def demo(args):
# input and output folder
image_path = args.input
save_path = args.output
if not os.path.exists(save_path):
os.makedirs(save_path)
# read BFM face model
facemodel = BFM()
# transfer original BFM model to our model
if not os.path.isfile('./BFM/BFM_model_front.mat'):
transferBFM09()
# read standard landmarks for preprocessing images
lm3D = load_lm3d()
batchsize = 1
# build reconstruction model
with tf.Graph().as_default() as graph, tf.device('/gpu:0'):
FaceReconstructor = Face3D()
images = tf.placeholder(name='input_imgs',
shape=[batchsize, 224, 224, 3],
dtype=tf.float32)
graph_def = load_graph('network/FaceReconModel.pb')
tf.import_graph_def(graph_def,
name='resnet',
input_map={'input_imgs:0': images})
# output coefficients of R-Net (dim = 257)
coeff = graph.get_tensor_by_name('resnet/coeff:0')
with tf.device('/cpu:0'):
# renderer layer
faceshaper = tf.placeholder(name="face_shape_r",
shape=[1, 35709, 3],
dtype=tf.float32)
facenormr = tf.placeholder(name="face_norm_r",
shape=[1, 35709, 3],
dtype=tf.float32)
facecolor = tf.placeholder(name="face_color",
shape=[1, 35709, 3],
dtype=tf.float32)
rendered = Render_layer(faceshaper, facenormr, facecolor,
facemodel, 1)
rstimg = tf.placeholder(name='rstimg',
shape=[224, 224, 4],
dtype=tf.uint8)
encode_png = tf.image.encode_png(rstimg)
# reconstructing faces
FaceReconstructor.Reconstruction_Block(coeff, batchsize)
face_shape = FaceReconstructor.face_shape_t
face_texture = FaceReconstructor.face_texture
face_color = FaceReconstructor.face_color
landmarks_2d = FaceReconstructor.landmark_p
recon_img = FaceReconstructor.render_imgs
tri = FaceReconstructor.facemodel.face_buf
# MTCNN Detector
detector = MTCNN()
img, lm = load_img_and_lm(image_path, detector)
with tf.Session() as sess:
print('reconstructing...')
# load images and corresponding 5 facial landmarks
# preprocess input image
input_img, lm_new, transform_params, posion = Preprocess(
img, lm, lm3D)
coeff_,face_shape_,face_texture_,face_color_,landmarks_2d_,recon_img_,tri_ = sess.run([coeff,\
face_shape,face_texture,face_color,landmarks_2d,recon_img,tri],feed_dict = {images: input_img})
# renderer output
face_shape_r, face_norm_r, face_color, tri = Reconstruction_for_render(
coeff_, facemodel)
final_images = sess.run(rendered,
feed_dict={
faceshaper:
face_shape_r.astype('float32'),
facenormr:
face_norm_r.astype('float32'),
facecolor: face_color.astype('float32')
})
result_image = final_images[0, :, :, :]
result_image = np.clip(result_image, 0., 1.).copy(order='C')
# save renderer output
result_bytes = sess.run(encode_png, {rstimg: result_image * 255.0})
result_output_path = os.path.join(
save_path,
image_path.split(os.path.sep)[-1].replace(
'.png', '_render.png').replace('jpg', '_render.png'))
with open(result_output_path, 'wb') as output_file:
output_file.write(result_bytes)
# get RGB image from RGBA
rgb_renderer_img, mask = RGBA2RGB(result_image)
# Paste the 3D rendered image back to the original image
renderer_3D_input_img = np.copy(img)
left0 = int(posion[0] * posion[4])
right0 = int(posion[1] * posion[4])
up0 = int(posion[2] * posion[4])
below0 = int(posion[3] * posion[4])
rgb_renderer_img = cv2.resize(rgb_renderer_img,
(right0 - left0, below0 - up0))
mask = cv2.resize(mask, (right0 - left0, below0 - up0))
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
mask.astype('uint32')
if left0 < 0:
mask = mask[:, -left0:]
rgb_renderer_img = rgb_renderer_img[:, -left0:]
left0 = 0
if up0 < 0:
mask = mask[-up0:, :]
rgb_renderer_img = rgb_renderer_img[-up0:, :]
up0 = 0
if right0 > renderer_3D_input_img.shape[1]:
mask = mask[:, :-(right0 - renderer_3D_input_img.shape[1])]
rgb_renderer_img = rgb_renderer_img[:, :-(
right0 - renderer_3D_input_img.shape[1])]
right0 = renderer_3D_input_img.shape[1]
if below0 > renderer_3D_input_img.shape[0]:
mask = mask[:-(below0 - renderer_3D_input_img.shape[0]), :]
rgb_renderer_img = rgb_renderer_img[:-(
below0 - renderer_3D_input_img.shape[0]), :]
below0 = renderer_3D_input_img.shape[0]
renderer_3D_input_img[
up0:below0, left0:right0] = renderer_3D_input_img[
up0:below0, left0:right0] * mask + rgb_renderer_img
renderer_3D_input_img = cv2.cvtColor(renderer_3D_input_img,
cv2.COLOR_BGR2RGB)
cv2.imwrite(
os.path.join(
save_path,
image_path.split(os.path.sep)[-1].replace(
'.png', '_renderer_in_original.png').replace(
'jpg', '_renderer_in_original.png')),
renderer_3D_input_img)
# reshape outputs
input_img = np.squeeze(input_img)
face_shape_ = np.squeeze(face_shape_, (0))
face_texture_ = np.squeeze(face_texture_, (0))
face_color_ = np.squeeze(face_color_, (0))
landmarks_2d_ = np.squeeze(landmarks_2d_, (0))
if not is_windows:
recon_img_ = np.squeeze(recon_img_, (0))
# save output files
if not is_windows:
savemat(os.path.join(save_path,image_path.split(os.path.sep)[-1].replace('.png','.mat').replace('jpg','mat')),{'cropped_img':input_img[:,:,::-1],'recon_img':recon_img_,'coeff':coeff_,\
'face_shape':face_shape_,'face_texture':face_texture_,'face_color':face_color_,'lm_68p':landmarks_2d_,'lm_5p':lm_new})
save_obj(
os.path.join(
save_path,
image_path.split(os.path.sep)[-1].replace(
'.png', '_mesh.obj').replace('jpg', '_mesh.obj')),
face_shape_, tri_,
np.clip(face_color_, 0, 255) /
255) # 3D reconstruction face (in canonical view)
def demo():
# input and output folder
args = parse_args()
image_path = 'input'
save_path = 'output'
if not os.path.exists(save_path):
os.makedirs(save_path)
img_list = glob.glob(image_path + '/' + '*.png')
img_list +=glob.glob(image_path + '/' + '*.jpg')
# read BFM face model
# transfer original BFM model to our model
if not os.path.isfile('./BFM/BFM_model_front.mat'):
transferBFM09()
# read standard landmarks for preprocessing images
lm3D = load_lm3d()
n = 0
# build reconstruction model
with tf.Graph().as_default() as graph:
with tf.device('/cpu:0'):
opt = Option(is_train=False)
opt.batch_size = 1
opt.pretrain_weights = args.pretrain_weights
FaceReconstructor = Face3D()
images = tf.placeholder(name = 'input_imgs', shape = [opt.batch_size,224,224,3], dtype = tf.float32)
if args.use_pb and os.path.isfile('network/FaceReconModel.pb'):
print('Using pre-trained .pb file.')
graph_def = load_graph('network/FaceReconModel.pb')
tf.import_graph_def(graph_def,name='resnet',input_map={'input_imgs:0': images})
# output coefficients of R-Net (dim = 257)
coeff = graph.get_tensor_by_name('resnet/coeff:0')
else:
print('Using pre-trained .ckpt file: %s'%opt.pretrain_weights)
import networks
coeff = networks.R_Net(images,is_training=False)
# reconstructing faces
FaceReconstructor.Reconstruction_Block(coeff,opt)
face_shape = FaceReconstructor.face_shape_t
face_texture = FaceReconstructor.face_texture
face_color = FaceReconstructor.face_color
landmarks_2d = FaceReconstructor.landmark_p
recon_img = FaceReconstructor.render_imgs
tri = FaceReconstructor.facemodel.face_buf
with tf.Session() as sess:
if not args.use_pb :
restore_weights(sess,opt)
print('reconstructing...')
