def test_write_video_with_audio(self):
f_name = os.path.join(VIDEO_DIR, "R6llTwEh07w.mp4")
video_tensor, audio_tensor, info = io.read_video(f_name,
pts_unit="sec")
with get_tmp_dir() as tmpdir:
out_f_name = os.path.join(tmpdir, "testing.mp4")
io.video.write_video(
out_f_name,
video_tensor,
round(info["video_fps"]),
video_codec="libx264rgb",
options={'crf': '0'},
audio_array=audio_tensor,
audio_fps=info["audio_fps"],
audio_codec="aac",
)
out_video_tensor, out_audio_tensor, out_info = io.read_video(
out_f_name, pts_unit="sec")
assert info["video_fps"] == out_info["video_fps"]
assert_equal(video_tensor, out_video_tensor)
audio_stream = av.open(f_name).streams.audio[0]
out_audio_stream = av.open(out_f_name).streams.audio[0]
assert info["audio_fps"] == out_info["audio_fps"]
assert audio_stream.rate == out_audio_stream.rate
assert pytest.approx(out_audio_stream.frames, rel=0.0,
abs=1) == audio_stream.frames
assert audio_stream.frame_size == out_audio_stream.frame_size
def test_read_partial_video_pts_unit_sec(self):
with temp_video(10, 300, 300, 5, lossless=True) as (f_name, data):
pts, _ = io.read_video_timestamps(f_name, pts_unit='sec')
for start in range(5):
for l in range(1, 4):
lv, _, _ = io.read_video(f_name,
pts[start],
pts[start + l - 1],
pts_unit='sec')
s_data = data[start:(start + l)]
self.assertEqual(len(lv), l)
self.assertTrue(s_data.equal(lv))
container = av.open(f_name)
stream = container.streams[0]
lv, _, _ = io.read_video(
f_name,
int(pts[4] * (1.0 / stream.time_base) + 1) * stream.time_base,
pts[7],
pts_unit='sec')
if get_video_backend() == "pyav":
# for "video_reader" backend, we don't decode the closest early frame
# when the given start pts is not matching any frame pts
self.assertEqual(len(lv), 4)
self.assertTrue(data[4:8].equal(lv))
container.close()
def test_write_video_with_audio(self):
f_name = os.path.join(VIDEO_DIR, "R6llTwEh07w.mp4")
video_tensor, audio_tensor, info = io.read_video(f_name, pts_unit="sec")
with get_tmp_dir() as tmpdir:
out_f_name = os.path.join(tmpdir, "testing.mp4")
io.video.write_video(
out_f_name,
video_tensor,
round(info["video_fps"]),
video_codec="libx264rgb",
options={'crf': '0'},
audio_array=audio_tensor,
audio_fps=info["audio_fps"],
audio_codec="aac",
)
out_video_tensor, out_audio_tensor, out_info = io.read_video(
out_f_name, pts_unit="sec"
)
self.assertEqual(info["video_fps"], out_info["video_fps"])
assert_equal(video_tensor, out_video_tensor)
audio_stream = av.open(f_name).streams.audio[0]
out_audio_stream = av.open(out_f_name).streams.audio[0]
self.assertEqual(info["audio_fps"], out_info["audio_fps"])
self.assertEqual(audio_stream.rate, out_audio_stream.rate)
self.assertAlmostEqual(audio_stream.frames, out_audio_stream.frames, delta=1)
self.assertEqual(audio_stream.frame_size, out_audio_stream.frame_size)
def test_read_partial_video_pts_unit_sec(self, start, offset):
with temp_video(10, 300, 300, 5, lossless=True) as (f_name, data):
pts, _ = io.read_video_timestamps(f_name, pts_unit='sec')
lv, _, _ = io.read_video(f_name,
pts[start],
pts[start + offset - 1],
pts_unit='sec')
s_data = data[start:(start + offset)]
assert len(lv) == offset
assert_equal(s_data, lv)
with av.open(f_name) as container:
stream = container.streams[0]
lv, _, _ = io.read_video(f_name,
int(pts[4] *
(1.0 / stream.time_base) + 1) *
stream.time_base,
pts[7],
pts_unit='sec')
if get_video_backend() == "pyav":
