"""
Author: Soubhik Sanyal
Copyright (c) 2019, Soubhik Sanyal
All rights reserved.
Max-Planck-Gesellschaft zur Foerderung der Wissenschaften e.V. (MPG) is holder of all proprietary rights on this
computer program.
You can only use this computer program if you have closed a license agreement with MPG or you get the right to use
the computer program from someone who is authorized to grant you that right.
Any use of the computer program without a valid license is prohibited and liable to prosecution.
Copyright 2019 Max-Planck-Gesellschaft zur Foerderung der Wissenschaften e.V. (MPG). acting on behalf of its
Max Planck Institute for Intelligent Systems and the Max Planck Institute for Biological Cybernetics.
All rights reserved.
More information about RingNet is available at https://ringnet.is.tue.mpg.de.
based on github.com/akanazawa/hmr
"""
## Demo of RingNet.
## Note that RingNet requires a loose crop of the face in the image.
## Sample usage:
## Run the following command to generate check the RingNet predictions on loosely cropped face images
# python -m demo --img_path *.jpg --out_folder ./RingNet_output
## To output the meshes run the following command
# python -m demo --img_path *.jpg --out_folder ./RingNet_output --save_obj_file=True
## To output both meshes and flame parameters run the following command
# python -m demo --img_path *.jpg --out_folder ./RingNet_output --save_obj_file=True --save_flame_parameters=True
## To output both meshes and flame parameters and generate a neutralized mesh run the following command
# python -m demo --img_path *.jpg --out_folder ./RingNet_output --save_obj_file=True --save_flame_parameters=True --neutralize_expression=True
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
from absl import flags
import numpy as np
import skimage.io as io
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf
from psbody.mesh import Mesh
from smpl_webuser.serialization import load_model
from util import renderer as vis_util
from util import image as img_util
from config_test import get_config
from run_RingNet import RingNet_inference
def visualize(img, proc_param, verts, cam, img_name='test_image'):
"""
Renders the result in original image coordinate frame.
"""
cam_for_render, vert_shifted = vis_util.get_original(
proc_param, verts, cam, img_size=img.shape[:2])
# Render results
rend_img_overlay = renderer(
vert_shifted*1.0, cam=cam_for_render, img=img, do_alpha=True)
rend_img = renderer(
vert_shifted*1.0, cam=cam_for_render, img_size=img.shape[:2])
rend_img_vp1 = renderer.rotated(
vert_shifted, 30, cam=cam_for_render, img_size=img.shape[:2])
import matplotlib.pyplot as plt
fig = plt.figure(1)
plt.clf()
plt.subplot(221)
plt.imshow(img)
plt.title('input')
plt.axis('off')
plt.subplot(222)
plt.imshow(rend_img_overlay)
plt.title('3D Mesh overlay')
plt.axis('off')
plt.subplot(223)
plt.imshow(rend_img)
plt.title('3D mesh')
plt.axis('off')
plt.subplot(224)
plt.imshow(rend_img_vp1)
plt.title('diff vp')
plt.axis('off')
plt.draw()
plt.show(block=False)
fig.savefig(img_name + '.png')
# import ipdb
# ipdb.set_trace()
def preprocess_image(img_path):
img = io.imread(img_path)
if np.max(img.shape[:2]) != config.img_size:
print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.0#scaling_factor
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,
config.img_size)
# import ipdb; ipdb.set_trace()
# Normalize image to [-1, 1]
# plt.imshow(crop/255.0)
# plt.show()
crop = 2 * ((crop / 255.) - 0.5)
return crop, proc_param, img
def main(config, template_mesh):
sess = tf.Session()
model = RingNet_inference(config, sess=sess)
input_img, proc_param, img = preprocess_image(config.img_path)
vertices, flame_parameters = model.predict(np.expand_dims(input_img, axis=0), get_parameters=True)
cams = flame_parameters[0][:3]
visualize(img, proc_param, vertices[0], cams, img_name=config.out_folder + '/images/' + config.img_path.split('/')[-1][:-4])
if config.save_obj_file:
if not os.path.exists(config.out_folder + '/mesh'):
os.mkdir(config.out_folder + '/mesh')
mesh = Mesh(v=vertices[0], f=template_mesh.f)
mesh.write_obj(config.out_folder + '/mesh/' + config.img_path.split('/')[-1][:-4] + '.obj')
if config.save_flame_parameters:
if not os.path.exists(config.out_folder + '/params'):
os.mkdir(config.out_folder + '/params')
flame_parameters_ = {'cam': flame_parameters[0][:3], 'pose': flame_parameters[0][3:3+config.pose_params], 'shape': flame_parameters[0][3+config.pose_params:3+config.pose_params+config.shape_params],
'expression': flame_parameters[0][3+config.pose_params+config.shape_params:]}
np.save(config.out_folder + '/params/' + config.img_path.split('/')[-1][:-4] + '.npy', flame_parameters_)
if config.neutralize_expression:
from util.using_flame_parameters import make_prdicted_mesh_neutral
if not os.path.exists(config.out_folder + '/neutral_mesh'):
os.mkdir(config.out_folder + '/neutral_mesh')
neutral_mesh = make_prdicted_mesh_neutral(config.out_folder + '/params/' + config.img_path.split('/')[-1][:-4] + '.npy', config.flame_model_path)
neutral_mesh.write_obj(config.out_folder + '/neutral_mesh/' + config.img_path.split('/')[-1][:-4] + '.obj')
if __name__ == '__main__':
config = get_config()
template_mesh = Mesh(filename='./flame_model/FLAME_sample.ply')
renderer = vis_util.SMPLRenderer(faces=template_mesh.f)
if not os.path.exists(config.out_folder):
os.makedirs(config.out_folder)
if not os.path.exists(config.out_folder + '/images'):
os.mkdir(config.out_folder + '/images')
main(config, template_mesh)
