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import numpy as np
import cv2, os, subprocess
from tqdm import tqdm
import torch
import platform
# import sys
# sys.path.append('..')
from src.models import Wav2Lip as wav2lip_mdoel
from src.utils import audio
import face_detection
class Wav2Lip:
def __init__(self, path = 'checkpoints/wav2lip.pth'):
self.fps = 25
self.resize_factor = 1
self.mel_step_size = 16
self.static = False
self.img_size = 96
self.face_det_batch_size = 2
self.box = [-1, -1, -1, -1]
self.pads = [0, 10, 0, 0]
self.nosmooth = False
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = self.load_model(path)
def load_model(self, checkpoint_path):
model = wav2lip_mdoel()
print("Load checkpoint from: {}".format(checkpoint_path))
if self.device == 'cuda':
checkpoint = torch.load(checkpoint_path)
else:
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
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(self.device)
return model.eval()
# def predict(self, face_path, audio_file, batch_size):
# if face_path.split('.')[1] in ['jpg', 'png', 'jpeg']:
# return self.predict_img(face_path, audio_file, batch_size)
# elif face_path.split('.')[1] == 'mp4':
# return self.predict_video(face_path, audio_file, batch_size)
# else:
# return None
def predict(self, face, audio_file, batch_size):
os.makedirs('results', exist_ok=True)
os.makedirs('temp', exist_ok=True)
frame = cv2.imread(face)
if self.resize_factor > 1:
frame = cv2.resize(frame, (frame.shape[1]//self.resize_factor, frame.shape[0]//self.resize_factor))
full_frames = [frame]
wav = audio.load_wav(audio_file, 16000)
mel = audio.melspectrogram(wav)
mel_chunks = []
mel_idx_multiplier = 80./self.fps
i = 0
while 1:
start_idx = int(i * mel_idx_multiplier)
if start_idx + self.mel_step_size > len(mel[0]):
mel_chunks.append(mel[:, len(mel[0]) - self.mel_step_size:])
break
mel_chunks.append(mel[:, start_idx : start_idx + self.mel_step_size])
i += 1
print("Length of mel chunks: {}".format(len(mel_chunks)))
full_frames = full_frames[:len(mel_chunks)]
batch_size = batch_size
gen = self.datagen(full_frames.copy(), mel_chunks, batch_size)
for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen,
total=int(np.ceil(float(len(mel_chunks))/batch_size)))):
if i == 0:
frame_h, frame_w = full_frames[0].shape[:-1]
out = cv2.VideoWriter('temp/result.avi',
cv2.VideoWriter_fourcc(*'DIVX'), self.fps, (frame_w, frame_h))
img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(self.device)
mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(self.device)
with torch.no_grad():
pred = self.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))
f[y1:y2, x1:x2] = p
out.write(f)
out.release()
command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(audio_file, 'temp/result.avi', 'results/example_answer.mp4')
subprocess.call(command, shell=platform.system() != 'Windows')
return 'results/example_answer.mp4'
def datagen(self, frames, mels, batch_size):
img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
if self.box[0] == -1:
if not self.static:
face_det_results = self.face_detect(frames) # BGR2RGB for CNN face detection
else:
face_det_results = self.face_detect([frames[0]])
else:
print('Using the specified bounding box instead of face detection...')
y1, y2, x1, x2 = self.box
face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames]
for i, m in enumerate(mels):
idx = 0 if self.static else i%len(frames)
frame_to_save = frames[idx].copy()
face, coords = face_det_results[idx].copy()
face = cv2.resize(face, (self.img_size, self.img_size))
img_batch.append(face)
mel_batch.append(m)
frame_batch.append(frame_to_save)
coords_batch.append(coords)
if len(img_batch) >= batch_size:
img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
img_masked = img_batch.copy()
img_masked[:, self.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[:, self.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 face_detect(self, images):
try:
detector = face_detection.FaceAlignment(face_detection.LandmarksType.TWO_D,
flip_input=False, device=self.device)
except:
detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D,
flip_input=False, device=self.device)
batch_size = self.face_det_batch_size
while 1:
predictions = []
try:
for i in tqdm(range(0, len(images), batch_size)):
# img_batch = torch.tensor(np.array(images[i:i + batch_size]), device=self.device)
# img_batch = img_batch.permute(0, 3, 1, 2)
# print(img_batch.shape, type(img_batch))
# predictions.extend(detector.get_landmarks_from_batch(img_batch))
predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size])))
except Exception as e:
print("Error in face detection: {}".format(e))
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 = self.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 self.nosmooth: boxes = self.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 get_smoothened_boxes(self, 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
if __name__ == '__main__':
wav2lip = Wav2Lip('../checkpoints/wav2lip.pth')
wav2lip.predict('../example.png', '../answer.wav', 2) |