lips(1).py

from config import *
import os
import numpy as np
import cv2, wav2lip.audio
import subprocess
from tqdm import tqdm
import glob
import torch, wav2lip.face_detection
from wav2lip.models import Wav2Lip
import platform


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):
	detector = wav2lip.face_detection.FaceAlignment(wav2lip.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 change resize_factor')
			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):
	img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

	if box[0] == -1:
		if not static:
			face_det_results = face_detect(frames) # BGR2RGB for CNN face detection
		else:
			face_det_results = face_detect([frames[0]])
	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]

	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):
	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):
	model = Wav2Lip()
	print("Load checkpoint from: {}".format(path))
	checkpoint = _load(path)
	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 modify_lips(path_id, audiofile, animatedfile, outfilePath):
	animatedfilePath = os.path.join("temp", path_id, animatedfile)
	audiofilePath = os.path.join("temp", path_id, audiofile)
	tempAudioPath = os.path.join("temp", path_id, "temp.wav")
	tempVideoPath  = os.path.join("temp", path_id, "temp.avi")

	if not os.path.isfile(animatedfilePath):
		raise ValueError('--face argument must be a valid path to video/image file')

	elif animatedfilePath.split('.')[1] in ['jpg', 'png', 'jpeg']:
		full_frames = [cv2.imread(animatedfilePath)]
		fps = fps

	else:
		video_stream = cv2.VideoCapture(animatedfilePath)
		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)))

	print('Extracting raw audio...')
	command = 'ffmpeg -y -i {} -strict -2 {}'.format(audiofilePath, tempAudioPath)
	subprocess.call(command, shell=True)
	

	wav = wav2lip.audio.load_wav(tempAudioPath, 16000)
	mel = wav2lip.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.copy(), mel_chunks)

	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:
			model = load_model(checkpoint_path)
			print ("Model loaded")

			frame_h, frame_w = full_frames[0].shape[:-1]
			out = cv2.VideoWriter(tempVideoPath, cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))

		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))

			f[y1:y2, x1:x2] = p
			out.write(f)

	out.release()

	command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(tempAudioPath, tempVideoPath, outfilePath)
	subprocess.call(command, shell=platform.system() != 'Windows')