- 同级新建fonts文件夹里拷贝一些字体文件，从这里找C:\Windows\Fonts，几十个就行。
- get_character_pic.py 生成字体
- cnn.py 训练数据
- main.py 裁剪指定图片并识别，素材图片新建imgs文件夹,在imgs/question.png下，结果文件保存在imgs/result.png。
- 注意如果识别不成功，很可能是question.png的字体你没有训练（这幅图的字体是方正书宋简体，但是你只训练了楷体），这时候可以使用楷体自己编一个算式图。
一、亮出效果
最近在线教育行业遭遇一点小波折，一些搜题、智能批改类的功能要下线。
退1024步讲，要不要自己做一个自动批改的功能啊？万一哪天孩子要用呢！
昨晚我做了一个梦，梦见我实现了这个功能，如下图所示：
功能简介：作对了，能打对号；做错了，能打叉号；没做的，能补上答案。
醒来后，我环顾四周，赶紧再躺下，希望梦还能接上。
二、实现步骤
基本思路
其实，搞定两点就成，第一是能识别数字，第二是能切分数字。
首先得能认识5是5，这是前提条件，其次是能找到5、6、7、8这些数字区域的位置。
前者是图像识别，后者是图像切割。
- 对于图像识别，一般的套路是下面这样的（CNN卷积神经网络）：
#mermaid-svg-19CB7BMTJEaRusAf .label{font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family);fill:#333;color:#333}#mermaid-svg-19CB7BMTJEaRusAf .label text{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf .node rect,#mermaid-svg-19CB7BMTJEaRusAf .node circle,#mermaid-svg-19CB7BMTJEaRusAf .node ellipse,#mermaid-svg-19CB7BMTJEaRusAf .node polygon,#mermaid-svg-19CB7BMTJEaRusAf .node path{fill:#ECECFF;stroke:#9370db;stroke-width:1px}#mermaid-svg-19CB7BMTJEaRusAf .node .label{text-align:center;fill:#333}#mermaid-svg-19CB7BMTJEaRusAf .node.clickable{cursor:pointer}#mermaid-svg-19CB7BMTJEaRusAf .arrowheadPath{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf .edgePath .path{stroke:#333;stroke-width:1.5px}#mermaid-svg-19CB7BMTJEaRusAf .flowchart-link{stroke:#333;fill:none}#mermaid-svg-19CB7BMTJEaRusAf .edgeLabel{background-color:#e8e8e8;text-align:center}#mermaid-svg-19CB7BMTJEaRusAf .edgeLabel rect{opacity:0.9}#mermaid-svg-19CB7BMTJEaRusAf .edgeLabel span{color:#333}#mermaid-svg-19CB7BMTJEaRusAf .cluster rect{fill:#ffffde;stroke:#aa3;stroke-width:1px}#mermaid-svg-19CB7BMTJEaRusAf .cluster text{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family);font-size:12px;background:#ffffde;border:1px solid #aa3;border-radius:2px;pointer-events:none;z-index:100}#mermaid-svg-19CB7BMTJEaRusAf .actor{stroke:#ccf;fill:#ECECFF}#mermaid-svg-19CB7BMTJEaRusAf text.actor>tspan{fill:#000;stroke:none}#mermaid-svg-19CB7BMTJEaRusAf .actor-line{stroke:grey}#mermaid-svg-19CB7BMTJEaRusAf .messageLine0{stroke-width:1.5;stroke-dasharray:none;stroke:#333}#mermaid-svg-19CB7BMTJEaRusAf .messageLine1{stroke-width:1.5;stroke-dasharray:2, 2;stroke:#333}#mermaid-svg-19CB7BMTJEaRusAf #arrowhead path{fill:#333;stroke:#333}#mermaid-svg-19CB7BMTJEaRusAf .sequenceNumber{fill:#fff}#mermaid-svg-19CB7BMTJEaRusAf #sequencenumber{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf #crosshead path{fill:#333;stroke:#333}#mermaid-svg-19CB7BMTJEaRusAf .messageText{fill:#333;stroke:#333}#mermaid-svg-19CB7BMTJEaRusAf .labelBox{stroke:#ccf;fill:#ECECFF}#mermaid-svg-19CB7BMTJEaRusAf .labelText,#mermaid-svg-19CB7BMTJEaRusAf .labelText>tspan{fill:#000;stroke:none}#mermaid-svg-19CB7BMTJEaRusAf .loopText,#mermaid-svg-19CB7BMTJEaRusAf .loopText>tspan{fill:#000;stroke:none}#mermaid-svg-19CB7BMTJEaRusAf .loopLine{stroke-width:2px;stroke-dasharray:2, 2;stroke:#ccf;fill:#ccf}#mermaid-svg-19CB7BMTJEaRusAf .note{stroke:#aa3;fill:#fff5ad}#mermaid-svg-19CB7BMTJEaRusAf .noteText,#mermaid-svg-19CB7BMTJEaRusAf .noteText>tspan{fill:#000;stroke:none}#mermaid-svg-19CB7BMTJEaRusAf .activation0{fill:#f4f4f4;stroke:#666}#mermaid-svg-19CB7BMTJEaRusAf .activation1{fill:#f4f4f4;stroke:#666}#mermaid-svg-19CB7BMTJEaRusAf .activation2{fill:#f4f4f4;stroke:#666}#mermaid-svg-19CB7BMTJEaRusAf .mermaid-main-font{font-family:"trebuchet ms", verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf .section{stroke:none;opacity:0.2}#mermaid-svg-19CB7BMTJEaRusAf .section0{fill:rgba(102,102,255,0.49)}#mermaid-svg-19CB7BMTJEaRusAf .section2{fill:#fff400}#mermaid-svg-19CB7BMTJEaRusAf .section1,#mermaid-svg-19CB7BMTJEaRusAf .section3{fill:#fff;opacity:0.2}#mermaid-svg-19CB7BMTJEaRusAf .sectionTitle0{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf .sectionTitle1{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf .sectionTitle2{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf .sectionTitle3{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf .sectionTitle{text-anchor:start;font-size:11px;text-height:14px;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf .grid .tick{stroke:#d3d3d3;opacity:0.8;shape-rendering:crispEdges}#mermaid-svg-19CB7BMTJEaRusAf .grid .tick text{font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf .grid path{stroke-width:0}#mermaid-svg-19CB7BMTJEaRusAf .today{fill:none;stroke:red;stroke-width:2px}#mermaid-svg-19CB7BMTJEaRusAf .task{stroke-width:2}#mermaid-svg-19CB7BMTJEaRusAf .taskText{text-anchor:middle;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf .taskText:not([font-size]){font-size:11px}#mermaid-svg-19CB7BMTJEaRusAf .taskTextOutsideRight{fill:#000;text-anchor:start;font-size:11px;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf .taskTextOutsideLeft{fill:#000;text-anchor:end;font-size:11px}#mermaid-svg-19CB7BMTJEaRusAf .task.clickable{cursor:pointer}#mermaid-svg-19CB7BMTJEaRusAf .taskText.clickable{cursor:pointer;fill:#003163 !important;font-weight:bold}#mermaid-svg-19CB7BMTJEaRusAf .taskTextOutsideLeft.clickable{cursor:pointer;fill:#003163 !important;font-weight:bold}#mermaid-svg-19CB7BMTJEaRusAf .taskTextOutsideRight.clickable{cursor:pointer;fill:#003163 !important;font-weight:bold}#mermaid-svg-19CB7BMTJEaRusAf .taskText0,#mermaid-svg-19CB7BMTJEaRusAf .taskText1,#mermaid-svg-19CB7BMTJEaRusAf .taskText2,#mermaid-svg-19CB7BMTJEaRusAf .taskText3{fill:#fff}#mermaid-svg-19CB7BMTJEaRusAf .task0,#mermaid-svg-19CB7BMTJEaRusAf .task1,#mermaid-svg-19CB7BMTJEaRusAf .task2,#mermaid-svg-19CB7BMTJEaRusAf .task3{fill:#8a90dd;stroke:#534fbc}#mermaid-svg-19CB7BMTJEaRusAf .taskTextOutside0,#mermaid-svg-19CB7BMTJEaRusAf .taskTextOutside2{fill:#000}#mermaid-svg-19CB7BMTJEaRusAf .taskTextOutside1,#mermaid-svg-19CB7BMTJEaRusAf .taskTextOutside3{fill:#000}#mermaid-svg-19CB7BMTJEaRusAf .active0,#mermaid-svg-19CB7BMTJEaRusAf .active1,#mermaid-svg-19CB7BMTJEaRusAf .active2,#mermaid-svg-19CB7BMTJEaRusAf .active3{fill:#bfc7ff;stroke:#534fbc}#mermaid-svg-19CB7BMTJEaRusAf .activeText0,#mermaid-svg-19CB7BMTJEaRusAf .activeText1,#mermaid-svg-19CB7BMTJEaRusAf .activeText2,#mermaid-svg-19CB7BMTJEaRusAf .activeText3{fill:#000 !important}#mermaid-svg-19CB7BMTJEaRusAf .done0,#mermaid-svg-19CB7BMTJEaRusAf .done1,#mermaid-svg-19CB7BMTJEaRusAf .done2,#mermaid-svg-19CB7BMTJEaRusAf .done3{stroke:grey;fill:#d3d3d3;stroke-width:2}#mermaid-svg-19CB7BMTJEaRusAf .doneText0,#mermaid-svg-19CB7BMTJEaRusAf .doneText1,#mermaid-svg-19CB7BMTJEaRusAf .doneText2,#mermaid-svg-19CB7BMTJEaRusAf .doneText3{fill:#000 !important}#mermaid-svg-19CB7BMTJEaRusAf .crit0,#mermaid-svg-19CB7BMTJEaRusAf .crit1,#mermaid-svg-19CB7BMTJEaRusAf .crit2,#mermaid-svg-19CB7BMTJEaRusAf .crit3{stroke:#f88;fill:red;stroke-width:2}#mermaid-svg-19CB7BMTJEaRusAf .activeCrit0,#mermaid-svg-19CB7BMTJEaRusAf .activeCrit1,#mermaid-svg-19CB7BMTJEaRusAf .activeCrit2,#mermaid-svg-19CB7BMTJEaRusAf .activeCrit3{stroke:#f88;fill:#bfc7ff;stroke-width:2}#mermaid-svg-19CB7BMTJEaRusAf .doneCrit0,#mermaid-svg-19CB7BMTJEaRusAf .doneCrit1,#mermaid-svg-19CB7BMTJEaRusAf .doneCrit2,#mermaid-svg-19CB7BMTJEaRusAf 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#aggregationEnd{fill:#ECECFF;stroke:#9370db;stroke-width:1}#mermaid-svg-19CB7BMTJEaRusAf #dependencyStart{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-19CB7BMTJEaRusAf #dependencyEnd{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-19CB7BMTJEaRusAf #extensionStart{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-19CB7BMTJEaRusAf #extensionEnd{fill:#9370db;stroke:#9370db;stroke-width:1}#mermaid-svg-19CB7BMTJEaRusAf .commit-id,#mermaid-svg-19CB7BMTJEaRusAf .commit-msg,#mermaid-svg-19CB7BMTJEaRusAf .branch-label{fill:lightgrey;color:lightgrey;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf .pieTitleText{text-anchor:middle;font-size:25px;fill:#000;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf .slice{font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf