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infer_smpl.py
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infer_smpl.py
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# Software License Agreement (BSD License)
#
# Copyright (c) 2019, Zerong Zheng (zzr18@mails.tsinghua.edu.cn)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the <organization> nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import division, print_function
import os
import sys
from absl import flags
import numpy as np
import skimage.io as io
import tensorflow as tf
from src.util import image as img_util
import src.config
from src.RunModel import RunModel
flags.DEFINE_string('img_path', 'data/im1963.jpg', 'Image to run')
flags.DEFINE_string('out_dir', './data', 'Output folder')
flags.DEFINE_string(
'json_path', None,
'If specified, uses the openpose output to crop the image.')
def preprocess_image(img_path, img_size=224):
img = io.imread(img_path)
if img.shape[2] == 4:
img = img[:, :, :3]
if np.max(img.shape[:2]) != img_size:
print('Resizing so the max image size is %d..' % img_size)
scale = (float(img_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
crop, proc_param = img_util.scale_and_crop(img, scale, center,
img_size)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
return crop, proc_param, img
def main(img_fname, out_dir):
sess = tf.Session()
model = RunModel(config, sess=sess)
input_img, proc_param, img = preprocess_image(img_fname)
input_img = np.expand_dims(input_img, 0)
joints, verts, cams, joints3d, theta \
= model.predict(input_img, get_theta=True)
cam_s = cams[0][0]
cam_t = cams[0][1:]
theta = theta[0]
verts = verts[0]
img_dir, img_name = os.path.split(img_fname)
with open(os.path.join(out_dir, img_name + '.smpl_vertex.txt'), 'w') as fp:
for p in verts:
fp.write('%f %f %f\n' % ((p[0] + cam_t[0]) * cam_s,
(p[1] + cam_t[1]) * cam_s, p[2] * cam_s))
with open(os.path.join(out_dir, img_name + '.cam_param.txt'), 'w') as fp:
fp.write('%f %f %f' % (cam_s, cam_t[0], cam_t[1]))
with open(os.path.join(out_dir, img_name + '.smpl_param.txt'), 'w') as fp:
for p in theta[3:75]:
fp.write('%f ' % p)
fp.write('\n')
for p in theta[75:]:
fp.write('%f ' % p)
if __name__ == '__main__':
config = flags.FLAGS
config(sys.argv)
config.load_path = src.config.PRETRAINED_MODEL
config.batch_size = 1
main(config.img_path, config.out_dir)