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evaluate.py
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evaluate.py
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import os
from scipy.ndimage import gaussian_filter1d
import torch
import argparse
import numpy as np
from dataset import get_meanpose, get_dataloader
from model import get_autoencoder
from functional.visualization import motion2video, hex2rgb
from functional.motion import preprocess_motion2d, postprocess_motion2d, openpose2motion
from functional.utils import ensure_dir, pad_to_height
from common import config
VIEW_ANGLES = [(0, 0, -np.pi / 2),
(0, 0, -np.pi / 3),
(0, 0, -np.pi / 6),
(0, 0, 0),
(0, 0, np.pi / 6),
(0, 0, np.pi / 3),
(0, 0, np.pi / 2)]
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)
v1 = VIEW_ANGLES[args.view1] if args.view1 is not None else None
v2 = VIEW_ANGLES[args.view2] if args.view2 is not None else None
input1 = dataloder.dataset.preprocessing(args.path1, v1).unsqueeze(0)
input2 = dataloder.dataset.preprocessing(args.path2, v2).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.")
def handle3x(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)
h3, w3, scale3 = pad_to_height(config.img_size[0], args.img2_height, args.img3_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)
input3 = openpose2motion(args.vid3_json_dir, scale=scale3, max_frame=args.max_length)
input1 = preprocess_motion2d(input1, mean_pose, std_pose)
input2 = preprocess_motion2d(input2, mean_pose, std_pose)
input3 = preprocess_motion2d(input3, mean_pose, std_pose)
input1 = input1.to(config.device)
input2 = input2.to(config.device)
input3 = input3.to(config.device)
# transfer by network
out = net.transfer_three(input1, input2, input3)
# 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)
input3 = postprocess_motion2d(input3, mean_pose, std_pose, w2 // 2, h2 // 2)
out = postprocess_motion2d(out, mean_pose, std_pose, w2 // 2, h2 // 2)
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)
color3 = hex2rgb(args.color3)
np.savez(os.path.join(save_dir, 'results.npz'),
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, h2, w2, os.path.join(save_dir,'out.mp4'), color2, args.transparency,
fps=args.fps, save_frame=args.save_frame)
print("Done.")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--name', type=str, choices=['skeleton', 'view', 'full'], required=True,
help='which structure to use.')
parser.add_argument('--model_path', type=str, required=True, help="filepath for trained model weights")
parser.add_argument('--path1', type=str)
parser.add_argument('--path2', type=str)
parser.add_argument('--view1', type=int)
parser.add_argument('--view2', type=int)
parser.add_argument('-o', '--out_dir', type=str, default='./outputs', help="output saving directory")
parser.add_argument('--render_video', type=bool, default=True, help="whether to save rendered video")
parser.add_argument('--fps', type=float, default=25, help="fps of output video")
parser.add_argument('--save_frame', action='store_true', help="to save rendered video frames")
parser.add_argument('--color1', type=str, default='#a50b69#b73b87#db9dc3', help='color1')
parser.add_argument('--color2', type=str, default='#4076e0#40a7e0#40d7e0', help='color2')
parser.add_argument('--color3', type=str, default='#ff8b06#ffb431#ffcd9d', help='color3')
parser.add_argument('--disable_smooth', action='store_true',
help="disable gaussian kernel smoothing")
parser.add_argument('--transparency', action='store_true',
help="make background transparent in resulting frames")
parser.add_argument('--max_length', type=int, default=120,
help='maximum input video length')
parser.add_argument('-g', '--gpu_ids', type=int, default=0, required=False)
args = parser.parse_args()
config.initialize(args)
if args.name == 'full':
handle3x(config, args)
else:
handle2x(config, args)
if __name__ == '__main__':
main()