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