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')
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 config.save_texture:
if not os.path.exists(config.flame_texture_data_path):
print('FLAME texture data not found')
return
texture_data = np.load(config.flame_texture_data_path, allow_pickle=True)[()]
texture = create_texture(img, proc_param, vertices[0], template_mesh.f, cams, texture_data)
if not os.path.exists(config.out_folder + '/texture'):
os.mkdir(config.out_folder + '/texture')
cv2.imwrite(config.out_folder + '/texture/' + config.img_path.split('/')[-1][:-4] + '.png', texture[:,:,::-1])
mesh = Mesh(v=vertices[0], f=template_mesh.f)
mesh.vt = texture_data['vt']
mesh.ft = texture_data['ft']
mesh.set_texture_image(config.out_folder + '/texture/' + config.img_path.split('/')[-1][:-4] + '.png')
mesh.write_obj(config.out_folder + '/texture/' + config.img_path.split('/')[-1][:-4] + '.obj')
def main(config):
print('Tensorflow version {}'.format(tf.__version__))
print("Input Dir: <{}>".format(config.in_folder))
print("Output Dir: <{}>".format(config.out_folder))
img_paths = glob.glob(os.path.join(config.in_folder, '*'))
if (config.save_viz or config.save_obj_file):
template_mesh = Mesh(filename='./flame_model/FLAME_sample.ply')
sess = tf.Session()
model = RingNet_inference(config, sess=sess)
pre_process_times = []
inference_times = []
start = time.time()
for img_path in img_paths:
pre_start = time.time()
input_img, proc_param, img = preprocess_image(img_path,
config.img_size)
pre_end = time.time()
duration = pre_end - pre_start
pre_process_times.append(duration)
inf_start = time.time()
vertices, flame_parameters = model.predict(np.expand_dims(input_img,
axis=0),
get_parameters=True)
inf_end = time.time()
duration = inf_end - inf_start
inference_times.append(duration)
if config.save_viz:
if not os.path.exists(config.out_folder + '/images'):
os.mkdir(config.out_folder + '/images')
cams = flame_parameters[0][:3]
renderer = vis_util.SMPLRenderer(faces=template_mesh.f)
# visualize(img, proc_param, vertices[0], cams, renderer, img_name=config.out_folder + '/images/' + img_path.split('/')[-1][:-4])
visualize_single_row(img,
proc_param,
vertices[0],
cams,
renderer,
img_name=config.out_folder + '/images/' +
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/' +
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/' + 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/' + img_path.split('/')[-1][:-4] +
'.npy', config.flame_model_path)
neutral_mesh.write_obj(config.out_folder + '/neutral_mesh/' +
img_path.split('/')[-1][:-4] + '.obj')
end = time.time()
overall_duration = end - start
mean_pre_process_time = np.mean(pre_process_times)
mean_inference_time = np.mean(inference_times)
print('mean_pre_process_time = {}'.format(mean_pre_process_time))
print('mean_inference_time = {}'.format(mean_inference_time))
n_images = len(img_paths)
throughput = n_images / (np.sum(pre_process_times) +
np.sum(inference_times))
print('total images = {} throughput = {}/s'.format(n_images, throughput))
throughput = n_images / overall_duration
print('total images = {} duration {} throughput = {}/s'.format(
n_images, overall_duration, throughput))