g.stateGroup text{fill:#9370db;stroke:none;font-size:10px;font-family:'trebuchet ms', verdana, arial;font-family:var(--mermaid-font-family)}#mermaid-svg-19CB7BMTJEaRusAf g.stateGroup text{fill:#9370db;fill:#333;stroke:none;font-size:10px}#mermaid-svg-19CB7BMTJEaRusAf g.statediagram-cluster .cluster-label text{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf g.stateGroup .state-title{font-weight:bolder;fill:#000}#mermaid-svg-19CB7BMTJEaRusAf g.stateGroup rect{fill:#ECECFF;stroke:#9370db}#mermaid-svg-19CB7BMTJEaRusAf g.stateGroup line{stroke:#9370db;stroke-width:1}#mermaid-svg-19CB7BMTJEaRusAf .transition{stroke:#9370db;stroke-width:1;fill:none}#mermaid-svg-19CB7BMTJEaRusAf .stateGroup .composit{fill:white;border-bottom:1px}#mermaid-svg-19CB7BMTJEaRusAf .stateGroup .alt-composit{fill:#e0e0e0;border-bottom:1px}#mermaid-svg-19CB7BMTJEaRusAf .state-note{stroke:#aa3;fill:#fff5ad}#mermaid-svg-19CB7BMTJEaRusAf .state-note text{fill:black;stroke:none;font-size:10px}#mermaid-svg-19CB7BMTJEaRusAf .stateLabel 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.edge-pattern-dashed{stroke-dasharray:3}#mermaid-svg-19CB7BMTJEaRusAf .edge-pattern-dotted{stroke-dasharray:2}#mermaid-svg-19CB7BMTJEaRusAf .marker{fill:#333}#mermaid-svg-19CB7BMTJEaRusAf .marker.cross{stroke:#333}
:root { --mermaid-font-family: "trebuchet ms", verdana, arial;}#mermaid-svg-19CB7BMTJEaRusAf {
color: rgba(0, 0, 0, 0.75);
font: ;
}
输入
识别输出
字符图片数据集
训练
训练结果
字符图片
字符值
- 对于图像切割，一般的套路是下面的这样（横向纵向投影法）：
既然思路能走得通，那么咱们先搞图像识别。
要自己搞图像识别，得准备数据->训练数据并保存模型->使用训练模型预测结果。
2.1 准备数据
对于男友，找一个油嘴滑舌的花花公子，不如找一个闷葫芦IT男，亲手把他培养成你期望的样子。
咱们不用什么官方的mnist数据集，因为那是官方的，不是你的，你想要添加±×÷它也没有。
有些通用的数据集，虽然很强大，很方便，但是一旦放到你的场景中，效果一点也不如你的愿。
只有训练自己手里的数据，然后自己用起来才顺手。更重要的是，我们享受创造的过程。
假设，我们只给口算做识别，那么我们需要的图片数据有如下几类：
索引：01234567891011121314
字符：0123456789=+- × ÷
如果能识别这些，基本上能满足整数的加减乘除运算了。
好了，图片哪里来？！
是啊，图片哪里来？
吓得我差点从梦里醒来，500万都规划好该怎么花了，居然双色球还没有选号！
梦里，一个老者跟我说，图片要自己生成。我问他如何生成，他呵呵一笑，消失在迷雾中……
仔细一想，其实也不难，打字我们总会吧，生成数字无非就是用代码把字写在图片上。
字之所以能展示，主要是因为有字体的支撑。
如果你用的是windows系统，那么打开KaTeX parse error: Undefined control sequence: \Windows at position 3: C:\̲W̲i̲n̲d̲o̲w̲s̲\Fonts这个文件夹，你会发现好多字体。
我们写代码调用这些字体，然后把它打印到一张图片上，是不是就有数据了。
而且这些数据完全是由我们控制的，想多就多，想少就少，想数字、字母、汉字、符号都可以，今天你搞出来数字识别，也就相当于你同时拥有了所有识别！想想还有点小激动呢！
看看，这就是打工和创业的区别。你用别人的数据相当于打工，你是不用操心，但是他给你什么你才有什么。自己造数据就相当于创业，虽然前期辛苦，你可以完全自己把握节奏，需要就加上，没用就去掉。
2.1.1 准备字体
建一个fonts文件夹，从字体库里拷一部分字体放进来，我这里是拷贝了13种字体文件。
好的，准备工作做好了，肯定很累吧，休息休息休息，一会儿再搞！
2.1.2 生成图片
代码如下，可以直接运行。
from __future__ import print_function
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
import os
import shutil
import time
# %% 要生成的文本
label_dict ={0:'0',1:'1',2:'2',3:'3',4:'4',5:'5',6:'6',7:'7',8:'8',9:'9',10:'=',11:'+',12:'-',13:'×',14:'÷'}# 文本对应的文件夹，给每一个分类建一个文件for value,char in label_dict.items():
train_images_dir ="dataset"+"/"+str(value)if os.path.isdir(train_images_dir):
shutil.rmtree(train_images_dir)
os.makedirs(train_images_dir)# %% 生成图片defmakeImage(label_dict, font_path, width=24, height=24, rotate =0):# 从字典中取出键值对for value,char in label_dict.items():# 创建一个黑色背景的图片，大小是24*24
img = Image.new("RGB",(width, height),"black")
draw = ImageDraw.Draw(img)# 加载一种字体,字体大小是图片宽度的90%
font = ImageFont.truetype(font_path,int(width*0.9))# 获取字体的宽高
font_width, font_height = draw.textsize(char, font)# 计算字体绘制的x,y坐标，主要是让文字画在图标中心
x =(width - font_width-font.getoffset(char)[0])/2
y =(height - font_height-font.getoffset(char)[1])/2# 绘制图片，在那里画，画啥，什么颜色，什么字体
draw.text((x,y), char,(255,255,255), font)# 设置图片倾斜角度
img = img.rotate(rotate)# 命名文件保存，命名规则：dataset/编号/img-编号_r-选择角度_时间戳.png
time_value =int(round(time.time()*1000))
img_path ="dataset/{}/img-{}_r-{}_{}.png".format(value,value,rotate,time_value)
img.save(img_path)# %% 存放字体的路径
font_dir ="./fonts"for font_name in os.listdir(font_dir):# 把每种字体都取出来，每种字体都生成一批图片
path_font_file = os.path.join(font_dir, font_name)# 倾斜角度从-10到10度，每个角度都生成一批图片for k inrange(-10,10,1):# 每个字符都生成图片
makeImage(label_dict, path_font_file, rotate = k)
上面纯代码不到30行，相信大家应该能看懂！看不懂不是我的读者。
