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142 lines
4.5 KiB
Python
142 lines
4.5 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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if image_size == 112:
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src = arcface_src
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else:
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src = float(image_size) / 112 * 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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def square_crop(im, S):
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if im.shape[0] > im.shape[1]:
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height = S
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width = int(float(im.shape[1]) / im.shape[0] * S)
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scale = float(S) / im.shape[0]
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else:
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width = S
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height = int(float(im.shape[0]) / im.shape[1] * S)
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scale = float(S) / im.shape[1]
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resized_im = cv2.resize(im, (width, height))
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det_im = np.zeros((S, S, 3), dtype=np.uint8)
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det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im
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return det_im, scale
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def transform(data, center, output_size, scale, rotation):
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scale_ratio = scale
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rot = float(rotation) * np.pi / 180.0
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#translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
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t1 = trans.SimilarityTransform(scale=scale_ratio)
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cx = center[0] * scale_ratio
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cy = center[1] * scale_ratio
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t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
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t3 = trans.SimilarityTransform(rotation=rot)
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t4 = trans.SimilarityTransform(translation=(output_size / 2,
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output_size / 2))
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t = t1 + t2 + t3 + t4
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M = t.params[0:2]
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cropped = cv2.warpAffine(data,
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M, (output_size, output_size),
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borderValue=0.0)
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return cropped, M
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def trans_points2d(pts, M):
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new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
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for i in range(pts.shape[0]):
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pt = pts[i]
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new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
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new_pt = np.dot(M, new_pt)
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#print('new_pt', new_pt.shape, new_pt)
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new_pts[i] = new_pt[0:2]
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return new_pts
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def trans_points3d(pts, M):
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scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
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#print(scale)
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new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
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for i in range(pts.shape[0]):
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pt = pts[i]
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new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
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new_pt = np.dot(M, new_pt)
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#print('new_pt', new_pt.shape, new_pt)
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new_pts[i][0:2] = new_pt[0:2]
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new_pts[i][2] = pts[i][2] * scale
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return new_pts
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def trans_points(pts, M):
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if pts.shape[1] == 2:
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return trans_points2d(pts, M)
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else:
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return trans_points3d(pts, M)
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