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inference.py
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inference.py
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import uuid
from typing import List
import numpy as np
import cv2, os, argparse, audio
import subprocess
from tqdm import tqdm
import torch, face_detection
from models import Wav2Lip
import platform
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
def get_smoothened_boxes(boxes, T):
for i in range(len(boxes)):
if i + T > len(boxes):
window = boxes[len(boxes) - T:]
else:
window = boxes[i: i + T]
boxes[i] = np.mean(window, axis=0)
return boxes
def face_detect(images, face_det_batch_size, pads, nosmooth, device):
detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D,
flip_input=False, device=device)
batch_size = face_det_batch_size
while 1:
predictions = []
try:
for i in tqdm(range(0, len(images), batch_size)):
predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size])))
except RuntimeError:
if batch_size == 1:
raise RuntimeError(
'Image too big to run face detection on GPU. Please use the --resize_factor argument')
batch_size //= 2
print('Recovering from OOM error; New batch size: {}'.format(batch_size))
continue
break
results = []
pady1, pady2, padx1, padx2 = pads
for rect, image in zip(predictions, images):
if rect is None:
cv2.imwrite('temp/faulty_frame.jpg', image) # check this frame where the face was not detected.
raise ValueError('Face not detected! Ensure the video contains a face in all the frames.')
y1 = max(0, rect[1] - pady1)
y2 = min(image.shape[0], rect[3] + pady2)
x1 = max(0, rect[0] - padx1)
x2 = min(image.shape[1], rect[2] + padx2)
results.append([x1, y1, x2, y2])
boxes = np.array(results)
if not nosmooth: boxes = get_smoothened_boxes(boxes, T=5)
results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)]
del detector
return results
def datagen(frames, mels, box, static, face_det_batch_size, pads, nosmooth, img_size, wav2lip_batch_size, device):
img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
if box[0] == -1:
if not static:
face_det_results = face_detect(frames,
face_det_batch_size=face_det_batch_size,
pads=pads,
nosmooth=nosmooth,
device=device) # BGR2RGB for CNN face detection
else:
face_det_results = face_detect([frames[0]],
face_det_batch_size=face_det_batch_size,
pads=pads,
nosmooth=nosmooth,
device=device)
else:
print('Using the specified bounding box instead of face detection...')
y1, y2, x1, x2 = box
face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames]
# 无限拼接
frames = np.concatenate((frames, np.flip(frames, axis=0)), axis=0)
face_det_results = face_det_results + face_det_results[::-1]
for i, m in enumerate(mels):
idx = 0 if static else i % len(frames)
frame_to_save = frames[idx].copy()
face, coords = face_det_results[idx].copy()
face = cv2.resize(face, (img_size, img_size))
img_batch.append(face)
mel_batch.append(m)
frame_batch.append(frame_to_save)
coords_batch.append(coords)
if len(img_batch) >= wav2lip_batch_size:
img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
img_masked = img_batch.copy()
img_masked[:, img_size // 2:] = 0
img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
yield img_batch, mel_batch, frame_batch, coords_batch
img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
if len(img_batch) > 0:
img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
img_masked = img_batch.copy()
img_masked[:, img_size // 2:] = 0
img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
yield img_batch, mel_batch, frame_batch, coords_batch
def _load(checkpoint_path, device):
if device == 'cuda':
checkpoint = torch.load(checkpoint_path)
else:
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
return checkpoint
def load_model(path, device):
model = Wav2Lip()
print("Load checkpoint from: {}".format(path))
checkpoint = _load(path, device)
s = checkpoint["state_dict"]
new_s = {}
for k, v in s.items():
new_s[k.replace('module.', '')] = v
model.load_state_dict(new_s)
model = model.to(device)
return model.eval()
def face_mask_from_image(image, face_landmarks_detector):
"""
Calculate face mask from image. This is done by
Args:
image: numpy array of an image
face_landmarks_detector: mediapipa face landmarks detector
