Delete face_align.py.txt

face_align.py.txt

@@ -1,141 +0,0 @@
-import cv2
-import numpy as np
-from skimage import transform as trans
-
-src1 = np.array([[51.642, 50.115], [57.617, 49.990], [35.740, 69.007],
-                 [51.157, 89.050], [57.025, 89.702]],
-                dtype=np.float32)
-#<--left
-src2 = np.array([[45.031, 50.118], [65.568, 50.872], [39.677, 68.111],
-                 [45.177, 86.190], [64.246, 86.758]],
-                dtype=np.float32)
-
-#---frontal
-src3 = np.array([[39.730, 51.138], [72.270, 51.138], [56.000, 68.493],
-                 [42.463, 87.010], [69.537, 87.010]],
-                dtype=np.float32)
-
-#-->right
-src4 = np.array([[46.845, 50.872], [67.382, 50.118], [72.737, 68.111],
-                 [48.167, 86.758], [67.236, 86.190]],
-                dtype=np.float32)
-
-#-->right profile
-src5 = np.array([[54.796, 49.990], [60.771, 50.115], [76.673, 69.007],
-                 [55.388, 89.702], [61.257, 89.050]],
-                dtype=np.float32)
-
-src = np.array([src1, src2, src3, src4, src5])
-src_map = {112: src, 224: src * 2}
-
-arcface_src = np.array(
-    [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
-     [41.5493, 92.3655], [70.7299, 92.2041]],
-    dtype=np.float32)
-
-arcface_src = np.expand_dims(arcface_src, axis=0)
-
-# In[66]:
-
-
-# lmk is prediction; src is template
-def estimate_norm(lmk, image_size=112, mode='arcface'):
-    assert lmk.shape == (5, 2)
-    tform = trans.SimilarityTransform()
-    lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
-    min_M = []
-    min_index = []
-    min_error = float('inf')
-    if mode == 'arcface':
-        if image_size == 112:
-            src = arcface_src
-        else:
-            src = float(image_size) / 112 * arcface_src
-    else:
-        src = src_map[image_size]
-    for i in np.arange(src.shape[0]):
-        tform.estimate(lmk, src[i])
-        M = tform.params[0:2, :]
-        results = np.dot(M, lmk_tran.T)
-        results = results.T
-        error = np.sum(np.sqrt(np.sum((results - src[i])**2, axis=1)))
-        #         print(error)
-        if error < min_error:
-            min_error = error
-            min_M = M
-            min_index = i
-    return min_M, min_index
-
-
-def norm_crop(img, landmark, image_size=112, mode='arcface'):
-    M, pose_index = estimate_norm(landmark, image_size, mode)
-    warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
-    return warped
-
-def square_crop(im, S):
-    if im.shape[0] > im.shape[1]:
-        height = S
-        width = int(float(im.shape[1]) / im.shape[0] * S)
-        scale = float(S) / im.shape[0]
-    else:
-        width = S
-        height = int(float(im.shape[0]) / im.shape[1] * S)
-        scale = float(S) / im.shape[1]
-    resized_im = cv2.resize(im, (width, height))
-    det_im = np.zeros((S, S, 3), dtype=np.uint8)
-    det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im
-    return det_im, scale
-
-
-def transform(data, center, output_size, scale, rotation):
-    scale_ratio = scale
-    rot = float(rotation) * np.pi / 180.0
-    #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
-    t1 = trans.SimilarityTransform(scale=scale_ratio)
-    cx = center[0] * scale_ratio
-    cy = center[1] * scale_ratio
-    t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
-    t3 = trans.SimilarityTransform(rotation=rot)
-    t4 = trans.SimilarityTransform(translation=(output_size / 2,
-                                                output_size / 2))
-    t = t1 + t2 + t3 + t4
-    M = t.params[0:2]
-    cropped = cv2.warpAffine(data,
-                             M, (output_size, output_size),
-                             borderValue=0.0)
-    return cropped, M
-
-
-def trans_points2d(pts, M):
-    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
-    for i in range(pts.shape[0]):
-        pt = pts[i]
-        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
-        new_pt = np.dot(M, new_pt)
-        #print('new_pt', new_pt.shape, new_pt)
-        new_pts[i] = new_pt[0:2]
-
-    return new_pts
-
-
-def trans_points3d(pts, M):
-    scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
-    #print(scale)
-    new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
-    for i in range(pts.shape[0]):
-        pt = pts[i]
-        new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
-        new_pt = np.dot(M, new_pt)
-        #print('new_pt', new_pt.shape, new_pt)
-        new_pts[i][0:2] = new_pt[0:2]
-        new_pts[i][2] = pts[i][2] * scale
-
-    return new_pts
-
-
-def trans_points(pts, M):
-    if pts.shape[1] == 2:
-        return trans_points2d(pts, M)
-    else:
-        return trans_points3d(pts, M)
-