核心代码就是画文字。
draw.text((x,y), char, (255, 255, 255), font)
翻译一下就是：使用某字体在黑底图片的(x,y)位置写白色的char符号。
核心逻辑就是三层循环。
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.statediagram-cluster.statediagram-cluster-alt .inner{fill:#e0e0e0}#mermaid-svg-DEUFBhZUwl8W6J0f .statediagram-cluster .inner{rx:0;ry:0}#mermaid-svg-DEUFBhZUwl8W6J0f .statediagram-state rect.basic{rx:5px;ry:5px}#mermaid-svg-DEUFBhZUwl8W6J0f .statediagram-state rect.divider{stroke-dasharray:10,10;fill:#efefef}#mermaid-svg-DEUFBhZUwl8W6J0f .note-edge{stroke-dasharray:5}#mermaid-svg-DEUFBhZUwl8W6J0f .statediagram-note rect{fill:#fff5ad;stroke:#aa3;stroke-width:1px;rx:0;ry:0}:root{--mermaid-font-family: '"trebuchet ms", verdana, arial';--mermaid-font-family: "Comic Sans MS", "Comic Sans", cursive}#mermaid-svg-DEUFBhZUwl8W6J0f .error-icon{fill:#522}#mermaid-svg-DEUFBhZUwl8W6J0f .error-text{fill:#522;stroke:#522}#mermaid-svg-DEUFBhZUwl8W6J0f .edge-thickness-normal{stroke-width:2px}#mermaid-svg-DEUFBhZUwl8W6J0f .edge-thickness-thick{stroke-width:3.5px}#mermaid-svg-DEUFBhZUwl8W6J0f .edge-pattern-solid{stroke-dasharray:0}#mermaid-svg-DEUFBhZUwl8W6J0f .edge-pattern-dashed{stroke-dasharray:3}#mermaid-svg-DEUFBhZUwl8W6J0f .edge-pattern-dotted{stroke-dasharray:2}#mermaid-svg-DEUFBhZUwl8W6J0f .marker{fill:#333}#mermaid-svg-DEUFBhZUwl8W6J0f .marker.cross{stroke:#333}
:root { --mermaid-font-family: "trebuchet ms", verdana, arial;}#mermaid-svg-DEUFBhZUwl8W6J0f {
color: rgba(0, 0, 0, 0.75);
font: ;
}
取一种字体
取一个角度
取一个字符
再来一次
再来一次
再来一次
字体库
角度库
字符库
写到图片上
如果代码你运行的没有问题，最终会生成如下结果：
好了，数据准备好了。总共15个文件夹，每个文件夹下对应的各种字体各种倾斜角的字符图片3900个（字符15类×字体13种×角度20个），图片的大小是24×24像素。
有了数据，我们就可以再进行下一步了，下一步是训练和使用数据。
2.2 训练数据
2.2.1 构建模型
你先看代码，外行感觉好深奥，内行偷偷地笑。
# %% 导入必要的包 import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import pathlib
import cv2
# %% 构建模型defcreate_model():
model = Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(24,24,1)),
layers.Conv2D(24,3,activation='relu'),
layers.MaxPooling2D((2,2)),
layers.Conv2D(64,3, activation='relu'),
layers.MaxPooling2D((2,2)),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(15)])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])return model
这个模型的序列是下面这样的，作用是输入一个图片数据，经过各个层揉搓，最终预测出这个图片属于哪个分类。
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输入: 图片24*24像素
卷积层1 Conv2D
池化层1 MaxPooling2D
卷积层2 Conv2D
池化层2 MaxPooling2D
全连接层1 Dense 128
全连接层2 Dense 15
0: 30%
1: 20%
2: 0.5%
...
=: 1%
这么多层都是干什么的，有什么用？和衣服一样，肯定是有用的，内衣、衬衣、毛衣、棉衣各有各的用处。
2.2.2 卷积层 Conv2D
各个职能部门的调查员，搜集和整理某单位区域内的特定数据。
我们输入的是一个图像，它是由像素组成的，这就是R e s c a l i n g ( 1. / 255 , i n p u t s h a p e = ( 24 , 24 , 1 ) ) Rescaling(1./255, input_shape=(24, 24, 1)) Rescaling(1./255,inputshape=(24,24,1))中，input_shape输入形状是24*24像素1个通道（彩色是RGB 3个通道）的图像。
卷积层代码中的定义是Conv2D(24,3),意思是用3*3像素的卷积核，去提取24个特征。
我把图转到地图上来，你就能理解了。以我大济南的市中区为例子。
卷积的作用就相当于从地图的某级单位区域中收集多组特定信息。比如以小区为单位去提取住宅数量、车位数量、学校数量、人口数、年收入、学历、年龄等等24个维度的信息。小区相当于卷积核。
提取完成之后是这样的。
第一次卷积之后，我们从市中区得到N个小区的数据。
卷积是可以进行多次的。
比如在小区卷积之后，我们还可在小区的基础上再来一次卷积，在卷积就是街道了。
通过再次以街道为单位卷积小区，我们就从市中区得到了N个街道的数据。
这就是卷积的作用。
通过一次次卷积，就把一张大图，通过特定的方法卷起来，最终留下来的是固定几组有目的数据，以此方便后续的评选决策。这是评选一个区的数据，要是评选济南市，甚至山东省，也是这么卷积。这和现实生活中评选文明城市、经济强省也是一个道理。
2.2.3 池化层 MaxPooling2D
说白了就是四舍五入。
计算机的计算能力是强大的，比你我快，但也不是不用考虑成本。我们当然希望它越快越好，如果一个方法能省一半的时间，我们肯定愿意用这种方法。
池化层干的就是这个事情。
池化的代码定义是这样的
M
a
x
P
o
o
l
i
n
g
2
D
(
(
2
,
2
)
)
MaxPooling2D((2,2))
MaxPooling2D((2,2))，这里是最大值池化。其中（2,2）是池化层的大小，其实就是在2*2的区域内，我们认为这一片可以合成一个单位。
再以地图举个例子，比如下面的16个格子里的数据，是16个街道的学校数量。
为了进一步提高计算效率，少计算一些数据，我们用2*2的池化层进行池化。
池化的方格是4个街道合成1个，新单位学校数量取成员中学校数量最大（也有取最小，取平均多种池化）的那一个。池化之后，16个格子就变为了4个格子，从而减少了数据。
这就是池化层的作用。
2.2.4 全连接层 Dense
弱水三千，只取一瓢。
在这里，它其实是一个分类器。
我们构建它时，代码是这样的
D
e
n
s
e
(
15
)
Dense(15)
Dense(15)。
它所做的事情，不管你前面是怎么样，有多少维度，到我这里我要强行转化为固定的通道。
比如识别字母a~z，我有500个神经元参与判断，但是最终输出结果就是26个通道(a,b,c,……,y,z)。
我们这里总共有15类字符，所以是15个通道。给定一个输入后，输出为每个分类的概率。
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font: ;
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其他类型的层
全连接层1 Dense 128
1
全连接层2 Dense 15
2
3
4
5
...
127
128
1: 30%
2: 20%
3: 0.5%
...
15: 1%
*注意：上面都是二维的输入，比如24×24，但是全连接层是一维的，所以代码中使用了
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layers.Flatten()
layers.Flatten()将二维数据拉平为一维数据([[11,12],[21,22]]->[11,12,21,22])。*
对于总体的模型，调用
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model.summary()
model.summary()打印序列的网络结构如下：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
rescaling_2 (Rescaling) (None, 24, 24, 1) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 22, 22, 24) 240
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 11, 11, 24) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 9, 9, 64) 13888
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 4, 4, 64) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 1024) 0
_________________________________________________________________
dense_4 (Dense) (None, 128) 131200
_________________________________________________________________
dense_5 (Dense) (None, 15) 1935
=================================================================
Total params: 147,263
Trainable params: 147,263
Non-trainable params: 0
_________________________________________________________________
我们看到
conv2d_5 (Conv2D) (None, 9, 9, 64)
经过2*2的池化之后变为
max_pooling2d_5 (MaxPooling2 (None, 4, 4, 64)
。
(None, 4, 4, 64)
再经过
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Flatten
Flatten拉成一维之后变为
(None, 1024)
，经过全连接变为
(None, 128)
再一次全连接变为
(None, 15)
，15就是我们的最终分类。这一切都是我们设计的。
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model.compile就是配置模型的几个参数，这个现阶段记住就可以。
2.2.5 训练数据
执行就完了。
# 统计文件夹下的所有图片数量
data_dir = pathlib.Path('dataset')# 从文件夹下读取图片，生成数据集
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,# 从哪个文件获取数据
color_mode="grayscale",# 获取数据的颜色为灰度
image_size=(24,24),# 图片的大小尺寸
batch_size=32# 多少个图片为一个批次)# 数据集的分类，对应dataset文件夹下有多少图片分类
class_names = train_ds.class_names
# 保存数据集分类
np.save("class_name.npy", class_names)# 数据集缓存处理
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)# 创建模型
model = create_model()# 训练模型，epochs=10，所有数据集训练10遍
model.fit(train_ds,epochs=10)# 保存训练后的权重
model.save_weights('checkpoint/char_checkpoint')
执行之后会输出如下信息：
Found 3900 files belonging to 15 classes.
Epoch 1/10 122/122 [=========] - 2s 19ms/step - loss: 0.5795 - accuracy: 0.8615
Epoch 2/10 122/122 [=========] - 2s 18ms/step - loss: 0.0100 - accuracy: 0.9992
Epoch 3/10 122/122 [=========] - 2s 19ms/step - loss: 0.0027 - accuracy: 1.0000
Epoch 4/10 122/122 [=========] - 2s 19ms/step - loss: 0.0013 - accuracy: 1.0000
Epoch 5/10 122/122 [=========] - 2s 20ms/step - loss: 8.4216e-04 - accuracy: 1.0000
Epoch 6/10 122/122 [=========] - 2s 18ms/step - loss: 5.5273e-04 - accuracy: 1.0000
Epoch 7/10 122/122 [=========] - 3s 21ms/step - loss: 4.0966e-04 - accuracy: 1.0000
Epoch 8/10 122/122 [=========] - 2s 20ms/step - loss: 3.0308e-04 - accuracy: 1.0000
Epoch 9/10 122/122 [=========] - 3s 23ms/step - loss: 2.3446e-04 - accuracy: 1.0000
Epoch 10/10 122/122 [=========] - 3s 21ms/step - loss: 1.8971e-04 - accuracy: 1.0000
我们看到，第3遍时候，准确率达到100%了。
最后结束的时候，我们发现文件夹checkpoint下多了几个文件：
char_checkpoint.data-00000-of-00001
char_checkpoint.index
checkpoint
上面那几个文件是训练结果，训练保存之后就不用动了。后面可以直接用这些数据进行预测。
2.3 预测数据
终于到了享受成果的时候了。
# 设置待识别的图片
img1=cv2.imread('img1.png',0)
img2=cv2.imread('img2.png',0)
imgs = np.array([img1,img2])# 构建模型
model = create_model()# 加载前期训练好的权重
model.load_weights('checkpoint/char_checkpoint')# 读出图片分类
class_name = np.load('class_name.npy')# 预测图片，获取预测值
predicts = model.predict(imgs)
results =[]# 保存结果的数组for predict in predicts:#遍历每一个预测结果
index = np.argmax(predict)# 寻找最大值
result = class_name[index]# 取出字符
results.append(result)print(results)
我们找两张图片img1.png
,img2.png
，一张是数字6，一张是数字8，两张图放到代码同级目录下，验证一下识别效果如何。
图片要通过
cv2.imread('img1.png',0)
转化为二维数组结构，0参数是灰度图片。经过处理后，图片转成的数组是如下所示(24,24)的结构：
