让图片动起来，特朗普和蒙娜丽莎深情合唱《Unravel》

import subprocessimport osfrom PIL import Image
def 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 argparse
import subprocessimport osfrom PIL import Image
def 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=False
if 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_file

python image_animation.py -i Inputs/trump2.png -c checkpoints/vox-cpk.pth.tar -v 1.mp4