for file in img_list:
n += 1
print(n)
# load images and corresponding 5 facial landmarks
img,lm = load_img(file,file.replace('png','txt').replace('jpg','txt'))
# preprocess input image
input_img,lm_new,transform_params = align_img(img,lm,lm3D)
coeff_,face_shape_,face_texture_,face_color_,landmarks_2d_,recon_img_,tri_ = sess.run([coeff,\
face_shape,face_texture,face_color,landmarks_2d,recon_img,tri],feed_dict = {images: input_img})
# reshape outputs
input_img = np.squeeze(input_img)
face_shape_ = np.squeeze(face_shape_, (0))
face_texture_ = np.squeeze(face_texture_, (0))
face_color_ = np.squeeze(face_color_, (0))
landmarks_2d_ = np.squeeze(landmarks_2d_, (0))
if not is_windows:
recon_img_ = np.squeeze(recon_img_, (0))
# save output files
if not is_windows:
savemat(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','.mat').replace('jpg','mat')),{'cropped_img':input_img[:,:,::-1],'recon_img':recon_img_,'coeff':coeff_,\
'face_shape':face_shape_,'face_texture':face_texture_,'face_color':face_color_,'lm_68p':landmarks_2d_,'lm_5p':lm_new})
save_obj(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','_mesh.obj').replace('.jpg','_mesh.obj')),face_shape_,tri_,np.clip(face_color_,0,255)/255) # 3D reconstruction face (in canonical view)
def FaceReconst_Spec_pics(self):
inDir = self.indir
# Text_Forth_lineEdit.text().split(';;')
save_path = self.savepath
self.mtcnn_Spec_pic()
# Text_Fifth_lineEdit.text()
# self.NoteTipsEdit.append("\n-------读取人脸信息-------")
# detector = MTCNN()
# cnt = 0
# for img_path in inDir:
# image = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
# try:
# result = detector.detect_faces(image)
# # Result is an array with all the bounding boxes detected. We know that for 'ivan.jpg' there is only one.
#
# bounding_box = result[0]['box']
# # print(result)
#
# keypoints = result[0]['keypoints']
# land_mask_file = img_path.replace(".jpg", ".txt")
# file_write = open(land_mask_file, "w")
# x, y = keypoints['left_eye']
# file_write.write(f"{x} {y}\n")
# x, y = keypoints['right_eye']
# file_write.write(f"{x} {y}\n")
# x, y = keypoints['nose']
# file_write.write(f"{x} {y}\n")
# x, y = keypoints['mouth_left']
# file_write.write(f"{x} {y}\n")
# x, y = keypoints['mouth_right']
# file_write.write(f"{x} {y}\n")
# file_write.close()
# except:
# cnt += 1
# self._trigger_text.emit("\n-------有{}张提取失败-------".format(cnt))
# # self.NoteTipsEdit.append("\n-------有{}张提取失败-------".format(cnt))
import time
# time.sleep(1)
if not os.path.isfile('./BFM/BFM_model_front.mat'):
transferBFM09()
# read standard landmarks for preprocessing images
lm3D = load_lm3d()
batchsize = 1
n = 0
with tf.Graph().as_default() as graph, tf.device('/cpu:0'):
FaceReconstructor = Face3D()
images = tf.placeholder(name='input_imgs',
shape=[batchsize, 224, 224, 3],
dtype=tf.float32)
graph_def = self.load_graph('network/FaceReconModel.pb')
tf.import_graph_def(graph_def,
name='resnet',
input_map={'input_imgs:0': images})
# output coefficients of R-Net (dim = 257)
coeff = graph.get_tensor_by_name('resnet/coeff:0')
# reconstructing faces
FaceReconstructor.Reconstruction_Block(coeff, batchsize)
face_shape = FaceReconstructor.face_shape_t
face_texture = FaceReconstructor.face_texture
face_color = FaceReconstructor.face_color
landmarks_2d = FaceReconstructor.landmark_p
# recon_img = FaceReconstructor.render_imgs
tri = FaceReconstructor.facemodel.face_buf
with tf.Session() as sess:
