study_name = get_file_name(environment.current_study.study_dir)
path_len = environment.current_study.image_index - warm_frames_num
print("{}:\t len {},\t done: {}".format(study_name, path_len, environment.current_study.finished_successfully))
environment.write_stats()
if __name__ == '__main__':
# if len(sys.argv) == 1:
# print("Error: missing run mode type (hand/fingers)")
# sys.exit(1)
#
# run_mode_type = sys.argv[1]
# set_run_config(run_mode_type)
sys.stdout = StdoutLog(log_dir, sys.stdout, print_to_log, print_to_stdout)
create_dir(checkpoint_dir)
if mode == "train":
if platform.system() == 'Linux':
if mode_type == "hand":
print("use hands dataset..")
train(["/home/ami/handsTrack/studies/full_hands/17.12.17",
"/home/ami/handsTrack/studies/full_hands/18.12.17",
"/home/ami/handsTrack/studies/full_hands/19.12.17"],
list_of_dirs=True)
else:
print("use fingers dataset..")
train("/home/ami/handsTrack/studies/fingers/20.12.17")
else:
image, point = study.next()
point = [point.x, point.y]
loss, next_state, p, check = sess.run([tracker.loss, tracker.out_state, tracker.predict, tracker.check],
feed_dict={tracker.input_frames: image,
tracker.target: [point],
tracker.keep_prob: keep_prob,
tracker.cell_state: current_cell_state,
tracker.hidden_state: current_hidden_state})
current_cell_state, current_hidden_state = next_state
# print("predict: {}, relative: {}, loss = {}".format(np.array(p), point, loss))
# print(np.mean(current_cell_state - old_state))
# old_state = current_cell_state
if __name__ == '__main__':
sys.stdout = StdoutLog(log_file, sys.stdout, print_to_log, print_to_stdout)
if mode == "train":
print("Start training ..")
if platform.system() == 'Linux':
train("/home/ami/fingersTracking/data/try")
#train(["/home/ami/fingersTracking/data/studies",
# "/home/ami/fingersTracking/data/studies9.1.18"], list_of_dirs=True)
else:
train("C:/Users/il115552/Desktop/New folder (6)")
if mode == "test":
print("Start testing ..")
if platform.system() == 'Linux':
test("/home/ami/fingersTracking/data/test")
else: