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data_processing.py
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data_processing.py
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import glob
import json
import os
import subprocess
import cv2
import numpy as np
from config.config import DataProcessingOptions
from utils.data_processing import compute_crop_radius, load_landmark_openface
from utils.deep_speech import DeepSpeech
def extract_audio(source_video_dir, res_audio_dir):
"""
extract audio files from videos
"""
if not os.path.exists(source_video_dir):
raise ("wrong path of video dir")
if not os.path.exists(res_audio_dir):
os.mkdir(res_audio_dir)
video_path_list = glob.glob(os.path.join(source_video_dir, "*.mp4"))
for video_path in video_path_list:
print("extract audio from video: {}".format(os.path.basename(video_path)))
audio_path = os.path.join(
res_audio_dir, os.path.basename(video_path).replace(".mp4", ".wav")
)
cmd = "ffmpeg -i {} -f wav -ar 16000 {}".format(video_path, audio_path)
subprocess.call(cmd, shell=True)
def extract_deep_speech(audio_dir, res_deep_speech_dir, deep_speech_model_path):
"""
extract deep speech feature
"""
if not os.path.exists(res_deep_speech_dir):
os.mkdir(res_deep_speech_dir)
DSModel = DeepSpeech(deep_speech_model_path)
wav_path_list = glob.glob(os.path.join(audio_dir, "*.wav"))
for wav_path in wav_path_list:
video_name = os.path.basename(wav_path).replace(".wav", "")
res_dp_path = os.path.join(res_deep_speech_dir, video_name + "_deepspeech.txt")
if os.path.exists(res_dp_path):
os.remove(res_dp_path)
print("extract deep speech feature from audio:{}".format(video_name))
ds_feature = DSModel.compute_audio_feature(wav_path)
np.savetxt(res_dp_path, ds_feature)
def extract_video_frame(source_video_dir, res_video_frame_dir):
"""
extract video frames from videos
"""
if not os.path.exists(source_video_dir):
raise ("wrong path of video dir")
if not os.path.exists(res_video_frame_dir):
os.mkdir(res_video_frame_dir)
video_path_list = glob.glob(os.path.join(source_video_dir, "*.mp4"))
for video_path in video_path_list:
video_name = os.path.basename(video_path)
frame_dir = os.path.join(res_video_frame_dir, video_name.replace(".mp4", ""))
if not os.path.exists(frame_dir):
os.makedirs(frame_dir)
print("extracting frames from {} ...".format(video_name))
videoCapture = cv2.VideoCapture(video_path)
fps = videoCapture.get(cv2.CAP_PROP_FPS)
if int(fps) != 25:
raise ("{} video is not in 25 fps".format(video_path))
frames = videoCapture.get(cv2.CAP_PROP_FRAME_COUNT)
for i in range(int(frames)):
ret, frame = videoCapture.read()
result_path = os.path.join(frame_dir, str(i).zfill(6) + ".jpg")
cv2.imwrite(result_path, frame)
def crop_face_according_openfaceLM(
openface_landmark_dir, video_frame_dir, res_crop_face_dir, clip_length
):
"""
crop face according to openface landmark
"""
if not os.path.exists(openface_landmark_dir):
raise ("wrong path of openface landmark dir")
if not os.path.exists(video_frame_dir):
raise ("wrong path of video frame dir")
if not os.path.exists(res_crop_face_dir):
os.mkdir(res_crop_face_dir)
landmark_openface_path_list = glob.glob(
os.path.join(openface_landmark_dir, "*.csv")
)
for landmark_openface_path in landmark_openface_path_list:
video_name = os.path.basename(landmark_openface_path).replace(".csv", "")
crop_face_video_dir = os.path.join(res_crop_face_dir, video_name)
if not os.path.exists(crop_face_video_dir):
os.makedirs(crop_face_video_dir)
print("cropping face from video: {} ...".format(video_name))
landmark_openface_data = load_landmark_openface(landmark_openface_path).astype(
np.int
)
frame_dir = os.path.join(video_frame_dir, video_name)
if not os.path.exists(frame_dir):
raise ("run last step to extract video frame")
if (
len(glob.glob(os.path.join(frame_dir, "*.jpg")))
!= landmark_openface_data.shape[0]
):
print(f"frames are {len(glob.glob(os.path.join(frame_dir, '*.jpg')))}")
print(f"landmarks are {landmark_openface_data.shape[0]}")
raise ("landmark length is different from frame length.")