我们要同时验证两张图，所以把两张图再组成
imgs
放到一起，
imgs
的结构是
（2,24,24）
。
下面是构建模型，然后加载权重。通过调用
predicts = model.predict(imgs)
将
imgs
传递给模型进行预测得出
predicts
。
predicts
的结构是
(2,15)
,数值如下面所示：
[[ 16.134243 -12.10675 -1.1994154 -27.766754 -43.4324 -9.633694 -12.214878 1.6287893 2.562174 3.2222707 13.834648 28.254173 -6.102874 16.76582 7.2586184] [ 5.022571 -8.762314 -6.7466817 -23.494259 -30.170597 2.4392672 -14.676962 5.8255725 8.855118 -2.0998626 6.820853 7.6578817 1.5132296 24.4664 2.4192357]]
意思是有2个预测结果，每一个图片的预测结果有15种可能。
然后根据
index = np.argmax(predict)
找出最大可能的索引。
根据索引找到字符的数值结果是[‘6’, ‘8’]。
下面是数据在内存中的监控：
可见，我们的预测是准确的。
下面，我们将要把图片中数字切割出来，进行识别了。
之前我们准备了数据，训练了数据，并且拿图片进行了识别，识别结果正确。
到目前为止，看来问题不大……没有大问题，有问题也大不了。
下面就是把图片进行切割识别了。
下面这张大图片，怎么把它搞一搞，搞成单个小数字的图片。
2.4 切割图像
上帝说要有光，就有了光。
于是，当光投过来时，物体的背后就有了影。
我们就知道了，有影的地方就有东西，没影的地方是空白。
这就是投影。
这个简单的道理放在图像切割上也很实用。
我们把文字的像素做个投影，这样我们就知道某个区间有没有文字，并且知道这个区间文字是否集中。
下面是示意图：
2.4.1 投影大法
最有效的方法，往往都是用循环实现的。
要计算投影，就得一个像素一个像素地数，查看有几个像素，然后记录下这一行有N个像素点。如此循环。
首先导入包：
import numpy as np
import cv2
from PIL import Image, ImageDraw, ImageFont
import PIL
import matplotlib.pyplot as plt
import os
import shutil
from numpy.core.records import array
from numpy.core.shape_base import block
import time
比如说要看垂直方向的投影，代码如下：
# 整幅图片的Y轴投影，传入图片数组，图片经过二值化并反色defimg_y_shadow(img_b):### 计算投影 ###(h,w)=img_b.shape
# 初始化一个跟图像高一样长度的数组，用于记录每一行的黑点个数
a=[0for z inrange(0,h)]# 遍历每一列，记录下这一列包含多少有效像素点for i inrange(0,h):for j inrange(0,w):if img_b[i,j]==255:
a[i]+=1return a
最终得到是这样的结构：
[0, 79, 67, 50, 50, 50, 109, 137, 145, 136, 125, 117, 123, 124, 134, 71, 62, 68, 104, 102, 83, 14, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, ……38, 44, 56, 106, 97, 83, 0, 0, 0, 0, 0, 0, 0]
表示第几行总共有多少个像素点，第1行是0，表示是空白的白纸，第2行有79个像素点。
如果我们想要从视觉呈现出来怎么处理呢？那可以把它立起来拉直画出来。
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# 展示图片defimg_show_array(a):
plt.imshow(a)
plt.show()# 展示投影图， 输入参数arr是图片的二维数组，direction是x,y轴defshow_shadow(arr, direction ='x'):
a_max =max(arr)if direction =='x':# x轴方向的投影
a_shadow = np.zeros((a_max,len(arr)), dtype=int)for i inrange(0,len(arr)):if arr[i]==0:continuefor j inrange(0, arr[i]):
a_shadow[j][i]=255elif direction =='y':# y轴方向的投影
a_shadow = np.zeros((len(arr),a_max), dtype=int)for i inrange(0,len(arr)):if arr[i]==0:continuefor j inrange(0, arr[i]):
a_shadow[i][j]=255
img_show_array(a_shadow)
我们来试验一下效果：
我们将上面的原图片命名为question.jpg放到代码同级目录。
# 读入图片
img_path ='question.jpg'
img=cv2.imread(img_path,0)
thresh =200# 二值化并且反色
ret,img_b=cv2.threshold(img,thresh,255,cv2.THRESH_BINARY_INV)
二值化并反色后的变化如下所示：
上面的操作很有作用，通过二值化，过滤掉杂色，通过反色将黑白对调，原来白纸区域都是255，现在黑色都是0，更利于计算。
计算投影并展示的代码：
img_y_shadow_a = img_y_shadow(img_b)
show_shadow(img_y_shadow_a,'y')# 如果要显示投影
下面的图是上面图在Y轴上的投影
从视觉上看，基本上能区分出来哪一行是哪一行。
2.4.2 根据投影找区域
最有效的方法，往往还得用循环来实现。
上面投影那张图，你如何计算哪里到哪里是一行，虽然肉眼可见，但是计算机需要规则和算法。
# 图片获取文字块，传入投影列表，返回标记的数组区域坐标[[左，上，右，下]]defimg2rows(a,w,h):### 根据投影切分图块 ###
inLine =False# 是否已经开始切分
start =0# 某次切分的起始索引
mark_boxs =[]for i inrange(0,len(a)):if inLine ==Falseand a[i]>10:
inLine =True
start = i
# 记录这次选中的区域[左，上，右，下]，上下就是图片，左右是start到当前elif i-start >5and a[i]<10and inLine:
inLine =Falseif i-start >10:
top =max(start-1,0)
bottom =min(h, i+1)
box =[0, top, w, bottom]
mark_boxs.append(box)return mark_boxs
通过投影，计算哪些区域在一定范围内是连续的，如果连续了很长时间，我们就认为是同一区域，如果断开了很长一段时间，我们就认为是另一个区域。
通过这项操作，我们就可以获得Y轴上某一行的上下两个边界点的坐标，再结合图片宽度，其实我们也就知道了一行图片的四个顶点的坐标了
mark_boxs
存下的是
[坐，上，右，下]
。
如果调用如下代码：
(img_h,img_w)=img.shape
row_mark_boxs = img2rows(img_y_shadow_a,img_w,img_h)print(row_mark_boxs)
我们获取到的是所有识别出来每行图片的坐标，格式是这样的：
[[0, 26, 596, 52], [0, 76, 596, 103], [0, 130, 596, 155], [0, 178, 596, 207], [0, 233, 596, 259], [0, 282, 596, 311], [0, 335, 596, 363], [0, 390, 596, 415]]
2.4.3 根据区域切图片
最有效的方法，最终也得用循环来实现。这也是计算机体现它强大的地方。
