让图片动起来，特朗普和蒙娜丽莎深情合唱《Unravel》
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第一时间关注技术干货！
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让一张图片，动起来，应该怎么做？
DeepFake 一阶运动模型，让万物皆可动。
利用这项技术，用特朗普和蒙娜丽莎的图片，合唱一首《Unravel》，是什么效果？
今天，它来了！
今天，继续手把手教学。
算法原理、环境搭建、效果实现，一条龙服务，尽在下文！
2
「特朗普和蒙娜丽莎的深情合唱」。
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》
python-mpipinstall-rrequirements.txt4
import subprocessimport osfrom PIL import Imagedef video2mp3(file_name): """ 将视频转为音频 :param file_name: 传入视频文件的路径 :return: """ outfile_name = file_name.split('.')[0] + '.mp3' cmd = 'ffmpeg -i ' + file_name + ' -f mp3 ' + outfile_name subprocess.call(cmd, shell=True)def video_add_mp3(file_name, mp3_file): """ 视频添加音频 :param file_name: 传入视频文件的路径 :param mp3_file: 传入音频文件的路径 :return: """ outfile_name = file_name.split('.')[0] + '-f.mp4' subprocess.call('ffmpeg -i ' + file_name + ' -i ' + mp3_file + ' -strict -2 -f mp4 ' + outfile_name, shell=True)import imageioimport torchfrom tqdm import tqdmfrom animate import normalize_kpfrom demo import load_checkpointsimport numpy as npimport matplotlib.pyplot as pltimport matplotlib.animation as animationfrom skimage import img_as_ubytefrom skimage.transform import resizeimport cv2import osimport argparseimport subprocessimport osfrom PIL import Imagedef video2mp3(file_name): """ 将视频转为音频 :param file_name: 传入视频文件的路径 :return: """ outfile_name = file_name.split('.')[0] + '.mp3' cmd = 'ffmpeg -i ' + file_name + ' -f mp3 ' + outfile_name print(cmd) subprocess.call(cmd, shell=True)def video_add_mp3(file_name, mp3_file): """ 视频添加音频 :param file_name: 传入视频文件的路径 :param mp3_file: 传入音频文件的路径 :return: """ outfile_name = file_name.split('.')[0] + '-f.mp4' subprocess.call('ffmpeg -i ' + file_name + ' -i ' + mp3_file + ' -strict -2 -f mp4 ' + outfile_name, shell=True)ap = argparse.ArgumentParser()ap.add_argument("-i", "--input_image", required=True,help="Path to image to animate")ap.add_argument("-c", "--checkpoint", required=True,help="Path to checkpoint")ap.add_argument("-v","--input_video", required=False, help="Path to video input")args = vars(ap.parse_args())print("[INFO] loading source image and checkpoint...")source_path = args['input_image']checkpoint_path = args['checkpoint']if args['input_video']: video_path = args['input_video']else: video_path = Nonesource_image = imageio.imread(source_path)source_image = resize(source_image,(256,256))[..., :3]generator, kp_detector = load_checkpoints(config_path='config/vox-256.yaml', checkpoint_path=checkpoint_path)if not os.path.exists('output'): os.mkdir('output')relative=Trueadapt_movement_scale=Truecpu=Falseif video_path: cap = cv2.VideoCapture(video_path) print("[INFO] Loading video from the given path")else: cap = cv2.VideoCapture(0) print("[INFO] Initializing front camera...")fps = cap.get(cv2.CAP_PROP_FPS)size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))video2mp3(file_name = video_path)fourcc = cv2.VideoWriter_fourcc('M','P','E','G')#out1 = cv2.VideoWriter('output/test.avi', fourcc, fps, (256*3 , 256), True)out1 = cv2.VideoWriter('output/test.mp4', fourcc, fps, size, True)cv2_source = cv2.cvtColor(source_image.astype('float32'),cv2.COLOR_BGR2RGB)with torch.no_grad() : predictions = [] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not cpu: source = source.cuda() kp_source = kp_detector(source) count = 0 while(True): ret, frame = cap.read() frame = cv2.flip(frame,1) if ret == True: if not video_path: x = 143 y = 87 w = 322 h = 322 frame = frame[y:y+h,x:x+w] frame1 = resize(frame,(256,256))[..., :3] if count == 0: source_image1 = frame1 source1 = torch.tensor(source_image1[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) kp_driving_initial = kp_detector(source1) frame_test = torch.tensor(frame1[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) driving_frame = frame_test if not cpu: driving_frame = driving_frame.cuda() kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale) out = generator(source, kp_source=kp_source, kp_driving=kp_norm) predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) im = np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0] im = cv2.cvtColor(im,cv2.COLOR_RGB2BGR) #joinedFrame = np.concatenate((cv2_source,im,frame1),axis=1) #joinedFrame = np.concatenate((cv2_source,im,frame1),axis=1) #cv2.imshow('Test',joinedFrame) #out1.write(img_as_ubyte(joinedFrame)) out1.write(img_as_ubyte(im)) count += 1# if cv2.waitKey(20) & 0xFF == ord('q'):# break else: break cap.release() out1.release() cv2.destroyAllWindows()video_add_mp3(file_name='output/test.mp4', mp3_file=video_path.split('.')[0] + '.mp3')
python image_animation.py -i path_to_input_file -c path_to_checkpoint -v path_to_video_filepython image_animation.py -i Inputs/trump2.png -c checkpoints/vox-cpk.pth.tar -v 1.mp45
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版权声明：以上内容为用户推荐收藏至CareerEngine平台，其内容（含文字、图片、视频、音频等）及知识版权均属用户或用户转发自的第三方网站，如涉嫌侵权，请通知[email protected]进行信息删除。如需查看信息来源，请点击“查看原文”。如需洽谈其它事宜，请联系[email protected]。