Returns:
A uint8 numpy array with the same height and width of the input image, containing a binary mask of the face in the image
"""
# initialize mask
mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
# detect face landmarks
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image)
detection = face_landmarks_detector.detect(mp_image)
if len(detection.face_landmarks) == 0:
# no face detected - set mask to all of the image
mask[:] = 1
return mask
# extract landmarks coordinates
face_coords = np.array([[lm.x * image.shape[1], lm.y * image.shape[0]] for lm in detection.face_landmarks[0]])
# calculate convex hull from face coordinates
convex_hull = cv2.convexHull(face_coords.astype(np.float32))
# apply convex hull to mask
return cv2.fillPoly(mask, pts=[convex_hull.squeeze().astype(np.int32)], color=1)
def main(face: str, audio_path: str, model: Wav2Lip,
fps: float = 25., resize_factor: int = 1, rotate: bool = False,
wav2lip_batch_size: int = 128, crop: List = [0, -1, 0, -1], outfile: str = '',
box: List = [-1, -1, -1, -1], static: bool = False, face_det_batch_size: int = 16, pads: List = [0, 10, 0, 0],
nosmooth: bool = False, img_size: int = 288,
device: str = "cuda", mel_step_size: int = 16,
face_landmarks_detector=None):
# 自动获取人脸遮罩点检测器
if face_landmarks_detector is None and os.path.exists(
os.path.join(os.path.dirname(__file__), "weights/face_landmarker_v2_with_blendshapes.task")):
face_landmarks_detector = vision.FaceLandmarker.create_from_options(
vision.FaceLandmarkerOptions(
base_options=python.BaseOptions(model_asset_path=os.path.join(os.path.dirname(__file__),
"weights/face_landmarker_v2_with_blendshapes.task")),
output_face_blendshapes=True,
output_facial_transformation_matrixes=True,
num_faces=1))
tmp_video = ''
tmp_audio = ''
try:
if not os.path.isfile(face):
raise ValueError('--face argument must be a valid path to video/image file')
elif face.split('.')[1] in ['jpg', 'png', 'jpeg']:
full_frames = [cv2.imread(face)]
else:
video_stream = cv2.VideoCapture(face)
fps = video_stream.get(cv2.CAP_PROP_FPS)
print('Reading video frames...')
full_frames = []
while 1:
still_reading, frame = video_stream.read()
if not still_reading:
video_stream.release()
break
if resize_factor > 1:
frame = cv2.resize(frame, (frame.shape[1] // resize_factor, frame.shape[0] // resize_factor))
if rotate:
frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)
y1, y2, x1, x2 = crop
if x2 == -1: x2 = frame.shape[1]
if y2 == -1: y2 = frame.shape[0]
frame = frame[y1:y2, x1:x2]
full_frames.append(frame)
print("Number of frames available for inference: " + str(len(full_frames)))
if not audio_path.endswith('.wav'):
print('Extracting raw audio...')
tmp_audio = f"/dev/shm/{uuid.uuid4()}.wav"
command = 'ffmpeg -y -i {} -strict -2 {}'.format(audio_path, tmp_audio)
subprocess.call(command, shell=True)
audio_path = tmp_audio
wav = audio.load_wav(audio_path, 16000)
mel = audio.melspectrogram(wav)
print(mel.shape)
if np.isnan(mel.reshape(-1)).sum() > 0:
raise ValueError(
'Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
mel_chunks = []
mel_idx_multiplier = 80. / fps
i = 0
while 1:
start_idx = int(i * mel_idx_multiplier)
if start_idx + mel_step_size > len(mel[0]):
mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
break
mel_chunks.append(mel[:, start_idx: start_idx + mel_step_size])
i += 1
print("Length of mel chunks: {}".format(len(mel_chunks)))
full_frames = full_frames[:len(mel_chunks)]
batch_size = wav2lip_batch_size
gen = datagen(full_frames, mel_chunks, box, static, face_det_batch_size,
pads, nosmooth, img_size, wav2lip_batch_size, device)
# 无声视频
frame_h, frame_w = full_frames[0].shape[:-1]
tmp_video = f"/dev/shm/{uuid.uuid4()}.avi"
out = cv2.VideoWriter(tmp_video, cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))
kernel_size = 21 # 高斯核的大小
sigma = 0 # 标准差,如果为0,则函数会根据核大小自动选择
for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen,
total=int(
np.ceil(
float(len(mel_chunks)) / batch_size)))):
img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
with torch.no_grad():
pred = model(mel_batch, img_batch)
pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
for p, f, c in zip(pred, frames, coords):
y1, y2, x1, x2 = c
p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
if face_landmarks_detector:
mask = face_mask_from_image(p, face_landmarks_detector)
blur_mask = cv2.GaussianBlur(mask.astype(float), (kernel_size, kernel_size), sigma)
blur_mask = blur_mask / blur_mask.max() # 归一化
# 应用渐变权重矩阵
# 这里的...代表所有的颜色通道
f[y1:y2, x1:x2] = f[y1:y2, x1:x2] * (1 - blur_mask[..., None]) + p * blur_mask[..., None]
else:
f[y1:y2, x1:x2] = p
out.write(f)
out.release()
command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(audio_path, tmp_video, outfile)
subprocess.call(command, shell=platform.system() != 'Windows')
finally:
# 清理临时文件
if os.path.exists(tmp_audio):
os.remove(tmp_audio)
if os.path.exists(tmp_video):
os.remove(tmp_video)
# 关闭人脸遮罩检测器
if face_landmarks_detector is not None:
face_landmarks_detector.close()
# 清理显存
torch.cuda.empty_cache()
_fa = None
if __name__ == '__main__':
# 命令行参数
parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models')
parser.add_argument('--checkpoint_path', type=str,
help='Name of saved checkpoint to load weights from',
required=True)
parser.add_argument('--face', type=str,
help='Filepath of video/image that contains faces to use',
required=True)
parser.add_argument('--audio', type=str,
help='Filepath of video/audio file to use as raw audio source',
required=True)
parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.',
default='')
parser.add_argument('--static', type=bool,
help='If True, then use only first video frame for inference', default=False)
parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)',
default=25., required=False)
parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0],
help='Padding (top, bottom, left, right). Please adjust to include chin at least')
parser.add_argument('--face_det_batch_size', type=int,
help='Batch size for face detection', default=16)
parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128)
parser.add_argument('--resize_factor', default=1, type=int,
help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p')
parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1],
help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. '
'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width')
parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1],
help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.'
'Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).')
parser.add_argument('--rotate', default=False, action='store_true',
help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.'
'Use if you get a flipped result, despite feeding a normal looking video')
parser.add_argument('--nosmooth', default=False, action='store_true',
help='Prevent smoothing face detections over a short temporal window')
parser.add_argument('--face_landmarks_detector_path',
default='weights/face_landmarker_v2_with_blendshapes.task',
type=str,
help='Path to face landmarks detector. Pass empty string to ignore face landmarks detection.')
args = parser.parse_args()
# args.img_size = 96
args.img_size = 288
# output file
if not args.outfile:
args.outfile = os.path.join("results", os.path.basename(args.checkpoint_path) + ".mp4")
if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
args.static = True
# 推理设备
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Using {} for inference.'.format(device))
# 推理模型加载
model = load_model(args.checkpoint_path, device)
print("Model loaded")
# 人脸遮罩检测器
# Create an FaceLandmarker object.
face_landmarks_detector = vision.FaceLandmarker.create_from_options(
vision.FaceLandmarkerOptions(
base_options=python.BaseOptions(model_asset_path=args.face_landmarks_detector_path),
output_face_blendshapes=True,
output_facial_transformation_matrixes=True,
num_faces=1)
) if args.face_landmarks_detector_path else None
try:
# 推理主过程
main(model=model, face=args.face, fps=args.fps, resize_factor=args.resize_factor, rotate=args.rotate,
audio_path=args.audio, wav2lip_batch_size=args.wav2lip_batch_size, crop=args.crop, outfile=args.outfile,
box=args.box, static=args.static, face_det_batch_size=args.face_det_batch_size,
pads=args.pads, nosmooth=args.nosmooth, img_size=args.img_size,
device=device, mel_step_size=16, face_landmarks_detector=face_landmarks_detector)
finally:
if hasattr(face_landmarks_detector, "close"):
face_landmarks_detector.close()