Upload animate.py

src/facerender/animate.py

@@ -0,0 +1,262 @@
+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)
+
+         # Wrap models in DataParallel for multi-GPU support
+        if torch.cuda.device_count() > 1:
+            print(f"Using {torch.cuda.device_count()} GPUs")
+            generator = torch.nn.DataParallel(generator)
+            kp_extractor = torch.nn.DataParallel(kp_extractor)
+            he_estimator = torch.nn.DataParallel(he_estimator)
+            mapping = torch.nn.DataParallel(mapping)
+        
+        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))
+        
+        def adjust_state_dict(state_dict, model):
+            new_state_dict = {}
+            if isinstance(model, torch.nn.DataParallel):
+                for k, v in state_dict.items():
+                    new_key = f"module.{k}"  # Add 'module.' prefix
+                    new_state_dict[new_key] = v
+            else:
+                new_state_dict = state_dict  # Keep original state_dict for single-GPU models
+            return new_state_dict
+        
+        if generator is not None:
+            generator_state_dict = adjust_state_dict(checkpoint['generator'], generator)
+            generator.load_state_dict(generator_state_dict)
+        if kp_detector is not None:
+            kp_state_dict = adjust_state_dict(checkpoint['kp_detector'],kp_detector)
+            kp_detector.load_state_dict(kp_state_dict)
+        if he_estimator is not None:
+            he_state_dict = adjust_state_dict(checkpoint['he_estimator'],he_estimator)
+            he_estimator.load_state_dict(he_state_dict)
+        if discriminator is not None:
+            try:
+               discriminator_dict =adjust_state_dict(checkpoint['discriminator'],discriminator)
+               discriminator.load_state_dict(discriminator_dict)
+            except:
+               print ('No discriminator in the state-dict. Dicriminator will be randomly initialized')
+        if optimizer_generator is not None:
+            optimizer_generator_dict =adjust_state_dict(checkpoint['optimizer_generator'],optimizer_generator)
+            optimizer_generator.load_state_dict(optimizer_generator_dict)
+        if optimizer_discriminator is not None:
+            try:
+                optimizer_discriminator_dict = adjust_state_dict(checkpoint['optimizer_discriminator'],optimizer_discriminator)
+                optimizer_discriminator.load_state_dict(optimizer_discriminator_dict)
+            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_dict = adjust_state_dict(checkpoint['optimizer_kp_detector'],optimizer_kp_detector)
+            optimizer_kp_detector.load_state_dict(optimizer_kp_detector_dict)
+        if optimizer_he_estimator is not None:
+            optimizer_he_estimator_dict = adjust_state_dict(checkpoint['optimizer_he_estimator'],optimizer_he_estimator)
+            optimizer_he_estimator.load_state_dict(optimizer_he_estimator_dict)
+
+        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))
+        
+        def adjust_state_dict(state_dict, model):
+            new_state_dict = {}
+            if isinstance(model, torch.nn.DataParallel):
+                for k, v in state_dict.items():
+                    new_key = f"module.{k}"  # Add 'module.' prefix
+                    new_state_dict[new_key] = v
+            else:
+                new_state_dict = state_dict  # Keep original state_dict for single-GPU models
+            return new_state_dict
+        
+        if mapping is not None:
+            mapping_dict = adjust_state_dict(checkpoint['mapping'],mapping)
+            mapping.load_state_dict(mapping_dict)
+        if discriminator is not None:
+            discriminator_dict = adjust_state_dict(checkpoint['discriminator'],discriminator)
+            discriminator.load_state_dict(discriminator_dict)
+        if optimizer_mapping is not None:
+            optimizer_mapping_dict = adjust_state_dict(checkpoint['optimizer_mapping'],optimizer_mapping)
+            optimizer_mapping.load_state_dict(optimizer_mapping_dict)
+        if optimizer_discriminator is not None:
+            optimizer_discriminator_dict = adjust_state_dict(checkpoint['optimizer_discriminator'],optimizer_discriminator)
+            optimizer_discriminator.load_state_dict(optimizer_discriminator_dict)
+
+        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
+