def _aggregate_per_chunk_results(self, algorithm_type):
lr_video_path = os.path.join(self.dataset_dir, 'video', self.lr_video_name)
lr_video_profile = get_video_profile(lr_video_path)
num_chunks = int(math.ceil(lr_video_profile['duration'] / (self.gop / lr_video_profile['frame_rate'])))
start_idx = 0
end_idx = num_chunks - 1
log_dir = os.path.join(self.dataset_dir, 'log', self.lr_video_name, self.model.name)
cache_profile_dir = os.path.join(self.dataset_dir, 'profile', self.lr_video_name, self.model.name)
log_name = os.path.join('quality_{}.txt'.format(algorithm_type))
cache_profile_name = os.path.join('{}.profile'.format(algorithm_type))
#log
log_path = os.path.join(log_dir, log_name)
with open(log_path, 'w') as f0:
#iterate over chunks
for chunk_idx in range(start_idx, end_idx + 1):
chunk_log_dir = os.path.join(log_dir, 'chunk{:04d}'.format(chunk_idx))
chunk_log_path= os.path.join(chunk_log_dir, log_name)
with open(chunk_log_path, 'r') as f1:
q_lines = f1.readlines()
f0.write('{}\t{}\n'.format(chunk_idx, q_lines[-1].strip()))
#cache profile
cache_profile_path = os.path.join(cache_profile_dir, cache_profile_name)
cache_data = b''
with open(cache_profile_path, 'wb') as f0:
for chunk_idx in range(start_idx, end_idx + 1):
chunk_cache_profile_path = os.path.join(cache_profile_dir, 'chunk{:04d}'.format(chunk_idx), cache_profile_name)
with open(chunk_cache_profile_path, 'rb') as f1:
f0.write(f1.read())
#log (bilinear, sr)
log_path = os.path.join(self.dataset_dir, 'log', self.lr_video_name, 'quality.txt')
with open(log_path, 'w') as f0:
#iterate over chunks
for chunk_idx in range(start_idx, end_idx + 1):
quality = []
chunk_log_path = os.path.join(self.dataset_dir, 'log', self.lr_video_name, 'chunk{:04d}'.format(chunk_idx), 'quality.txt')
with open(chunk_log_path, 'r') as f1:
lines = f1.readlines()
for line in lines:
line = line.strip()
quality.append(float(line.split('\t')[1]))
f0.write('{}\t{:.4f}\n'.format(chunk_idx, np.average(quality)))
log_path = os.path.join(self.dataset_dir, 'log', self.lr_video_name, self.model.name, 'quality.txt')
with open(log_path, 'w') as f0:
#iterate over chunks
for chunk_idx in range(start_idx, end_idx + 1):
quality = []
chunk_log_path = os.path.join(self.dataset_dir, 'log', self.lr_video_name, self.model.name, 'chunk{:04d}'.format(chunk_idx), 'quality.txt')
with open(chunk_log_path, 'r') as f1:
lines = f1.readlines()
for line in lines:
line = line.strip()
quality.append(float(line.split('\t')[1]))
f0.write('{}\t{:.4f}\n'.format(chunk_idx, np.average(quality)))
def select_anchor_point_set(self, algorithm_type, chunk_idx=None, max_nemo_num_anchor_points=None):
if chunk_idx is not None:
if algorithm_type == 'nemo':
self._select_anchor_point_set_nemo(chunk_idx)
elif algorithm_type == 'uniform':
self._select_anchor_point_set_uniform(chunk_idx)
elif algorithm_type == 'random':
self._select_anchor_point_set_random(chunk_idx)
else:
lr_video_path = os.path.join(self.dataset_dir, 'video', self.lr_video_name)
lr_video_profile = get_video_profile(lr_video_path)
num_chunks = int(math.ceil(lr_video_profile['duration'] / (self.gop / lr_video_profile['frame_rate'])))
for i in range(num_chunks):
if algorithm_type == 'nemo':
self._select_anchor_point_set_nemo(i)
elif algorithm_type == 'uniform':
self._select_anchor_point_set_uniform(i)
elif algorithm_type == 'random':
self._select_anchor_point_set_random(i)
#path
parser.add_argument('--ffmpeg_path', type=str, default='/usr/bin/ffmpeg')
parser.add_argument('--input_video_path', type=str, required=True)
parser.add_argument('--output_video_dir', type=str, required=True)
#video
parser.add_argument('--mode', type=str, required=True)
parser.add_argument('--bitrate', type=int, default=None)
parser.add_argument('--output_width', type=int, default=None)
