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generate_visual_gradient_map.py
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generate_visual_gradient_map.py
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import cv2
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Get_gradient(nn.Module):
def __init__(self):
super(Get_gradient, self).__init__()
kernel_v = [[0, -1, 0],
[0, 0, 0],
[0, 1, 0]]
kernel_h = [[0, 0, 0],
[-1, 0, 1],
[0, 0, 0]]
kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
self.weight_h = nn.Parameter(data = kernel_h, requires_grad = False).cuda()
self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False).cuda()
def forward(self, x):
x0 = x[:, 0]
x1 = x[:, 1]
x2 = x[:, 2]
x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding=2)
x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding=2)
x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding=2)
x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding=2)
x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding=2)
x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding=2)
x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-6)
x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-6)
x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-6)
x_mean = (x0+x1+x2)/3
x = torch.cat([x_mean,x_mean,x_mean],dim=1)
return x
class getgradient_function(nn.Module):
def __init__(self):
super(getgradient_function, self).__init__()
self.gradient = Get_gradient()
def forward(self,x):
x = x.unsqueeze(0)
out = self.gradient(x)
return out
def main():
getgradient = getgradient_function()
method = 'DPT-X2' # DPT-X4 HR
input_root_path = '/media/wsz/FAB9B702EAEE0235/szwang/test/DPT_private/SRimages'
save_root_path = '/media/wsz/FAB9B702EAEE0235/szwang/test/DPT_private/GRAimages'
allresults_path = os.path.join(input_root_path, method)
gradresults_path = os.path.join(save_root_path, method)
if os.path.exists(gradresults_path):
print('next')
else:
os.mkdir(gradresults_path)
datset_names = sorted(os.listdir(allresults_path))
dataset_num = len(datset_names)
for i in range(dataset_num):
dataset_result_path = os.path.join(allresults_path,datset_names[i])
save_result_path = os.path.join(gradresults_path,datset_names[i])
if os.path.exists(save_result_path):
print('next')
else:
os.mkdir(save_result_path)
scene_names = sorted(os.listdir(dataset_result_path))
scene_num = len(scene_names)
for j in range(scene_num):
img_path = os.path.join(dataset_result_path,scene_names[j])
grad_path = os.path.join(save_result_path,scene_names[j])
if os.path.exists(grad_path):
print('next')
else:
os.mkdir(grad_path)
img_names = sorted(os.listdir(img_path))
img_num = len(img_names)
for k in range(img_num):
image_path = os.path.join(img_path, img_names[k])
gradient_path = os.path.join(grad_path, img_names[k])
img = cv2.imread(image_path)
img = img.astype(np.float32) / 255.
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
tensor_img = torch.from_numpy(np.transpose(img, (2, 0, 1))).cuda()
tensor_img = tensor_img.float()
gradient = getgradient(tensor_img)
gradient = gradient.squeeze(0).cpu()
gradient = np.transpose(gradient.numpy(), (1, 2, 0))
gradient = gradient * 255.
cv2.imwrite(gradient_path,gradient)
if __name__ == "__main__":
main()