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89 lines
2.1 KiB
Python
89 lines
2.1 KiB
Python
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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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src1 = np.array([
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[51.642,50.115],
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[57.617,49.990],
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[35.740,69.007],
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[51.157,89.050],
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[57.025,89.702]], dtype=np.float32)
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#<--left
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src2 = np.array([
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[45.031,50.118],
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[65.568,50.872],
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[39.677,68.111],
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[45.177,86.190],
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[64.246,86.758]], dtype=np.float32)
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#---frontal
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src3 = np.array([
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[39.730,51.138],
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[72.270,51.138],
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[56.000,68.493],
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[42.463,87.010],
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[69.537,87.010]], dtype=np.float32)
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#-->right
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src4 = np.array([
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[46.845,50.872],
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[67.382,50.118],
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[72.737,68.111],
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[48.167,86.758],
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[67.236,86.190]], dtype=np.float32)
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#-->right profile
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src5 = np.array([
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[54.796,49.990],
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[60.771,50.115],
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[76.673,69.007],
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[55.388,89.702],
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[61.257,89.050]], 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],
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[73.5318, 51.5014],
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[56.0252, 71.7366],
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[41.5493, 92.3655],
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[70.7299, 92.2041] ], 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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