# print('-----------reconstructing-----------')
# self.NoteTipsEdit.append("\n-------正在重建-------")
self._trigger_text.emit("\n-------正在重建-------")
QApplication.processEvents()
# self.Tipstext.append("\n-------正在重建-------")
cntt = 0
for file in inDir:
try:
n += 1
print(n)
# print(file)
# self.NoteTipsEdit.append("\n第"+n+"张正在重建")
# load images and corresponding 5 facial landmarks
filetxt = os.path.basename(file)
# print(filetxt)
img, lm = load_img(
file,
file.replace('png', 'txt').replace('jpg', 'txt'))
# print(lm)
# preprocess input image
input_img, lm_new, transform_params = Preprocess(
img, lm, lm3D)
# coeff_,face_shape_,face_texture_,face_color_,landmarks_2d_,recon_img_,tri_ = sess.run([coeff,\
# face_shape,face_texture,face_color,landmarks_2d,recon_img,tri],feed_dict = {images: input_img})
coeff_, face_shape_, face_texture_, face_color_, landmarks_2d_, tri_ = sess.run(
[
coeff, face_shape, face_texture, face_color,
landmarks_2d, tri
],
feed_dict={images: input_img})
# reshape outputs
input_img = np.squeeze(input_img)
face_shape_ = np.squeeze(face_shape_, (0))
face_texture_ = np.squeeze(face_texture_, (0))
face_color_ = np.squeeze(face_color_, (0))
landmarks_2d_ = np.squeeze(landmarks_2d_, (0))
# recon_img_ = np.squeeze(recon_img_, (0))
# save output files
# savemat(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','.mat').replace('jpg','mat')),{'cropped_img':input_img[:,:,::-1],'recon_img':recon_img_,'coeff':coeff_,\
# 'face_shape':face_shape_,'face_texture':face_texture_,'face_color':face_color_,'lm_68p':landmarks_2d_,'lm_5p':lm_new})
# savemat(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','.mat').replace('jpg','mat')),{'cropped_img':input_img[:,:,::-1],'coeff':coeff_,\
# 'face_shape':face_shape_,'face_texture':face_texture_,'face_color':face_color_,'lm_68p':landmarks_2d_,'lm_5p':lm_new})
save_obj(
os.path.join(
save_path,
filetxt.replace('.png', '_mesh.obj').replace(
'.jpg', '_mesh.obj')), face_shape_, tri_,
np.clip(face_color_, 0, 255) /
255) # 3D reconstruction face (in canonical view)
except:
cntt += 1
# self.NoteTipsEdit.append("\n-------有{}张重建失败-------".format(cnt))
# self.NoteTipsEdit.append("\n-------完毕-------")
print(cntt)
self._trigger_text.emit(
"\n-------有{}张重建失败-------".format(cntt))
self._trigger_text.emit("\n-------完毕-------")
QApplication.processEvents()
# self.Tipstext.append("\n-------有{}张重建失败-------".format(cntt))
# self.Tipstext.append("\n-------完毕-------")
self._trigger.emit()
def d3dr(file):
# read BFM face model
# transfer original BFM model to our model
if not os.path.isfile('./BFM/BFM_model_front.mat'):
transferBFM09()
# read standard landmarks for preprocessing images
lm3D = load_lm3d()
batchsize = 1
n = 0
# build reconstruction model
with tf.Graph().as_default() as graph, tf.device('/cpu:0'):
FaceReconstructor = Face3D()
images = tf.placeholder(name='input_imgs',
shape=[batchsize, 224, 224, 3],
dtype=tf.float32)
graph_def = load_graph('network/FaceReconModel.pb')
tf.import_graph_def(graph_def,
name='resnet',
input_map={'input_imgs:0': images})
# output coefficients of R-Net (dim = 257)
coeff = graph.get_tensor_by_name('resnet/coeff:0')
# reconstructing faces
FaceReconstructor.Reconstruction_Block(coeff, batchsize)
face_shape = FaceReconstructor.face_shape_t
face_texture = FaceReconstructor.face_texture
face_color = FaceReconstructor.face_color
landmarks_2d = FaceReconstructor.landmark_p
recon_img = FaceReconstructor.render_imgs
tri = FaceReconstructor.facemodel.face_buf
with tf.Session() as sess:
print('reconstructing...')
