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86 lines
3.7 KiB
86 lines
3.7 KiB
5 months ago
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import cv2
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import numpy as np
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'''
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基于Opencv图像处理的车牌定位和分割
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'''
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def car_plate_recognize(car):
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"""=========================== 1. 定位车牌（车牌检测）==========================="""
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# 蓝色、黄色和绿色所对应的色彩空间
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lower_blue = np.array([100, 110, 110])
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upper_blue = np.array([130, 255, 255])
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lower_yellow = np.array([15, 55, 55])
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upper_yellow = np.array([50, 255, 255])
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lower_green = np.array([35, 100, 100])
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upper_green = np.array([85, 255, 255])
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hsv = cv2.cvtColor(car, cv2.COLOR_BGR2HSV) # 将BGR图像转化到HSV的颜色空间
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mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
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mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
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mask_green = cv2.inRange(hsv, lower_green, upper_green)
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mask_plate = cv2.bitwise_or(mask_blue, mask_yellow)
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mask_plate = cv2.bitwise_or(mask_plate, mask_green)
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# 根据阈值找到对应颜色
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mask = cv2.cvtColor(mask_plate, cv2.COLOR_GRAY2BGR)
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mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
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Matrix = np.ones((20, 20), np.uint8)
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mask1 = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, Matrix)
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mask = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, Matrix) # 形态学开运算
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ret, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY) # 二值化进而获取轮廓
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contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 获取轮廓 contours
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# 初始化 box
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box = None
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# 寻找轮廓最大的 定位车牌
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for i in range(len(contours)):
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cnt = contours[i]
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area = cv2.contourArea(cnt)
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if area > 3000:
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rect = cv2.minAreaRect(cnt)
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box = cv2.boxPoints(rect)
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box = np.int_(box)
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break # 找到一个符合条件的就跳出循环
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if box is None:
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raise ValueError("No contours found that meet the size requirement.")
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plate = cv2.drawContours(car.copy(), [box], -1, (0, 255, 0), 3)
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"""=========================== 2. 分割车牌中的每个字符 ==========================="""
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ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]
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xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]
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ys_sorted_index = np.argsort(ys)
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xs_sorted_index = np.argsort(xs)
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x1 = box[xs_sorted_index[0], 0]
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x2 = box[xs_sorted_index[3], 0]
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y1 = box[ys_sorted_index[0], 1]
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y2 = box[ys_sorted_index[3], 1]
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ROI_plate = plate[y1:y2, x1:x2]
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ROI_plate_gray = cv2.cvtColor(ROI_plate, cv2.COLOR_BGR2GRAY) # 灰度化
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ROI_plate_blur = cv2.GaussianBlur(ROI_plate_gray, (5, 5), 0) # 高斯滤波
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ret, ROI_plate_Binary = cv2.threshold(ROI_plate_blur, 127, 255, cv2.THRESH_BINARY) # 二值化
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# 形态学腐蚀 去除边框
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kernel = np.ones((5, 5), dtype=np.uint8)
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ROI_erode = cv2.erode(ROI_plate_Binary, kernel, iterations=1)
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# 根据宽度 裁剪7个字符
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width = ROI_erode.shape[1]
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height = ROI_erode.shape[0]
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word_0 = ROI_erode[0:height, 0:np.uint8(height / 2)]
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word_1 = ROI_erode[0:height, np.uint8(height / 2):height]
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size = np.uint8((width - height) / 5)
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word_2 = ROI_erode[0:height, height + 0 * size:height + 1 * size]
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word_3 = ROI_erode[0:height, height + 1 * size:height + 2 * size]
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word_4 = ROI_erode[0:height, height + 2 * size:height + 3 * size]
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word_5 = ROI_erode[0:height, height + 3 * size:height + 4 * size]
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word_6 = ROI_erode[0:height, height + 4 * size:height + 5 * size]
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word_all = [word_0, word_1, word_2, word_3, word_4, word_5, word_6]
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return plate, word_all
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if __name__ == "__main__":
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car = cv2.imread(r'/mnt/data/cropped_license_plate_0.jpg', 1)
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plate, _ = car_plate_recognize(car)
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cv2.imwrite("plate.jpg", plate)
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