Method read_img_list

sat/data_share.py:180–195

(self, img_path_list)

            random.shuffle(self.fut_traj_list)

    def read_img_list(self, img_path_list):
        video_size, fps, max_num_frames, skip_frms_num = \
            self.video_size, self.fps, self.max_num_frames, self.skip_frms_num
        
        img_path_list = img_path_list[skip_frms_num:]   # skip some frames
        
        tensor_frms = load_image_list_to_tensors(img_path_list)
        tensor_frms = torch.stack(tensor_frms, dim=0)  # T, H, W, C
        
        tensor_frms = pad_last_frame(
            tensor_frms, max_num_frames
        )  # the len of indices may be less than num_frames, due to round error\
        tensor_frms = tensor_frms.permute(0, 3, 1, 2)  # [T, H, W, C] -> [T, C, H, W]
        tensor_frms = resize_for_rectangle_crop(tensor_frms, video_size, reshape_mode=self.reshape_mode)
        tensor_frms = (tensor_frms - 127.5) / 127.5
        return max_num_frames, tensor_frms

    def __getitem__(self, index):
        while True: