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Add dynamic video processing script

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This commit is contained in:
James Betker 2020-05-27 17:09:11 -06:00
parent f745be9dea
commit 41c1efbf56
2 changed files with 183 additions and 20 deletions
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41
codes/options/options.py
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@ -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():

162
codes/process_video.py Normal file
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@ -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