验证码测试

接上面，验证码的验证

import tensorflow as tf
import numpy as np
from PIL import Image
import os
import matplotlib.pyplot as plt 

CAPTCHA_LEN = 4

MODEL_SAVE_PATH = 'F:\\YanZhengMa\\1\\yz\\'
TEST_IMAGE_PATH = 'F:\\YanZhengMa\\1\\valid\\'

def get_image_data_and_name(fileName, filePath=TEST_IMAGE_PATH):
    pathName = os.path.join(filePath, fileName)
    img = Image.open(pathName)
    #转为灰度图
    img = img.convert("L")       
    image_array = np.array(img)
    #归一化
    image_data = image_array.flatten()/255
    image_name = fileName[0:CAPTCHA_LEN]
    return image_data, image_name

def digitalStr2Array(digitalStr):
    digitalList = []
    for c in digitalStr:
        digitalList.append(ord(c) - ord('0'))
    return np.array(digitalList)

def model_test():
    nameList = []
    for pathName in os.listdir(TEST_IMAGE_PATH):
        nameList.append(pathName.split('/')[-1])
    totalNumber = len(nameList)
    #加载graph
    saver = tf.train.import_meta_graph(MODEL_SAVE_PATH+"crack_captcha.model-1800.meta")
    graph = tf.get_default_graph()
    #从graph取得 tensor，他们的name是在构建graph时定义的(查看上面captcha-test.py里的代码)
    input_holder = graph.get_tensor_by_name("data-input:0")
    keep_prob_holder = graph.get_tensor_by_name("keep-prob:0")
    predict_max_idx = graph.get_tensor_by_name("predict_max_idx:0")
    with tf.Session() as sess:
        saver.restore(sess, tf.train.latest_checkpoint(MODEL_SAVE_PATH))
        count = 0
        for fileName in nameList:
            img_data, img_name = get_image_data_and_name(fileName, TEST_IMAGE_PATH)
            predict = sess.run(predict_max_idx, feed_dict={input_holder:[img_data], keep_prob_holder : 1.0})            
            filePathName = TEST_IMAGE_PATH + fileName
            print(filePathName)
            img = Image.open(filePathName)
            plt.imshow(img)
            plt.axis('off')
            plt.show()
            predictValue = np.squeeze(predict)
            rightValue = digitalStr2Array(img_name)
            if np.array_equal(predictValue, rightValue):
                result = '正确'
                count += 1
            else: 
                result = '错误'            
            print('实际值：{}， 预测值：{}，测试结果：{}'.format(rightValue, predictValue, result))
            print('\n')

        print('正确率：%.2f%%(%d/%d)' % (count*100/totalNumber, count, totalNumber))

if __name__ == '__main__':
    model_test()