# 裁剪图片,img 图片数组， mark_boxs 区域标记defcut_img(img, mark_boxs):
img_items =[]# 存放裁剪好的图片for i inrange(0,len(mark_boxs)):
img_org = img.copy()
box = mark_boxs[i]# 裁剪图片
img_item = img_org[box[1]:box[3], box[0]:box[2]]
img_items.append(img_item)return img_items
这一步骤是拿着方框，从大图上用小刀划下小图，核心代码是
img_org[box[1]:box[3], box[0]:box[2]]
图片裁剪，参数是数组的
[上：下，左：右]
，获取的数据还是二维的数组。
如果保存下来：
# 保存图片defsave_imgs(dir_name, imgs):if os.path.exists(dir_name):
shutil.rmtree(dir_name)ifnot os.path.exists(dir_name):
os.makedirs(dir_name)
img_paths =[]for i inrange(0,len(imgs)):
file_path = dir_name+'/part_'+str(i)+'.jpg'
cv2.imwrite(file_path,imgs[i])
img_paths.append(file_path)return img_paths
# 切图并保存
row_imgs = cut_img(img, row_mark_boxs)
imgs = save_imgs('rows', row_imgs)# 如果要保存切图print(imgs)
图片是下面这样的：
2.4.4 循环可去油腻
还是循环。
横着行我们掌握了，那么针对每一行图片，我们竖着切成三块是不是也会了，一个道理。
需要注意的是，横竖是稍微有区别的，下面是上图的x轴投影。
横着的时候，字与字之间本来就是有空隙的，然后块与块也有空隙，这个空隙的度需要掌握好，以便更好地区分出来是字的间距还是算式块的间距。
幸好，有种方法叫膨胀。
膨胀对人来说不积极，但是对于技术来说，不管是膨胀（dilate），还是腐蚀（erode），只要能达到目的，都是好的。
kernel=np.ones((3,3),np.uint8)# 膨胀核大小
row_img_b=cv2.dilate(img_b,kernel,iterations=6)# 图像膨胀6次
膨胀之后再投影，就很好地区分出了块。
根据投影裁剪之后如下图所示：
同理，不膨胀可截取单个字符。
这样，这是一块区域的字符。
一行的，一页的，通过循环，都可以截取出来。
有了图片，就可以识别了。有了位置，就可以判断识别结果的关系了。
下面提供一些代码，这些代码不全，有些函数你可能找不到，但是思路可以参考，详细的代码可以去我的github去看。
defdivImg(img_path, save_file =False):
img_o=cv2.imread(img_path,1)# 读入图片
img=cv2.imread(img_path,0)(img_h,img_w)=img.shape
thresh =200# 二值化整个图，用于分行
ret,img_b=cv2.threshold(img,thresh,255,cv2.THRESH_BINARY_INV)# 计算投影，并截取整个图片的行
img_y_shadow_a = img_y_shadow(img_b)
row_mark_boxs = img2rows(img_y_shadow_a,img_w,img_h)# 切行的图片，切的是原图
row_imgs = cut_img(img, row_mark_boxs)
all_mark_boxs =[]
all_char_imgs =[]# ===============从行切块======================for i inrange(0,len(row_imgs)):
row_img = row_imgs[i](row_img_h,row_img_w)=row_img.shape
# 二值化一行的图，用于切块
ret,row_img_b=cv2.threshold(row_img,thresh,255,cv2.THRESH_BINARY_INV)
kernel=np.ones((3,3),np.uint8)#图像膨胀6次
row_img_b_d=cv2.dilate(row_img_b,kernel,iterations=6)
img_x_shadow_a = img_x_shadow(row_img_b_d)
block_mark_boxs = row2blocks(img_x_shadow_a, row_img_w, row_img_h)
row_char_boxs =[]
row_char_imgs =[]# 切块的图，切的是原图
block_imgs = cut_img(row_img, block_mark_boxs)if save_file:
b_imgs = save_imgs('cuts/row_'+str(i), block_imgs)# 如果要保存切图print(b_imgs)# =============从块切字====================for j inrange(0,len(block_imgs)):
block_img = block_imgs[j](block_img_h,block_img_w)=block_img.shape
# 二值化块,因为要切字符图片了
ret,block_img_b=cv2.threshold(block_img,thresh,255,cv2.THRESH_BINARY_INV)
block_img_x_shadow_a = img_x_shadow(block_img_b)
row_top = row_mark_boxs[i][1]
block_left = block_mark_boxs[j][0]
char_mark_boxs,abs_char_mark_boxs = block2chars(block_img_x_shadow_a, block_img_w, block_img_h,row_top,block_left)
row_char_boxs.append(abs_char_mark_boxs)# 切的是二值化的图
char_imgs = cut_img(block_img_b, char_mark_boxs,True)
row_char_imgs.append(char_imgs)if save_file:
c_imgs = save_imgs('cuts/row_'+str(i)+'/blocks_'+str(j), char_imgs)# 如果要保存切图print(c_imgs)
all_mark_boxs.append(row_char_boxs)
all_char_imgs.append(row_char_imgs)return all_mark_boxs,all_char_imgs,img_o
最后返回的值是
3
个，
all_mark_boxs
是标记的字符位置的坐标集合。
[左,上,右,下]
是指某个字符在一张大图里的坐标，打印一下是这样的：
[[[[19, 26, 34, 53], [36, 26, 53, 53], [54, 26, 65, 53], [66, 26, 82, 53], [84, 26, 101, 53], [102, 26, 120, 53], [120, 26, 139, 53]], [[213, 26, 229, 53], [231, 26, 248, 53], [249, 26, 268, 53], [268, 26, 285, 53]], [[408, 26, 426, 53], [427, 26, 437, 53], [438, 26, 456, 53], [456, 26, 474, 53], [475, 26, 492, 53]]], [[[20, 76, 36, 102], [38, 76, 48, 102], [50, 76, 66, 102], [67, 76, 85, 102], [85, 76, 104, 102]], [[214, 76, 233, 102], [233, 76, 250, 102], [252, 76, 268, 102], [270, 76, 287, 102]], [[411, 76, 426, 102], [428, 76, 445, 102], [446, 76, 457, 102], [458, 76, 474, 102], [476, 76, 493, 102], [495, 76, 511, 102]]]]
