Spaces:
Running
on
Zero
Running
on
Zero
downscaling input video
Browse files
app.py
CHANGED
@@ -104,26 +104,47 @@ def remove_directory_contents(directory):
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@spaces.GPU
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@torch.inference_mode()
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@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
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def process_video(video_path, prompt):
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try:
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# Get video info
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probe = ffmpeg.probe(video_path)
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video_info = next(s for s in probe['streams'] if s['codec_type'] == 'video')
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width = int(video_info['width'])
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height = int(video_info['height'])
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fps = eval(video_info['r_frame_rate'])
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# Read frames
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out, _ = (
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ffmpeg
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.input(video_path)
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.output('pipe:', format='rawvideo', pix_fmt='rgb24')
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.run(capture_stdout=True)
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)
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frames = np.frombuffer(out, np.uint8).reshape([-1,
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print(f"Read {len(frames)} frames")
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@@ -196,7 +217,7 @@ def process_video(video_path, prompt):
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output_path = "segmented_video.mp4"
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process = (
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ffmpeg
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.input('pipe:', format='rawvideo', pix_fmt='rgb24', s=f'{
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.output(output_path, pix_fmt='yuv420p')
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.overwrite_output()
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.run_async(pipe_stdin=True)
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@@ -219,7 +240,7 @@ def process_video(video_path, prompt):
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print(traceback.format_exc()) # This will print the full stack trace
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return None
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@spaces.GPU(duration=
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def segment_video(video_file, prompt):
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if video_file is None:
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return None
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@spaces.GPU
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@torch.inference_mode()
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@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
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def process_video(video_path, prompt, target_fps=30, max_dimension=640):
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try:
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# Get video info
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probe = ffmpeg.probe(video_path)
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video_info = next(s for s in probe['streams'] if s['codec_type'] == 'video')
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width = int(video_info['width'])
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height = int(video_info['height'])
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original_fps = eval(video_info['r_frame_rate'])
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# Calculate new dimensions while maintaining aspect ratio
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if width > height:
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if width > max_dimension:
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new_width = max_dimension
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new_height = int(height * (max_dimension / width))
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else:
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new_width = width
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new_height = height
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else:
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if height > max_dimension:
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new_height = max_dimension
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new_width = int(width * (max_dimension / height))
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else:
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new_width = width
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new_height = height
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# Determine target fps
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fps = min(original_fps, target_fps)
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print(f"Original video: {width}x{height}, {original_fps} fps")
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print(f"Processing at: {new_width}x{new_height}, {fps} fps")
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# Read and resize frames
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out, _ = (
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ffmpeg
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.input(video_path)
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.filter('fps', fps=fps)
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.filter('scale', width=new_width, height=new_height)
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.output('pipe:', format='rawvideo', pix_fmt='rgb24')
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.run(capture_stdout=True)
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)
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frames = np.frombuffer(out, np.uint8).reshape([-1, new_height, new_width, 3])
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print(f"Read {len(frames)} frames")
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output_path = "segmented_video.mp4"
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process = (
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ffmpeg
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.input('pipe:', format='rawvideo', pix_fmt='rgb24', s=f'{new_width}x{new_height}', r=fps)
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.output(output_path, pix_fmt='yuv420p')
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.overwrite_output()
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.run_async(pipe_stdin=True)
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print(traceback.format_exc()) # This will print the full stack trace
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return None
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@spaces.GPU(duration=600)
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def segment_video(video_file, prompt):
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if video_file is None:
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return None
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