parser.add_argument('--output_height', type=int, default=None)
parser.add_argument('--gop', type=int, default=120)
parser.add_argument('--start', type=int, default=None)
parser.add_argument('--duration', type=int, default=None)
args = parser.parse_args()
print(args.input_video_path)
input_video_height = get_video_profile(args.input_video_path)['height']
if args.mode == 'cut_and_resize_and_encode':
assert(args.start is not None and args.duration is not None)
enc = LibvpxEncoder(args.output_video_dir, args.input_video_path, input_video_height, args.start, args.duration, args.ffmpeg_path)
enc.cut_and_resize_and_encode(args.output_width, args.output_height, args.bitrate, args.gop)
elif args.mode == 'resize_and_encode':
assert(args.bitrate is not None and args.output_height is not None and args.output_width is not None)
enc = LibvpxEncoder(args.output_video_dir, args.input_video_path, input_video_height, args.start,
args.duration, args.ffmpeg_path)
enc.resize_and_encode(args.output_width, args.output_height, args.bitrate, args.gop)
else:
raise ValueError('Unsupported mode')
choices=['nemo', 'uniform', 'random'],
required=True)
args = parser.parse_args()
if args.vpxdec_path is None:
args.vpxdec_path = os.path.join(os.environ['NEMO_CODE_ROOT'],
'third_party', 'libvpx', 'bin',
'vpxdec_nemo_ver2_x86')
assert (os.path.exists(args.vpxdec_path))
# setup
dataset_dir = os.path.join(args.data_dir, args.content)
lr_video_path = os.path.join(dataset_dir, 'video', args.lr_video_name)
hr_video_path = os.path.join(dataset_dir, 'video', args.hr_video_name)
lr_video_profile = get_video_profile(lr_video_path)
hr_video_profile = get_video_profile(hr_video_path)
scale = args.output_height // lr_video_profile['height']
model = build_model(args.model_type, args.num_blocks, args.num_filters,
scale, args.upsample_type)
# prepare metadata
metadata_file = os.path.join(dataset_dir, 'log', args.lr_video_name,
model.name, 'metadata.txt')
cache_profile_name = '{}_{}'.format(args.algorithm, args.quality_margin)
if not os.path.exists(metadata_file):
offline_cache_metadata(args.vpxdec_path, dataset_dir,
args.lr_video_name, model.name,
cache_profile_name, args.output_width,
args.output_height)
def _select_anchor_point_set_nemo(self, chunk_idx):
postfix = 'chunk{:04d}'.format(chunk_idx)
cache_profile_dir = os.path.join(self.dataset_dir, 'profile', self.lr_video_name, self.model.name, postfix)
log_dir = os.path.join(self.dataset_dir, 'log', self.lr_video_name, self.model.name, postfix)
os.makedirs(log_dir, exist_ok=True)
os.makedirs(cache_profile_dir, exist_ok=True)
algorithm_type = 'nemo_{}'.format(self.quality_margin)
###########step 1: analyze anchor points##########
#calculate num_skipped_frames and num_decoded frames
start_time = time.time()
lr_video_path = os.path.join(self.dataset_dir, 'video', self.lr_video_name)
lr_video_profile = get_video_profile(lr_video_path)
num_total_frames = int(round(lr_video_profile['frame_rate'], 3) * round(lr_video_profile['duration']))
num_left_frames = num_total_frames - chunk_idx * self.gop
assert(num_total_frames == math.floor(num_total_frames))
num_skipped_frames = chunk_idx * self.gop
num_decoded_frames = self.gop if num_left_frames >= self.gop else num_left_frames
#save low-resolution, super-resoluted, high-resolution frames to local storage
libvpx.save_rgb_frame(self.vpxdec_path, self.dataset_dir, self.lr_video_name, skip=num_skipped_frames, limit=num_decoded_frames, postfix=postfix)
libvpx.save_yuv_frame(self.vpxdec_path, self.dataset_dir, self.hr_video_name, self.output_width, self.output_height, num_skipped_frames, num_decoded_frames, postfix)
libvpx.setup_sr_frame(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.model, postfix)
#measure bilinear, per-frame super-resolution quality
