### OpenCV 和 SVM 实现车牌检测与识别的方法 #### 数据预处理 在实现车牌检测与识别之前，需要对输入图像进行一系列的预处理操作。这些操作通常包括灰度转换、高斯模糊以及边缘检测等步骤。通过这些操作可以减少噪声并突出目标区域。 ```python import cv2 def preprocess_image(image_path): image = cv2.imread(image_path) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 将彩色图像转为灰度图[^1] blurred = cv2.GaussianBlur(gray, (5, 5), 0) # 使用高斯滤波去除噪点 edged = cv2.Canny(blurred, 30, 200) # 边缘检测以定位轮廓 return edged ``` #### 车牌定位 利用形态学变换和轮廓查找技术来精确定位车牌的位置。此过程涉及膨胀腐蚀操作以便更好地分离连通域，并筛选出可能属于车牌形状的候选区域。 ```python def locate_license_plate(edged_image): contours, _ = cv2.findContours(edged_image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:] contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10] screenCnt = None for c in contours: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.02 * peri, True) if len(approx) == 4: # 假设车牌大致呈矩形 screenCnt = approx break mask = np.zeros(gray.shape, dtype=np.uint8) new_image = cv2.drawContours(mask, [screenCnt], 0, 255, -1,) new_image = cv2.bitwise_and(img, img, mask=mask) (x, y) = np.where(mask == 255) (topx, topy) = (np.min(x), np.min(y)) (bottomx, bottomy) = (np.max(x), np.max(y)) cropped = gray[topx:bottomx+1, topy:bottomy+1] return cropped ``` #### 字符分割 一旦获取到完整的车牌图像，则需进一步将其划分为单独的字符部分。这一步骤对于后续分类至关重要。 ```python def segment_characters(plate_image): _, thresh = cv2.threshold(plate_image, 127, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[^2] output = cv2.connectedComponentsWithStats(thresh, connectivity=8, ltype=cv2.CV_32S) stats = output[2][1:] # 排除背景标签 labels = output[1] char_images = [] for stat in stats: x, y, w, h, area = stat if not is_valid_char_stat(stat): continue # 自定义函数判断是否有效字符统计信息 char_img = plate_image[y:y+h,x:x+w] resized = resize_to_fit(char_img, 20, 20) char_images.append(resized) return char_images ``` #### 训练和支持向量机(SVM)应用 最后阶段就是构建支持向量机模型来进行字符预测。这里分别创建两个不同的SVM实例——一个针对汉字（省份缩写），另一个面向英文字母及阿拉伯数字。 ```python from sklearn.svm import SVC from sklearn.preprocessing import LabelEncoder # 加载训练样本... label_encoder_province = LabelEncoder() svm_province = SVC(kernel='linear', C=1.0, probability=False) svm_province.fit(X_train_provinces, label_encoder_province.transform(Y_train_provinces)) label_encoder_alnum = LabelEncoder() svm_alphanum = SVC(kernel='rbf', gamma='scale') svm_alphanum.fit(X_train_alphanumeric, label_encoder_alnum.transform(Y_train_alphanumeric)) ``` 当准备完毕之后就可以依次调用上述各模块完成整个流程： ```python if __name__ == "__main__": path = 'path/to/your/image.jpg' edge_detected = preprocess_image(path) license_plate_region = locate_license_plate(edge_detected) chars = segment_characters(license_plate_region) predictions = [] for ch in chars[:1]: # 只取第一个作为省名代号 pred_probabilities = svm_province.predict_proba([ch.reshape(-1)])[:,1] predicted_label = label_encoder_province.inverse_transform([pred_probabilities.argmax()])[0] predictions.append(predicted_label) for ch in chars[1:]: pred_probabilities = svm_alphanum.predict_proba([ch.reshape(-1)]) predicted_label = label_encoder_alnum.inverse_transform([pred_probabilities.argmax()])[0] predictions.append(predicted_label) print(''.join(predictions)) ```