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########手写数字数据集##########
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###########保存模型############
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########1层隐含层（全连接层）##########
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#60000条训练数据和10000条测试数据，28x28像素的灰度图像
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#隐含层激活函数：ReLU函数
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#输出层激活函数：softmax函数（实现多分类）
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#损失函数：稀疏交叉熵损失函数
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#输入层有784个节点，隐含层有128个神经元，输出层有10个节点
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import tensorflow as tf
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import matplotlib.pyplot as plt
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import numpy as np
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import tkinter as tk
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from tkinter import filedialog
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import cv2
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import utilsl
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import numpy as np
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import argparse
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import imutils
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from imutils import contours
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import tensorflow as tf
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import time
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print('--------------')
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nowtime = time.strftime('%Y-%m-%d %H:%M:%S')
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print(nowtime)
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#指定GPU
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#import os
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#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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#gpus = tf.config.experimental.list_physical_devices('GPU')
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#tf.config.experimental.set_memory_growth(gpus[0],True)
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#初始化
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plt.rcParams['font.sans-serif'] = ['SimHei']
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#加载数据
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mnist = tf.keras.datasets.mnist
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(train_x,train_y),(test_x,test_y) = mnist.load_data()
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print('\n train_x:%s, train_y:%s, test_x:%s, test_y:%s'%(train_x.shape,train_y.shape,test_x.shape,test_y.shape))
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#数据预处理
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#X_train = train_x.reshape((60000,28*28))
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#Y_train = train_y.reshape((60000,28*28)) #后面采用tf.keras.layers.Flatten()改变数组形状
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X_train,X_test = tf.cast(train_x/255.0,tf.float32),tf.cast(test_x/255.0,tf.float32) #归一化
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y_train,y_test = tf.cast(train_y,tf.int16),tf.cast(test_y,tf.int16)
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#建立模型
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model = tf.keras.Sequential()
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model.add(tf.keras.layers.Flatten(input_shape=(28,28))) #添加Flatten层说明输入数据的形状
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model.add(tf.keras.layers.Dense(128,activation='relu')) #添加隐含层，为全连接层，128个节点，relu激活函数
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model.add(tf.keras.layers.Dense(10,activation='softmax')) #添加输出层，为全连接层，10个节点，softmax激活函数
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print('\n',model.summary()) #查看网络结构和参数信息
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#配置模型训练方法
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#adam算法参数采用keras默认的公开参数，损失函数采用稀疏交叉熵损失函数，准确率采用稀疏分类准确率函数
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model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['sparse_categorical_accuracy'])
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#训练模型
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#批量训练大小为64，迭代5次，测试集比例0.2（48000条训练集数据，12000条测试集数据）
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print('--------------')
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nowtime = time.strftime('%Y-%m-%d %H:%M:%S')
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print('训练前时刻：'+str(nowtime))
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history = model.fit(X_train,y_train,batch_size=64,epochs=5,validation_split=0.2)
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print('--------------')
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nowtime = time.strftime('%Y-%m-%d %H:%M:%S')
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print('训练后时刻：'+str(nowtime))
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#评估模型
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model.evaluate(X_test,y_test,verbose=2) #每次迭代输出一条记录，来评价该模型是否有比较好的泛化能力
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#保存模型参数
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#model.save_weights('C:\\Users\\xuyansong\\Desktop\\深度学习\\python\\MNIST\\模型参数\\mnist_weights.h5')
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#保存整个模型
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model.save('mnist_weights.h5')
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#结果可视化
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print(history.history)
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loss = history.history['loss'] #训练集损失
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val_loss = history.history['val_loss'] #测试集损失
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acc = history.history['sparse_categorical_accuracy'] #训练集准确率
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val_acc = history.history['val_sparse_categorical_accuracy'] #测试集准确率
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plt.figure(figsize=(10,3))
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plt.subplot(121)
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plt.plot(loss,color='b',label='train')
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plt.plot(val_loss,color='r',label='test')
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plt.ylabel('loss')
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plt.legend()
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plt.subplot(122)
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plt.plot(acc,color='b',label='train')
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plt.plot(val_acc,color='r',label='test')
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plt.ylabel('Accuracy')
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plt.legend()