它是有结构的。它的结构是：
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color: rgba(0, 0, 0, 0.75);
font: ;
}
第一层-数组
行1
行2
第二层-行3
行4
行5
块1
第三层-块2
块3
字符1
第四层-字符2
字符3
左上右下 495, 76, 511, 102
all_char_imgs
这个返回值，里面是上面坐标结构对应位置的图片。
img_o
就是原图了。
2.5 识别
循环，循环，还是TM循环！
对于识别，2.3 预测数据已经讲过了，那次是对于2张独立图片的识别，现在我们要对整张大图切分后的小图集合进行识别，这就又用到了循环。
翠花，上代码！
all_mark_boxs,all_char_imgs,img_o = divImg(path,save)
model = cnn.create_model()
model.load_weights('checkpoint/char_checkpoint')
class_name = np.load('class_name.npy')# 遍历行for i inrange(0,len(all_char_imgs)):
row_imgs = all_char_imgs[i]# 遍历块for j inrange(0,len(row_imgs)):
block_imgs = row_imgs[j]
block_imgs = np.array(block_imgs)
results = cnn.predict(model, block_imgs, class_name)print('recognize result:',results)
上面代码做的就是以块为单位，传递给神经网络进行预测，然后返回识别结果。
针对这张图，我们来进行裁剪和识别。
看底部的最后一行
recognize result: ['1', '0', '12', '2', '10']
recognize result: ['8', '12', '6', '10']
recognize result: ['1', '0', '12', '7', '10']
结果是索引，不是真实的字符，我们根据字典
10: '=', 11: '+', 12: '-', 13: '×', 14: '÷'
转换过来之后结果是：
recognize result: ['1', '0', '-', '2', '=']
recognize result: ['8', '-', '6', '=']
recognize result: ['1', '0', '-', '7', '=']
和图片是对应的：
2.6 计算并反馈
循环……
我们获取到了
10-2=
、
8-6=2
，也获取到了他们在原图的位置坐标
[左,上,右,下]
，那么怎么把结果反馈到原图上呢？
往往到这里就剩最后一步了。
再来温习一遍需求：作对了，能打对号；做错了，能打叉号；没做的，能补上答案。
实现分两步走：计算（是作对做错还是没错）和反馈（把预期结果写到原图上）。
2.6.1 计算
python有个函数很强大，就是
eval
函数，能计算字符串算式，比如直接计算
eval("5+3-2")
。
所以，一切都靠它了。
# 计算数值并返回结果 参数chars：['8', '-', '6', '=']defcalculation(chars):
cstr =''.join(chars)
result =''if("="in cstr):# 有等号
str_arr = cstr.split('=')
c_str = str_arr[0]
r_str = str_arr[1]
c_str = c_str.replace("×","*")
c_str = c_str.replace("÷","/")try:
c_r =int(eval(c_str))except Exception as e:print("Exception",e)if r_str =="":
result = c_r
else:ifstr(c_r)==str(r_str):
result ="√"else:
result ="×"return result
执行之后获得的结果是：
recognize result: ['8', '×', '4', '=']
calculate result: 32
recognize result: ['2', '-', '1', '=', '1']
calculate result: √
recognize result: ['1', '0', '-', '5', '=']
calculate result: 5
2.6.2 反馈
有了结果之后，把结果写到图片上，这是最后一步，也是最简单的一步。
但是实现起来，居然很繁琐。
得找坐标吧，得计算结果呈现的位置吧，我们还想标记不同的颜色，比如对了是绿色，错了是红色，补齐答案是灰色。
下面代码是在一个图img上，把文本内容text画到(left,top)位置，以特定颜色和大小。
# 绘制文本defcv2ImgAddText(img, text, left, top, textColor=(255,0,0), textSize=20):if(isinstance(img, np.ndarray)):# 判断是否OpenCV图片类型
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))# 创建一个可以在给定图像上绘图的对象
draw = ImageDraw.Draw(img)# 字体的格式
fontStyle = ImageFont.truetype("fonts/fangzheng_shusong.ttf", textSize, encoding="utf-8")# 绘制文本
draw.text((left, top), text, textColor, font=fontStyle)# 转换回OpenCV格式return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
结合着切图的信息、计算的信息，下面代码提供思路参考：
# 获取切图标注，切图图片，原图图图片
all_mark_boxs,all_char_imgs,img_o = divImg(path,save)# 恢复模型，用于图片识别
model = cnn.create_model()
model.load_weights('checkpoint/char_checkpoint')
class_name = np.load('class_name.npy')# 遍历行for i inrange(0,len(all_char_imgs)):
row_imgs = all_char_imgs[i]# 遍历块for j inrange(0,len(row_imgs)):
block_imgs = row_imgs[j]
block_imgs = np.array(block_imgs)# 图片识别
results = cnn.predict(model, block_imgs, class_name)print('recognize result:',results)# 计算结果
result = calculation(results)print('calculate result:',result)# 获取块的标注坐标
block_mark = all_mark_boxs[i][j]# 获取结果的坐标，写在块的最后一个字
answer_box = block_mark[-1]# 计算最后一个字的位置
x = answer_box[2]
y = answer_box[3]
iw = answer_box[2]- answer_box[0]
ih = answer_box[3]- answer_box[1]# 计算字体大小
textSize =max(iw,ih)# 根据结果设置字体颜色ifstr(result)=="√":
color =(0,255,0)elifstr(result)=="×":
color =(255,0,0)else:
color =(192,192,192)# 将结果写到原图上
img_o = cv2ImgAddText(img_o,str(result), answer_box[2], answer_box[1],color, textSize)# 将写满结果的原图保存
cv2.imwrite('result.jpg', img_o)
结果是下面这样的：
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