新手上手Tensorflow之手写数字识别应用（3）

本系列为应用TensorFlow实现手写数字识别应用的全过程的代码实现及细节讨论。按照实现流程，分为如下几部分：
1. 模型训练并保存模型
2. 通过鼠标输入数字并保存
2. 图像预处理
4. 读入模型对输入的图片进行识别
本文重点讨论图像预处理的问题。
所谓的图像预处理，这里是指对由鼠标输入数字的图像进行分割，并缩放到和样本相同的尺寸。
这一块没有什么难点，直接上代码，注释写的比较明确了。

'''边缘检测，裁剪'''#  -*- coding: utf-8 -*import cv2import numpy as npimport os#import the imageimg = cv2.imread('./img/number.jpg',1)#cv2.imshow('img',img)#转化为灰度图img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #高斯平滑除噪#img_blur = cv2.GaussianBlur(img,(5,5),0)img_blur = cv2.medianBlur(img_gray,5)#cv2.imshow('Gaussian',img_blur)#canny算子 边缘检测img_canny = cv2.Canny(img_blur,200,100)#cv2.imshow('canny',img_canny)#二值化处理_, img_bin = cv2.threshold(img_canny, 80, 255, cv2.THRESH_BINARY )#cv2.imshow('bin',img_bin)#积分运算imgI = cv2.integral(img_bin)#######分块#定义分块的尺寸(xh,yw) = img_gray.shapep = int(xh / 10) #高度方向上等分的快数q = int(yw / 10) #宽度方向上等分的快数#分块矩阵sat = np.arange(p*q).reshape(p,q)#获取原图的尺寸#计算每块的宽和高；w = int(yw / q)h = int(xh / p)if w <= 5:    print('the image is too small to split!')    os._exit(0)#print(w,h)#合并块sated = np.ones((p,q))p = range(p)q = range(q)# 计算各块的能量sat[0][0] = imgI[h-1][w-1]for n in q[1:]: #先计算第0行的能量    sat[0][n] = imgI[h-1][w * (n+1) -1] - imgI[h-1][w * n -1]for m in p[1:]: #计算第0列的能量    sat[m][0] = imgI[h * (m+1) - 1][w-1] - imgI[h * m - 1][w-1]for m in p[1:]: #计算其余的能量    for n in q[1:]:        sat[m][n] = imgI[h * (m+1) - 1][w * (n+1) -1]  - imgI[h * (m+1) - 1][w * n -1] -imgI[h * m - 1][w * (n+1) -1] + imgI[h * m - 1][w * n -1] #print(sat)#计算各块的能量密度sat = sat / (w * h) #选出能量密度较高的块print('to draw:')threshold1 = 10##8邻域搜索算法合并区域def eightSearch(sated, m, n,mleft,ntop,mright,nbottom):    #sat_search = [sat[m-1][n-1],sat[m-1][n],sat[m-1][n+1],sat[m][n-1],sat[m][n+1],sat[m+1][n-1],sat[m+1][n],sat[m+1][n+1]]    sat_search = [(m-1,n-1),(m-1,n),(m-1,n+1),(m,n-1),(m,n+1),(m+1,n-1),(m+1,n),(m+1,n+1)]    for sati in sat_search:        s0 = sati[0]        s1 = sati[1]        if sated[s0][s1] != -1:            left = s1 * w -1            top = s0 * h -1            right = s1 * w + w -1            bottom = s0 * h + h -1            sated[s0][s1] = -1            if sat[s0][s1] > 10:                #记录边界                if left < mleft:                    mleft = left                elif right > mright:                    mright = right                if top < ntop:                    ntop = top                elif bottom > nbottom:                    nbottom = bottom                #循环                sated,mleft,ntop,mright,nbottom = eightSearch(sated,s0,s1,mleft,ntop,mright,nbottom)    return sated,mleft,ntop,mright,nbottom##保存框选出的区域#@param img 原图#@param left, right ,top,bottom 裁切区域的上下左右坐标#@param pad 是否添加边（添加20%的黑边）#@param name 保存图片的名字,默认为None,则不保存def saveRect(img, mleft, ntop, mright, nbottom, pad = True, name = None):    subimg = img[ntop:nbottom, mleft:mright]    if pad is False:        if name is not None:            cv2.imwrite(name,subimg)        return subimg    else:        subimgshape = subimg.shape        addpad =(2*int(0.1 * subimgshape[0]),2*int(0.1 * subimgshape[1]))        frame = np.zeros((addpad[0] + subimgshape[0], addpad[1] + subimgshape[1], 3), np.uint8)        frame[int(addpad[0]/2):(int(addpad[0]/2)+subimgshape[0]),int(addpad[1]/2):(int(addpad[1]/2)+subimgshape[1])] = subimg        if name is not None:            cv2.imwrite(name,frame)        return frame #返回裁切结果count = 0;#图中数字计数 for m in p[1:-2]: #因为8邻域，所以排除    for n in q[1:-2]:        if sated[m][n] != -1 and sat[m][n]> 1:            print('has number!')            sated[m][n] = -1            sated,mleft,ntop,mright,nbottom = eightSearch(sated,m,n,n*w-1,m*h-1,n*w-1+w,m*h-1+h)            #cv2.rectangle(img,(mleft,ntop),(mright,nbottom),(0,0,255),1)            saimg = saveRect(img,mleft,ntop,mright,nbottom)            cv2.imshow('subimg' + str(count), saimg)            res = cv2.resize(saimg,(28, 28), interpolation = cv2.INTER_AREA )            cv2.imwrite('./img/num1202' + str(count) + '.jpg',res)            count = count + 1cv2.waitKey()cv2.destroyAllWindows()