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#暂停5秒关闭画布，否则画布一直打开的同时，会持续占用GPU内存
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#根据需要自行选择
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#plt.ion() #打开交互式操作模式
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#plt.show()
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#plt.pause(5)
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#plt.close()
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#使用模型
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#plt.figure()
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#for i in range(10):
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# num = np.random.randint(1,10000)
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# plt.subplot(2,5,i+1)
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# plt.axis('off')
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# plt.imshow(test_x[num],cmap='gray')
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# demo = tf.reshape(X_test[num],(1,28,28))
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# y_pred = np.argmax(model.predict(demo))
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# plt.title('标签值：'+str(test_y[num])+'\n预测值：'+str(y_pred))
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#y_pred = np.argmax(model.predict(X_test[0:5]),axis=1)
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#print('X_test[0:5]: %s'%(X_test[0:5].shape))
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#print('y_pred: %s'%(y_pred))
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#plt.ion() #打开交互式操作模式
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#plt.show()
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#plt.pause(5)
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#plt.close()
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# 创建tkinter根窗口并立即隐藏
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root = tk.Tk()
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root.withdraw()
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# 弹出文件选择对话框让用户选择模板文件
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#template_file_path = filedialog.askopenfilename(title="选择模板文件", filetypes=[("PNG files", "*.png"), ("JPEG files", "*.jpg"), ("All files", "*.*")])
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# 弹出文件选择对话框让用户选择信用卡图片
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image_file_path = filedialog.askopenfilename(title="选择信用卡图片", filetypes=[("PNG files", "*.png"), ("JPEG files", "*.jpg"), ("All files", "*.*")])
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# 使用用户选择的路径读取模板文件和信用卡图片
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#img = cv2.imread(template_file_path)
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image = cv2.imread(image_file_path)
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#指定信用卡类型
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FIRST_NUMBER={
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"3":"American Express",
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"4":"Visa",
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"5":"MasterCard",
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"6":"Discover Card"
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}
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#绘图展示
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def cv_show(name,img):
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cv2.imshow(name,img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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def preprocess_image(roi):
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# 调整图像大小并归一化
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roi = cv2.resize(roi, (28, 28))
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roi = roi / 255.0
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roi = roi.reshape(1, 28, 28, 1) # 为模型输入调整形状
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return roi
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def predict_digit(roi, model):
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roi = preprocess_image(roi)
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prediction = model.predict(roi)
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digit = np.argmax(prediction)
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return str(digit)
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#读取一个模板文件
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#img=cv2.imread(template_file_path)
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#cv_show("img",img)
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#灰度图
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#ref=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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#cv_show('ref',ref)
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#二值图像
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#ref=cv2.threshold(ref,10,255,cv2.THRESH_BINARY_INV)[1]
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#cv_show('ref',ref)
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#model = tf.keras.models.load_model('mnist_weights.h5')
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#计算轮廓
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#cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图）
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#cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_SIMPLE只保留终点坐标
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#返回的list中每个元素都是图像中的一个轮廓
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#refCnts,hierarchy=cv2.findContours(ref.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
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#cv2.drawContours(img,refCnts,-1,(0,0,255),3)
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#cv_show('img',img)
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#refCnts=utilsl.sort_contours(refCnts,method="left-to-right")[0]#排序从左到右，从上到下
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#digits={}
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'''
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第一个参数：img是原图
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第二个参数：（x，y）是矩阵的左上点坐标
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第三个参数：（x+w，y+h）是矩阵的右下点坐标
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第四个参数：（0,255,0）是画线对应的rgb颜色
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'''
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#遍历每一个轮廓
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#for(i,c) in enumerate(refCnts):
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#计算外接矩形并且resize成合适大小
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# (x,y,w,h)=cv2.boundingRect(c)
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# roi=ref[y:y+h,x:x+w]
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# roi=cv2.resize(roi,(57,58))
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#每一个数字对应一个模板
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# digits[i]=roi
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#初始化卷积核