quality_bilinear = libvpx.bilinear_quality(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.hr_video_name, self.output_width, self.output_height,
num_skipped_frames, num_decoded_frames, postfix)
quality_dnn = libvpx.offline_dnn_quality(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.hr_video_name, self.model.name, \
self.output_width, self.output_height, num_skipped_frames, num_decoded_frames, postfix)
end_time = time.time()
print('{} video chunk: (Step1-profile bilinear, dnn quality) {}sec'.format(chunk_idx, end_time - start_time))
#create multiple processes for parallel quality measurements
start_time = time.time()
q0 = mp.Queue()
q1 = mp.Queue()
decoders = [mp.Process(target=libvpx.offline_cache_quality_mt, args=(q0, q1, self.vpxdec_path, self.dataset_dir, \
self.lr_video_name, self.hr_video_name, self.model.name, self.output_width, self.output_height)) for i in range(self.num_decoders)]
for decoder in decoders:
decoder.start()
#select a single anchor point and measure the resulting quality
single_anchor_point_sets = []
frames = libvpx.load_frame_index(self.dataset_dir, self.lr_video_name, postfix)
for idx, frame in enumerate(frames):
anchor_point_set = libvpx.AnchorPointSet.create(frames, cache_profile_dir, frame.name)
anchor_point_set.add_anchor_point(frame)
anchor_point_set.save_cache_profile()
q0.put((anchor_point_set.get_cache_profile_name(), num_skipped_frames, num_decoded_frames, postfix, idx))
single_anchor_point_sets.append(anchor_point_set)
for frame in frames:
item = q1.get()
idx = item[0]
quality = item[1]
single_anchor_point_sets[idx].set_measured_quality(quality)
single_anchor_point_sets[idx].remove_cache_profile()
#remove multiple processes
for decoder in decoders:
q0.put('end')
for decoder in decoders:
decoder.join()
end_time = time.time()
print('{} video chunk: (Step1-profile anchor point quality) {}sec'.format(chunk_idx, end_time - start_time))
###########step 2: order anchor points##########
start_time = time.time()
multiple_anchor_point_sets = []
anchor_point_set = None
FAST_anchor_point_set = single_anchor_point_sets[0]
while len(single_anchor_point_sets) > 0:
anchor_point_idx, estimated_quality = self._select_anchor_point(anchor_point_set, single_anchor_point_sets)
selected_anchor_point = single_anchor_point_sets.pop(anchor_point_idx)
if len(multiple_anchor_point_sets) == 0:
anchor_point_set = libvpx.AnchorPointSet.load(selected_anchor_point, cache_profile_dir, '{}_{}'.format(algorithm_type, 1))
anchor_point_set.set_estimated_quality(selected_anchor_point.measured_quality)
else:
anchor_point_set = libvpx.AnchorPointSet.load(multiple_anchor_point_sets[-1], cache_profile_dir, '{}_{}'.format(algorithm_type, multiple_anchor_point_sets[-1].get_num_anchor_points() + 1))
anchor_point_set.add_anchor_point(selected_anchor_point.anchor_points[0])
anchor_point_set.set_estimated_quality(estimated_quality)
multiple_anchor_point_sets.append(anchor_point_set)
end_time = time.time()
print('{} video chunk: (Step2) {}sec'.format(chunk_idx, end_time - start_time))
###########step 3: select anchor points##########
start_time = time.time()
log_path0 = os.path.join(log_dir, 'quality_{}.txt'.format(algorithm_type))
log_path1 = os.path.join(log_dir, 'quality_{}_8.txt'.format(algorithm_type))
log_path2 = os.path.join(log_dir, 'quality_{}_16.txt'.format(algorithm_type))
log_path3 = os.path.join(log_dir, 'quality_fast.txt')
with open(log_path0, 'w') as f0, open(log_path1, 'w') as f1, open(log_path2, 'w') as f2, open(log_path3, 'w') as f3:
for idx, anchor_point_set in enumerate(multiple_anchor_point_sets):
#log quality
anchor_point_set.save_cache_profile()
quality_cache = libvpx.offline_cache_quality(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.hr_video_name, \
