### 使用 OpenCV 进行车牌字符检测 #### 一、环境准备 为了顺利运行车牌字符检测程序，需安装 Python 和 OpenCV 库。可以通过 pip 安装 OpenCV： ```bash pip install opencv-python-headless ``` #### 二、代码实现 下面是一个完整的车牌字符检测实例： ```python import cv2 import numpy as np def preprocess_image(image_path): """图像预处理""" img = cv2.imread(image_path) gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 缩放图片到固定大小 resized_img = cv2.resize(gray_img, (int(gray_img.shape[1]/2), int(gray_img.shape[0]/2))) # 高斯模糊去噪 blurred_img = cv2.GaussianBlur(resized_img, (5, 5), 0) # Canny边缘检测 edged_img = cv2.Canny(blurred_img, 30, 200) return img, edged_img def find_possible_plates(edged_img): """查找可能的车牌区域""" contours, _ = cv2.findContours(edged_img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) possible_plates = [] for contour in contours: perimeter = cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True) if len(approx) == 4 and 800 < cv2.contourArea(contour) < 7000: rect = cv2.minAreaRect(contour) box = cv2.boxPoints(rect) box = np.intp(box) possible_plates.append((box, contour)) return possible_plates def extract_plate_characters(plate_region, original_image): """提取并矫正车牌字符""" mask = np.zeros_like(original_image[:, :, 0]) cv2.drawContours(mask, [plate_region], -1, color=255, thickness=-1) extracted_plate = cv2.bitwise_and(original_image, original_image, mask=mask) x, y, w, h = cv2.boundingRect(plate_region) cropped_plate = extracted_plate[y:y+h, x:x+w] corrected_plate = four_point_transform(cropped_plate, plate_region.reshape(-1, 2)) return corrected_plate def four_point_transform(image, pts): """四点透视变换""" rect = order_points(pts) (tl, tr, br, bl) = rect widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype="float32") M = cv2.getPerspectiveTransform(rect.astype('float32'), dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) return warped def order_points(pts): """对四个角点按顺序排列""" rect = np.zeros((4, 2), dtype="float32") s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect if __name__ == '__main__': image_path = 'car.jpg' orig_img, edge_detected_img = preprocess_image(image_path=image_path) plates = find_possible_plates(edge_detected_img) for i, (possible_plate_contour, _) in enumerate(plates[:]): plate_img = extract_plate_characters(possible_plate_contour, orig_img) # 显示找到的车牌 cv2.imshow(f'Plate {i}', plate_img) cv2.waitKey(0) cv2.destroyAllWindows() ``` 此段代码实现了从读取原始车辆照片至最终展示分离出来的车牌图像的过程[^3]。 #### 三、注意事项 - 输入图像是彩色形式，在转换成灰度之前会先调整尺寸以减少计算量。 - 边缘检测采用Canny算子，能够有效捕捉边界特征。 - 查找轮廓时过滤掉面积过小或过大以及形状不符合矩形特性的候选对象。 - 对疑似车牌部分做几何校正，确保后续文字识别准确性更高。