##檢測圖像顏色小程序 設計一個界面，用來檢測一幅圖像的顏色分布，開發平臺為Qt5.3.2+OpenCV2.4.9。 該程序的主要步驟如下：? 1\. 載入圖像，選定一種顏色；? 2\. 設定閾值，在該值范圍內判定像素屬于預設的顏色；? 3\. 在界面的Label中輸出結果。 首先，新建一個Qt Widgets Application，其中基類選擇為QWidget，在創建完項目后，添加一個檢測圖像顏色的類ColorDetector。并在在Qt項目的.pro文件中添加： ~~~ INCLUDEPATH+=C:\OpenCV\install\include\opencv\ C:\OpenCV\install\include\opencv2\ C:\OpenCV\install\include LIBS+=C:\OpenCV\lib\libopencv_calib3d249.dll.a\ C:\OpenCV\lib\libopencv_contrib249.dll.a\ C:\OpenCV\lib\libopencv_core249.dll.a\ C:\OpenCV\lib\libopencv_features2d249.dll.a\ C:\OpenCV\lib\libopencv_flann249.dll.a\ C:\OpenCV\lib\libopencv_gpu249.dll.a\ C:\OpenCV\lib\libopencv_highgui249.dll.a\ C:\OpenCV\lib\libopencv_imgproc249.dll.a\ C:\OpenCV\lib\libopencv_legacy249.dll.a\ C:\OpenCV\lib\libopencv_ml249.dll.a\ C:\OpenCV\lib\libopencv_nonfree249.dll.a\ C:\OpenCV\lib\libopencv_objdetect249.dll.a\ C:\OpenCV\lib\libopencv_ocl249.dll.a\ C:\OpenCV\lib\libopencv_video249.dll.a\ C:\OpenCV\lib\libopencv_photo249.dll.a\ C:\OpenCV\lib\libopencv_stitching249.dll.a\ C:\OpenCV\lib\libopencv_superres249.dll.a\ C:\OpenCV\lib\libopencv_ts249.a\ C:\OpenCV\lib\libopencv_videostab249.dll.a ~~~ 進入圖形界面文件widget.ui，在其中拖入三個按鍵，改名為”Open Image”，”Process”，”Color”，他們分別執行載入圖像、顏色檢測、顏色設定的操作。同時，拖入一個Vertical Slider，用于調整顏色的閾值。最后在下方設置一個Label，圖像輸出在該區域中。  設置各個按鈕的objectName，分別為：? “Open Image”設定為”openImage”? “Color”設定為”colorButton”? “Process”設定為”ImageProcess”? “Vertical Slider”設定為”verticalSlider”? 這是為了在程序中將信號與槽函數相對應： ~~~ Widget::Widget(QWidget *parent) : QWidget(parent), ui(new Ui::Widget) { ui -> setupUi(this); connect(ui -> openImage, SIGNAL(clicked()), this, SLOT(openImage())); connect(ui -> ImageProcess, SIGNAL(clicked()), this, SLOT(ImageProcess())); connect(ui -> colorButton, SIGNAL(clicked()), this, SLOT(colorSelect())); connect(ui -> verticalSlider, SIGNAL(valueChanged(int)), this, SLOT(changeDis(int))); } ~~~ 這里規定當未有輸入圖像時，圖像處理按鈕不可用，在以上的Widget構造函數中添加： ~~~ // 在未輸入圖像時，屏蔽圖像處理的按鈕 ui -> ImageProcess -> setEnabled(false); ui -> colorButton -> setEnabled(false); ui -> verticalSlider -> setEnabled(false); ~~~ 接著，定義處理圖像所用的類，首先是colordetector.h： ~~~ #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(); // 提供靜態的接口來獲得ColorDetector對象 static ColorDetector *singleton; public: cv::Mat getInputImage() const; // 載入圖像 cv::Mat getResult() const; // 返回圖像處理結果 void process(); // 將閾值范圍內的像素點置為255，其余為0 int getDistance(const cv::Vec3b&) const; bool setInputImage(std::string); // 判斷是否已輸入圖像 static ColorDetector * getInstance(); // 使用單例模式創建類的實例 static void destory(); // 一個對象的析構函數 // 設置顏色閾值 void setColorDistanceThreshold(int); int getColorDistanceThreshold() const; // 設置顏色 void setTargetColor(unsigned char, unsigned char, unsigned char); // 顏色通道轉換 void setTargetColor(cv::Vec3b); cv::Vec3b getTargetColor() const; }; #endif /* COLORDETECTOR_H_ */ ~~~ 接著在colordetector.cpp中添加各函數的定義： ~~~ #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) { // 顏色通道轉換 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; } ~~~ 接下來定義圖形界面的處理函數，首先在widget.h中添加： ~~~ #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 ImageProcess(); void colorSelect(); void changeDis(int); }; #endif // WIDGET_H ~~~ 接著在widget.cpp中定義信號與槽函數及相關操作： ~~~ #include "widget.h" #include "ui_widget.h" #include <QColorDialog> #include <QDebug> Widget::Widget(QWidget *parent) : QWidget(parent), ui(new Ui::Widget) { ui -> setupUi(this); // 關聯信號與槽函數 connect(ui -> openImage, SIGNAL(clicked()), this, SLOT(openImage())); connect(ui -> ImageProcess, SIGNAL(clicked()), this, SLOT(ImageProcess())); connect(ui -> colorButton, SIGNAL(clicked()), this, SLOT(colorSelect())); connect(ui -> verticalSlider, SIGNAL(valueChanged(int)), this, SLOT(changeDistance(int))); // 在未輸入圖像時，屏蔽圖像處理的按鈕 ui -> ImageProcess -> setEnabled(false); ui -> colorButton -> setEnabled(false); ui -> verticalSlider -> setEnabled(false); } 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.toLatin1().data()); cv::Mat input; input = ColorDetector::getInstance()->getInputImage(); if (!input.data) { qDebug() << "No Input Image"; } else { // 當檢測到輸入圖像，可激活圖像處理按鍵，并顯示原圖像 ui->ImageProcess->setEnabled(true); ui->colorButton->setEnabled(true); ui->verticalSlider->setEnabled(true); cv::namedWindow("image"); cv::imshow("image",ColorDetector::getInstance()->getInputImage()); } } void Widget::ImageProcess() { 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)); } void Widget::colorSelect() { QColor color = QColorDialog::getColor(Qt::green,this); if(color.isValid()) { ColorDetector::getInstance()->setTargetColor( color.red(),color.green(),color.blue()); } } void Widget::changeDistance(int value) { ColorDetector::getInstance()->setColorDistanceThreshold(value); } ~~~ 效果如下，未輸入圖像時，圖像處理、顏色選擇按鈕部分不可操作： 1.未輸入圖像時：  2.載入圖像后：  3.打開color按鈕，可選擇要檢測的顏色：  4.選定顏色后，拖動可調節閾值，點擊Process可輸出檢測結果。