self.model.name, anchor_point_set.get_cache_profile_name(), self.output_width, self.output_height, \
num_skipped_frames, num_decoded_frames, postfix)
anchor_point_set.remove_cache_profile()
quality_diff = np.asarray(quality_dnn) - np.asarray(quality_cache)
quality_log = '{}\t{}\t{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}\n'.format(anchor_point_set.get_num_anchor_points(), len(frames), \
np.average(quality_cache), np.average(quality_dnn), np.average(quality_bilinear), np.average(anchor_point_set.estimated_quality))
f0.write(quality_log)
if idx < 8:
f1.write(quality_log)
if idx < 16:
f2.write(quality_log)
if idx == 0:
f3.write(quality_log)
#terminate
if np.average(quality_diff) <= self.quality_margin:
#case 1: does not restrict #anchor points
anchor_point_set.set_cache_profile_name(algorithm_type)
anchor_point_set.save_cache_profile()
#case 2: limit #anchor points to 8
if anchor_point_set.get_num_anchor_points() > 8:
anchor_point_set_ = multiple_anchor_point_sets[7]
else:
anchor_point_set_ = anchor_point_set
anchor_point_set_.set_cache_profile_name('{}_8'.format(algorithm_type))
anchor_point_set_.save_cache_profile()
#case 3: limit #anchor points to 16
if anchor_point_set.get_num_anchor_points() > 16:
anchor_point_set_ = multiple_anchor_point_sets[15]
else:
anchor_point_set_ = anchor_point_set
anchor_point_set_.set_cache_profile_name('{}_16'.format(algorithm_type))
anchor_point_set_.save_cache_profile()
#case 4: FAST
anchor_point_set_ = FAST_anchor_point_set
anchor_point_set_.set_cache_profile_name('fast'.format(algorithm_type))
anchor_point_set_.save_cache_profile()
break
end_time = time.time()
print('{} video chunk: (Step3) {}sec'.format(chunk_idx, end_time - start_time))
#remove images
lr_image_dir = os.path.join(self.dataset_dir, 'image', self.lr_video_name, postfix)
hr_image_dir = os.path.join(self.dataset_dir, 'image', self.hr_video_name, postfix)
sr_image_dir = os.path.join(self.dataset_dir, 'image', self.lr_video_name, self.model.name, postfix)
shutil.rmtree(lr_image_dir, ignore_errors=True)
shutil.rmtree(hr_image_dir, ignore_errors=True)
shutil.rmtree(sr_image_dir, ignore_errors=True)
def _select_anchor_point_set_random(self, chunk_idx=None):
postfix = 'chunk{:04d}'.format(chunk_idx)
cache_profile_dir = os.path.join(self.dataset_dir, 'profile', self.lr_video_name, self.model.name, postfix)
log_dir = os.path.join(self.dataset_dir, 'log', self.lr_video_name, self.model.name, postfix)
os.makedirs(log_dir, exist_ok=True)
os.makedirs(cache_profile_dir, exist_ok=True)
algorithm_type = 'random_{}'.format(self.quality_margin)
###########step 1: measure bilinear, dnn quality##########
#calculate num_skipped_frames and num_decoded frames
start_time = time.time()
lr_video_path = os.path.join(self.dataset_dir, 'video', self.lr_video_name)
lr_video_profile = get_video_profile(lr_video_path)
num_total_frames = int(round(lr_video_profile['frame_rate'], 3) * round(lr_video_profile['duration']))
num_left_frames = num_total_frames - chunk_idx * self.gop
assert(num_total_frames == math.floor(num_total_frames))
num_skipped_frames = chunk_idx * self.gop
num_decoded_frames = self.gop if num_left_frames >= self.gop else num_left_frames
#save low-resolution, super-resoluted, high-resolution frames to local storage
libvpx.save_rgb_frame(self.vpxdec_path, self.dataset_dir, self.lr_video_name, skip=num_skipped_frames, limit=num_decoded_frames, postfix=postfix)
libvpx.save_yuv_frame(self.vpxdec_path, self.dataset_dir, self.hr_video_name, self.output_width, self.output_height, num_skipped_frames, num_decoded_frames, postfix)
libvpx.setup_sr_frame(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.model, postfix)
#measure bilinear, per-frame super-resolution quality
