import os import cv2 import yaml import numpy as np import warnings from skimage import img_as_ubyte warnings.filterwarnings('ignore') import imageio import torch import torchvision from src.facerender.modules.keypoint_detector import HEEstimator, KPDetector from src.facerender.modules.mapping import MappingNet from src.facerender.modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator from src.facerender.modules.make_animation import make_animation from pydub import AudioSegment from src.utils.face_enhancer import enhancer as face_enhancer from src.utils.paste_pic import paste_pic from src.utils.videoio import save_video_with_watermark class AnimateFromCoeff(): def __init__(self, free_view_checkpoint, mapping_checkpoint, config_path, device): with open(config_path) as f: config = yaml.safe_load(f) generator = OcclusionAwareSPADEGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) kp_extractor = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) he_estimator = HEEstimator(**config['model_params']['he_estimator_params'], **config['model_params']['common_params']) mapping = MappingNet(**config['model_params']['mapping_params']) generator.to(device) kp_extractor.to(device) he_estimator.to(device) mapping.to(device) for param in generator.parameters(): param.requires_grad = False for param in kp_extractor.parameters(): param.requires_grad = False for param in he_estimator.parameters(): param.requires_grad = False for param in mapping.parameters(): param.requires_grad = False if free_view_checkpoint is not None: self.load_cpk_facevid2vid(free_view_checkpoint, kp_detector=kp_extractor, generator=generator, he_estimator=he_estimator) else: raise AttributeError("Checkpoint should be specified for video head pose estimator.") if mapping_checkpoint is not None: self.load_cpk_mapping(mapping_checkpoint, mapping=mapping) else: raise AttributeError("Checkpoint should be specified for video head pose estimator.") self.kp_extractor = kp_extractor self.generator = generator self.he_estimator = he_estimator self.mapping = mapping self.kp_extractor.eval() self.generator.eval() self.he_estimator.eval() self.mapping.eval() self.device = device def load_cpk_facevid2vid(self, checkpoint_path, generator=None, discriminator=None, kp_detector=None, he_estimator=None, optimizer_generator=None, optimizer_discriminator=None, optimizer_kp_detector=None, optimizer_he_estimator=None, device="cpu"): checkpoint = torch.load(checkpoint_path, map_location=torch.device(device)) if generator is not None: generator.load_state_dict(checkpoint['generator']) if kp_detector is not None: kp_detector.load_state_dict(checkpoint['kp_detector']) if he_estimator is not None: he_estimator.load_state_dict(checkpoint['he_estimator']) if discriminator is not None: try: discriminator.load_state_dict(checkpoint['discriminator']) except: print ('No discriminator in the state-dict. Dicriminator will be randomly initialized') if optimizer_generator is not None: optimizer_generator.load_state_dict(checkpoint['optimizer_generator']) if optimizer_discriminator is not None: try: optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator']) except RuntimeError as e: print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized') if optimizer_kp_detector is not None: optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector']) if optimizer_he_estimator is not None: optimizer_he_estimator.load_state_dict(checkpoint['optimizer_he_estimator']) return checkpoint['epoch'] def load_cpk_mapping(self, checkpoint_path, mapping=None, discriminator=None, optimizer_mapping=None, optimizer_discriminator=None, device='cpu'): checkpoint = torch.load(checkpoint_path, map_location=torch.device(device)) if mapping is not None: mapping.load_state_dict(checkpoint['mapping']) if discriminator is not None: discriminator.load_state_dict(checkpoint['discriminator']) if optimizer_mapping is not None: optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping']) if optimizer_discriminator is not None: optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator']) return checkpoint['epoch'] def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, background_enhancer=None, preprocess='crop'): source_image=x['source_image'].type(torch.FloatTensor) source_semantics=x['source_semantics'].type(torch.FloatTensor) target_semantics=x['target_semantics_list'].type(torch.FloatTensor) source_image=source_image.to(self.device) source_semantics=source_semantics.to(self.device) target_semantics=target_semantics.to(self.device) if 'yaw_c_seq' in x: yaw_c_seq = x['yaw_c_seq'].type(torch.FloatTensor) yaw_c_seq = x['yaw_c_seq'].to(self.device) else: yaw_c_seq = None if 'pitch_c_seq' in x: pitch_c_seq = x['pitch_c_seq'].type(torch.FloatTensor) pitch_c_seq = x['pitch_c_seq'].to(self.device) else: pitch_c_seq = None if 'roll_c_seq' in x: roll_c_seq = x['roll_c_seq'].type(torch.FloatTensor) roll_c_seq = x['roll_c_seq'].to(self.device) else: roll_c_seq = None frame_num = x['frame_num'] predictions_video = make_animation(source_image, source_semantics, target_semantics, self.generator, self.kp_extractor, self.he_estimator, self.mapping, yaw_c_seq, pitch_c_seq, roll_c_seq, use_exp = True) predictions_video = predictions_video.reshape((-1,)+predictions_video.shape[2:]) predictions_video = predictions_video[:frame_num] video = [] for idx in range(predictions_video.shape[0]): image = predictions_video[idx] image = np.transpose(image.data.cpu().numpy(), [1, 2, 0]).astype(np.float32) video.append(image) result = img_as_ubyte(video) ### the generated video is 256x256, so we keep the aspect ratio, original_size = crop_info[0] if original_size: result = [ cv2.resize(result_i,(256, int(256.0 * original_size[1]/original_size[0]) )) for result_i in result ] video_name = x['video_name'] + '.mp4' path = os.path.join(video_save_dir, 'temp_'+video_name) imageio.mimsave(path, result, fps=float(25)) av_path = os.path.join(video_save_dir, video_name) return_path = av_path audio_path = x['audio_path'] audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0] new_audio_path = os.path.join(video_save_dir, audio_name+'.wav') print('new_audio_path',new_audio_path) start_time = 0 # cog will not keep the .mp3 filename sound = AudioSegment.from_file(audio_path) frames = frame_num end_time = start_time + frames*1/25*1000 word1=sound.set_frame_rate(16000) word = word1[start_time:end_time] word.export(new_audio_path, format="wav") base64_video,temp_file_path = save_video_with_watermark(path, new_audio_path, av_path, watermark= False) print(f'The generated video is named {video_name} in {video_save_dir}') if preprocess.lower() == 'full': # only add watermark to the full image. video_name_full = x['video_name'] + '_full.mp4' full_video_path = os.path.join(video_save_dir, video_name_full) return_path = full_video_path base64_video,temp_file_path = paste_pic(path, pic_path, crop_info, new_audio_path, full_video_path) print(f'The generated video is named {video_save_dir}/{video_name_full}') else: full_video_path = av_path #### paste back then enhancers if enhancer: video_name_enhancer = x['video_name'] + '_enhanced.mp4' enhanced_path = os.path.join(video_save_dir, 'temp_'+video_name_enhancer) av_path_enhancer = os.path.join(video_save_dir, video_name_enhancer) return_path = av_path_enhancer enhanced_images = face_enhancer(temp_file_path, method=enhancer, bg_upsampler=background_enhancer) imageio.mimsave(enhanced_path, enhanced_images, fps=float(25)) base64_video,temp_file_path = save_video_with_watermark(enhanced_path, new_audio_path, av_path_enhancer, watermark= False) print(f'The generated video is named {video_save_dir}/{video_name_enhancer}') os.remove(enhanced_path) os.remove(path) os.remove(new_audio_path) return return_path,base64_video,temp_file_path