classify.py

import tensorflow as tf
import sys
import os
from PIL import Image
import pandas as pd
import numpy as np

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

image_path = sys.argv[1]
filename = []
predict = []

for file in os.listdir(image_path):
    # Read the image_data
    image_data = tf.gfile.FastGFile(image_path + '/' + file, 'rb').read()

    # Loads label file, strips off carriage return
    label_lines = [line.rstrip() for line
                   in tf.gfile.GFile("logs/trained_labels.txt")]

    # Unpersists graph from file
    with tf.gfile.FastGFile("logs/trained_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]
        filename.append(file)
        predict.append(label_lines[top_k[0]])
        print(label_lines[top_k[0]])
df = pd.DataFrame(data=np.array((filename, predict)), index=['Filename', 'Hand Posture'])
df.to_csv('predicted_output3.csv', index=False)
# for node_id in top_k:
#     human_string = label_lines[node_id]
#     score = predictions[0][node_id]
#     print('%s (score = %.5f)' % (human_string, score))
#     print("--------------------------------")