原文鏈接：http://www.cnblogs.com/emouse/archive/2013/03/31/2991333.html

作者寫作一系列：http://www.cnblogs.com/emouse/category/449213.html

Qt圖像的縮放顯示

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實(shí)現(xiàn)圖像縮放的方法很多，在 OpenCV&Qt學(xué)習(xí)之一——打開圖片文件并顯示 的例程中，label控件是通過

ui->imagelabel->resize(ui->imagelabel->pixmap()->size());
來實(shí)現(xiàn)適應(yīng)圖像顯示的，但是由于窗口固定，在打開的圖像小于控件大小時(shí)就會(huì)縮在左上角顯示，在打開圖像過大時(shí)則顯示不全。因此這個(gè)例程中首先實(shí)現(xiàn)圖像適合窗口的縮放顯示。

由于是基于OpenCV和Qt的圖像處理，因此圖像的縮放處理在OpenCV和Qt都可以完成，我這里就把OpenCV用作圖像的原始處理，Qt用作顯示處理，因此縮放顯示由Qt完成。

Qt中QImage提供了用于縮放的基本函數(shù)，而且功能非常強(qiáng)大，使用Qt自帶的幫助可以檢索到相關(guān)信息。

函數(shù)原型：

QImage QImage::scaled ( const QSize & size, Qt::AspectRatioMode aspectRatioMode = Qt::IgnoreAspectRatio, Qt::TransformationMode transformMode = Qt::FastTransformation ) const
這是直接獲取大小，還有另一種形式：
QImage QImage::scaled ( int width, int height, Qt::AspectRatioMode aspectRatioMode = Qt::IgnoreAspectRatio, Qt::TransformationMode transformMode = Qt::FastTransformation ) const
函數(shù)說明以及參數(shù)在文檔中已經(jīng)說的非常清楚了，文檔摘錄如下：

Returns a copy of the image scaled to a rectangle defined by the givensize according to the givenaspectRatioMode andtransformMode.

If aspectRatioMode is Qt::IgnoreAspectRatio, the image is scaled tosize.

If aspectRatioMode is Qt::KeepAspectRatio, the image is scaled to a rectangle as large as possible insidesize, preserving the aspect ratio.

If aspectRatioMode is Qt::KeepAspectRatioByExpanding, the image is scaled to a rectangle as small as possible outsidesize, preserving the aspect ratio.

官方文檔中已經(jīng)說的比較清楚了，代碼實(shí)現(xiàn)也比較簡(jiǎn)單，代碼如下：

{QImage imgScaled ;imgScaled = img.scaled(ui->imagelabel->size(),Qt::KeepAspectRatio); // imgScaled = img.QImage::scaled(ui->imagelabel->width(),ui->imagelabel->height(),Qt::KeepAspectRatio);ui->imagelabel->setPixmap(QPixmap::fromImage(imgScaled)); }

顯示效果如下：

QImage的一點(diǎn)疑問與理解

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在查找資料時(shí)參考了這篇? Qt中圖像的顯示與基本操作 博客，但是存在一些疑點(diǎn)，博客中相關(guān)代碼如下：

QImage* imgScaled = new QImage； *imgScaled=img->scaled(width,height,Qt::KeepAspectRatio); ui->label->setPixmap(QPixmap::fromImage(*imgScaled));

對(duì)于以上代碼通過和我之前的代碼做簡(jiǎn)單對(duì)比，發(fā)現(xiàn)有幾點(diǎn)不一樣的地方：

圖像的定義方式，這里的定義方式為QImage* imgScale = new QImage

scaled函數(shù)的調(diào)用方式，一個(gè)是imgScaled = img.scaled后者為*imgScaled=img->scaled，我最開始也是將.寫為->一直沒找出錯(cuò)誤，提示base operand of '->' has non-pointer type 'QImage'

繼續(xù)查找Qt的幫助手冊(cè)，發(fā)現(xiàn)QImage的構(gòu)造函數(shù)還真是多：

Public Functions

QImage ()

QImage ( const QSize &size, Formatformat )

QImage ( int width, intheight, Format format )

QImage ( uchar *data, intwidth, intheight, Formatformat )

QImage ( const uchar *data, intwidth, intheight, Formatformat )

QImage ( uchar *data, intwidth, intheight, intbytesPerLine, Formatformat )