# for "video_reader" backend, we don't decode the closest early frame
# when the given start pts is not matching any frame pts
assert len(lv) == 4
assert_equal(data[4:8], lv)
def test_invalid_file(self):
set_video_backend('video_reader')
with self.assertRaises(RuntimeError):
io.read_video('foo.mp4')
set_video_backend('pyav')
with self.assertRaises(RuntimeError):
io.read_video('foo.mp4')
def test_invalid_file(self):
set_video_backend("video_reader")
with pytest.raises(RuntimeError):
io.read_video("foo.mp4")
set_video_backend("pyav")
with pytest.raises(RuntimeError):
io.read_video("foo.mp4")
def test_read_partial_video(self):
with temp_video(10, 300, 300, 5, lossless=True) as (f_name, data):
pts, _ = io.read_video_timestamps(f_name)
for start in range(5):
for l in range(1, 4):
lv, _, _ = io.read_video(f_name, pts[start], pts[start + l - 1])
s_data = data[start:(start + l)]
self.assertEqual(len(lv), l)
self.assertTrue(s_data.equal(lv))
lv, _, _ = io.read_video(f_name, pts[4] + 1, pts[7])
self.assertEqual(len(lv), 4)
self.assertTrue(data[4:8].equal(lv))
def test_read_partial_video_bframes(self):
# do not use lossless encoding, to test the presence of B-frames
options = {'bframes': '16', 'keyint': '10', 'min-keyint': '4'}
with temp_video(100, 300, 300, 5, options=options) as (f_name, data):
pts, _ = io.read_video_timestamps(f_name)
for start in range(0, 80, 20):
for l in range(1, 4):
lv, _, _ = io.read_video(f_name, pts[start], pts[start + l - 1])
s_data = data[start:(start + l)]
self.assertEqual(len(lv), l)
self.assertTrue((s_data.float() - lv.float()).abs().max() < self.TOLERANCE)
lv, _, _ = io.read_video(f_name, pts[4] + 1, pts[7])
self.assertEqual(len(lv), 4)
self.assertTrue((data[4:8].float() - lv.float()).abs().max() < self.TOLERANCE)
def blur_background(video_path,
respth='./res/test_res',
cp='model_final_diss.pth'):
frames, audio, info = read_video(video_path, 61, 65, pts_unit="sec")
scale_labels = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((frames.shape[1], frames.shape[2]),
interpolation=Image.NEAREST),
transforms.ToTensor()
])
labels = label_images(frames, cp)
new_frames = []
for frame_inx in tqdm.tqdm(
list(range(frames.shape[0])),
desc="generating segmented frames with background"):
scaled_label = scale_labels(labels[frame_inx].type(
torch.uint8)).squeeze(0)
scaled_label = torch.stack([scaled_label, scaled_label, scaled_label],
dim=2)
blurred_img = torch.from_numpy(
cv2.blur(frames[frame_inx].numpy(), (15, 15)))
new_frames.append(
torch.where(scaled_label > 0, frames[frame_inx], blurred_img))
new_frames = torch.stack(new_frames)
write_video(
os.path.join(respth, "blurred_background" +
os.path.basename(video_path)) + ".mp4", new_frames,
round(info["video_fps"]))
def __getitem__(self, idx):
sample = {}
row = self.clip_metadata_df.iloc[idx]
filename, fps = row['filename'], row['fps']
filename, fps, clip_t_start, is_last_clip = row['filename'], row[
'fps'], row['clip-t-start'], row['is-last-clip']
# compute clip_t_start and clip_t_end
clip_length_in_sec = self.clip_length / self.frame_rate
clip_t_end = clip_t_start + clip_length_in_sec
# get a tensor [clip_length, H, W, C] of the video frames between clip_t_start and clip_t_end seconds
vframes, _, _ = read_video(filename=filename,
start_pts=clip_t_start,
end_pts=clip_t_end,
pts_unit='sec')
idxs = EvalVideoDataset._resample_video_idx(self.clip_length, fps,
self.frame_rate)
vframes = vframes[
idxs][:self.
