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| 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 | |
| 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 | |
| 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']) | |
| mapping = MappingNet(**config['model_params']['mapping_params']) | |
| generator.to(device) | |
| kp_extractor.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 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) | |
| 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.mapping = mapping | |
| self.kp_extractor.eval() | |
| self.generator.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, enhancer=None, original_size=None): | |
| 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) | |
| yaw_c_seq = x['yaw_c_seq'].type(torch.FloatTensor) | |
| pitch_c_seq = x['pitch_c_seq'].type(torch.FloatTensor) | |
| roll_c_seq = x['roll_c_seq'].type(torch.FloatTensor) | |
| source_image=source_image.to(self.device) | |
| source_semantics=source_semantics.to(self.device) | |
| target_semantics=target_semantics.to(self.device) | |
| yaw_c_seq = x['yaw_c_seq'].to(self.device) | |
| pitch_c_seq = x['pitch_c_seq'].to(self.device) | |
| roll_c_seq = x['roll_c_seq'].to(self.device) | |
| frame_num = x['frame_num'] | |
| predictions_video = make_animation(source_image, source_semantics, target_semantics, | |
| self.generator, self.kp_extractor, 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, | |
| 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)) | |
| if enhancer: | |
| video_name_enhancer = x['video_name'] + '_enhanced.mp4' | |
| av_path_enhancer = os.path.join(video_save_dir, video_name_enhancer) | |
| enhanced_path = os.path.join(video_save_dir, 'temp_'+video_name_enhancer) | |
| enhanced_images = face_enhancer(result, method=enhancer) | |
| if original_size: | |
| enhanced_images = [ cv2.resize(result_i,(256, int(256.0 * original_size[1]/original_size[0]) )) for result_i in enhanced_images ] | |
| imageio.mimsave(enhanced_path, enhanced_images, fps=float(25)) | |
| av_path = os.path.join(video_save_dir, video_name) | |
| 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') | |
| start_time = 0 | |
| sound = AudioSegment.from_mp3(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") | |
| cmd = r'ffmpeg -y -i "%s" -i "%s" -vcodec copy "%s"' % (path, new_audio_path, av_path) | |
| os.system(cmd) | |
| if enhancer: | |
| cmd = r'ffmpeg -y -i "%s" -i "%s" -vcodec copy "%s"' % (enhanced_path, new_audio_path, av_path_enhancer) | |
| os.system(cmd) | |
| os.remove(enhanced_path) | |
| os.remove(path) | |
| os.remove(new_audio_path) | |