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# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import mediapipe as mp
from latentsync.utils.util import read_video
import os
import tqdm
import shutil
from multiprocessing import Pool
paths = []
def gather_video_paths(input_dir, output_dir, resolution):
for video in sorted(os.listdir(input_dir)):
if video.endswith(".mp4"):
video_input = os.path.join(input_dir, video)
video_output = os.path.join(output_dir, video)
if os.path.isfile(video_output):
continue
paths.append([video_input, video_output, resolution])
elif os.path.isdir(os.path.join(input_dir, video)):
gather_video_paths(os.path.join(input_dir, video), os.path.join(output_dir, video), resolution)
class FaceDetector:
def __init__(self, resolution=256):
self.face_detection = mp.solutions.face_detection.FaceDetection(
model_selection=0, min_detection_confidence=0.5
)
self.resolution = resolution
def detect_face(self, image):
height, width = image.shape[:2]
# Process the image and detect faces.
results = self.face_detection.process(image)
if not results.detections: # Face not detected
raise Exception("Face not detected")
if len(results.detections) != 1:
return False
detection = results.detections[0] # Only use the first face in the image
bounding_box = detection.location_data.relative_bounding_box
face_width = int(bounding_box.width * width)
face_height = int(bounding_box.height * height)
if face_width < self.resolution or face_height < self.resolution:
return False
return True
def detect_video(self, video_path):
video_frames = read_video(video_path, change_fps=False)
if len(video_frames) == 0:
return False
for frame in video_frames:
if not self.detect_face(frame):
return False
return True
def close(self):
self.face_detection.close()
def filter_video(video_input, video_out, resolution):
if os.path.isfile(video_out):
return
face_detector = FaceDetector(resolution)
try:
save = face_detector.detect_video(video_input)
except Exception as e:
# print(f"Exception: {e} Input video: {video_input}")
face_detector.close()
return
if save:
os.makedirs(os.path.dirname(video_out), exist_ok=True)
shutil.copy(video_input, video_out)
face_detector.close()
def multi_run_wrapper(args):
return filter_video(*args)
def filter_high_resolution_multiprocessing(input_dir, output_dir, resolution, num_workers):
print(f"Recursively gathering video paths of {input_dir} ...")
gather_video_paths(input_dir, output_dir, resolution)
print(f"Filtering high resolution videos in {input_dir} ...")
with Pool(num_workers) as pool:
for _ in tqdm.tqdm(pool.imap_unordered(multi_run_wrapper, paths), total=len(paths)):
pass
if __name__ == "__main__":
input_dir = "/mnt/bn/maliva-gen-ai/lichunyu/HDTF/original/train"
output_dir = "/mnt/bn/maliva-gen-ai/lichunyu/HDTF/detected/train"
resolution = 256
num_workers = 50
filter_high_resolution_multiprocessing(input_dir, output_dir, resolution, num_workers)
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