def __getitem__(self, index):
img_path = self.filelist[index]
img = convert_to_rgb(io.imread(img_path))
img = resize_max_length(img, 240)
img = img[:, :, 0].astype(np.float32)
img /= 255.0
return img
def read_img(img_path, scale):
img = convert_to_rgb(io.imread(img_path))
oh, ow = img.shape[:2]
if isinstance(scale, int):
img = resize_max_length(img, scale)
elif isinstance(scale, tuple):
img = cv2.resize(img, scale)
h, w = img.shape[:2]
scale_h, scale_w = h / oh, w / ow
return img, scale_h, scale_w
def __getitem__(self, index):
img_path = self.img_paths[index]
img = io.imread(img_path)
img = convert_to_rgb(img)
# img0 = img
img0 = resize_max_length(img, 640)
img1, H = homography_adaption(img0, angle=self.angle)
pix_pos0, pix_pos1 = sample_ground_truth(img0, H)
# img = draw_corspd_region(img0, img1, H)
# import matplotlib.pyplot as plt
# plt.imshow(img)
# plt.show()
if self.transforms is not None:
img0, pix_pos0 = self.transforms(img0, pix_pos0)
img1, pix_pos1 = self.transforms(img1, pix_pos1)
pix_pos2 = sample_negative(img1, pix_pos1)
img0 = torch.tensor(img0).permute(2, 0, 1).float()
img1 = torch.tensor(img1).permute(2, 0, 1).float()
# img = draw_triplet(img0, pix_pos0, img1, pix_pos1, img1, pix_pos2)
# import matplotlib.pyplot as plt
# plt.imshow(img)
# plt.show()
# return
pix_pos0 = torch.tensor(pix_pos0).float()
pix_pos1 = torch.tensor(pix_pos1).float()
target = dict(
kps0=pix_pos0,
kps1=pix_pos1,
kps2=pix_pos2,
H=H,
)
return img0, img1, target, index