1.OpenCV 下载及安装配置
opencv的下载地址： http://opencv.org/downloads.html
最新版本：opencv3.0.0
注意：支持的visual studio2013
我们可以下载稳定版本：opencv2.4.11
安装：双击opencv-2.4.11解压到某一目录下即可
配置：在系统环境变量Path中，添加相应的路径。
32位添加：C:\opencv\ opencv2.4.11\build\x86\vc10\bin
64位添加：C:\opencv\ opencv2.4.11\build\x86\vc10\bin 和 C:\opencv \ opencv2.4.11\build\x86\vc10\bin
2.VisualStudio 下 OpenCV调试
新建一个控制台程序
按照导航一步步完成项目创建
配置项目的include目录，指向opencv的安装目录，如下图所示
配置项目依赖的lib目录及依赖的库，如下图所示
环境配置好后，编写测试代码
#include
"
stdafx.h
"
#include
<opencv2/opencv.hpp>
#include
<iostream>
#include
<fstream>
#include
<sstream>
#include
<math.h>
void
train_and_test_lda()
{
string
fn_csv =
string
(
"
at.txt
"
);
//
string fn_csv = string("feret.txt");
vector<Mat>
allImages,train_images,test_images;
vector
<
int
>
allLabels,train_labels,test_labels;
try
{
read_csv(fn_csv, allImages, allLabels);
}
catch
(cv::Exception&
e) {
cerr
<<
"
Error opening file
"
<< fn_csv <<
"
. Reason:
"
<< e.msg <<
endl;
//
文件有问题，我们啥也做不了了，退出了
exit(
1
);
}
if
(allImages.size()<=
1
) {
string
error_message =
"
This demo needs at least 2 images to work. Please add more images to your data set!
"
;
CV_Error(CV_StsError, error_message);
}
for
(
int
i=
0
; i<allImages.size() ; i++
)
equalizeHist(allImages[i],allImages[i]);
int
photoNumber =
allImages.size();
for
(
int
i=
0
; i<photoNumber ; i++
)
{
if
((i%g_photoNumberOfOnePerson)<
g_howManyPhotoForTraining)
{
train_images.push_back(allImages[i]);
train_labels.push_back(allLabels[i]);
}
else
{
test_images.push_back(allImages[i]);
test_labels.push_back(allLabels[i]);
}
}
/*
Ptr<FaceRecognizer> model = createEigenFaceRecognizer();//定义pca模型
model->train(train_images, train_labels);//训练pca模型，这里的model包含了所有特征值和特征向量，没有损失
model->save("eigenface.yml");//保存训练结果，供检测时使用
*/
Ptr
<FaceRecognizer> fishermodel =
createFisherFaceRecognizer();
fishermodel
->train(train_images,train_labels);
//
用保存的降维后的图片来训练fishermodel，后面的内容与原始代码就没什么变化了
fishermodel->save(
"
fisherlda.yml
"
);
int
iCorrectPrediction =
0
;
int
predictedLabel;
int
testPhotoNumber =
test_images.size();
for
(
int
i=
0
;i<testPhotoNumber;i++
)
{
predictedLabel
= fishermodel->
predict(test_images[i]);
if
(predictedLabel ==
test_labels[i])
iCorrectPrediction
++
;
}
string
result_message = format(
"
Test Number = %d / Actual Number = %d.
"
, testPhotoNumber, iCorrectPrediction);
cout
<< result_message <<
endl;
cout
<<
"
accuracy =
"
<<
float
(iCorrectPrediction)/testPhotoNumber<<
endl;
}
int
main() {
cout
<<
"
lda =
"
<<
endl;
train_and_test_lda();
return
0
;
}
编译通过后，正常运行说明opencv环境配置正确。
3.Eclipse下OpenCV开发环境配置
在eclipse中，新建java project，如下图所示
新建成功后，修改项目的build path,增加外部jar，如下图所示
选择C:\opencv\ opencv2.4.11\build\java\opencv-2411.jar
点开opencv-2411.jar，选择Native library location， 点击edit
32位系统指向：C:\opencv\ opencv2.4.11\build\java\x86
64位系统指向：C:\opencv\ opencv2.4.11\build\java\x64
编写测试代码：
import
org.opencv.core.Core;
import
org.opencv.core.CvType;
import
org.opencv.core.Mat;
public
class
TestOpencv {
public
static
void
main( String[] args )
{
System.loadLibrary( Core.NATIVE_LIBRARY_NAME );
Mat mat
= Mat.eye( 3, 3
, CvType.CV_8UC1 );
System.out.println(
"mat = " +
mat.dump() );
}
}
运行输出如下，即环境配置正确：
