# Insect_Identification
## 项目介绍:
#### 名称:
基于机器视觉的害虫种类及数量检测
#### 要求:
数目检测和昆虫种类识别
## 项目进度
- [x] 2017/4/8---------二值化
- [x] 2017/4/9---------图片中昆虫虫体计数
- [x] 2017/4/22-------PyQt和OpenCV_VideoFrame结合做出基本界面
- [x] 摄像头Frame中检测虫体数目,并在界面中显示标出
- [x] 学习昆虫图像特征的提取,参考论文中提出的几个特征量
- [x] 提取特征量并进行保存
- [x] 按照神经网络方法搭建训练模型
- [x] 搭建了线性SVM分类训练器
- [x] 将特征提取和UI界面建立连接,实现拍照和预测判断一体
## 机器学习训练算法(参考Python机器学习)
* LogisticRegression
* SGDClassfier 还没有尝试
* LinearSVM
* 朴素贝叶斯 (文本分类,不用)
* K邻近(分类)
* 决策树,不用
* 集成模型,不用
## 文件介绍
* 用户界面
* MainWindow.ui-----------------------PyQtDesigner设计的主界面文件
* MainWindow.py----------------------PyUIC转换而成的主界面程序
* 运行逻辑
* VideoMainWindow.py--------------PyQt结合OpenCV实现在界面中显示视频画面
* PreProcess.py-------------------------对源数据样本进行预处理
* 特征提取
* P_circle.py------------------------------似圆度
* P_extend.py----------------------------延长度
* P_leaf.py--------------------------------叶状性
* P_rect.py--------------------------------矩形度
* P_spherical.py------------------------球形度
* GetFeatures.py-----------------------提取特征的模块
* GetFiveFeatures.py-----------------五个特征提取的测试代码
* FeatureExtract.py-------------------提取样本库特征保存到CSV文件
*
* 机器学习模块
* LinearSVM.py-------------------------线性SVM分类器的训练和模型保存
* LinearRegression.py---------------逻辑回归分类器的训练和模型保存
* KneiborsClassfier.py---------------KNN分类器的训练和模型保存
* Predict.py------------------------------加载预训练模型,对特征进行预测
* Thresholding.py---------------------------大津法程序实现和OpenCV大津法函数的效果对比
* Count.py-------------------------------------实现加载图片,二值化(大津法),查找轮廓进行计数的效果
* GetChineseName.py--------------------分类中英文转换
## 参考书籍
1. 《OpenCV3 计算机视觉Python语言实现》
2. 《机器学习》
3. 《Python机器学习实践与Kaggle实战》
## 参考链接及对应解决方案
#### 1. 计数
1. 大米计数(http://blog.csdn.net/jia20003/article/details/7605653)
2. 二值化大津法原理介绍(http://www.cnblogs.com/herway/archive/2011/09/23/2186698.html)
3. OpenCV二值化教程(http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html#thresholding)
#### 2. 界面
1. PyQt结合OpenCVVideoFrame(https://github.com/seym45/webcamViewer)
2. PyQt基础教程(http://zetcode.com/gui/pyqt5/)
#### 3. 分类
久远,找不到资源了。
## 后记
这个是大四的时候的毕业设计。
其实并不是很实用,只能拿其清晰的标本图像来做测试。
用到了基本的图像处理、特征提取、机器学习分类器一些知识。
没有合适的资源?快使用搜索试试~ 我知道了~
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