http://kivantium.hateblo.jp/entry/2015/11/18/233834
こちらのサイトを参考に、pythonでの学習モデルを利用した画像判定プログラムを作りたいのですが、このプログラムをどう実行すればいいのかがいまひとつ解りませんので質問させていただきます。
例えばこれをeval.pyという名前で保存して、ターミナルで
python eval.pyと入力するだけではダメなのは解るんですが、コマンドライン引数で画像や学習済みモデルを指定すればいいのでしょうか。
浅学なもので恐縮ですが、ご教授いただければ幸いです。

以下参考部分のコードです。

#!/usr/bin/env python
#! -*- coding: utf-8 -*-

import sys
import numpy as np
import tensorflow as tf
import cv2


NUM_CLASSES = 2
IMAGE_SIZE = 28
IMAGE_PIXELS = IMAGE_SIZE*IMAGE_SIZE*3

def inference(images_placeholder, keep_prob):
""" モデルを作成する関数

引数: 
  images_placeholder: inputs()で作成した画像のplaceholder
  keep_prob: dropout率のplace_holder

返り値:
  cross_entropy: モデルの計算結果
"""
def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

x_image = tf.reshape(images_placeholder, [-1, 28, 28, 3])

with tf.name_scope('conv1') as scope:
    W_conv1 = weight_variable([5, 5, 3, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

with tf.name_scope('pool1') as scope:
    h_pool1 = max_pool_2x2(h_conv1)

with tf.name_scope('conv2') as scope:
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

with tf.name_scope('pool2') as scope:
    h_pool2 = max_pool_2x2(h_conv2)

with tf.name_scope('fc1') as scope:
    W_fc1 = weight_variable([7*7*64, 1024])
    b_fc1 = bias_variable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

with tf.name_scope('fc2') as scope:
    W_fc2 = weight_variable([1024, NUM_CLASSES])
    b_fc2 = bias_variable([NUM_CLASSES])

with tf.name_scope('softmax') as scope:
    y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

return y_conv

if __name__ == '__main__':
test_image = []
for i in range(1, len(sys.argv)):
    img = cv2.imread(sys.argv[i])
    img = cv2.resize(img, (28, 28))
    test_image.append(img.flatten().astype(np.float32)/255.0)
test_image = np.asarray(test_image)

images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS))
labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES))
keep_prob = tf.placeholder("float")

logits = inference(images_placeholder, keep_prob)
sess = tf.InteractiveSession()

saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
saver.restore(sess, "model.ckpt")

for i in range(len(test_image)):
    pred = np.argmax(logits.eval(feed_dict={ 
        images_placeholder: [test_image[i]],
        keep_prob: 1.0 })[0])
    print pred