n += 1
print(n)
# load images and corresponding 5 facial landmarks
img, lm = load_img(
file,
file.replace('png', 'txt').replace('jpg', 'txt'))
# preprocess input image
input_img, lm_new, transform_params = Preprocess(img, lm, lm3D)
coeff_, face_shape_, face_texture_, face_color_, landmarks_2d_, recon_img_, tri_ = sess.run(
[
coeff, face_shape, face_texture, face_color, landmarks_2d,
recon_img, tri
],
feed_dict={images: input_img})
# reshape outputs
input_img = np.squeeze(input_img)
face_shape_ = np.squeeze(face_shape_, (0))
face_texture_ = np.squeeze(face_texture_, (0))
face_color_ = np.squeeze(face_color_, (0))
landmarks_2d_ = np.squeeze(landmarks_2d_, (0))
recon_img_ = np.squeeze(recon_img_, (0))
# save output files
savemat(
change_file_extension(file, "mat"), {
'cropped_img': input_img[:, :, ::-1],
'recon_img': recon_img_,
'coeff': coeff_,
'face_shape': face_shape_,
'face_texture': face_texture_,
'face_color': face_color_,
'lm_68p': landmarks_2d_,
'lm_5p': lm_new
})
save_obj(change_file_extension(file, "obj"), face_shape_, tri_,
np.clip(face_color_, 0, 255) /
255) # 3D reconstruction face (in canonical view)
def FaceReconst(self):
self.MtcnnDectect()
inFileDir = self.indir
save_path = self.savepath
img_list = glob.glob(inFileDir + '/' + '*.png')
img_list += glob.glob(inFileDir + '/' + '*.jpg')
# read BFM face model
# transfer original BFM model to our model
if not os.path.isfile('./BFM/BFM_model_front.mat'):
transferBFM09()
# read standard landmarks for preprocessing images
lm3D = load_lm3d()
batchsize = 1
n = 0
with tf.Graph().as_default() as graph, tf.device('/cpu:0'):
FaceReconstructor = Face3D()
images = tf.placeholder(name='input_imgs',
shape=[batchsize, 224, 224, 3],
dtype=tf.float32)
graph_def = self.load_graph('network/FaceReconModel.pb')
tf.import_graph_def(graph_def,
name='resnet',
input_map={'input_imgs:0': images})
# output coefficients of R-Net (dim = 257)
coeff = graph.get_tensor_by_name('resnet/coeff:0')
# reconstructing faces
FaceReconstructor.Reconstruction_Block(coeff, batchsize)
face_shape = FaceReconstructor.face_shape_t
face_texture = FaceReconstructor.face_texture
face_color = FaceReconstructor.face_color
landmarks_2d = FaceReconstructor.landmark_p
# recon_img = FaceReconstructor.render_imgs
tri = FaceReconstructor.facemodel.face_buf
with tf.Session() as sess:
# print('-----------reconstructing-----------')
# self.TextEdit.append("\n-------正在重建-------")
# self.NoteTipsEdit.append("\n-------正在重建-------")
self._trigger_pic_text.emit("\n-------正在重建-------")
QApplication.processEvents()
cnt = 0
for file in img_list:
try:
n += 1
print(n)
# self.NoteTipsEdit.append("\n第"+n+"张正在重建")
# load images and corresponding 5 facial landmarks
img, lm = load_img(
file,
file.replace('png', 'txt').replace('jpg', 'txt'))
# preprocess input image
input_img, lm_new, transform_params = Preprocess(
img, lm, lm3D)
# coeff_,face_shape_,face_texture_,face_color_,landmarks_2d_,recon_img_,tri_ = sess.run([coeff,\
# face_shape,face_texture,face_color,landmarks_2d,recon_img,tri],feed_dict = {images: input_img})
coeff_, face_shape_, face_texture_, face_color_, landmarks_2d_, tri_ = sess.run(
[
coeff, face_shape, face_texture, face_color,
landmarks_2d, tri
],
feed_dict={images: input_img})
# reshape outputs
input_img = np.squeeze(input_img)
face_shape_ = np.squeeze(face_shape_, (0))
face_texture_ = np.squeeze(face_texture_, (0))
face_color_ = np.squeeze(face_color_, (0))
landmarks_2d_ = np.squeeze(landmarks_2d_, (0))
# recon_img_ = np.squeeze(recon_img_, (0))
# save output files
# savemat(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','.mat').replace('jpg','mat')),{'cropped_img':input_img[:,:,::-1],'recon_img':recon_img_,'coeff':coeff_,\
# 'face_shape':face_shape_,'face_texture':face_texture_,'face_color':face_color_,'lm_68p':landmarks_2d_,'lm_5p':lm_new})
# savemat(os.path.join(save_path,file.split(os.path.sep)[-1].replace('.png','.mat').replace('jpg','mat')),{'cropped_img':input_img[:,:,::-1],'coeff':coeff_,\
# 'face_shape':face_shape_,'face_texture':face_texture_,'face_color':face_color_,'lm_68p':landmarks_2d_,'lm_5p':lm_new})
save_obj(
os.path.join(
save_path,
file.split(os.path.sep)[-1].replace(
'.png',
'_mesh.obj').replace('jpg', '_mesh.obj')),
face_shape_, tri_,
np.clip(face_color_, 0, 255) /
255) # 3D reconstruction face (in canonical view)
except:
cnt += 1
# self.NoteTipsEdit.append("\n-------有{}张重建失败-------".format(cnt))
# self.NoteTipsEdit.append("\n-------完毕-------")
self._trigger_pic_text.emit(
"\n-------有{}张重建失败-------".format(cnt))
self._trigger_pic_text.emit("\n-------完毕-------")
# self.TextEdit.append("\n-------有{}张重建失败-------".format(cnt))
# self.TextEdit.append("\n-------完毕-------")
QApplication.processEvents()
self.trigger.emit()