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OpenCV2 颜色识别

在这个例子中,我们开始选定一种颜色,并设置一个阈值

然后把图片中和所选颜色的差别在阈值中的点标定出来

在这个例子中,主要要注意这两点:

1. OpenCV与QT的结合,包括Mat 与 QImage 的转换

2. 我们使用了类来实现此功能,创建了一个单例模式的类

首先我们创建一个简单的图形界面,使用的是QT

创建处理图像用的类

#ifndef COLORDETECTOR_H_
#define COLORDETECTOR_H_

#include
<opencv2/core/core.hpp>
#include
<opencv2/highgui/highgui.hpp>
#include
<string>

class ColorDetector{
private:
int minDist;
cv::Vec3b target;
cv::Mat result;
cv::Mat image;
ColorDetector();
static ColorDetector *singleton;

public:
static ColorDetector * getInstance();
static void destory();
void setColorDistanceThreshold(int);
int getColorDistanceThreshold() const;
void setTargetColor(unsigned char, unsigned char, unsigned char);
void setTargetColor(cv::Vec3b);
cv::Vec3b getTargetColor()
const;
void process();
int getDistance(const cv::Vec3b&) const;
cv::Mat getResult()
const;
bool setInputImage(std::string);
cv::Mat getInputImage()
const;
};


#endif /* COLORDETECTOR_H_ */

将其构造函数声明为private,提供静态的接口来获得ColorDetector对象

void setColorDistanceThreshold(int) 用于设置阈值
void setTargetColor(unsigned char, unsigned char, unsigned char)
void setTargetColor(cv::Vec3b) 用于设置颜色
bool setInputImage(std::string) 用于载入待处理图像
cv::Mat getResult() const 用于返回处理结果,结果用一副图像表示

其具体实现如下

#include "ColorDetector.h"

ColorDetector
* ColorDetector::singleton = 0;

ColorDetector::ColorDetector():minDist(
100){
target[
0] = target[1] = target[2] = 0;
}

ColorDetector
* ColorDetector::getInstance(){
if(singleton == 0){
singleton
= new ColorDetector;
}
return singleton;
}

void ColorDetector::destory(){
if(singleton!=0){
delete singleton;
}
singleton
= 0;
}

void ColorDetector::setColorDistanceThreshold(int distance){
if(distance < 0){
distance
= 0;
}
minDist
= distance;
}

int ColorDetector::getColorDistanceThreshold() const{
return minDist;
}

void ColorDetector::setTargetColor(unsigned char red,
unsigned
char green, unsigned char blue){
target[
2] = red;
target[
1] = green;
target[
0] = blue;
}

void ColorDetector::setTargetColor(cv::Vec3b color){
target
= color;
}

cv::Vec3b ColorDetector::getTargetColor()
const{
return target;
}

int ColorDetector::getDistance(const cv::Vec3b& color) const{
return abs(color[0]-target[0])+abs(color[1]-target[1])+abs(color[2]-target[2]);
}

void ColorDetector::process(){
result.create(image.rows, image.cols, CV_8U);
cv::Mat_
<cv::Vec3b>::const_iterator it = image.begin<cv::Vec3b>();
cv::Mat_
<cv::Vec3b>::const_iterator itend = image.end<cv::Vec3b>();
cv::Mat_
<uchar>::iterator itout = result.begin<uchar>();
for(; it!=itend; ++it, ++itout){
if(getDistance(*it) < minDist){
*itout = 255;
}
else{
*itout = 0;
}
}
}

cv::Mat ColorDetector::getResult()
const{
return result;
}

bool ColorDetector::setInputImage(std::string filename){
image
= cv::imread(filename);
if(!image.data){
return false;
}
return true;
}

cv::Mat ColorDetector::getInputImage()
const{
return image;
}

在前面文章中,讲了遍历像素点的方法,这里用迭代器实现

然后是图像界面中的处理函数

#ifndef WIDGET_H
#define WIDGET_H

#include
<QWidget>
#include
<QFileDialog>
#include
<QImage>
#include
<opencv2/core/core.hpp>
#include
<opencv2/highgui/highgui.hpp>
#include
<opencv2/imgproc/imgproc.hpp>
#include
"colordetector.h"

namespace Ui {
class Widget;
}

class Widget : public QWidget
{
Q_OBJECT

public:
explicit Widget(QWidget *parent = 0);
~Widget();

private:
Ui::Widget
*ui;
QImage qimage;
cv::Mat image;

private slots:
void openImage();
void dealImage();
void colorSelect();
void changeDis(int);
};

#endif // WIDGET_H

其实现

#include "widget.h"
#include
"ui_widget.h"
#include
<QColorDialog>


Widget::Widget(QWidget
*parent) :
QWidget(parent),
ui(
new Ui::Widget)
{
ui
->setupUi(this);
connect(ui
->openImage,SIGNAL(clicked()),this,SLOT(openImage()));
connect(ui
->dealImage,SIGNAL(clicked()),this,SLOT(dealImage()));
connect(ui
->colorButton,SIGNAL(clicked()),this,SLOT(colorSelect()));
connect(ui
->verticalSlider,SIGNAL(valueChanged(int)),this,SLOT(changeDis(int)));
}

Widget::
~Widget()
{
delete ui;
}



void Widget::openImage(){
QString fileName
= QFileDialog::getOpenFileName(this,
tr(
"Open Image"), ".",
tr(
"Image Files (*.png *.jpg *.jpeg *.bmp)"));

ColorDetector::getInstance()
->setInputImage(fileName.toAscii().data());
cv::namedWindow(
"image");
cv::imshow(
"image",ColorDetector::getInstance()->getInputImage());
dealImage();
}

void Widget::dealImage(){
ColorDetector::getInstance()
->process();
cv::cvtColor(ColorDetector::getInstance()
->getResult(),image,CV_GRAY2RGB);
qimage
= QImage((const unsigned char*)(image.data),image.cols,image.rows,QImage::Format_RGB888);
ui
->label->setPixmap(QPixmap::fromImage(qimage).scaledToHeight(300));
//ui->label->resize(ui->label->pixmap()->size());
}

void Widget::colorSelect(){
QColor color
= QColorDialog::getColor(Qt::green,this);
if(color.isValid()){
ColorDetector::getInstance()
->setTargetColor(
color.red(),color.green(),color.blue());
}
dealImage();
}

void Widget::changeDis(int value){
ColorDetector::getInstance()
->setColorDistanceThreshold(value);
dealImage();
}

在QT项目的pro文件中要加入如下几句

INCLUDEPATH += D:\OpenCV\include
LIBS +
= -LD:\OpenCV\lib \
-lopencv_core230 \
-lopencv_highgui230 \
-lopencv_imgproc230

看看最后效果

不同颜色,不同阈值的比较效果

posted @ 2011-07-31 14:21  xiatwhu  阅读(15866)  评论(1编辑  收藏  举报
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