分类一个numpy数组图像

Question

I am using the Tensorflow for Poets tutorial to classify an image. I am using the code below to classify an image, but would like to feed in a numpy array as the image instead of a jpeg, how would the code have to change?

import tensorflow as tf
import sys

# 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("/tf_files/retrained_labels.txt")]

# Unpersists graph from file
with tf.gfile.FastGFile("/tf_files/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]

    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))

image_data = tf.gfile.FastGFile(image_path, 'rb').read() - If I'm not reading from a file, I imagine I don't need this.

predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0': image_data}) - I know I don't need to override this aspect of the feed_dict, but what should I do instead?

Overall, how can I make sure that a nparray I have that represents an image be used properly for prediction?

Answer 1

Thank you all, I have found the answer:

supposed I have a 3-dimensional numpy array with size (100,132,3) called image .

All I have to do is pass it into the softmax classifier using 'DecodeJpeg:0 rather than DecodeJpeg/contents:0 like so...

predictions = sess.run(softmax_tensor, {'DecodeJpeg:0': image})

... and there you have it