate(img: cv.typing.MatLike) -> typing.Optional[cv.typing.MatLike]:
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"""Extract the car plate part from given image.
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:param img: The image containing car plate in BGR format.
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:return: The image of binary car plate in U8 format if succeed, otherwise None.
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"""
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# Reference: https://www.cnblogs.com/linuxAndMcu/p/19144795
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# ── 1. 利用蓝色车牌颜色在 HSV 空间定位车牌 ──────────────────────────
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hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
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# 中国蓝牌 HSV 范围
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lower_blue = np.array([100, 80, 60])
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upper_blue = np.array([130, 255, 255])
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mask_blue = cv.inRange(hsv, lower_blue, upper_blue)
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# Resize the image to make following step works about finding proper contours.
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img = _uniform_car_plate(img)
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# Convert to grayscale image
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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# 形态学：闭运算填孔 + 开运算去噪
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kernel_close = cv.getStructuringElement(cv.MORPH_RECT, (25, 10))
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kernel_open = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
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mask_blue = cv.morphologyEx(mask_blue, cv.MORPH_CLOSE, kernel_close)
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mask_blue = cv.morphologyEx(mask_blue, cv.MORPH_OPEN, kernel_open)
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# Histogram balance to increase contrast
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hist_gray = cv.equalizeHist(gray)
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# Apply Gaussian blur to reduce noise
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blurred = cv.GaussianBlur(hist_gray, (5, 5), 0)
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# Edge detection using Canny
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edges = cv.Canny(blurred, 50, 150)
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# ── 2. 连通域分析，筛选最符合车牌长宽比的区域 ──────────────────────
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num_labels, labels, stats, _ = cv.connectedComponentsWithStats(mask_blue, connectivity=8)
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# cv.imshow('contours', edges)
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# k = cv.waitKey(0)
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# return None
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# Morphological operations to connect broken edges
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kernel_close = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
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closed = cv.morphologyEx(edges, cv.MORPH_CLOSE, kernel_close)
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kernel_open = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
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opened = cv.morphologyEx(closed, cv.MORPH_OPEN, kernel_open)
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kernel_dilate = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
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dilated = cv.dilate(edges, kernel_dilate, iterations=2)
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best = None
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best_score = 0
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h_img, w_img = img.shape[:2]
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cv.imshow('contours', opened)
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k = cv.waitKey(0)
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return None
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# Find contours
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contours, _ = cv.findContours(closed, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
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if not contours:
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logging.error("No contours found")
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return None
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# List all contours
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logging.debug(f'Total {len(contours)} contours.')
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for i, contour in enumerate(contours):
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logging.debug(f'Contour[{i}] has {contour.shape[0]} points.')
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cv.drawContours(img, contours, -1, (0, 0, 255), 3)
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cv.imshow('contours', img)
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k = cv.waitKey(0)
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return None
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# Filter contours
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candidates: list[cv.typing.MatLike] = []
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MIN_AREA: float = 2000
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MAX_AREA: float = 100000
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MIN_ASPECT_RATIO: float = 2.5
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MAX_ASPECT_RATIO: float = 6.0
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for i, contour in enumerate(contours):
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# Calculate the area
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area = cv.contourArea(contour)
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if area < MIN_AREA or area > MAX_AREA:
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logging.debug(f'Contour[{i}] failed at area limit. The area of this contour is {area}.')
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for i in range(1, num_labels):
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x, y, w, h, area = stats[i]
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if area < 3000:
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continue
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ratio = w / (h + 1e-5)
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# 标准车牌宽高比约 3:1 ~ 5:1
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if 2.5 < ratio < 6.0:
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score = area * (1 - abs(ratio - 3.5) / 3.5)
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if score > best_score:
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best_score = score
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best = (x, y, w, h)
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# Get bounding rectangle
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bouding_rect = cv.boundingRect(contour)
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(x, y, w, h) = bouding_rect
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# Calaulate aspect ratio
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aspect_ratio = w / h
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if aspect_ratio < MIN_ASPECT_RATIO or aspect_ratio > MAX_ASPECT_RATIO:
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logging.debug(f'Contour[{i}] failed at aspect ratio limit. The aspect ratio of this contour is {aspect_ratio}.')
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continue
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assert best is not None, "未找到车牌区域"
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x, y, w, h = best
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# 稍微扩边
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pad = 6
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x1 = max(x - pad, 0)
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y1 = max(y - pad, 0)
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x2 = min(x + w + pad, w_img)
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y2 = min(y + h + pad, h_img)
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# Get the convex hull of contour
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hull = cv.convexHull(contour)
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# Compute the occupation of contour area in convex hull area
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hull_area = cv.contourArea(hull)
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solidity = area / hull_area
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plate_color = img[y1:y2, x1:x2].copy()
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print(f"车牌区域: x={x1}, y={y1}, w={x2-x1}, h={y2-y1}")
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# Filter more regular contour
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if solidity > 0.6:
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# Extra check for the rectangle fill rate
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fill_ratio = area / (w * h)
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if fill_ratio > 0.3:
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logging.debug(f'Contour[{i}] is perfect.')
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candidates.append(contour)
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continue
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else:
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logging.debug(f'Contour[{i}] failed at rectangle fill ratio limit. The fill ratio of this contour is {fill_ratio}')
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else:
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logging.debug(f'Contour[{i}] failed at solidity limit. The solidity of this contour is {solidity}.')
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# # 在原图上标记（仅供调试）
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# debug = img.copy()
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# cv.rectangle(debug, (x1, y1), (x2, y2), (0, 255, 0), 3)
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# cv.imwrite('./debug_detected.jpg', debug)
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if len(candidates) == 0:
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logging.error("No candidate contour")
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return None
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cv.drawContours(img, contours, -1, (0, 0, 255), 3)
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cv.imshow('contours', img)
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k = cv.waitKey(0)
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return None
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# ── 3. 二值化：文字/边缘 → 黑色，背景 → 白色 ─────────────────────
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gray = cv.cvtColor(plate_color, cv.COLOR_BGR2GRAY)
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# 高斯模糊降噪
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blurred = cv.GaussianBlur(gray, (3, 3), 0)
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# Step 6: Find the most likely license plate contour
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# License plates are typically rectangular with specific aspect ratios
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max_area = 0
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best_contour = None
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for contour in contours:
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area = cv.contourArea(contour)
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if area < 500: # Filter out small contours
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continue
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# Approximate the contour
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peri = cv.arcLength(contour, True)
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approx = cv.approxPolyDP(contour, 0.02 * peri, True)
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# Look for quadrilateral shapes (4 corners)
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if len(approx) == 4:
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x, y, w, h = cv.boundingRect(contour)
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aspect_ratio = float(w) / h if h > 0 else 0
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# Typical license plate aspect ratio is between 2 and 5
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if 2 <= aspect_ratio <= 5 and area > max_area:
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max_area = area
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best_contour = approx
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if best_contour is None:
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# If no perfect quadrilateral found, try with largest rectangular contour
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for contour in contours:
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area = cv.contourArea(contour)
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if area < 500:
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continue
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x, y, w, h = cv.boundingRect(contour)
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aspect_ratio = float(w) / h if h > 0 else 0
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if 1.5 <= aspect_ratio <= 6 and area > max_area:
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max_area = area
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rect = cv.minAreaRect(contour)
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box = cv.boxPoints(rect)
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best_contour = np.int0(box)
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if best_contour is None:
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logging.error("No valid contour found")
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return None
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# Step 7: Perspective transformation to get front view
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# Order points: top-left, top-right, bottom-right, bottom-left
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pts = best_contour.reshape(4, 2)
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rect = np.zeros((4, 2), dtype="float32")
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# Sum and difference of coordinates to find corners
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)] # Top-left has smallest sum
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rect[2] = pts[np.argmax(s)] # Bottom-right has largest sum
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diff = np.diff(pts, axis=1)
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rect[1] = pts[np.argmin(diff)] # Top-right has smallest difference
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rect[3] = pts[np.argmax(diff)] # Bottom-left has largest difference
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# Calculate width and height of new image
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width_a = np.linalg.norm(rect[0] - rect[1])
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width_b = np.linalg.norm(rect[2] - rect[3])
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max_width = max(int(width_a), int(width_b))
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height_a = np.linalg.norm(rect[0] - rect[3])
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height_b = np.linalg.norm(rect[1] - rect[2])
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max_height = max(int(height_a), int(height_b))
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# Destination points for perspective transform
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dst_pts = np.array([
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[0, 0],
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[max_width - 1, 0],
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[max_width - 1, max_height - 1],
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[0, max_height - 1]
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], dtype="float32")
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# Get perspective transform matrix and apply it
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M = cv.getPerspectiveTransform(rect, dst_pts)
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warped = cv.warpPerspective(img, M, (max_width, max_height))
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# Otsu 自动阈值，得到白字黑底，再取反 → 黑字白底
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_, binary_otsu = cv.threshold(blurred, 0, 255,
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cv.THRESH_BINARY + cv.THRESH_OTSU)
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binary = cv.bitwise_not(binary_otsu) # 反转：字符变黑，背景变白
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# 去除小噪点（开运算）
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kernel_denoise = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))
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binary = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel_denoise)
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return binary
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# cv.imwrite('./plate_binary.png', binary)
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# print("二值化结果已保存: plate_binary.png")
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# ── 4. 叠加边框轮廓（细化文字边缘，参考效果图）─────────────────────
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# Canny 边缘叠加让效果更接近参考图
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edges = cv.Canny(blurred, 40, 120)
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edges_inv = cv.bitwise_not(edges) # 边缘→黑色
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combined = cv.bitwise_and(binary, edges_inv) # 合并
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# 再做一次轻微腐蚀让字体略粗
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kernel_dilate = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))
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combined = cv.erode(combined, kernel_dilate, iterations=1)
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return combined
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# cv.imwrite('./plate_final.png', combined)
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# print("最终结果已保存: plate_final.png")
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# Step 8: Convert warped image to grayscale
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warped_gray = cv.cvtColor(warped, cv.COLOR_BGR2GRAY)
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# Step 9: Apply adaptive thresholding for better binarization
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# First, enhance contrast
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clahe = cv.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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enhanced = clahe.apply(warped_gray)
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# Apply Otsu's thresholding to get binary image
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_, binary = cv.threshold(enhanced, 0, 255, cv.THRESH_BINARY_INV + cv.THRESH_OTSU)
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# Step 10: Clean up the binary image with morphological operations
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kernel_clean = cv.getStructuringElement(cv.MORPH_RECT, (2, 2))
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cleaned = cv.morphologyEx(binary, cv.MORPH_CLOSE, kernel_clean)
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# Ensure the output is in the correct format (U8)
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result = cleaned.astype(np.uint8)
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return result
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@dataclass
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class Cli:
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