Pythonfirst-order-model实现让照片动起来

#!/user/bin/env python
# coding=utf-8
"""
@project : first-order-model
@author  : 剑客阿良_ALiang
@file   : hy_gradio.py
@ide    : PyCharm
@time   : 2022-06-23 14:35:28
"""
import uuid
from typing import Optional
 
import gradio as gr
import matplotlib
 
matplotlib.use('Agg')
import os, sys
import yaml
from argparse import ArgumentParser
from tqdm import tqdm
 
import imageio
import numpy as np
from skimage.transform import resize
from skimage import img_as_ubyte
import torch
from sync_batchnorm import DataParallelWithCallback
 
from modules.generator import OcclusionAwareGenerator
from modules.keypoint_detector import KPDetector
from animate import normalize_kp
from scipy.spatial import ConvexHull
 
if sys.version_info[0] < 3:
    raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7")
 
 
def load_checkpoints(config_path, checkpoint_path, cpu=False):
    with open(config_path) as f:
        config = yaml.load(f)
 
    generator = OcclusionAwareGenerator(**config['model_params']['generator_params'],
                                        **config['model_params']['common_params'])
    if not cpu:
        generator.cuda()
 
    kp_detector = KPDetector(**config['model_params']['kp_detector_params'],
                             **config['model_params']['common_params'])
    if not cpu:
        kp_detector.cuda()
 
    if cpu:
        checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
    else:
        checkpoint = torch.load(checkpoint_path)
 
    generator.load_state_dict(checkpoint['generator'])
    kp_detector.load_state_dict(checkpoint['kp_detector'])
 
    if not cpu:
        generator = DataParallelWithCallback(generator)
        kp_detector = DataParallelWithCallback(kp_detector)
 
    generator.eval()
    kp_detector.eval()
 
    return generator, kp_detector
 
 
def make_animation(source_image, driving_video, generator, kp_detector, relative=True, adapt_movement_scale=True,
                   cpu=False):
    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()
        driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3)
        kp_source = kp_detector(source)
        kp_driving_initial = kp_detector(driving[:, :, 0])
 
        for frame_idx in tqdm(range(driving.shape[2])):
            driving_frame = driving[:, :, frame_idx]
            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])
    return predictions
 
 
def find_best_frame(source, driving, cpu=False):
    import face_alignment
 
    def normalize_kp(kp):
        kp = kp - kp.mean(axis=0, keepdims=True)
        area = ConvexHull(kp[:, :2]).volume
        area = np.sqrt(area)
        kp[:, :2] = kp[:, :2] / area
        return kp
 
    fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True,
                                      device='cpu' if cpu else 'cuda')
    kp_source = fa.get_landmarks(255 * source)[0]
    kp_source = normalize_kp(kp_source)
    norm = float('inf')
    frame_num = 0
    for i, image in tqdm(enumerate(driving)):
        kp_driving = fa.get_landmarks(255 * image)[0]
        kp_driving = normalize_kp(kp_driving)
        new_norm = (np.abs(kp_source - kp_driving) ** 2).sum()
        if new_norm < norm:
            norm = new_norm
            frame_num = i
    return frame_num
 
 
def h_interface(input_image: str):
    parser = ArgumentParser()
    opt = parser.parse_args()
    opt.config = "./config/vox-256.yaml"
    opt.checkpoint = "./checkpoint/vox-cpk.pth.tar"
    opt.source_image = input_image
    opt.driving_video = "./data/input/ts.mp4"
    opt.result_video = "./data/result/{}.mp4".format(uuid.uuid1().hex)
    opt.relative = True
    opt.adapt_scale = True
    opt.cpu = True
    opt.find_best_frame = False
    opt.best_frame = False
    # source_image = imageio.imread(opt.source_image)
    source_image = opt.source_image
    reader = imageio.get_reader(opt.driving_video)
    fps = reader.get_meta_data()['fps']
    driving_video = []
    try:
        for im in reader:
            driving_video.append(im)
    except RuntimeError:
        pass
    reader.close()
 
    source_image = resize(source_image, (256, 256))[..., :3]
    driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]
    generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu)
 
    if opt.find_best_frame or opt.best_frame is not None:
        i = opt.best_frame if opt.best_frame is not None else find_best_frame(source_image, driving_video, cpu=opt.cpu)
        print("Best frame: " + str(i))
        driving_forward = driving_video[i:]
        driving_backward = driving_video[:(i + 1)][::-1]
        predictions_forward = make_animation(source_image, driving_forward, generator, kp_detector,
                                             relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
        predictions_backward = make_animation(source_image, driving_backward, generator, kp_detector,
                                              relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
        predictions = predictions_backward[::-1] + predictions_forward[1:]
    else:
        predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=opt.relative,
                                     adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
    imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)
    return opt.result_video
 
 
if __name__ == "__main__":
    demo = gr.Interface(h_interface, inputs=[gr.Image(shape=(500, 500))], outputs=[gr.Video()])
 
    demo.launch()
    # h_interface("C:\\Users\\huyi\\Desktop\\xx3.jpg")