def handle2x(config, args):
# resize input
h1, w1, scale1 = pad_to_height(config.img_size[0], args.img1_height, args.img1_width)
h2, w2, scale2 = pad_to_height(config.img_size[0], args.img2_height, args.img2_width)
# load trained model
net = get_autoencoder(config)
net.load_state_dict(torch.load(args.model_path))
net.to(config.device)
net.eval()
# mean/std pose
mean_pose, std_pose = get_meanpose(config)
# get input
input1 = openpose2motion(args.vid1_json_dir, scale=scale1, max_frame=args.max_length)
input2 = openpose2motion(args.vid2_json_dir, scale=scale2, max_frame=args.max_length)
input1 = preprocess_motion2d(input1, mean_pose, std_pose)
input2 = preprocess_motion2d(input2, mean_pose, std_pose)
input1 = input1.to(config.device)
input2 = input2.to(config.device)
# transfer by network
out12 = net.transfer(input1, input2)
out21 = net.transfer(input2, input1)
# postprocessing the outputs
input1 = postprocess_motion2d(input1, mean_pose, std_pose, w1 // 2, h1 // 2)
input2 = postprocess_motion2d(input2, mean_pose, std_pose, w2 // 2, h2 // 2)
out12 = postprocess_motion2d(out12, mean_pose, std_pose, w2 // 2, h2 // 2)
out21 = postprocess_motion2d(out21, mean_pose, std_pose, w1 // 2, h1 // 2)
if not args.disable_smooth:
out12 = gaussian_filter1d(out12, sigma=2, axis=-1)
out21 = gaussian_filter1d(out21, sigma=2, axis=-1)
if args.out_dir is not None:
save_dir = args.out_dir
ensure_dir(save_dir)
color1 = hex2rgb(args.color1)
color2 = hex2rgb(args.color2)
np.savez(os.path.join(save_dir, 'results.npz'),
input1=input1,
input2=input2,
out12=out12,
out21=out21)
if args.render_video:
print("Generating videos...")
motion2video(input1, h1, w1, os.path.join(save_dir, 'input1.mp4'), color1, args.transparency,
fps=args.fps, save_frame=args.save_frame)
motion2video(input2, h2, w2, os.path.join(save_dir,'input2.mp4'), color2, args.transparency,
fps=args.fps, save_frame=args.save_frame)
motion2video(out12, h2, w2, os.path.join(save_dir,'out12.mp4'), color2, args.transparency,
fps=args.fps, save_frame=args.save_frame)
motion2video(out21, h1, w1, os.path.join(save_dir,'out21.mp4'), color1, args.transparency,
fps=args.fps, save_frame=args.save_frame)
print("Done.")
def motion2video(motion, h, w, save_path, colors, transparency=False, motion_tgt=None, fps=25, save_frame=False):
nr_joints = motion.shape[0]
videowriter = imageio.get_writer(save_path, fps=fps)
vlen = motion.shape[-1]
if save_frame:
frames_dir = save_path[:-4] + '-frames'
ensure_dir(frames_dir)
for i in tqdm(range(vlen)):
[img, img_cropped] = joints2image(motion[:, :, i], colors, transparency, H=h, W=w, nr_joints=nr_joints)
if motion_tgt is not None:
[img_tgt, img_tgt_cropped] = joints2image(motion_tgt[:, :, i], colors, transparency, H=h, W=w, nr_joints=nr_joints)
img_ori = img.copy()
img = cv2.addWeighted(img_tgt, 0.3, img_ori, 0.7, 0)
img_cropped = cv2.addWeighted(img_tgt, 0.3, img_ori, 0.7, 0)
bb = bounding_box(img_cropped)
img_cropped = img_cropped[:, bb[2]:bb[3], :]
if save_frame:
save_image(img_cropped, os.path.join(frames_dir, "%04d.png" % i))
videowriter.append_data(img)
videowriter.close()
def motion_feature_extract(config, args):
# resize input
h1, w1, scale1 = pad_to_height(config.img_size[0], args.img1_height, args.img1_width)
# load trained model
net = get_autoencoder(config)
net.load_state_dict(torch.load(args.model_path))
net.to(config.device)
net.eval()
# mean/std pose
mean_pose, std_pose = get_meanpose(config)
# get input
input1 = openpose2motion(args.vid1_json_dir, scale=scale1, max_frame=args.max_length)
print("after motion")
print(input1.shape)
input1 = preprocess_motion2d(input1, mean_pose, std_pose)
print("after preprocess")
print(input1.shape)
if args.out_dir is not None:
save_dir = args.out_dir
ensure_dir(save_dir)
# color1 = hex2rgb(args.color1)
# color2 = hex2rgb(args.color2)
np.savez(os.path.join(save_dir, 'pose_feature.npz'), pose=input1)
input1 = input1.to(config.device)
# transfer by network
# out = net.transfer_three(input1, input2, input3)
out = net.forward(input1)
mot = net.mot_encoder(input1)
print(mot.shape)
# postprocessing the outputs
input1 = postprocess_motion2d(input1, mean_pose, std_pose, w1 // 2, h1 // 2)
out = postprocess_motion2d(out, mean_pose, std_pose, w1 // 2, h1 // 2)
print("after postprocess")
print(input1.shape)
if not args.disable_smooth:
out = gaussian_filter1d(out, sigma=2, axis=-1)
