Open zqq-judy opened 3 years ago
Hello!
Q1、In the 8_generate_posmap_300WLP.py file, does the following code perform affine transformation and calculate the tfrom matrix?
kpt = image_vertices[bfm.kpt_ind, :].astype(np.int32) left = np.min(kpt[:, 0]) right = np.max(kpt[:, 0]) top = np.min(kpt[:, 1]) bottom = np.max(kpt[:, 1]) center = np.array([right - (right - left) / 2.0 ,bottom - (bottom - top) / 2.0]) old_size = (right - left + bottom - top)/2 size = int(old_size*1.5) # random pertube. you can change the numbers marg = old_size*0.1 t_xv = np.random.rand()*marg*2 - marg t_y = np.random.rand()*marg*2 - marg center[0] = center[0]+t_x; center[1] = center[1]+t_y size = size*(np.random.rand()*0.2 + 0.9) # crop and record the transform parameters src_pts = np.array([[center[0]-size/2, center[1]-size/2], [center[0] - size/2, center[1]+size/2], [center[0]+size/2, center[1]-size/2]]) DST_PTS = np.array([[0, 0], [0, image_h - 1], [image_w - 1, 0]]) tform = skimage.transform.estimate_transform('similarity', src_pts, DST_PTS)
kpt = image_vertices[bfm.kpt_ind, :].astype(np.int32)
left = np.min(kpt[:, 0])
right = np.max(kpt[:, 0])
top = np.min(kpt[:, 1])
bottom = np.max(kpt[:, 1])
center = np.array([right - (right - left) / 2.0 ,bottom - (bottom - top) / 2.0])
old_size = (right - left + bottom - top)/2
size = int(old_size*1.5)
# random pertube. you can change the numbers
marg = old_size*0.1
t_xv = np.random.rand()*marg*2 - marg
t_y = np.random.rand()*marg*2 - marg
center[0] = center[0]+t_x; center[1] = center[1]+t_y
size = size*(np.random.rand()*0.2 + 0.9)
# crop and record the transform parameters
src_pts = np.array([[center[0]-size/2, center[1]-size/2], [center[0] - size/2, center[1]+size/2], [center[0]+size/2, center[1]-size/2]])
DST_PTS = np.array([[0, 0], [0, image_h - 1], [image_w - 1, 0]])
tform = skimage.transform.estimate_transform('similarity', src_pts, DST_PTS)
Q2、So how to calculate the inverse_tfrom matrix of the inverse affine transformation? Thanks!
Hello!
Q1、In the 8_generate_posmap_300WLP.py file, does the following code perform affine transformation and calculate the tfrom matrix?
kpt = image_vertices[bfm.kpt_ind, :].astype(np.int32)left = np.min(kpt[:, 0])right = np.max(kpt[:, 0])top = np.min(kpt[:, 1])bottom = np.max(kpt[:, 1])center = np.array([right - (right - left) / 2.0 ,bottom - (bottom - top) / 2.0])old_size = (right - left + bottom - top)/2size = int(old_size*1.5)# random pertube. you can change the numbersmarg = old_size*0.1t_xv = np.random.rand()*marg*2 - margt_y = np.random.rand()*marg*2 - margcenter[0] = center[0]+t_x; center[1] = center[1]+t_ysize = size*(np.random.rand()*0.2 + 0.9)# crop and record the transform parameterssrc_pts = np.array([[center[0]-size/2, center[1]-size/2], [center[0] - size/2, center[1]+size/2], [center[0]+size/2, center[1]-size/2]])DST_PTS = np.array([[0, 0], [0, image_h - 1], [image_w - 1, 0]])tform = skimage.transform.estimate_transform('similarity', src_pts, DST_PTS)Q2、So how to calculate the inverse_tfrom matrix of the inverse affine transformation? Thanks!