clip_length] # [:self.clip_length] for removing extra frames if isinstance(idxs, slice)
if vframes.shape[0] != self.clip_length:
raise RuntimeError(
f'<EvalVideoDataset>: got clip of length {vframes.shape[0]} != {self.clip_length}.'
f'filename={filename}, clip_t_start={clip_t_start}, clip_t_end={clip_t_end}, '
f'fps={fps}')
sample['clip'] = self.transforms(vframes)
sample['filename'] = filename
sample['is-last-clip'] = is_last_clip
return sample
def load_rgbf(args,is_cropped,path):
render_size = args.inference_size
rgb_frames,_,_ = read_video(path,pts_unit='sec')
rgb_frames= rgb_frames.to(torch.float32)
# Step 1 crop the images
frame_size = rgb_frames[0].shape
if (render_size[0] < 0) or (render_size[1] < 0) or (frame_size[0]%64) or (frame_size[1]%64):
render_size[0] = ( (frame_size[0])//64 ) * 64
render_size[1] = ( (frame_size[1])//64 ) * 64
cropped_frames = []
for rgb_frame in rgb_frames:
image_size = rgb_frame.shape[:2]
if is_cropped:
cropper = StaticRandomCrop(image_size, crop_size)
else:
cropper = StaticCenterCrop(image_size, render_size)
frame = cropper.crop(rgb_frame)
cropped_frames.append(frame)
rgb_frames = cropped_frames
# Step 2 load the flow images
flow_frames = flow_from_frames(args,rgb_frames)
frames = []
# Step 3 create the rgbf images
for (index, flow_img) in enumerate(flow_frames):
flow_img = flow_img.cpu().numpy()
mcm = MCM(flow_img)
rgb_img = rgb_frames[index]
rgbf = RGBF(rgb_img, mcm)
frames.append(rgbf)
return frames
def bug_due_to_unit_in_audio_align():
fname = osp.expanduser('~/tv_host.mp4')
vframes, aframes, info = read_video(fname,
start_pts=0,
end_pts=1,
pts_unit='sec')
return vframes
def _get_video_frame(self, video_year, video_ID, frame_id):
p = self._get_video_path(video_year, video_ID)
# videos are all at 25 fps, so to get rought seconds cound we divide that
s_entry = (frame_id - 1) / 25
video, _, _ = IO.read_video(p, s_entry, s_entry + 0.5, pts_unit="sec")
return F.to_pil_image(video[0, ...].permute(2, 0, 1))
def vgpt(invid):
try:
os.remove("output.mp4")
except FileNotFoundError:
pass
clip = VideoFileClip(invid)
rate = clip.fps
sequence_length = int(clip.fps * clip.duration)
pts = read_video_timestamps(invid, pts_unit='sec')[0]
video = read_video(invid,
pts_unit='sec',
start_pts=pts[0],
end_pts=pts[sequence_length - 1])[0]
video = preprocess(video, resolution,
sequence_length).unsqueeze(0).to(device)
with torch.no_grad():
encodings = vqvae.encode(video)
video_recon = vqvae.decode(encodings)
video_recon = torch.clamp(video_recon, -0.5, 0.5)
videos = video_recon[0].permute(1, 2, 3, 0) # CTHW -> THWC
videos = ((videos + 0.5) * 255).cpu().numpy().astype('uint8')
imageio.mimwrite('output.mp4', videos, fps=int(rate))
return './output.mp4'
def __init__(self,
video_path,
start_time=0,
end_time=None,
stride=None,
transforms=None):
"""
Args:
video_path: path to the video file
start_time (seconds): the start time to read the video
end_time (seconds): the end time to read the video
stride (seconds): time interval between frames
transforms (torchvision.transforms): transform to apply to each frame
"""
assert path.exists(video_path), f'wrong video path'
self.video_path = video_path
self.start_time = start_time
self.end_time = end_time
self.stride = stride
self.transforms = transforms
self.video_frames, _, self.info = read_video(filename=video_path,
start_pts=self.start_time,
end_pts=self.end_time,
pts_unit='sec')
self.frame_stride = int(stride * self.info['video_fps'])
self.video_frames = self.video_frames[
list(range(0, self.video_frames.shape[0], self.frame_stride)), ...]
