@ amirid
Hi, i have solved it.Code is below.
import cv2
import torch.nn.functional as F
import skimage.transform as trans
import numpy as np
def convert_image_np(inp):
“”“Convert a Tensor to numpy image.”""
inp = inp.numpy().transpose((1, 2, 0))
inp = (inp*255).astype(np.uint8)
return inp
def param2theta(param, w, h):
param = np.linalg.inv(param)
theta = np.zeros([2,3])
theta[0,0] = param[0,0]
theta[0,1] = param[0,1]*h/w
theta[0,2] = param[0,2]*2/w + param[0,0] + param[0,1] - 1
theta[1,0] = param[1,0]*w/h
theta[1,1] = param[1,1]
theta[1,2] = param[1,2]*2/h + param[1,0] + param[1,1] - 1
return theta
tr = trans.estimate_transform(‘affine’, src=src, dst=dst)
M = tr.params[0:2,:]
img = cv2.warpAffine(image,M,(w,h))
theta = param2theta(tr.params, w, h)
image = Variable(image).unsqueeze(0).cuda()
theta = Variable(theta).unsqueeze(0).cuda()
grid = F.affine_grid(theta,image.size())
img_ = F.grid_sample(image,grid)
img_ = convert_image_np(img_.data.cpu().squeeze(0))
The visualizations of img and img_ should be same. Hope it can help you!