Delete src/facerender/animate.py

src/facerender/animate.py

@@ -1,219 +0,0 @@
-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, new_audio_path
-