| import os |
|
|
| from data import common |
|
|
| import cv2 |
| import numpy as np |
| import imageio |
|
|
| import torch |
| import torch.utils.data as data |
|
|
| class Video(data.Dataset): |
| def __init__(self, args, name='Video', train=False, benchmark=False): |
| self.args = args |
| self.name = name |
| self.scale = args.scale |
| self.idx_scale = 0 |
| self.train = False |
| self.do_eval = False |
| self.benchmark = benchmark |
|
|
| self.filename, _ = os.path.splitext(os.path.basename(args.dir_demo)) |
| self.vidcap = cv2.VideoCapture(args.dir_demo) |
| self.n_frames = 0 |
| self.total_frames = int(self.vidcap.get(cv2.CAP_PROP_FRAME_COUNT)) |
|
|
| def __getitem__(self, idx): |
| success, lr = self.vidcap.read() |
| if success: |
| self.n_frames += 1 |
| lr, = common.set_channel(lr, n_channels=self.args.n_colors) |
| lr_t, = common.np2Tensor(lr, rgb_range=self.args.rgb_range) |
|
|
| return lr_t, -1, '{}_{:0>5}'.format(self.filename, self.n_frames) |
| else: |
| vidcap.release() |
| return None |
|
|
| def __len__(self): |
| return self.total_frames |
|
|
| def set_scale(self, idx_scale): |
| self.idx_scale = idx_scale |
|
|
|
|
