From eb43b3df31d9a092d6985af5b3baf3ced244ec6c Mon Sep 17 00:00:00 2001 From: yyc12345 Date: Thu, 9 Apr 2026 07:47:20 +0800 Subject: [PATCH] revoke: revoke persepctive correction code and fix yellow car plate issue --- mv-and-ip/.gitignore | 1 + mv-and-ip/car_plate.py | 126 +++++++++-------------------------------- 2 files changed, 28 insertions(+), 99 deletions(-) diff --git a/mv-and-ip/.gitignore b/mv-and-ip/.gitignore index 12d44ef..a8dfc42 100644 --- a/mv-and-ip/.gitignore +++ b/mv-and-ip/.gitignore @@ -4,6 +4,7 @@ # All image files *.jpg +*.jpeg *.png *.webp diff --git a/mv-and-ip/car_plate.py b/mv-and-ip/car_plate.py index d941da4..c477de2 100644 --- a/mv-and-ip/car_plate.py +++ b/mv-and-ip/car_plate.py @@ -49,21 +49,30 @@ def _uniform_car_plate(img: cv.typing.MatLike) -> cv.typing.MatLike: class CarPlateHsvBoundary: lower_bound: cv.typing.MatLike upper_bound: cv.typing.MatLike + need_revert: bool + """是否取反黑白颜色，因为蓝牌和黄牌的操作正好是反的""" CAR_PLATE_HSV_BOUNDARIES: tuple[CarPlateHsvBoundary, ...] = ( # 中国蓝牌 HSV 范围 - CarPlateHsvBoundary(np.array([100, 80, 60]), np.array([130, 255, 255])), + CarPlateHsvBoundary(np.array([100, 80, 60]), np.array([130, 255, 255]), True), # 中国绿牌 HSV 范围 - CarPlateHsvBoundary(np.array([35, 43, 46]), np.array([99, 255, 255])), + CarPlateHsvBoundary(np.array([35, 43, 46]), np.array([99, 255, 255]), False), # 中国黄牌 HSV 范围 - CarPlateHsvBoundary(np.array([32, 43, 46]), np.array([68, 255, 255])), + CarPlateHsvBoundary(np.array([16, 43, 46]), np.array([34, 255, 255]), False), ) +@dataclass +class CarPlateMask: + mask: cv.typing.MatLike + need_revert: bool + """是否对颜色取反，与CarPlateHsvBoundary中的同名字段含义一致""" + + def _batchly_mask_car_plate( hsv: cv.typing.MatLike, -) -> typing.Iterator[cv.typing.MatLike]: +) -> typing.Iterator[CarPlateMask]: """ """ for boundary in CAR_PLATE_HSV_BOUNDARIES: # 以给定HSV范围检测符合该颜色的位置 @@ -76,7 +85,7 @@ def _batchly_mask_car_plate( mask = cv.morphologyEx(mask, cv.MORPH_OPEN, kernel_open) # Return value - yield mask + yield CarPlateMask(mask, boundary.need_revert) @dataclass @@ -85,6 +94,8 @@ class CarPlateRegion: y: int w: int h: int + need_revert: bool + """是否对颜色取反，与CarPlateHsvBoundary中的同名字段含义一致""" MIN_AREA: float = 3000 @@ -94,10 +105,12 @@ BEST_ASPECT_RATIO: float = 3.5 def _analyse_car_plate_connection( - mask: cv.typing.MatLike, + mask: CarPlateMask, ) -> typing.Optional[CarPlateRegion]: # 连通域分析，筛选最符合车牌长宽比的区域 - num_labels, labels, stats, _ = cv.connectedComponentsWithStats(mask, connectivity=8) + num_labels, labels, stats, _ = cv.connectedComponentsWithStats( + mask.mask, connectivity=8 + ) best: typing.Optional[CarPlateRegion] = None best_score = 0 @@ -113,77 +126,11 @@ def _analyse_car_plate_connection( score = area * (1 - abs(ratio - BEST_ASPECT_RATIO) / BEST_ASPECT_RATIO) if score > best_score: best_score = score - best = CarPlateRegion(x, y, w, h) + best = CarPlateRegion(x, y, w, h, mask.need_revert) return best -@dataclass -class PerspectiveData: - top_left: tuple[int, int] - top_right: tuple[int, int] - bottom_left: tuple[int, int] - bottom_right: tuple[int, int] - - new_width: int - new_height: int - - -def _extract_perspective_data( - gray: cv.typing.MatLike, -) -> typing.Optional[PerspectiveData]: - """ """ - # Histogram balance to increase contrast - hist_gray = cv.equalizeHist(gray) - - # Apply Gaussian blur to reduce noise - blurred = cv.GaussianBlur(hist_gray, (5, 5), 0) - - # Edge detection using Canny - edges = cv.Canny(blurred, 50, 150) - - # Find contours - contours, _ = cv.findContours(edges, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) - if not contours: - return None - # Find the largest one because all image is car plate - max_area_contour = max(contours, key=lambda contour: cv.contourArea(contour)) - - # Approximate the contour - peri = cv.arcLength(max_area_contour, True) - approx = cv.approxPolyDP(max_area_contour, 0.02 * peri, True) - if len(approx) != 4: - return None - - # Perspective transformation to get front view - # Order points: top-left, top-right, bottom-right, bottom-left - pts = approx.reshape(4, 2) - rect = np.zeros((4, 2), dtype="float32") - - # Sum and difference of coordinates to find corners - s = pts.sum(axis=1) - top_left = pts[np.argmin(s)] # Top-left has smallest sum - bottom_right = pts[np.argmax(s)] # Bottom-right has largest sum - - diff = np.diff(pts, axis=1) - top_right = pts[np.argmin(diff)] # Top-right has smallest difference - bottom_left = pts[np.argmax(diff)] # Bottom-left has largest difference - - # Calculate width and height of new image - width_a = np.linalg.norm(rect[0] - rect[1]) - width_b = np.linalg.norm(rect[2] - rect[3]) - max_width = max(int(width_a), int(width_b)) - - height_a = np.linalg.norm(rect[0] - rect[3]) - height_b = np.linalg.norm(rect[1] - rect[2]) - max_height = max(int(height_a), int(height_b)) - - # Return value - return PerspectiveData( - top_left, top_right, bottom_left, bottom_right, max_width, max_height - ) - - def extract_car_plate(img: cv.typing.MatLike) -> typing.Optional[cv.typing.MatLike]: """ Extract the car plate part from given image. @@ -232,39 +179,20 @@ def extract_car_plate(img: cv.typing.MatLike) -> typing.Optional[cv.typing.MatLi # Otsu 自动阈值，得到白字黑底，再取反 → 黑字白底 _, binary_otsu = cv.threshold(blurred, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU) # 反转：字符变黑，背景变白 - binary = cv.bitwise_not(binary_otsu) + if candidate.need_revert: + binary = cv.bitwise_not(binary_otsu) + else: + binary = binary_otsu # 去除小噪点（开运算） kernel_denoise = cv.getStructuringElement(cv.MORPH_RECT, (2, 2)) binary = cv.morphologyEx(binary, cv.MORPH_OPEN, kernel_denoise) - # 尝试获取视角矫正数据 - perspective_data = _extract_perspective_data(gray) - if perspective_data is None: - logging.warning(f'Can not fetch perspective data. The output image has no perspective correction.') - return binary - - # 执行视角矫正 - perspective_src = np.array([ - list(perspective_data.top_left), - list(perspective_data.top_right), - list(perspective_data.bottom_right), - list(perspective_data.bottom_left) - ], dtype="float32") - perspective_dst = np.array([ - [0, 0], - [perspective_data.new_width - 1, 0], - [perspective_data.new_width - 1, perspective_data.new_height - 1], - [0, perspective_data.new_height - 1] - ], dtype="float32") - M = cv.getPerspectiveTransform(perspective_src, perspective_dst) - warped = cv.warpPerspective(binary, M, (perspective_data.new_width, perspective_data.new_height)) - - return warped + return binary # cv.imwrite('./plate_binary.png', binary) # print("二值化结果已保存: plate_binary.png") - # ── 4. 叠加边框轮廓（细化文字边缘，参考效果图）───────────────────── + # 叠加边框轮廓（细化文字边缘，参考效果图） # Canny 边缘叠加让效果更接近参考图 edges = cv.Canny(blurred, 40, 120) edges_inv = cv.bitwise_not(edges) # 边缘→黑色