# if args.out_dir is not None:
# save_dir = args.out_dir
# ensure_dir(save_dir)
# # color1 = hex2rgb(args.color1)
# # color2 = hex2rgb(args.color2)
# np.savez(os.path.join(save_dir, 'results.npz'), pose=input1)
# input1=input1,
# input2=input2,
# input3=input3,
# out=out)
# if args.render_video:
# print("Generating videos...")
# motion2video(input1, h1, w1, os.path.join(save_dir,'input1.mp4'), color1, args.transparency,
# fps=args.fps, save_frame=args.save_frame)
# motion2video(input2, h2, w2, os.path.join(save_dir,'input2.mp4'), color2, args.transparency,
# fps=args.fps, save_frame=args.save_frame)
# motion2video(input3, h3, w3, os.path.join(save_dir,'input3.mp4'), color3, args.transparency,
# fps=args.fps, save_frame=args.save_frame)
# motion2video(out, h1, w1, os.path.join(save_dir,'out.mp4'), color2, args.transparency,
# fps=args.fps, save_frame=args.save_frame)
print("Done.")
def handle2x(config, args):
w1 = h1 = w2 = h2 = 512
# load trained model
net = get_autoencoder(config)
net.load_state_dict(torch.load(args.model_path))
net.to(config.device)
net.eval()
# mean/std pose
mean_pose, std_pose = get_meanpose(config)
# get input
dataloder = get_dataloader('test', config)
input1 = dataloder.dataset.preprocessing(args.path1,
args.view1).unsqueeze(0)
input2 = dataloder.dataset.preprocessing(args.path2,
args.view2).unsqueeze(0)
input1 = input1.to(config.device)
input2 = input2.to(config.device)
# transfer by network
out12 = net.transfer(input1, input2)
out21 = net.transfer(input2, input1)
# postprocessing the outputs
input1 = postprocess_motion2d(input1, mean_pose, std_pose, w1 // 2,
h1 // 2)
input2 = postprocess_motion2d(input2, mean_pose, std_pose, w2 // 2,
h2 // 2)
out12 = postprocess_motion2d(out12, mean_pose, std_pose, w2 // 2, h2 // 2)
out21 = postprocess_motion2d(out21, mean_pose, std_pose, w1 // 2, h1 // 2)
if not args.disable_smooth:
out12 = gaussian_filter1d(out12, sigma=2, axis=-1)
out21 = gaussian_filter1d(out21, sigma=2, axis=-1)
if args.out_dir is not None:
save_dir = args.out_dir
ensure_dir(save_dir)
color1 = hex2rgb(args.color1)
color2 = hex2rgb(args.color2)
np.savez(os.path.join(save_dir, 'results.npz'),
input1=input1,
input2=input2,
out12=out12,
out21=out21)
if args.render_video:
print("Generating videos...")
motion2video(input1,
h1,
w1,
os.path.join(save_dir, 'input1.mp4'),
color1,
args.transparency,
fps=args.fps,
save_frame=args.save_frame)
motion2video(input2,
h2,
w2,
os.path.join(save_dir, 'input2.mp4'),
color2,
args.transparency,
fps=args.fps,
save_frame=args.save_frame)
motion2video(out12,
h2,
w2,
os.path.join(save_dir, 'out12.mp4'),
color2,
args.transparency,
fps=args.fps,
save_frame=args.save_frame)
motion2video(out21,
h1,
w1,
os.path.join(save_dir, 'out21.mp4'),
color1,
args.transparency,
fps=args.fps,
save_frame=args.save_frame)
print("Done.")
input2 = postprocess_motion2d(input2, mean_pose, std_pose, w2 // 2, h2 // 2)
# interpolated motions [(J, 2, L), ..., (J, 2, L)]
interp_motions = [postprocess_motion2d(out12[i:i+1, :, :], mean_pose, std_pose) for i in range(out12.shape[0])]
# each cell's position
if args.form == 'line':
position = [str(i) for i in range(len(interp_motions))]
else:
position = [str(i // args.nr_sample) + '.' + str(i % args.nr_sample) for i in range(len(interp_motions))]
# write output video
out_path = args.out_path
if out_path is not None:
pardir = os.path.split(out_path)[0]
ensure_dir(pardir)
print('generating video...')
cell_height = cell_width = args.cell_height
color1 = hex2rgb(args.color1)
color2 = hex2rgb(args.color2)
vlen = min(input1.shape[-1], input2.shape[-1])
videowriter = imageio.get_writer(out_path, fps=25)
for i in tqdm(range(vlen)):
img_iterps = []
for j, motion in enumerate(interp_motions):
if args.form == 'line':
color = interpolate_color(color1, color2, j / (args.nr_sample - 1))
else:
color = interpolate_color(color1, color2, (j // args.nr_sample) / (args.nr_sample - 1))
img, img_cropped = joints2image(motion[:, :, i], color, transparency=args.transparency,