def test_read_video_pts_unit_sec(self):
with temp_video(10, 300, 300, 5, lossless=True) as (f_name, data):
lv, _, info = io.read_video(f_name, pts_unit='sec')
assert_equal(data, lv)
assert info["video_fps"] == 5
assert info == {"video_fps": 5}
def get_clip(self, idx):
"""
Gets a subclip from a list of videos.
Arguments:
idx (int): index of the subclip. Must be between 0 and num_clips().
Returns:
video (Tensor)
audio (Tensor)
info (Dict)
video_idx (int): index of the video in `video_paths`
"""
if idx >= self.num_clips():
raise IndexError("Index {} out of range "
"({} number of clips)".format(
idx, self.num_clips()))
video_idx, clip_idx = self.get_clip_location(idx)
video_path = self.video_paths[video_idx]
clip_pts = self.clips[video_idx][clip_idx]
start_pts = clip_pts[0].item()
end_pts = clip_pts[-1].item()
video, audio, info = read_video(video_path, start_pts, end_pts)
if self.frame_rate is not None:
resampling_idx = self.resampling_idxs[video_idx][clip_idx]
if isinstance(resampling_idx, torch.Tensor):
resampling_idx = resampling_idx - resampling_idx[0]
video = video[resampling_idx]
info["video_fps"] = self.frame_rate
assert len(video) == self.num_frames, "{} x {}".format(
video.shape, self.num_frames)
return video, audio, info, video_idx
def test_read_partial_video(self, start, offset):
with temp_video(10, 300, 300, 5, lossless=True) as (f_name, data):
pts, _ = io.read_video_timestamps(f_name)
lv, _, _ = io.read_video(f_name, pts[start],
pts[start + offset - 1])
s_data = data[start:(start + offset)]
assert len(lv) == offset
assert_equal(s_data, lv)
if get_video_backend() == "pyav":
# for "video_reader" backend, we don't decode the closest early frame
# when the given start pts is not matching any frame pts
lv, _, _ = io.read_video(f_name, pts[4] + 1, pts[7])
assert len(lv) == 4
assert_equal(data[4:8], lv)
def __getitem__(self, idx):
"""
obtain the image (read and transform)
:param idx: index of the file required
:return: img => image array
"""
rand = np.random.RandomState()
video = self.files[idx]
target = self.get_class(video)
video, audio, info = io.read_video(video)
video_duration = video.shape[0] // info['video_fps']
# Less than one second video
if video_duration == 0:
return self.preprocess_video(video), self.preprocess_audio(audio, info['audio_fps']), target
else:
second_idx = rand.randint(0, video_duration - 1)
second_idx_vid = int(second_idx * info['video_fps'])
second_idx_aud = int(second_idx * info['audio_fps'])
video = video[second_idx_vid:second_idx_vid + int(info['video_fps'])]
audio = audio[:, second_idx_aud:second_idx_aud + int(info['audio_fps'])]
return self.preprocess_video(video), self.preprocess_audio(audio, info['audio_fps']), target
def test_read_video_pts_unit_sec(self):
with temp_video(10, 300, 300, 5, lossless=True) as (f_name, data):
lv, _, info = io.read_video(f_name, pts_unit='sec')
self.assertTrue(data.equal(lv))
self.assertEqual(info["video_fps"], 5)
self.assertEqual(info, {"video_fps": 5})
def __init__(self,
args,
is_cropped,
path='/path/to/frames/only/folder',
replicates=1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
