classify_original.py

import tensorflow as tf	
import sys
import os
import picamera
from time import sleep 

# Disable tensorflow compilation warnings
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

image_path = "testing.jpg"

#camera = picamera.PiCamera()
camera.resolution = (2592,1944)

camera.capture(image_path)

if image_path:
    
    # Read 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]
        score = predictions[0][1]
        print(score)
        #    print('%s (score = %.5f)' % (human_string, score))
    if score > 0.8:
        exec(open("servotest.py").read())
        sleep(10)
    else:
        print('not a bottle')  
        sleep(10)