def predict(image, sess, softmax_tensor):
"""
Function used by classification workers to get prediction on image.
This method was adapted based on run_inference_on_image() method from classify_image.py found in TensorFlow official tutorial.
:param image: filename of the image to be classified
:param sess: TensorFlow session
:param softmax_tensor: tensor used for computing the predictions
:return: (img_id, result) with img_id being Instagram Image ID and the result being dictionary with 5 most probable
objects depicted in the image as keys and corresponding prediction confidences as values.
"""
img_id = os.path.splitext(os.path.basename(image))[0]
image_data = tf.gfile.FastGFile(image, 'rb').read()
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
# encoding of the image.
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = NodeLookup()
top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
result = {}
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
result[human_string] = float(score)
return img_id, result
def run_inference_on_image(self, imageList, num_top_predictions=5):
"""Runs inference on an image.
Args:
image: Image file name.
Returns:
Nothing
"""
for image in imageList:
if not tf.gfile.Exists(image):
tf.logging.fatal('File does not exist %s', image)
image_data = tf.gfile.FastGFile(image, 'rb').read()
# Creates graph from saved GraphDef.
self.__create_graph()
with tf.Session() as sess:
# Some useful tensors:
# 'softmax:0': A tensor containing the normalized prediction across
# 1000 labels.
# 'pool_3:0': A tensor containing the next-to-last layer containing 2048
# float description of the image.
# 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
# encoding of the image.
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
# Creates node ID --> English string lookup.
node_lookup = NodeLookup()
top_k = predictions.argsort()[-num_top_predictions:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))