frame_length = min(
len(glob.glob(os.path.join(frame_dir, "*.jpg"))),
landmark_openface_data.shape[0],
)
end_frame_index = list(range(clip_length, frame_length, clip_length))
video_clip_num = len(end_frame_index)
for i in range(video_clip_num):
first_image = cv2.imread(os.path.join(frame_dir, "000000.jpg"))
video_h, video_w = first_image.shape[0], first_image.shape[1]
crop_flag, radius_clip = compute_crop_radius(
(video_w, video_h),
landmark_openface_data[
end_frame_index[i] - clip_length : end_frame_index[i], :, :
],
)
if not crop_flag:
continue
radius_clip_1_4 = radius_clip // 4
print(
"cropping {}/{} clip from video:{}".format(
i, video_clip_num, video_name
)
)
res_face_clip_dir = os.path.join(crop_face_video_dir, str(i).zfill(6))
if not os.path.exists(res_face_clip_dir):
os.mkdir(res_face_clip_dir)
for frame_index in range(
end_frame_index[i] - clip_length, end_frame_index[i]
):
source_frame_path = os.path.join(
frame_dir, str(frame_index).zfill(6) + ".jpg"
)
source_frame_data = cv2.imread(source_frame_path)
frame_landmark = landmark_openface_data[frame_index, :, :]
crop_face_data = source_frame_data[
frame_landmark[29, 1]
- radius_clip : frame_landmark[29, 1]
+ radius_clip * 2
+ radius_clip_1_4,
frame_landmark[33, 0]
- radius_clip
- radius_clip_1_4 : frame_landmark[33, 0]
+ radius_clip
+ radius_clip_1_4,
:,
].copy()
res_crop_face_frame_path = os.path.join(
res_face_clip_dir, str(frame_index).zfill(6) + ".jpg"
)
if os.path.exists(res_crop_face_frame_path):
os.remove(res_crop_face_frame_path)
cv2.imwrite(res_crop_face_frame_path, crop_face_data)
def generate_training_json(crop_face_dir, deep_speech_dir, clip_length, res_json_path):
video_name_list = os.listdir(crop_face_dir)
video_name_list.sort()
res_data_dic = {}
for video_index, video_name in enumerate(video_name_list):
print(
"generate training json file :{} {}/{}".format(
video_name, video_index, len(video_name_list)
)
)
tem_dic = {}
deep_speech_feature_path = os.path.join(
deep_speech_dir, video_name + "_deepspeech.txt"
)
if not os.path.exists(deep_speech_feature_path):
raise ("wrong path of deep speech")
deep_speech_feature = np.loadtxt(deep_speech_feature_path)
video_clip_dir = os.path.join(crop_face_dir, video_name)
clip_name_list = os.listdir(video_clip_dir)
clip_name_list.sort()
video_clip_num = len(clip_name_list)
clip_data_list = []
for clip_index, clip_name in enumerate(clip_name_list):
tem_tem_dic = {}
clip_frame_dir = os.path.join(video_clip_dir, clip_name)
frame_path_list = glob.glob(os.path.join(clip_frame_dir, "*.jpg"))
frame_path_list.sort()
assert len(frame_path_list) == clip_length
start_index = int(float(clip_name) * clip_length)
assert (
int(float(os.path.basename(frame_path_list[0]).replace(".jpg", "")))
== start_index
)
frame_name_list = [
video_name + "/" + clip_name + "/" + os.path.basename(item)
for item in frame_path_list
]
deep_speech_list = deep_speech_feature[
start_index : start_index + clip_length, :
].tolist()
if len(frame_name_list) != len(deep_speech_list):
print(
" skip video: {}:{}/{} clip:{}:{}/{} because of different length: {} {}".format(
video_name,
video_index,
len(video_name_list),
clip_name,
clip_index,
len(clip_name_list),
len(frame_name_list),
len(deep_speech_list),
)
)
tem_tem_dic["frame_name_list"] = frame_name_list
tem_tem_dic["frame_path_list"] = frame_path_list
tem_tem_dic["deep_speech_list"] = deep_speech_list
clip_data_list.append(tem_tem_dic)
tem_dic["video_clip_num"] = video_clip_num
tem_dic["clip_data_list"] = clip_data_list
res_data_dic[video_name] = tem_dic
if os.path.exists(res_json_path):
os.remove(res_json_path)
with open(res_json_path, "w") as f:
json.dump(res_data_dic, f)
if __name__ == "__main__":
opt = DataProcessingOptions().parse_args()
# step1: extract video frames
if opt.extract_video_frame:
extract_video_frame(opt.source_video_dir, opt.video_frame_dir)
# step2: extract audio files
if opt.extract_audio:
extract_audio(opt.source_video_dir, opt.audio_dir)
# step3: extract deep speech features
if opt.extract_deep_speech:
extract_deep_speech(opt.audio_dir, opt.deep_speech_dir, opt.deep_speech_model)
# step4: crop face images
if opt.crop_face:
crop_face_according_openfaceLM(
opt.openface_landmark_dir,
opt.video_frame_dir,
opt.crop_face_dir,
opt.clip_length,
)
# step5: generate training json file
if opt.generate_training_json:
generate_training_json(
opt.crop_face_dir, opt.deep_speech_dir, opt.clip_length, opt.json_path
)