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45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
import os
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import cv2
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import numpy as np
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from skimage import transform as trans
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src = np.array([
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[30.2946, 51.6963],
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[65.5318, 51.5014],
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[48.0252, 71.7366],
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[33.5493, 92.3655],
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[62.7299, 92.2041]], dtype=np.float32)
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src[:, 0] += 8.0
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img_path = '/data/anxiang/datasets/IJB_release/IJBC/loose_crop'
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img_path_align = '/data/anxiang/datasets/IJB_release/IJBC/loose_crop_align'
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img_list_path = '/data/anxiang/datasets/IJB_release/IJBC/meta/ijbc_name_5pts_score.txt'
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img_list = open(img_list_path)
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files = img_list.readlines()
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for img_index, each_line in enumerate(files):
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if img_index % 500 == 0:
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print('processing', img_index)
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name_lmk_score = each_line.strip().split(' ')
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img_name = os.path.join(img_path, name_lmk_score[0])
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img = cv2.imread(img_name)
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landmark = np.array([float(x) for x in name_lmk_score[1:-1]], dtype=np.float32)
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landmark = landmark.reshape((5, 2))
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if landmark.shape[0] == 68:
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landmark5 = np.zeros((5, 2), dtype=np.float32)
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landmark5[0] = (landmark[36] + landmark[39]) / 2
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landmark5[1] = (landmark[42] + landmark[45]) / 2
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landmark5[2] = landmark[30]
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landmark5[3] = landmark[48]
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landmark5[4] = landmark[54]
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else:
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landmark5 = landmark
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tform = trans.SimilarityTransform()
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tform.estimate(landmark5, src)
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M = tform.params[0:2, :]
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img = cv2.warpAffine(img, M, (112, 112), borderValue=0.0)
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cv2.imwrite(os.path.join(img_path_align, name_lmk_score[0]), img)
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