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import os, sys
import cv2
import numpy as np
from time import time
from scipy.io import savemat
import argparse
from tqdm import tqdm, trange
import torch
import face_alignment
import deep_3drecon
from moviepy.editor import VideoFileClip
import copy
from utils.commons.multiprocess_utils import multiprocess_run_tqdm, multiprocess_run
from utils.commons.meters import Timer
from decord import VideoReader
from decord import cpu, gpu
from utils.commons.face_alignment_utils import mediapipe_lm478_to_face_alignment_lm68
import mediapipe
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
# fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, network_size=4, device='cuda')
mp_face_mesh = mediapipe.solutions.face_mesh
face_reconstructor = deep_3drecon.Reconstructor()
def chunk(iterable, chunk_size):
final_ret = []
cnt = 0
ret = []
for record in iterable:
if cnt == 0:
ret = []
ret.append(record)
cnt += 1
if len(ret) == chunk_size:
final_ret.append(ret)
ret = []
if len(final_ret[-1]) != chunk_size:
final_ret.append(ret)
return final_ret
# landmark detection in Deep3DRecon
def lm68_2_lm5(in_lm):
assert in_lm.ndim == 2
# in_lm: shape=[68,2]
lm_idx = np.array([31,37,40,43,46,49,55]) - 1
# 将上述特殊角点的数据取出，得到5个新的角点数据，拼接起来。
lm = np.stack([in_lm[lm_idx[0],:],np.mean(in_lm[lm_idx[[1,2]],:],0),np.mean(in_lm[lm_idx[[3,4]],:],0),in_lm[lm_idx[5],:],in_lm[lm_idx[6],:]], axis = 0)
# 将第一个角点放在了第三个位置
lm = lm[[1,2,0,3,4],:2]
return lm
def extract_frames_job(fname):
out_name=fname.replace(".mp4", "_coeff_pt.npy").replace("datasets/raw/cropped_clips", "datasets/processed/coeff")
if os.path.exists(out_name):
return None
video_reader = VideoReader(fname, ctx=cpu(0))
frame_rgb_lst = video_reader.get_batch(list(range(0,len(video_reader)))).asnumpy()
return frame_rgb_lst
def extract_lms_mediapipe_job(frames):
if frames is None:
return None
with mp_face_mesh.FaceMesh(
static_image_mode=False,
max_num_faces=1,
refine_landmarks=True,
min_detection_confidence=0.5) as face_mesh:
ldms_normed = []
frame_i = 0
frame_ids = []
for i in range(len(frames)):
# Convert the BGR image to RGB before processing.
ret = face_mesh.process(frames[i])
# Print and draw face mesh landmarks on the image.
if not ret.multi_face_landmarks:
print(f"Skip Item: Caught errors when mediapipe get face_mesh, maybe No face detected in some frames!")
return None
else:
myFaceLandmarks = []
lms = ret.multi_face_landmarks[0]
for lm in lms.landmark:
myFaceLandmarks.append([lm.x, lm.y, lm.z])
ldms_normed.append(myFaceLandmarks)
frame_ids.append(frame_i)
frame_i += 1
bs, H, W, _ = frames.shape
ldms478 = np.array(ldms_normed)
lm68 = mediapipe_lm478_to_face_alignment_lm68(ldms478, H, W, return_2d=True)
lm5_lst = [lm68_2_lm5(lm68[i]) for i in range(lm68.shape[0])]
lm5 = np.stack(lm5_lst)
return ldms478, lm68, lm5
def process_video_batch(fname_lst, out_name_lst=None):
frames_lst = []
with Timer("load_frames", True):
for (i, res) in multiprocess_run_tqdm(extract_frames_job, fname_lst, num_workers=2, desc="decord is loading frames in the batch videos..."):
frames_lst.append(res)
lm478s_lst = []
lm68s_lst = []
lm5s_lst = []
with Timer("mediapipe_faceAlign", True):
for (i, res) in multiprocess_run_tqdm(extract_lms_mediapipe_job, frames_lst, num_workers=2, desc="mediapipe is predicting face mesh in batch videos..."):
if res is None:
res = (None, None, None)
lm478s, lm68s, lm5s = res
lm478s_lst.append(lm478s)
lm68s_lst.append(lm68s)
lm5s_lst.append(lm5s)
processed_cnt_in_this_batch = 0
with Timer("deep_3drecon_pytorch", True):
for i, fname in tqdm(enumerate(fname_lst), total=len(fname_lst), desc="extracting 3DMM in the batch videos..."):
