|
|
import cv2
|
|
|
import numpy as np
|
|
|
from skimage import transform as trans
|
|
|
|
|
|
arcface_dst = 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)
|
|
|
|
|
|
|
|
|
def estimate_norm(lmk, image_size=112, mode='arcface'):
|
|
|
assert lmk.shape == (5, 2)
|
|
|
assert image_size % 112 == 0 or image_size % 128 == 0
|
|
|
if image_size % 112 == 0:
|
|
|
ratio = float(image_size) / 112.0
|
|
|
diff_x = 0
|
|
|
else:
|
|
|
ratio = float(image_size) / 128.0
|
|
|
diff_x = 8.0 * ratio
|
|
|
dst = arcface_dst * ratio
|
|
|
dst[:, 0] += diff_x
|
|
|
tform = trans.SimilarityTransform()
|
|
|
tform.estimate(lmk, dst)
|
|
|
M = tform.params[0:2, :]
|
|
|
return M
|
|
|
|
|
|
|
|
|
def norm_crop(img, landmark, image_size=112, mode='arcface'):
|
|
|
M = estimate_norm(landmark, image_size, mode)
|
|
|
warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
|
|
|
return warped
|
|
|
|
|
|
|
|
|
def norm_crop2(img, landmark, image_size=112, mode='arcface'):
|
|
|
M = estimate_norm(landmark, image_size, mode)
|
|
|
warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
|
|
|
return warped, M
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
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)
|
|
|
|
|
|
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])
|
|
|
|
|
|
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)
|
|
|
|
|
|
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)
|
|
|
|
