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datasets_4cor_img.py
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datasets_4cor_img.py
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# Data loading based on https://github.com/NVIDIA/flownet2-pytorch
import numpy as np
import torch
import torch.utils.data as data
import torchgeometry as tgm
import random
from glob import glob
import os.path as osp
import cv2
marginal = 32
patch_size = 128
class homo_dataset(data.Dataset):
def __init__(self):
self.is_test = False
self.init_seed = True
self.image_list_img1 = []
self.image_list_img2 = []
self.dataset=[]
self.colorjit = False
def __getitem__(self, index):
if not self.init_seed:
worker_info = torch.utils.data.get_worker_info()
if worker_info is not None:
torch.manual_seed(worker_info.id)
np.random.seed(worker_info.id)
random.seed(worker_info.id)
self.init_seed = True
img1 = cv2.imread(self.image_list_img1[index])
img2 = cv2.imread(self.image_list_img2[index])
if self.dataset=='mscoco':
img1 = cv2.resize(img1, (320, 240))
img2 = cv2.resize(img2, (320, 240))
(height, width, _) = img1.shape
y = random.randint(marginal, height - marginal - patch_size)
x = random.randint(marginal, width - marginal - patch_size)
top_left_point = (x, y)
bottom_right_point = (patch_size + x, patch_size + y)
perturbed_four_points_cord = []
top_left_point_cord = (x, y)
bottom_left_point_cord = (x, patch_size + y - 1)
bottom_right_point_cord = (patch_size + x - 1, patch_size + y - 1)
top_right_point_cord = (x + patch_size - 1, y)
four_points_cord = [top_left_point_cord, bottom_left_point_cord, bottom_right_point_cord, top_right_point_cord]
try:
perturbed_four_points_cord = []
for i in range(4):
t1 = random.randint(-marginal, marginal)
t2 = random.randint(-marginal, marginal)
perturbed_four_points_cord.append((four_points_cord[i][0] + t1,
four_points_cord[i][1] + t2))
y_grid, x_grid = np.mgrid[0:img1.shape[0], 0:img1.shape[1]]
point = np.vstack((x_grid.flatten(), y_grid.flatten())).transpose()
org = np.float32(four_points_cord)
dst = np.float32(perturbed_four_points_cord)
H = cv2.getPerspectiveTransform(org, dst)
H_inverse = np.linalg.inv(H)
except:
perturbed_four_points_cord = []
for i in range(4):
t1 = 32//(i+1)
t2 = -32//(i+1)
perturbed_four_points_cord.append((four_points_cord[i][0] + t1,
four_points_cord[i][1] + t2))
y_grid, x_grid = np.mgrid[0:img1.shape[0], 0:img1.shape[1]]
point = np.vstack((x_grid.flatten(), y_grid.flatten())).transpose()
org = np.float32(four_points_cord)
dst = np.float32(perturbed_four_points_cord)
H = cv2.getPerspectiveTransform(org, dst)
H_inverse = np.linalg.inv(H)
warped_image = cv2.warpPerspective(img2, H_inverse, (img1.shape[1], img1.shape[0]))
img_patch_ori = img1[top_left_point[1]:bottom_right_point[1], top_left_point[0]:bottom_right_point[0], :]
img_patch_pert = warped_image[top_left_point[1]:bottom_right_point[1], top_left_point[0]:bottom_right_point[0],:]
point_transformed_branch1 = cv2.perspectiveTransform(np.array([point], dtype=np.float64), H).squeeze()
diff_branch1 = point_transformed_branch1 - np.array(point, dtype=np.float64)
diff_x_branch1 = diff_branch1[:, 0]
diff_y_branch1 = diff_branch1[:, 1]
diff_x_branch1 = diff_x_branch1.reshape((img1.shape[0], img1.shape[1]))