self.frames = []
frames, _, _ = read_video(path, pts_unit='sec')
frames = frames.to(torch.float32)
for i in range(len(frames) - 1):
im1 = frames[i]
im2 = frames[i + 1]
self.frames += [[im1, im2]]
self.size = len(self.frames)
self.frame_size = self.frames[0][0].shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (
self.frame_size[0] % 64) or (self.frame_size[1] % 64):
self.render_size[0] = ((self.frame_size[0]) // 64) * 64
self.render_size[1] = ((self.frame_size[1]) // 64) * 64
args.inference_size = self.render_size
def test_read_partial_video(self):
with temp_video(10, 300, 300, 5, lossless=True) as (f_name, data):
pts, _ = io.read_video_timestamps(f_name)
for start in range(5):
for l in range(1, 4):
lv, _, _ = io.read_video(f_name, pts[start], pts[start + l - 1])
s_data = data[start:(start + l)]
self.assertEqual(len(lv), l)
self.assertTrue(s_data.equal(lv))
if get_video_backend() == "pyav":
# for "video_reader" backend, we don't decode the closest early frame
# when the given start pts is not matching any frame pts
lv, _, _ = io.read_video(f_name, pts[4] + 1, pts[7])
self.assertEqual(len(lv), 4)
self.assertTrue(data[4:8].equal(lv))
def batch_iter(self, batch_size):
for dir_name in os.listdir(self._data_task_dir):
fp = self._data_task_dir / dir_name / "recording.mp4"
vframes, _, _ = read_video(fp.as_posix(), pts_unit="sec")
for batch in torch.split(vframes, batch_size):
if batch.size(0) == batch_size:
yield batch
def __init__(self, args, path='/path/to/frames/only/folder'):
self.args = args
self.render_size = args.inference_size
self.frames, _, _ = read_video(path, pts_unit='sec')
self.frames = self.frames.to(torch.float32)
self.size = len(self.frames)
self.frame_size = self.frames[0][0].shape
args.inference_size = self.render_size
def get_clip(self, idx):
"""
Gets a subclip from a list of videos.
Arguments:
idx (int): index of the subclip. Must be between 0 and num_clips().
Returns:
video (Tensor)
audio (Tensor)
info (Dict)
video_idx (int): index of the video in `video_paths`
"""
if idx >= self.num_clips():
raise IndexError("Index {} out of range "
"({} number of clips)".format(
idx, self.num_clips()))
video_path = self.video_paths[idx]
clip_pts = self.clips[idx]
from torchvision import get_video_backend
backend = get_video_backend()
if backend == "pyav":
# check for invalid options
if self._video_width != 0:
raise ValueError(
"pyav backend doesn't support _video_width != 0")
if self._video_height != 0:
raise ValueError(
"pyav backend doesn't support _video_height != 0")
if self._video_min_dimension != 0:
raise ValueError(
"pyav backend doesn't support _video_min_dimension != 0")
if self._video_max_dimension != 0:
raise ValueError(
"pyav backend doesn't support _video_max_dimension != 0")
if self._audio_samples != 0:
raise ValueError(
"pyav backend doesn't support _audio_samples != 0")
if backend == "pyav":
assert len(clip_pts) > 0
start_pts = clip_pts[0].item()
end_pts = clip_pts[-1].item()
video, audio, info = read_video(video_path, start_pts, end_pts)
else:
raise NotImplementedError(f"backend {backend} is not implemented.")