video_rgb = frames_lst[i] # [t, 224,224, 3]
lm478_arr = lm478s_lst[i]
lm68_arr = lm68s_lst[i]
lm5_arr = lm5s_lst[i]
if lm5_arr is None:
continue
num_frames = len(video_rgb)
batch_size = 32
iter_times = num_frames // batch_size
last_bs = num_frames % batch_size
coeff_lst = []
for i_iter in range(iter_times):
start_idx = i_iter * batch_size
batched_images = video_rgb[start_idx: start_idx + batch_size]
batched_lm5 = lm5_arr[start_idx: start_idx + batch_size]
coeff, align_img = face_reconstructor.recon_coeff(batched_images, batched_lm5, return_image = True)
coeff_lst.append(coeff)
if last_bs != 0:
batched_images = video_rgb[-last_bs:]
batched_lm5 = lm5_arr[-last_bs:]
coeff, align_img = face_reconstructor.recon_coeff(batched_images, batched_lm5, return_image = True)
coeff_lst.append(coeff)
coeff_arr = np.concatenate(coeff_lst,axis=0)
result_dict = {
'coeff': coeff_arr.reshape([num_frames, -1]).astype(np.float32),
'lm478': lm478_arr.reshape([num_frames, 478, 3]).astype(np.float32),
'lm68': lm68_arr.reshape([num_frames, 68, 2]).astype(np.int16),
'lm5': lm5_arr.reshape([num_frames, 5, 2]).astype(np.int16),
}
np.save(out_name_lst[i], result_dict)
processed_cnt_in_this_batch +=1
print(f"In this batch {processed_cnt_in_this_batch} files are processed")
def split_wav(mp4_name):
wav_name = mp4_name[:-4] + '.wav'
if os.path.exists(wav_name):
return
video = VideoFileClip(mp4_name,verbose=False)
dur = video.duration
audio = video.audio
assert audio is not None
audio.write_audiofile(wav_name,fps=16000,verbose=False,logger=None)
if __name__ == '__main__':
### Process Single Long video for NeRF dataset
# video_id = 'May'
# video_fname = f"data/raw/videos/{video_id}.mp4"
# out_fname = f"data/processed/videos/{video_id}/coeff.npy"
# process_video(video_fname, out_fname)
### Process short video clips for LRS3 dataset
import random
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--lrs3_path', type=str, default='/home/yezhenhui/projects/TalkingHead-1KH/datasets/raw/cropped_clips', help='')
parser.add_argument('--process_id', type=int, default=0, help='')
parser.add_argument('--total_process', type=int, default=1, help='')
args = parser.parse_args()
import os, glob
lrs3_dir = args.lrs3_path
out_dir = lrs3_dir.replace("raw/cropped_clips", "processed/coeff")
os.makedirs(out_dir, exist_ok=True)
# mp4_name_pattern = os.path.join(lrs3_dir, "*.mp4")
# mp4_names = glob.glob(mp4_name_pattern)
with open('/home/yezhenhui/projects/LDMAvatar/clean.txt', 'r') as f:
txt = f.read()
mp4_names = txt.split("\n")
mp4_names = sorted(mp4_names)
if args.total_process > 1:
assert args.process_id <= args.total_process-1
num_samples_per_process = len(mp4_names) // args.total_process
if args.process_id == args.total_process-1:
mp4_names = mp4_names[args.process_id * num_samples_per_process : ]
else:
mp4_names = mp4_names[args.process_id * num_samples_per_process : (args.process_id+1) * num_samples_per_process]
random.seed(111)
random.shuffle(mp4_names)
batched_mp4_names_lst = chunk(mp4_names, chunk_size=8)
for batch_mp4_names in tqdm(batched_mp4_names_lst, desc='[ROOT]: extracting face mesh and 3DMM in batches...'):
try:
for mp4_name in batch_mp4_names:
split_wav(mp4_name)
out_names = [mp4_name.replace(".mp4", "_coeff_pt.npy").replace("datasets/raw/cropped_clips", "datasets/processed/coeff") for mp4_name in batch_mp4_names]
process_video_batch(batch_mp4_names, out_names)
# process_video(mp4_name,out_name=mp4_name.replace(".mp4", "_coeff_pt.npy").replace("datasets/raw/cropped_clips", "datasets/processed/coeff"))
except Exception as e:
print(e)
continue
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