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| import cv2 | |
| import numpy as np | |
| from MobileAgent.crop import crop_image, calculate_size | |
| from PIL import Image | |
| def order_point(coor): | |
| arr = np.array(coor).reshape([4, 2]) | |
| sum_ = np.sum(arr, 0) | |
| centroid = sum_ / arr.shape[0] | |
| theta = np.arctan2(arr[:, 1] - centroid[1], arr[:, 0] - centroid[0]) | |
| sort_points = arr[np.argsort(theta)] | |
| sort_points = sort_points.reshape([4, -1]) | |
| if sort_points[0][0] > centroid[0]: | |
| sort_points = np.concatenate([sort_points[3:], sort_points[:3]]) | |
| sort_points = sort_points.reshape([4, 2]).astype('float32') | |
| return sort_points | |
| def longest_common_substring_length(str1, str2): | |
| m = len(str1) | |
| n = len(str2) | |
| dp = [[0] * (n + 1) for _ in range(m + 1)] | |
| for i in range(1, m + 1): | |
| for j in range(1, n + 1): | |
| if str1[i - 1] == str2[j - 1]: | |
| dp[i][j] = dp[i - 1][j - 1] + 1 | |
| else: | |
| dp[i][j] = max(dp[i - 1][j], dp[i][j - 1]) | |
| return dp[m][n] | |
| def ocr(image_path, ocr_detection, ocr_recognition): | |
| text_data = [] | |
| coordinate = [] | |
| image_full = cv2.imread(image_path) | |
| det_result = ocr_detection(image_full) | |
| det_result = det_result['polygons'] | |
| for i in range(det_result.shape[0]): | |
| pts = order_point(det_result[i]) | |
| image_crop = crop_image(image_full, pts) | |
| try: | |
| result = ocr_recognition(image_crop)['text'][0] | |
| except: | |
| continue | |
| box = [int(e) for e in list(pts.reshape(-1))] | |
| box = [box[0], box[1], box[4], box[5]] | |
| text_data.append(result) | |
| coordinate.append(box) | |
| else: | |
| return text_data, coordinate |