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catordog.py
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catordog.py
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import tensorflow as tf
import sys
import os
# change this as you see fit
# Loads label file, strips off carriage return
class CatOrDog():
def __init__(self):
self.dir_path = os.path.dirname(os.path.realpath(__file__))
self.label_lines = [line.rstrip() for line
in tf.gfile.GFile(os.path.join(self.dir_path, "output/retrained_labels.txt"))]
self.create_graph()
self.sess = tf.Session()
def create_graph(self):
with tf.gfile.FastGFile(os.path.join(self.dir_path, "output/retrained_graph.pb"), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
def run(self, image_path):
image_data = tf.gfile.FastGFile(image_path, 'rb').read()
# Feed the image_data as input to the graph and get first prediction
softmax_tensor = self.sess.graph.get_tensor_by_name('final_result:0')
predictions = self.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]
print('Result for %s' % (image_path))
ret = ''
for node_id in top_k:
human_string = self.label_lines[node_id]
score = predictions[0][node_id]
print('%s (score = %.5f)' % (human_string, score))
if (score > 0.5):
ret += human_string
return ret