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OpenCV-Python身份证信息识别( 三 )

Python


for index in range(len(positions)):position = positions[index]data_area = data_areas['{}-{}'.format(position[0], position[1])]ocr_data = https://www.isolves.com/it/cxkf/yy/Python/2022-04-11/ocr.ocr(data_area)ocr_result = ''.join([''.join(result[0]) for result in ocr_data]).replace(' ', '')# print('{}：{}'.format(labels[index], ocr_result))result.append('{}：{}'.format(labels[index], ocr_result))show(data_area, "data_area")for item in result:print(item)show(res, "res")

OpenCV-Python身份证信息识别

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结语通过以上的步骤 ， 便成功地将身份证信息进行了提取 ， 过程中的一些数字参数 ， 可能会在不同的场景中有些许的调整 。
以下放上所有的代码：
代码import cv2import numpy as npfrom cnocr import CnOcrdef show(image, window_name):cv2.namedWindow(window_name, 0)cv2.imshow(window_name, image)# 0任意键终止窗口cv2.waitKey(0)cv2.destroyAllWindows()ocr = CnOcr(model_name='densenet_lite_136-gru')image = cv2.imread('card.png')show(image, "image")gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)show(gray, "gray")blur = cv2.medianBlur(gray, 7)show(blur, "blur")threshold = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]show(threshold, "threshold")canny = cv2.Canny(threshold, 100, 150)show(canny, "canny")kernel = np.ones((3, 3), np.uint8)dilate = cv2.dilate(canny, kernel, iterations=5)show(dilate, "dilate")binary, contours, hierarchy = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)image_copy = image.copy()res = cv2.drawContours(image_copy, contours, -1, (255, 0, 0), 20)show(res, "res")contours = sorted(contours, key=cv2.contourArea, reverse=True)[0]image_copy = image.copy()res = cv2.drawContours(image_copy, contours, -1, (255, 0, 0), 20)show(res, "contours")epsilon = 0.02 * cv2.arcLength(contours, True)approx = cv2.approxPolyDP(contours, epsilon, True)n = []for x, y in zip(approx[:, 0, 0], approx[:, 0, 1]):n.append((x, y))n = sorted(n)sort_point = []n_point1 = n[:2]n_point1.sort(key=lambda x: x[1])sort_point.extend(n_point1)n_point2 = n[2:4]n_point2.sort(key=lambda x: x[1])n_point2.reverse()sort_point.extend(n_point2)p1 = np.array(sort_point, dtype=np.float32)h = sort_point[1][1] - sort_point[0][1]w = sort_point[2][0] - sort_point[1][0]pts2 = np.array([[0, 0], [0, h], [w, h], [w, 0]], dtype=np.float32)M = cv2.getPerspectiveTransform(p1, pts2)dst = cv2.warpPerspective(image, M, (w, h))# print(dst.shape)show(dst, "dst")if w < h:dst = np.rot90(dst)resize = cv2.resize(dst, (1084, 669), interpolation=cv2.INTER_AREA)show(resize, "resize")temp_image = resize.copy()gray = cv2.cvtColor(resize, cv2.COLOR_BGR2GRAY)show(gray, "gray")threshold = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]show(threshold, "threshold")blur = cv2.medianBlur(threshold, 5)show(blur, "blur")kernel = np.ones((3, 3), np.uint8)morph_open = cv2.morphologyEx(blur, cv2.MORPH_OPEN, kernel)show(morph_open, "morph_open")kernel = np.ones((7, 7), np.uint8)dilate = cv2.dilate(morph_open, kernel, iterations=6)show(dilate, "dilate")binary, contours, hierarchy = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)resize_copy = resize.copy()res = cv2.drawContours(resize_copy, contours, -1, (255, 0, 0), 2)show(res, "res")labels = ['姓名', '性别', '民族', '出生年', '出生月', '出生日', '住址', '公民身份证号码']positions = []data_areas = {}resize_copy = resize.copy()for contour in contours:epsilon = 0.002 * cv2.arcLength(contour, True)approx = cv2.approxPolyDP(contour, epsilon, True)x, y, w, h = cv2.boundingRect(approx)if h > 50 and x < 670:res = cv2.rectangle(resize_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)area = gray[y:(y + h), x:(x + w)]blur = cv2.medianBlur(area, 3)data_area = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]positions.append((x, y))data_areas['{}-{}'.format(x, y)] = data_areashow(res, "res")positions.sort(key=lambda p: p[1])result = []index = 0while index < len(positions) - 1:if positions[index + 1][1] - positions[index][1] < 10:temp_list = [positions[index + 1], positions[index]]for i in range(index + 1, len(positions)):if positions[i + 1][1] - positions[i][1] < 10:temp_list.append(positions[i + 1])else:breaktemp_list.sort(key=lambda p: p[0])positions[index:(index + len(temp_list))] = temp_listindex = index + len(temp_list) - 1else:index += 1for index in range(len(positions)):position = positions[index]data_area = data_areas['{}-{}'.format(position[0], position[1])]ocr_data = https://www.isolves.com/it/cxkf/yy/Python/2022-04-11/ocr.ocr(data_area)ocr_result = ''.join([''.join(result[0]) for result in ocr_data]).replace(' ', '')# print('{}：{}'.format(labels[index], ocr_result))result.append('{}：{}'.format(labels[index], ocr_result))show(data_area, "data_area")for item in result:print(item)show(res, "res")


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