217 lines
9.1 KiB
Python
217 lines
9.1 KiB
Python
import argparse
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import logging
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import os
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import os.path as osp
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import subprocess
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import time
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import torch
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import torch.utils.data as data
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import torchvision.transforms.functional as F
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from PIL import Image
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from tqdm import tqdm
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from trainer.ExtensibleTrainer import ExtensibleTrainer
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from utils import options as option
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import utils.util as util
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from data import create_dataloader
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class FfmpegBackedVideoDataset(data.Dataset):
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'''Pulls frames from a video one at a time using FFMPEG.'''
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def __init__(self, opt, working_dir):
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super(FfmpegBackedVideoDataset, self).__init__()
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self.opt = opt
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self.video = self.opt['video_file']
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self.working_dir = working_dir
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self.frame_rate = self.opt['frame_rate']
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self.start_at = self.opt['start_at_seconds']
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self.end_at = self.opt['end_at_seconds']
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self.force_multiple = self.opt['force_multiple']
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self.frame_count = (self.end_at - self.start_at) * self.frame_rate
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# The number of (original) video frames that will be stored on the filesystem at a time.
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self.max_working_files = 20
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self.data_type = self.opt['data_type']
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self.vertical_splits = self.opt['vertical_splits'] if 'vertical_splits' in opt.keys() else 1
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def get_time_for_it(self, it):
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secs = it / self.frame_rate + self.start_at
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mins = int(secs / 60)
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hours = int(mins / 60)
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secs = secs - (mins * 60) - (hours * 3600)
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mins = mins % 60
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return '%02d:%02d:%06.3f' % (hours, mins, secs)
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def __getitem__(self, index):
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if self.vertical_splits > 0:
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actual_index = int(index / self.vertical_splits)
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else:
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actual_index = index
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# Extract the frame. Command template: `ffmpeg -ss 17:00.0323 -i <video file>.mp4 -vframes 1 destination.png`
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working_file_name = osp.join(self.working_dir, "working_%d.png" % (actual_index % self.max_working_files,))
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vid_time = self.get_time_for_it(actual_index)
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ffmpeg_args = ['ffmpeg', '-y', '-ss', vid_time, '-i', self.video, '-vframes', '1', working_file_name]
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process = subprocess.Popen(ffmpeg_args, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL)
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process.wait()
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# get LQ image
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LQ_path = working_file_name
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img_LQ = Image.open(LQ_path)
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split_index = (index % self.vertical_splits)
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if self.vertical_splits > 0:
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w, h = img_LQ.size
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w_per_split = int(w / self.vertical_splits)
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left = w_per_split * split_index
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img_LQ = F.crop(img_LQ, 0, left, h, w_per_split)
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img_LQ = F.to_tensor(img_LQ)
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mask = torch.ones(1, img_LQ.shape[1], img_LQ.shape[2])
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ref = torch.cat([img_LQ, mask], dim=0)
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if self.force_multiple > 1:
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assert self.vertical_splits <= 1 # This is not compatible with vertical splits for now.
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c, h, w = img_LQ.shape
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h_, w_ = h, w
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height_removed = h % self.force_multiple
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width_removed = w % self.force_multiple
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if height_removed != 0:
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h_ = self.force_multiple * ((h // self.force_multiple) + 1)
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if width_removed != 0:
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w_ = self.force_multiple * ((w // self.force_multiple) + 1)
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lq_template = torch.zeros(c,h_,w_)
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lq_template[:,:h,:w] = img_LQ
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ref_template = torch.zeros(c,h_,w_)
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ref_template[:,:h,:w] = img_LQ
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img_LQ = lq_template
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ref = ref_template
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return {'lq': img_LQ, 'lq_fullsize_ref': ref,
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'lq_center': torch.tensor([img_LQ.shape[1] // 2, img_LQ.shape[2] // 2], dtype=torch.long) }
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def __len__(self):
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return self.frame_count * self.vertical_splits
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def merge_images(files, output_path):
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"""Merges several image files together across the vertical axis
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"""
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images = [Image.open(f) for f in files]
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w, h = images[0].size
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result_width = w * len(images)
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result_height = h
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result = Image.new('RGB', (result_width, result_height))
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for i in range(len(images)):
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result.paste(im=images[i], box=(i * w, 0))
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result.save(output_path)
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if __name__ == "__main__":