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rectKernel=cv2.getStructuringElement(cv2.MORPH_RECT,(9,3))
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sqKernel=cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
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#读取输入图像，预处理
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image=cv2.imread(image_file_path)
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cv_show('image',image)
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image=utilsl.resize(image,width=300)
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gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
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cv_show('gray',gray)
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#礼帽操作，突出更明亮的区域
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tophat=cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,rectKernel)
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cv_show('tophat',tophat)
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#计算
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gradX=cv2.Sobel(tophat,ddepth=cv2.CV_32F,dx=1,dy=0,ksize=1)
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gradX=np.absolute(gradX)
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(minVal,maxVal)=(np.min(gradX),np.max(gradX))
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gradX=(255*((gradX-minVal)/(maxVal-minVal)))
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gradX=gradX.astype("uint8")
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print(np.array(gradX).shape)
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cv_show('gradX',gradX)
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#通过闭操作，（先膨胀，在腐蚀）将数字连在一起
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gradX=cv2.morphologyEx(gradX,cv2.MORPH_CLOSE,rectKernel)
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cv_show('gradX',gradX)
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#THRESH_OTSU会自动寻找合适的阈值，适合双峰，需把阈值参数设置为0
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thresh=cv2.threshold(gradX,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)[1]
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cv_show('thresh',thresh)
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#再来一个闭操作
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thresh=cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,sqKernel)
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cv_show('thresh',thresh)
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thresh=cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,sqKernel)
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cv_show('thresh',thresh)
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#计算轮廓
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threshCnts,hierarchy=cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
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cnts=threshCnts
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cur_img=image.copy()
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cv2.drawContours(cur_img,cnts,-1,(0,0,255),3)
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cv_show('img',cur_img)
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locs=[]
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#遍历轮廓
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for (i,c) in enumerate(cnts):
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#计算矩形
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(x,y,w,h)=cv2.boundingRect(c)
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ar=w/float(h)
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#适合合适的区域，根据实际任务来，这里的基本是四个数字一组
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if ar>2.5 and ar <4:
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if(w>40 and w<55) and (h>10 and h<20):
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#符合的留下来
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locs.append((x,y,w,h))
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#将符合的轮廓从左到右排序
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locs=sorted(locs,key=lambda x:x[0])
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output=[]
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#遍历每一个轮廓中的数字
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for (i,(gX,gY,gW,gH)) in enumerate(locs):
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#initialize the list of group digits
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groupOutput=[]
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#根据坐标提取每一个组
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group=gray[gY-5:gY+gH+5,gX-5:gX+gW+5]
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cv_show('group',group)
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#预处理
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group=cv2.threshold(group,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)[1]
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cv_show('group',group)
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#计算每一个轮廓
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digitCnts,hierarchy=cv2.findContours(group.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
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digitCnts=contours.sort_contours(digitCnts,method="left-to-right")[0]
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#计算每一组总的每一个数值
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for c in digitCnts:
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(x, y, w, h) = cv2.boundingRect(c)
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roi = group[y:y + h, x:x + w]
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roi = cv2.resize(roi, (28, 28))
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cv_show('roi', roi)
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digit = predict_digit(roi, model)
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groupOutput.append(digit)
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print("识别的数字：", groupOutput)
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#画出来
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cv2.rectangle(image,(gX-5,gY-5),(gX+gW+5,gY+gH+5),(0,0,255),1)
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cv2.putText(image,"".join(groupOutput),(gX,gY-15),cv2.FONT_HERSHEY_SIMPLEX,0.65,(0,0,255),2)
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#得到结果
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output.extend(groupOutput)
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# 打印结果
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#print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
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print("Credit Card #: {}".format("".join(output)))
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cv2.imshow("Image", image)
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cv2.waitKey()
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cv2.destroyAllWindows()
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