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step1_1_manipulate_expression.py
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import argparse
import tensorflow as tf
import numpy as np
import cv2
from PIL import Image
import os
from scipy.io import loadmat,savemat
from utils.preprocess import POS, headrecon_preprocess_withmask, facerecon_preprocess_yu_5p, facerecon_preprocess
from utils.loader import load_data, load_lm3d, load_center3d, read_facemodel
from utils.recon_face import compute_center2d, compute_faceshape
from utils.create_renderer import create_renderer_graph
from PIL import Image
import math as m
tf.enable_eager_execution()
def load_facerecon_graph(graph_filename):
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:
input = tf.placeholder(name='input_imgs', shape=[
None, 224, 224, 3], dtype=tf.float32)
tf.import_graph_def(graph_def, name='resnet', input_map={
'input_imgs:0': input})
output = graph.get_tensor_by_name('resnet/coeff:0')
return graph, input, output
def face_recon(src_path, tgt_path, input_path, output_path, output, vis_path=None, s_factor=1.5, focal=1015, center=112, align_nums=10, degree=100):
# load BFM
facemodel = read_facemodel()
# read standard landmarks for face recon preprocessing
lm3D = load_lm3d(align_nums)
# read head center for depth recon preprocessing
head_center3d = load_center3d()
# create face recon graph
face_recon_graph, images, coef = load_facerecon_graph('model/model_mask3_white_light.pb')
face_recon_sess = tf.Session(graph=face_recon_graph)
# create renderer graph
depth_render_graph, input_focal, input_center, input_depth, \
input_vertex, input_tri, output_depthmap = create_renderer_graph()
render_sess = tf.Session(graph=depth_render_graph)
print("source", src_path.split(os.path.sep)[-1].split('.')[0])
# print("target", tgt_path.split(os.path.sep)[-1].split('.')[0])
print("target", tgt_path.split('.')[0])
#TODO: # get source image's coefficient
mask = loadmat(os.path.join(input_path, src_path.split(os.path.sep)[-1].split('.')[0] + '.mat'))['mask']
## load images and corresponding 5 facial landmarks
if align_nums == 5:
img, lm = load_data(src_path,
os.path.join(input_path, src_path.split(os.path.sep)[-1].split('.')[0] + '_detection.txt'))
lm = lm[-10:].reshape([5, 2])
input_img, inv_params = facerecon_preprocess_yu_5p(img, lm, lm3D)
elif align_nums == 10:
img, lm = load_data(src_path,
os.path.join(input_path, src_path.split(os.path.sep)[-1].split('.')[0] + '_landmark.txt'))
lm = lm.reshape([68, 2])
input_img, inv_params = facerecon_preprocess(img, lm, lm3D)
# recon face
coeff = face_recon_sess.run(coef, feed_dict={images: np.expand_dims(input_img, 0)})[..., :-1]
#TODO: # get target image's coefficient
tgt_mask = loadmat(tgt_path.split('.')[0] + '.mat')['mask']
## load images and corresponding 5 facial landmarks
if align_nums == 5:
tgt_img, tgt_lm = load_data(tgt_path,
tgt_path.split('.')[0] + '_detection.txt')
tgt_lm = tgt_lm[-10:].reshape([5, 2])
tgt_input_img, tgt_inv_params = facerecon_preprocess_yu_5p(tgt_img, tgt_lm, lm3D)
elif align_nums == 10:
tgt_img, tgt_lm = load_data(tgt_path,
tgt_path.split('.')[0] + '_landmark.txt')
tgt_lm = tgt_lm.reshape([68, 2])
tgt_input_img, tgt_inv_params = facerecon_preprocess(tgt_img, tgt_lm, lm3D)
# recon face
tgt_coeff = face_recon_sess.run(coef, feed_dict={images: np.expand_dims(tgt_input_img, 0)})[..., :-1]
# transfer target expression to source expression
degree = degree/100
coeff[:, 80:144] = tgt_coeff[:, 80:144] * degree + coeff[:, 80:144] * (1-degree)
# preprocess input image for depth recon net
# reproject the reconstructed face to raw image with adjusted focal and center
f = focal * inv_params[0]
p_center = inv_params[0] * center + inv_params[1]
face_shape, face_projection, landmarks_2d = compute_faceshape(coeff, facemodel, inv_params)
# crop the raw image with head center as the image center
center2d, displacement = compute_center2d(head_center3d, coeff, facemodel, f, p_center)
_, s = POS(face_projection.transpose(), facemodel.meanshape.reshape([-1, 3]).transpose())
crop_img, crop_mask, inv_params_, crop_lm, crop_param = headrecon_preprocess_withmask(img, mask, landmarks_2d, center2d.reshape([2]), s*s_factor/100)
#TODO:
# save processed data
data = np.zeros([3 + 257 + 136])
data[0] = f / inv_params_[0]
data[1: 3] = (p_center - inv_params_[1].reshape([2]))/inv_params_[0]
data[3: 260] = coeff.reshape([257])
data[257: 260] = data[257: 260] - displacement.reshape([3])
data[260:] = crop_lm.reshape([136])
face_projection_cropped, _ = compute_center2d(np.expand_dims(face_shape, 0),
np.expand_dims(data[3:260], 0), facemodel, data[0], data[1:3], displace_flag=False, apply_pose=False)
# render face depth
d = 10 - face_shape[:, 2:]
d = np.tile(np.expand_dims(d, 0), [1, 1, 3])
d_map = render_sess.run(output_depthmap, feed_dict={
input_focal: data[0].reshape([1]),
input_center: data[1: 3].reshape([1, 1, 2]),
input_depth: d,
input_vertex: np.expand_dims(face_shape, 0),
input_tri: np.expand_dims(facemodel.tri, 0) - 1 # start from 0
})
if vis_path:
cv2.imwrite(os.path.join(vis_path, output + '.png'),
crop_img.astype(np.uint8))
cv2.imwrite(os.path.join(vis_path, output + '_dmap.png'), d_map[0] * 255)
savemat(os.path.join(output_path, output + '.mat'),
{'img': crop_img.astype(np.uint8),
'mask': crop_mask.astype(np.uint8),
'crop_param': crop_param.astype(np.float32),
'face3d': data.astype(np.float32), # data --> face3d #TODO:
# 0: focal; [1, 3) center; [3,260): face coeff; [260~396): landmark
'face_shape':face_shape.astype(np.float32),
'face_projection': face_projection_cropped.squeeze(0).astype(np.float32),
'face_depthmap': d_map[..., 0].squeeze(0),
'face_mask': d_map[..., -1].squeeze(0),
'face_tri': facemodel.tri}, do_compression=True)
face_recon_sess.close()
render_sess.close()
if __name__ == '__main__':
input_path = 'inputs'
save_path = 'outputs/step1'
vis_path = 'outputs/step1/vis'
if not os.path.isdir(save_path):
os.makedirs(save_path)
if not os.path.isdir(vis_path):
os.makedirs(vis_path)
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# define parser
parser = argparse.ArgumentParser()
parser.add_argument('--src_path', default='inputs/junghoo.jpg')
parser.add_argument('--tgt_path', default='expressions/wide_smile.png')
parser.add_argument('--output', default='edit_expression')
args = parser.parse_args()
degree = 100
# recon 3d face and prepare the input to depth recon
face_recon(args.src_path, args.tgt_path, input_path, save_path, args.output, vis_path, degree=degree)