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#### options
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torch.backends.cudnn.benchmark = True
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want_just_images = True
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/use_video_upsample.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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util.mkdirs(
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(path for key, path in opt['path'].items()
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if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
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util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
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screen=True, tofile=True)
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logger = logging.getLogger('base')
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logger.info(option.dict2str(opt))
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util.loaded_options = opt
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#### Create test dataset and dataloader
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test_loaders = []
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test_set = FfmpegBackedVideoDataset(opt['dataset'], opt['path']['results_root'])
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test_loader = create_dataloader(test_set, opt['dataset'])
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logger.info('Number of test images in [{:s}]: {:d}'.format(opt['dataset']['name'], len(test_set)))
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test_loaders.append(test_loader)
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model = ExtensibleTrainer(opt)
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test_set_name = test_loader.dataset.opt['name']
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logger.info('\nTesting [{:s}]...'.format(test_set_name))
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test_start_time = time.time()
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dataset_dir = osp.join(opt['path']['results_root'], test_set_name)
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util.mkdir(dataset_dir)
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frame_counter = 0
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frames_per_vid = opt['frames_per_mini_vid']
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minivid_crf = opt['minivid_crf']
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vid_output = opt['mini_vid_output_folder'] if 'mini_vid_output_folder' in opt.keys() else dataset_dir
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vid_counter = opt['minivid_start_no'] if 'minivid_start_no' in opt.keys() else 0
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img_index = opt['generator_img_index']
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recurrent_mode = opt['recurrent_mode']
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if recurrent_mode:
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assert opt['dataset']['batch_size'] == 1 # Can only do 1 frame at a time in recurrent mode, by definition.
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scale = opt['scale']
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first_frame = True
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ffmpeg_proc = None
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tq = tqdm(test_loader)
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for data in tq:
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need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True
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if recurrent_mode and first_frame:
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b, c, h, w = data['lq'].shape
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recurrent_entry = torch.zeros((b,c,h*scale,w*scale), device=data['lq'].device)
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# Optionally swap out the 'generator' for the first frame to create a better image that the recurrent generator works off of.
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if 'recurrent_hr_generator' in opt.keys():
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recurrent_gen = model.env['generators']['generator']
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model.env['generators']['generator'] = model.env['generators'][opt['recurrent_hr_generator']]
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first_frame = False
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if recurrent_mode:
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data['recurrent'] = recurrent_entry
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model.feed_data(data, 0, need_GT=need_GT)
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model.test()
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visuals = model.get_current_visuals()['rlt']
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if recurrent_mode:
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recurrent_entry = visuals
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visuals = visuals.cpu().float()
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for i in range(visuals.shape[0]):
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sr_img = util.tensor2img(visuals[i]) # uint8
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# save images
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save_img_path = osp.join(dataset_dir, '%08d.png' % (frame_counter,))
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util.save_img(sr_img, save_img_path)
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frame_counter += 1
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if frame_counter % frames_per_vid == 0:
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if ffmpeg_proc is not None:
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print("Waiting for last encode..")
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ffmpeg_proc.wait()
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print("Encoding minivid %d.." % (vid_counter,))
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# Perform stitching.
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num_splits = opt['dataset']['vertical_splits'] if 'vertical_splits' in opt['dataset'].keys() else 1
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if num_splits > 1:
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procs = []
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src_imgs_path = osp.join(dataset_dir, "joined")
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os.makedirs(src_imgs_path, exist_ok=True)
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for i in range(int(frames_per_vid / num_splits)):
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to_join = [osp.join(dataset_dir, "%08d.png" % (j,)) for j in range(i * num_splits, i * num_splits + num_splits)]
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merge_images(to_join, osp.join(src_imgs_path, "%08d.png" % (i,)))
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else:
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src_imgs_path = dataset_dir
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# Encoding command line:
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# ffmpeg -framerate 30 -i %08d.png -c:v libx265 -crf 12 -preset slow -pix_fmt yuv444p test.mkv
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cmd = ['ffmpeg', '-y', '-framerate', str(opt['dataset']['frame_rate']), '-f', 'image2', '-i', osp.join(src_imgs_path, "%08d.png"),
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'-c:v', 'libx265', '-crf', str(minivid_crf), '-preset', 'slow', '-pix_fmt', 'yuv444p', osp.join(vid_output, "mini_%06d.mkv" % (vid_counter,))]
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print(ffmpeg_proc)
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ffmpeg_proc = subprocess.Popen(cmd)#, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL)
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vid_counter += 1
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frame_counter = 0
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print("Done.")
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if want_just_images:
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continue |