".index"):
print(
"#######################Restored all weights ###########################"
)
model_saver.restore(
sess, configs.restore_model_path_fn(cur_model_iterations))
else:
print(configs.restore_model_path_fn(cur_model_iterations))
print("The prev model is not existing!")
quit()
##################### First get the depth scale from the subset of the training set ######################
cur_model_depth_scale = my_utils.mAverageCounter(shape=[1])
scale_data_index = my_utils.mRangeVariable(
min_val=scale_data_from,
max_val=scale_data_to - 1,
initial_val=scale_data_from)
while not scale_data_index.isEnd():
batch_images_np = np.zeros([
configs.scale_batch_size, configs.img_size,
configs.img_size, 3
],
dtype=np.float32)
batch_relation_table_np = np.zeros([
configs.scale_batch_size, configs.nJoints, configs.nJoints
],
dtype=np.float32)
batch_loss_table_log_np = np.zeros([
configs.scale_batch_size, configs.nJoints, configs.nJoints
],
dtype=np.float32)
@classmethod
def reset(cls):
cls.next_flag = 0
@classmethod
def get_next(cls):
return cls.next_flag
@classmethod
def get_going(cls):
return cls.keep_going
if __name__ == "__main__":
total_sum = len(os.listdir(os.path.join(data_path, "images")))
data_index = my_utils.mRangeVariable(0, total_sum, -1)
wnd_width = 1000
wnd_height = 1000
# proj_mat = vtools.OpenGLUtils.perspective(np.radians(45), float(wnd_width) / wnd_height, 0.1, 10000.0)
# view_mat = vtools.OpenGLUtils.lookAt((0, 0, 6), (0, 0, 0), (0, 1, 0))
view_mat = np.identity(4)
proj_mat, _ = h36m_camera.get_cam_mat(1, 54138969)
visualBox = vtools.mVisualBox(
wnd_width,
wnd_height,
title="ordinal show",
btn_callback=m_btn_callback,
proj_mat=proj_mat,
view_mat=view_mat,