diff_y_branch1 = diff_y_branch1.reshape((img1.shape[0], img1.shape[1]))
pf_patch_x_branch1 = diff_x_branch1[top_left_point[1]:bottom_right_point[1],
top_left_point[0]:bottom_right_point[0]]
pf_patch_y_branch1 = diff_y_branch1[top_left_point[1]:bottom_right_point[1],
top_left_point[0]:bottom_right_point[0]]
pf_patch = np.zeros((patch_size, patch_size, 2))
pf_patch[:, :, 0] = pf_patch_x_branch1
pf_patch[:, :, 1] = pf_patch_y_branch1
img_patch_ori = img_patch_ori[:, :, ::-1].copy()
img_patch_pert = img_patch_pert[:, :, ::-1].copy()
img1 = torch.from_numpy((img_patch_ori)).float().permute(2, 0, 1)
img2 = torch.from_numpy((img_patch_pert)).float().permute(2, 0, 1)
if self.colorjit:
img1 = composed_transform1(img1/255) * 255
img2 = composed_transform1(img2/255) * 255
flow = torch.from_numpy(pf_patch).permute(2, 0, 1).float()
### homo
four_point_org = torch.zeros((2, 2, 2))
four_point_org[:, 0, 0] = torch.Tensor([0, 0])
four_point_org[:, 0, 1] = torch.Tensor([128 - 1, 0])
four_point_org[:, 1, 0] = torch.Tensor([0, 128 - 1])
four_point_org[:, 1, 1] = torch.Tensor([128 - 1, 128 - 1])
four_point = torch.zeros((2, 2, 2))
four_point[:, 0, 0] = flow[:, 0, 0] + torch.Tensor([0, 0])
four_point[:, 0, 1] = flow[:, 0, -1] + torch.Tensor([128 - 1, 0])
four_point[:, 1, 0] = flow[:, -1, 0] + torch.Tensor([0, 128 - 1])
four_point[:, 1, 1] = flow[:, -1, -1] + torch.Tensor([128 - 1, 128 - 1])
four_point_org = four_point_org.flatten(1).permute(1, 0).unsqueeze(0)
four_point = four_point.flatten(1).permute(1, 0).unsqueeze(0)
H = tgm.get_perspective_transform(four_point_org, four_point)
H = H.squeeze()
return img2, img1, flow, H
class MYDATA(homo_dataset):
def __init__(self, split='train', dataset='msococo'):
super(MYDATA, self).__init__()
if split == 'train':
if dataset=='mscoco':
root_img1 = './mscoco2017/train2017'
root_img2 = './mscoco2017/train2017'
if dataset=='ggmap':
root_img1 = './GoogleMap/train2014_input'
root_img2 = './GoogleMap/train2014_template'
if dataset=='moveobj':
root_img1 = './moving_object/img_pair_train_new/img1'
root_img2 = './moving_object/img_pair_train_new/img2'
else:
if dataset=='mscoco':
root_img1 = './mscoco2017/test2017'
root_img2 = './mscoco2017/test2017'
if dataset=='ggmap':
root_img1 = './GoogleMap/val2014_input'
root_img2 = './GoogleMap/val2014_template'
if dataset=='moveobj':
root_img1 = './moving_object/img_pair_test_new/img1'
root_img2 = './moving_object/img_pair_test_new/img2'
self.colorjit = False
self.dataset = dataset
self.image_list_img1 = sorted(glob(osp.join(root_img1, '*.jpg')))
self.image_list_img2 = sorted(glob(osp.join(root_img2, '*.jpg')))
def __len__(self):
return int(len(self.image_list_img1))
def fetch_dataloader(args, split='train'):
if split == 'train':
train_dataset = MYDATA(split='train',dataset=args.dataset)
train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size,
pin_memory=True, shuffle=True, num_workers=8, drop_last=False)
print('Training with %d image pairs' % len(train_dataset))
else:
train_dataset = MYDATA(split='val',dataset=args.dataset)
train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size,
pin_memory=True, shuffle=True, num_workers=8, drop_last=False)
return train_loader