quality_bilinear = libvpx.bilinear_quality(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.hr_video_name, self.output_width, self.output_height,
num_skipped_frames, num_decoded_frames, postfix)
quality_dnn = libvpx.offline_dnn_quality(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.hr_video_name, self.model.name, \
self.output_width, self.output_height, num_skipped_frames, num_decoded_frames, postfix)
end_time = time.time()
print('{} video chunk: (Step1-profile bilinear, dnn quality) {}sec'.format(chunk_idx, end_time - start_time))
###########step 2: select anchor points##########
start_time = time.time()
frames = libvpx.load_frame_index(self.dataset_dir, self.lr_video_name, postfix)
log_path = os.path.join(log_dir, 'quality_{}.txt'.format(algorithm_type))
with open(log_path, 'w') as f:
for i in range(len(frames)):
#select anchor point uniformly
num_anchor_points = i + 1
anchor_point_set = libvpx.AnchorPointSet.create(frames, cache_profile_dir, '{}_{}'.format(algorithm_type, num_anchor_points))
random_frames = random.sample(frames, num_anchor_points)
for frame in random_frames:
anchor_point_set.add_anchor_point(frame)
#measure the quality
anchor_point_set.save_cache_profile()
quality_cache = libvpx.offline_cache_quality(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.hr_video_name, \
self.model.name, anchor_point_set.get_cache_profile_name(), self.output_width, self.output_height, \
num_skipped_frames, num_decoded_frames, postfix)
anchor_point_set.remove_cache_profile()
quality_diff = np.asarray(quality_dnn) - np.asarray(quality_cache)
quality_log = '{}\t{}\t{:.4f}\t{:.4f}\t{:.4f}\n'.format(anchor_point_set.get_num_anchor_points(), len(frames), \
np.average(quality_cache), np.average(quality_dnn), np.average(quality_bilinear))
f.write(quality_log)
#terminate
if np.average(quality_diff) <= self.quality_margin:
anchor_point_set.set_cache_profile_name(algorithm_type)
anchor_point_set.save_cache_profile()
libvpx.offline_cache_quality(self.vpxdec_path, self.dataset_dir, self.lr_video_name, self.hr_video_name, \
self.model.name, anchor_point_set.get_cache_profile_name(), self.output_width, self.output_height, \
num_skipped_frames, num_decoded_frames, postfix)
break
end_time = time.time()
print('{} video chunk: (Step2) {}sec'.format(chunk_idx, end_time - start_time))
#remove images
lr_image_dir = os.path.join(self.dataset_dir, 'image', self.lr_video_name, postfix)
hr_image_dir = os.path.join(self.dataset_dir, 'image', self.hr_video_name, postfix)
sr_image_dir = os.path.join(self.dataset_dir, 'image', self.lr_video_name, self.model.name, postfix)
shutil.rmtree(lr_image_dir, ignore_errors=True)
shutil.rmtree(hr_image_dir, ignore_errors=True)
shutil.rmtree(sr_image_dir, ignore_errors=True)
#directory, path
parser.add_argument('--dataset_dir', type=str, required=True)
parser.add_argument('--content', type=str, required=True)
parser.add_argument('--video_name', type=str, required=True)
#architecture
parser.add_argument('--model_type', type=str, default='nemo_s')
parser.add_argument('--num_filters', type=int, required=True)
parser.add_argument('--num_blocks', type=int, required=True)
parser.add_argument('--upsample_type', type=str, default='deconv')
parser.add_argument('--scale', type=int, required=True)
args = parser.parse_args()
#scale, dnn
video_path = os.path.join(args.dataset_dir, args.content, 'video',
args.video_name)
video_profile = get_video_profile(video_path)
with tf.Graph().as_default(), tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
model = build_model(args.model_type, args.num_blocks, args.num_filters,
args.scale, args.upsample_type)
log_dir = os.path.join(args.dataset_dir, args.content, 'log',
args.video_name, model.name)
os.makedirs(log_dir, exist_ok=True)
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
