Spaces:
Running
Running
File size: 2,068 Bytes
67a9b5d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
import cv2
import numpy as np
def get_similarity_transform(src_pts, dst_pts):
"""Get similarity transform matrix from src_pts to dst_pts
Args:
src_pts: Kx2 np.array
source points matrix, each row is a pair of coordinates (x, y)
dst_pts: Kx2 np.array
destination points matrix, each row is a pair of coordinates (x, y)
Returns:
xform_matrix: 3x3 np.array
transform matrix from src_pts to dst_pts
"""
src_pts = np.asarray(src_pts)
dst_pts = np.asarray(dst_pts)
assert src_pts.shape == dst_pts.shape
assert (src_pts.ndim == 2) and (src_pts.shape[-1] == 2)
npts = src_pts.shape[0]
src_x = src_pts[:, 0].reshape((-1, 1))
src_y = src_pts[:, 1].reshape((-1, 1))
tmp1 = np.hstack((src_x, -src_y, np.ones((npts, 1)), np.zeros((npts, 1))))
tmp2 = np.hstack((src_y, src_x, np.zeros((npts, 1)), np.ones((npts, 1))))
A = np.vstack((tmp1, tmp2))
dst_x = dst_pts[:, 0].reshape((-1, 1))
dst_y = dst_pts[:, 1].reshape((-1, 1))
b = np.vstack((dst_x, dst_y))
x = np.linalg.lstsq(A, b, rcond=-1)[0]
x = np.squeeze(x)
sc, ss, tx, ty = x[0], x[1], x[2], x[3]
xform_matrix = np.array([
[sc, -ss, tx],
[ss, sc, ty],
[ 0, 0, 1]
])
return xform_matrix
def align_and_crop(image, landmarks, std_landmarks, align_size,
border_value=0, return_transform_matrix=False):
landmarks = np.asarray(landmarks)
std_landmarks = np.asarray(std_landmarks)
xform_matrix = get_similarity_transform(landmarks, std_landmarks)
landmarks_ex = np.pad(landmarks, ((0,0),(0,1)), mode='constant', constant_values=1)
dst_landmarks = np.dot(landmarks_ex, xform_matrix[:2,:].T)
dst_image = cv2.warpAffine(image, xform_matrix[:2,:], dsize=align_size,
borderValue=border_value)
if return_transform_matrix:
return dst_image, dst_landmarks, xform_matrix
else:
return dst_image, dst_landmarks
|