diff --git a/codes/options/options.py b/codes/options/options.py
index 935fc8a4..090addb1 100644
--- a/codes/options/options.py
+++ b/codes/options/options.py
@@ -20,26 +20,27 @@ def parse(opt_path, is_train=True):
scale = opt['scale']
# datasets
- for phase, dataset in opt['datasets'].items():
- phase = phase.split('_')[0]
- dataset['phase'] = phase
- if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample':
- dataset['scale'] = scale
- is_lmdb = False
- ''' LMDB is not supported at this point with the mods I've been making.
- if dataset.get('dataroot_GT', None) is not None:
- dataset['dataroot_GT'] = osp.expanduser(dataset['dataroot_GT'])
- if dataset['dataroot_GT'].endswith('lmdb'):
- is_lmdb = True
- if dataset.get('dataroot_LQ', None) is not None:
- dataset['dataroot_LQ'] = osp.expanduser(dataset['dataroot_LQ'])
- if dataset['dataroot_LQ'].endswith('lmdb'):
- is_lmdb = True
- '''
- dataset['data_type'] = 'lmdb' if is_lmdb else 'img'
- if dataset['mode'].endswith('mc'): # for memcached
- dataset['data_type'] = 'mc'
- dataset['mode'] = dataset['mode'].replace('_mc', '')
+ if 'datasets' in opt.keys():
+ for phase, dataset in opt['datasets'].items():
+ phase = phase.split('_')[0]
+ dataset['phase'] = phase
+ if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample':
+ dataset['scale'] = scale
+ is_lmdb = False
+ ''' LMDB is not supported at this point with the mods I've been making.
+ if dataset.get('dataroot_GT', None) is not None:
+ dataset['dataroot_GT'] = osp.expanduser(dataset['dataroot_GT'])
+ if dataset['dataroot_GT'].endswith('lmdb'):
+ is_lmdb = True
+ if dataset.get('dataroot_LQ', None) is not None:
+ dataset['dataroot_LQ'] = osp.expanduser(dataset['dataroot_LQ'])
+ if dataset['dataroot_LQ'].endswith('lmdb'):
+ is_lmdb = True
+ '''
+ dataset['data_type'] = 'lmdb' if is_lmdb else 'img'
+ if dataset['mode'].endswith('mc'): # for memcached
+ dataset['data_type'] = 'mc'
+ dataset['mode'] = dataset['mode'].replace('_mc', '')
# path
for key, path in opt['path'].items():
diff --git a/codes/process_video.py b/codes/process_video.py
new file mode 100644
index 00000000..f442a334
--- /dev/null
+++ b/codes/process_video.py
@@ -0,0 +1,162 @@
+import argparse
+import argparse
+import logging
+import os.path as osp
+import os
+import subprocess
+import time
+
+import torch
+import torch.utils.data as data
+import torchvision.transforms.functional as F
+from PIL import Image
+from tqdm import tqdm
+
+import options.options as option
+import utils.util as util
+from data import create_dataloader
+from models import create_model
+import glob
+
+
+class FfmpegBackedVideoDataset(data.Dataset):
+ '''Pulls frames from a video one at a time using FFMPEG.'''
+
+ def __init__(self, opt, working_dir):
+ super(FfmpegBackedVideoDataset, self).__init__()
+ self.opt = opt
+ self.video = self.opt['video_file']
+ self.working_dir = working_dir
+ self.frame_rate = self.opt['frame_rate']
+ self.start_at = self.opt['start_at_seconds']
+ self.end_at = self.opt['end_at_seconds']
+ self.frame_count = (self.end_at - self.start_at) * self.frame_rate
+ # The number of (original) video frames that will be stored on the filesystem at a time.
