Question

我正在使用此网站https://medium.com/enpit-developer-blog/image-recognition-for-custom-categories-with-tensorflow-68196e589efa上的代码训练我自己的模型以检测图像上的物体。因为我是初学者，所以我遇到了问题。我想将检测到的对象显示在单独的帧中（裁剪）。修改后的代码在这里，但是我显然遗漏了一些东西。

import os, sys

import tensorflow as tf
import matplotlib.pyplot as plt


os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# change this as you see fit
image_path = sys.argv[1]

# Read in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()

# Loads label file, strips off carriage return
label_lines = [line.rstrip() for line 
                   in tf.gfile.GFile("retrained_labels.txt")]

# Unpersists graph from file
with tf.gfile.FastGFile("retrained_graph.pb", 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')

with tf.Session() as sess:
    # Feed the image_data as input to the graph and get first prediction
    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')

    predictions = sess.run(softmax_tensor, \
             {'DecodeJpeg/contents:0': image_data})

    # Sort to show labels of first prediction in order of confidence
    top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
    plt.imshow(predictions[0])
    plt.show()
    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
        print('%s (score = %.5f)' % (human_string, score))

修改代码以在单独的框架中显示检测到的对象

0 个答案: