82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import mediapipe as mp
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from latentsync.utils.util import read_video, gather_video_paths_recursively
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import os
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import tqdm
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from multiprocessing import Pool
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class FaceDetector:
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def __init__(self):
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self.face_detection = mp.solutions.face_detection.FaceDetection(
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model_selection=0, min_detection_confidence=0.5
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)
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def detect_face(self, image):
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# Process the image and detect faces.
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results = self.face_detection.process(image)
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if not results.detections: # Face not detected
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return False
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if len(results.detections) != 1:
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return False
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return True
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def detect_video(self, video_path):
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try:
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video_frames = read_video(video_path, change_fps=False)
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except Exception as e:
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print(f"Exception: {e} - {video_path}")
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return False
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if len(video_frames) == 0:
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return False
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for frame in video_frames:
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if not self.detect_face(frame):
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return False
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return True
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def close(self):
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self.face_detection.close()
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def remove_incorrect_affined(video_path):
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if not os.path.isfile(video_path):
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return
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face_detector = FaceDetector()
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has_face = face_detector.detect_video(video_path)
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if not has_face:
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os.remove(video_path)
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print(f"Removed: {video_path}")
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face_detector.close()
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def remove_incorrect_affined_multiprocessing(input_dir, num_workers):
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video_paths = gather_video_paths_recursively(input_dir)
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print(f"Total videos: {len(video_paths)}")
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print(f"Removing incorrect affined videos in {input_dir} ...")
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with Pool(num_workers) as pool:
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for _ in tqdm.tqdm(pool.imap_unordered(remove_incorrect_affined, video_paths), total=len(video_paths)):
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pass
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if __name__ == "__main__":
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input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/multilingual_dcc/high_visual_quality"
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num_workers = 50
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remove_incorrect_affined_multiprocessing(input_dir, num_workers)
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