+ self.max_working_files = 20
+
+ self.data_type = self.opt['data_type']
+ self.vertical_splits = self.opt['vertical_splits'] if 'vertical_splits' in opt.keys() else 1
+
+ def get_time_for_it(self, it):
+ secs = it / self.frame_rate + self.start_at
+ mins = int(secs / 60)
+ secs = secs - (mins * 60)
+ return '%02d:%06.3f' % (mins, secs)
+
+ def __getitem__(self, index):
+ if self.vertical_splits > 0:
+ actual_index = int(index / self.vertical_splits)
+ else:
+ actual_index = index
+
+ # Extract the frame. Command template: `ffmpeg -ss 17:00.0323 -i <video file>.mp4 -vframes 1 destination.png`
+ working_file_name = osp.join(self.working_dir, "working_%d.png" % (actual_index % self.max_working_files,))
+ vid_time = self.get_time_for_it(actual_index)
+ ffmpeg_args = ['ffmpeg', '-y', '-ss', vid_time, '-i', self.video, '-vframes', '1', working_file_name]
+ process = subprocess.Popen(ffmpeg_args, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL)
+ process.wait()
+
+ # get LQ image
+ LQ_path = working_file_name
+ img_LQ = Image.open(LQ_path)
+ split_index = (index % self.vertical_splits)
+ if self.vertical_splits > 0:
+ w, h = img_LQ.size
+ w_per_split = int(w / self.vertical_splits)
+ left = w_per_split * split_index
+ img_LQ = F.crop(img_LQ, 0, left, h, w_per_split)
+ img_LQ = F.to_tensor(img_LQ)
+
+ return {'LQ': img_LQ}
+
+ def __len__(self):
+ return self.frame_count * self.vertical_splits
+
+
+if __name__ == "__main__":
+ #### options
+ torch.backends.cudnn.benchmark = True
+ want_just_images = True
+ parser = argparse.ArgumentParser()
+ parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='../options/use_video_upsample.yml')
+ opt = option.parse(parser.parse_args().opt, is_train=False)
+ opt = option.dict_to_nonedict(opt)
+
+ util.mkdirs(
+ (path for key, path in opt['path'].items()
+ if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
+ util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
+ screen=True, tofile=True)
+ logger = logging.getLogger('base')
+ logger.info(option.dict2str(opt))
+
+ #### Create test dataset and dataloader
+ test_loaders = []
+
+ test_set = FfmpegBackedVideoDataset(opt['dataset'], opt['path']['results_root'])
+ test_loader = create_dataloader(test_set, opt['dataset'])
+ logger.info('Number of test images in [{:s}]: {:d}'.format(opt['dataset']['name'], len(test_set)))
+ test_loaders.append(test_loader)
+
+ model = create_model(opt)
+ test_set_name = test_loader.dataset.opt['name']
+ logger.info('\nTesting [{:s}]...'.format(test_set_name))
+ test_start_time = time.time()
+ dataset_dir = osp.join(opt['path']['results_root'], test_set_name)
+ util.mkdir(dataset_dir)
+
+ frame_counter = 0
+ frames_per_vid = opt['frames_per_mini_vid']
+ minivid_bitrate = opt['mini_vid_bitrate']
+ vid_counter = 0
+
+ tq = tqdm(test_loader)
+ for data in tq:
+ need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
+ model.feed_data(data, need_GT=need_GT)
+ model.test()
+
+ if isinstance(model.fake_H, tuple):
+ visuals = model.fake_H[0].detach().float().cpu()
+ else:
+ visuals = model.fake_H.detach().float().cpu()
+ for i in range(visuals.shape[0]):
+ sr_img = util.tensor2img(visuals[i]) # uint8
+
+ # save images
+ save_img_path = osp.join(dataset_dir, '%08d.png' % (frame_counter,))
+ util.save_img(sr_img, save_img_path)
+ frame_counter += 1
+
+
+ if frame_counter % frames_per_vid == 0:
+ print("Encoding minivid %d.." % (vid_counter,))
+ # Perform stitching.
+ num_splits = opt['dataset']['vertical_splits'] if 'vertical_splits' in opt['dataset'].keys() else 1
+ if num_splits > 1:
+ imgs = glob.glob(osp.join(dataset_dir, "*.png"))
+ procs = []
+ src_imgs_path = osp.join(dataset_dir, "joined")
+ os.makedirs(src_imgs_path, exist_ok=True)
+ for i in range(int(frames_per_vid / num_splits)):
+ to_join = [imgs[j] for j in range(i * num_splits, i * num_splits + num_splits)]
+ cmd = ['magick', 'convert'] + to_join + ['+append', osp.join(src_imgs_path, "%08d.png" % (i,))]
+ procs.append(subprocess.Popen(cmd))
+ for p in procs:
+ p.wait()
+ else:
+ src_imgs_path = dataset_dir
+
+ # Encoding command line:
+ # ffmpeg -r 29.97 -f image2 -start_number 0 -i %08d.png -i ../wha_audio.mp3 -vcodec mpeg4 -vb 80M -r 29.97 -q:v 0 test.avi
+ cmd = ['ffmpeg', '-y', '-r', str(opt['dataset']['frame_rate']), '-f', 'image2', '-start_number', '0', '-i', osp.join(src_imgs_path, "%08d.png"),
+ '-vcodec', 'mpeg4', '-vb', minivid_bitrate, '-r', str(opt['dataset']['frame_rate']), '-q:v', '0', osp.join(dataset_dir, "mini_%06d.mp4" % (vid_counter,))]
+ process = subprocess.Popen(cmd, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL)
+ process.wait()
+ vid_counter += 1
+ frame_counter = 0
+ print("Done.")
+
+
+ if want_just_images:
+ continue
\ No newline at end of file