resampling_idx = self.resampling_idxs[idx]
if isinstance(resampling_idx, torch.Tensor):
resampling_idx = resampling_idx - resampling_idx[0]
video = video[resampling_idx]
info["video_fps"] = self.frame_rate
assert len(video) == self.num_frames, "{} x {}".format(
video.shape, self.num_frames)
return video, audio, info
def test_read_partial_video_pts_unit_sec(self):
with temp_video(10, 300, 300, 5, lossless=True) as (f_name, data):
pts, _ = io.read_video_timestamps(f_name, pts_unit='sec')
for start in range(5):
for l in range(1, 4):
lv, _, _ = io.read_video(f_name, pts[start], pts[start + l - 1], pts_unit='sec')
s_data = data[start:(start + l)]
self.assertEqual(len(lv), l)
self.assertTrue(s_data.equal(lv))
container = av.open(f_name)
stream = container.streams[0]
lv, _, _ = io.read_video(f_name,
int(pts[4] * (1.0 / stream.time_base) + 1) * stream.time_base, pts[7],
pts_unit='sec')
self.assertEqual(len(lv), 4)
self.assertTrue(data[4:8].equal(lv))
def read_frame(self, timestamp = None):
"""Reads the next frame or from timestamp.
If no timestamp is provided this method just returns the next frame from
the video. This is significantly (up to 10x) faster if the `video_loader`
backend is available. If a timestamp is provided we first have to seek
to the right position and then load the frame.
Args:
timestamp: Specific timestamp of frame in seconds or None (default: None)
Returns:
A PIL Image
"""
if timestamp is not None:
self.current_timestamp_idx = self.timestamps.index(timestamp)
else:
# no timestamp provided -> set current timestamp index to next frame
if self.current_timestamp_idx < len(self.timestamps):
self.current_timestamp_idx += 1
if self.reader:
if timestamp is not None:
# Calling seek is slow. If we read next frame we can skip it!
if self.timestamps.index(timestamp) != self.last_timestamp_idx + 1:
self.reader.seek(timestamp)
# make sure we have the tensor in correct shape (we want H x W x C)
frame = next(self.reader)['data'].permute(1,2,0)
self.last_timestamp_idx = self.current_timestamp_idx
else: # fallback on pyav
if timestamp is None:
# read next frame if no timestamp is provided
timestamp = self.timestamps[self.current_timestamp_idx]
frame, _, _ = io.read_video(self.path,
start_pts=timestamp,
end_pts=timestamp,
pts_unit=self.pts_unit)
self.last_timestamp_idx = self.timestamps.index(timestamp)
if len(frame.shape) < 3:
raise ValueError('Unexpected error during loading of frame')
# sometimes torchvision returns multiple frames for one timestamp (bug?)
if len(frame.shape) > 3 and frame.shape[0] > 1:
frame = frame[0]
# make sure we return a H x W x C tensor and not (1 x H x W x C)
if len(frame.shape) == 4:
frame = frame.squeeze()
# convert to PIL image
image = Image.fromarray(frame.numpy())
return image
def test_read_video_corrupted_file(self):
with tempfile.NamedTemporaryFile(suffix='.mp4') as f:
f.write(b'This is not an mpg4 file')
video, audio, info = io.read_video(f.name)
self.assertIsInstance(video, torch.Tensor)
self.assertIsInstance(audio, torch.Tensor)
self.assertEqual(video.numel(), 0)
self.assertEqual(audio.numel(), 0)
self.assertEqual(info, {})
def test_read_video_corrupted_file(self):
with tempfile.NamedTemporaryFile(suffix='.mp4') as f:
f.write(b'This is not an mpg4 file')
video, audio, info = io.read_video(f.name)
assert isinstance(video, torch.Tensor)
assert isinstance(audio, torch.Tensor)
assert video.numel() == 0
assert audio.numel() == 0
assert info == {}
def _read_video(filename, start_pts=0, end_pts=None):
if _video_backend == "pyav":
return io.read_video(filename, start_pts, end_pts)
else:
if end_pts is None:
end_pts = -1
return io._read_video_from_file(
filename,
video_pts_range=(start_pts, end_pts),
)