predict.py

import tensorflow as tf, sys


def classifySkinLesion(image_path):
    """
        Function to classify skin lesion images as benign or malignant
    """

    # 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("trained_nets_Mumbai_hackathon/ISIC_3/retrained_labels.txt")]

    # Unpersists graph from file
    with tf.gfile.FastGFile("trained_nets_Mumbai_hackathon/ISIC_3/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))



def classifyDiabeticRetinopathy(image_path):
    """
        Function to classify images of retina based on sevirity of retinopathy
        Severity Index:
        0 - No Retinopathy detected
        1 - Mild case of retinopathy
        2 - Severe case of retinopathy 
    """

    # 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("trained_nets_Mumbai_hackathon/retino/retrained_labels.txt")]

    # Unpersists graph from file
    with tf.gfile.FastGFile("trained_nets_Mumbai_hackathon/retino/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))