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