在task2.py
,我正在执行一个这样的python文件:
text = os.system("python new_image.py --image_file " + "/home/roots/" + str(nc) + ".jpg --num_top_predictions 1")
new_image.py
外观的基本线条
def main(_):
maybe_download_and_extract()
image = (FLAGS.image_file if FLAGS.image_file else
os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
score = run_inference_on_image(image)
return score
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--model_dir',
type=str,
default='/tmp/imagenet',
help="""\
Path to classify_image_graph_def.pb,
imagenet_synset_to_human_label_map.txt, and
imagenet_2012_challenge_label_map_proto.pbtxt.\
"""
)
parser.add_argument(
'--image_file',
type=str,
default='',
help='Absolute path to image file.'
)
parser.add_argument(
'--num_top_predictions',
type=int,
default=5,
help='Display this many predictions.'
)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
我已经classify_image.py
在 tensorflow 模型 imagenet 中进行了修改,以便run_inference_on_image()
返回一个列表。我现在想在task2.py
. 我将如何做到这一点?