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72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
import cv2
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import numpy as np
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from skimage import transform as trans
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src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007],
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[51.157, 89.050], [57.025, 89.702]],
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dtype=np.float32)
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#<--left
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src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111],
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[45.177, 86.190], [64.246, 86.758]],
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dtype=np.float32)
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#---frontal
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src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493],
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[42.463, 87.010], [69.537, 87.010]],
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dtype=np.float32)
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#-->right
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src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111],
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[48.167, 86.758], [67.236, 86.190]],
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dtype=np.float32)
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#-->right profile
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src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007],
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[55.388, 89.702], [61.257, 89.050]],
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dtype=np.float32)
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src = np.array([src1, src2, src3, src4, src5])
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src_map = {112: src, 224: src * 2}
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arcface_src = np.array(
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[[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
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[41.5493, 92.3655], [70.7299, 92.2041]],
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dtype=np.float32)
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arcface_src = np.expand_dims(arcface_src, axis=0)
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# In[66]:
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# lmk is prediction; src is template
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def estimate_norm(lmk, image_size=112, mode='arcface'):
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assert lmk.shape == (5, 2)
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tform = trans.SimilarityTransform()
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lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
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min_M = []
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min_index = []
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min_error = float('inf')
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if mode == 'arcface':
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assert image_size == 112
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src = arcface_src
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else:
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src = src_map[image_size]
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for i in np.arange(src.shape[0]):
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tform.estimate(lmk, src[i])
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M = tform.params[0:2, :]
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results = np.dot(M, lmk_tran.T)
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results = results.T
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error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1)))
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# print(error)
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if error < min_error:
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min_error = error
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min_M = M
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min_index = i
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return min_M, min_index
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def norm_crop(img, landmark, image_size=112, mode='arcface'):
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M, pose_index = estimate_norm(landmark, image_size, mode)
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warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
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return warped
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