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step3_get_head_geometry.py
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import os
import numpy as np
import cv2
import tensorflow as tf
from utils.recon_depth import split_data, get_pixel_value, get_face_texture, uvd2xyz
from utils.create_renderer import create_renderer_graph
from scipy.io import savemat, loadmat
_FACE_V_NUM = 35709
_FACE_T_NUM = 70789
_HAIREAR_V_NUM = 28000
_HAIREAR_T_NUM = 54000
def load_depthrecon_graph(graph_filename, image_size=256):
with tf.gfile.GFile(graph_filename,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
inputs = tf.placeholder(name='inputs', shape=[
None, image_size, image_size, 5], dtype=tf.float32)
tf.import_graph_def(graph_def, name='resnet', input_map={
'inputs:0': inputs})
output = graph.get_tensor_by_name('resnet/depth_map:0')
return graph, inputs, output
def create_shaperecon_graph(image_size=256):
with tf.Graph().as_default() as graph:
imgs = tf.placeholder(dtype=tf.float32, shape=[1, image_size, image_size, 3])
hairear_uv = tf.placeholder(dtype=tf.float32, shape=[1, _HAIREAR_V_NUM, 2])
hairear_dmap = tf.placeholder(dtype=tf.float32, shape=[1, image_size, image_size, 1])
face3d_data = tf.placeholder(dtype=tf.float32, shape=[1, 396])
face_shape2d = tf.placeholder(dtype=tf.float32, shape=[1, _FACE_V_NUM, 2])
focal, center, _ = split_data(face3d_data)
face_texture = get_face_texture(imgs, face_shape2d)
hairear_d = get_pixel_value(hairear_dmap, hairear_uv)
hairear_uv_trans = hairear_uv + 0.5
hairear_uv_trans = tf.concat([hairear_uv_trans[:, :, 0:1],
256 - hairear_uv_trans[:, :, 1:]], axis=-1)
hairear_uvd = tf.concat([hairear_uv_trans, hairear_d], axis=-1)
hairear_xyz = uvd2xyz(hairear_uvd, focal, center)
hairear_texture = get_pixel_value(imgs, hairear_uv)
return graph, imgs, hairear_uv, hairear_dmap, face3d_data, face_shape2d, face_texture, hairear_xyz, hairear_texture
def depth_recon(data_path, save_path):
# create face recon graph
depthrecon_graph, inputs, depth_map = load_depthrecon_graph('model/depth_net.pb')
depth_recon_sess = tf.Session(graph=depthrecon_graph)
# create shape recon graph
shaperecon_graph, input_imgs, input_uv, input_dmap, input_facedata, input_face2d, \
output_face_texture, output_hairear_xyz, output_hairear_texture = create_shaperecon_graph()
shape_recon_sess = tf.Session(graph=shaperecon_graph)
# create renderer graph
depth_render_graph, input_focal, input_center, input_depth, \
input_vertex, input_tri, output_depthmap = create_renderer_graph(v_num=_FACE_V_NUM + _HAIREAR_V_NUM, t_num=_FACE_T_NUM + _HAIREAR_T_NUM)
render_sess = tf.Session(graph=depth_render_graph)
names = [i for i in os.listdir(data_path) if i.endswith('mat')]
for i, name in enumerate(names):
print(i, name.split('.')[0])
# read and prepare data
data_input = loadmat(os.path.join(data_path, name))
imgs_input = data_input['img'].astype(np.float32).reshape([1, 256, 256, 3])
face_d_input = data_input['face_depthmap'].astype(np.float32).reshape([1, 256, 256, 1])
face_xyz_input = np.expand_dims(data_input['face_projection'].astype(np.float32), 0)
facewoh_m_input = data_input['facemask_withouthair'].astype(np.float32).reshape([1, 256, 256, 1])
face3d_data_input = data_input['face3d'].astype(np.float32).reshape([1, 396])
xy = data_input['points_index']
uv = np.concatenate([xy[:, 1:], xy[:, :1]], axis=1)
hairear_uv_input = uv.astype(np.float32).reshape([1, _HAIREAR_V_NUM, 2])
hairear_m_input = data_input['input_mask'].astype(np.float32).reshape([1, 256, 256, 1])
depth_input = np.concatenate([imgs_input/255, (10 - face_d_input) * facewoh_m_input,
hairear_m_input], -1)
# recon hairear depth
depth_output = depth_recon_sess.run(depth_map, feed_dict={
inputs: depth_input
})
# recover head shape from hairear depth
h_xyz, h_texture, f_texture = shape_recon_sess.run([
output_hairear_xyz, output_hairear_texture, output_face_texture],
feed_dict={
input_imgs: imgs_input,
input_dmap: depth_output,
input_face2d: face_xyz_input,
input_uv: hairear_uv_input,
input_facedata: face3d_data_input
})
# render head depth
head_xyz = np.concatenate([
np.expand_dims(data_input['face_shape'], 0), h_xyz], axis=-2)
head_d = np.tile(10 - head_xyz[..., -1:], [1, 1, 3])
head_tri = np.concatenate([
data_input['face_tri'], data_input['points_tri'] + _FACE_V_NUM], axis=0)
head_dmap = render_sess.run(output_depthmap, feed_dict={
input_focal: data_input['face3d'][:, 0].reshape([1]),
input_center: data_input['face3d'][:, 1:3].reshape([1, 1, 2]),
input_depth: head_d,
input_vertex: head_xyz,
input_tri: np.expand_dims(head_tri, 0) - 1,
})
result = {
'hairear_shape': h_xyz.squeeze(0),
'hairear_texture': h_texture.squeeze(0),
'hairear_tri': data_input['points_tri'],
'face_shape': data_input['face_shape'],
'face_texture': f_texture.squeeze(0),
'face_tri': data_input['face_tri'],
'hairear_index': data_input['points_index'],
'facemask_withouthair': data_input['facemask_withouthair'],
'depth': head_dmap[..., 0].squeeze(0),
'mask': head_dmap[..., -1].squeeze(0)
}
savemat(os.path.join(save_path, name), result, do_compression=True)
depth_recon_sess.close()
shape_recon_sess.close()
render_sess.close()
if __name__ == '__main__':
data_path = 'output/step2'
save_path = 'output/step3'
if not os.path.isdir(save_path):
os.makedirs(save_path)
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# recon depth and recover the head geometry
depth_recon(data_path, save_path)