QImage ( const uchar *data, intwidth, intheight, intbytesPerLine, Formatformat )

QImage ( const char * const[]xpm )

QImage ( const QString &fileName, const char *format = 0 )

QImage ( const char *fileName, const char *format = 0 )

QImage ( const QImage &image )

~QImage ()

QImage提供了適用于不同場(chǎng)合的構(gòu)造方式，在手冊(cè)中對(duì)他們也有具體的應(yīng)用，但是我仍然沒找到QImage image;和QImage* image = new QImage這兩種究竟對(duì)應(yīng)的是哪兩種，有什么區(qū)別和不同。 在上一篇博文?OpenCV&Qt學(xué)習(xí)之二——QImage的進(jìn)一步認(rèn)識(shí)? 中提到了對(duì)于圖像數(shù)據(jù)的一點(diǎn)認(rèn)識(shí)，其中提到QImage是對(duì)現(xiàn)有數(shù)據(jù)的一種重新整合，是一種格式，但是數(shù)據(jù)還是指向原來的。從這里來看還需要根據(jù)構(gòu)造方式具體區(qū)別，并不完全正確。

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凌亂查了查資料，網(wǎng)上的資料就那幾個(gè)，互相轉(zhuǎn)來轉(zhuǎn)去的，而且多數(shù)比較老，仍然沒有幫助我想通關(guān)于這里面數(shù)據(jù)結(jié)構(gòu)的一些疑問，Qt 和 OpenCV對(duì)C和指針的要求還是比較高的，長(zhǎng)時(shí)間從單片機(jī)類的程序過來那點(diǎn)功底還真不夠，具體的C應(yīng)用都忘光了。這個(gè)問題只能暫時(shí)擱置，在后面的學(xué)習(xí)中慢慢理解。

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基于OpenCV的圖像初步處理

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以下兩個(gè)例程根據(jù)書籍 OpenCV 2 Computer Vision Application Programming Cookbook中的相關(guān)例程整理，這是一本比較新也比較基礎(chǔ)的入門書籍。

salt-and-pepper noise

關(guān)于圖像數(shù)據(jù)的基礎(chǔ)知識(shí)參見這段介紹：

Fundamentally, an image is a matrix of numerical values. This is why OpenCV 2 manipulates them using the cv::Mat data structure. Each element of the matrix represents one pixel. For a gray-level image (a "black-and-white" image), pixels are unsigned 8-bit values where 0 corresponds to black and corresponds 255 to white. For a color image, three such values per pixel are required to represent the usual three primary color channels {Red, Green, Blue}. A matrix element is therefore made, in this case, of a triplet of values.

這兒以想圖像中添加saltand-pepper noise為例，來說明如何訪問圖像矩陣中的獨(dú)立元素。saltand-pepper noise就是圖片中一些像素點(diǎn)，隨機(jī)的被黑色或者白色的像素點(diǎn)所替代，因此添加saltand-pepper noise也比較簡(jiǎn)單，只需要隨機(jī)的產(chǎn)生行和列，將這些行列值對(duì)應(yīng)的像素值更改即可，當(dāng)然通過上面的介紹，需要更改RGB3個(gè)通道。程序如下：

void Widget::salt(cv::Mat &image,int n) {int i,j;for (int k=0; k<n; k++){i= qrand()%image.cols;j= qrand()%image.rows;if (image.channels() == 1) { // gray-level imageimage.at<uchar>(j,i)= 255;} else if (image.channels() == 3) { // color imageimage.at<cv::Vec3b>(j,i)[0]= 255;image.at<cv::Vec3b>(j,i)[1]= 255;image.at<cv::Vec3b>(j,i)[2]= 255;}} }

對(duì)Win 7系統(tǒng)中的自帶圖像考拉進(jìn)行處理后的效果如下圖所示（程序是Ubuntu 12.04下的）：

減少色彩位數(shù)

在很多處理中需要對(duì)圖片中的所有像素進(jìn)行遍歷操作，采用什么方式進(jìn)行這個(gè)操作是需要思考的問題，關(guān)于這個(gè)問題的論述可以參考下面一段簡(jiǎn)介：

Color images are composed of 3-channel pixels. Each of these channels corresponds to the intensity value of one of the three primary colors (red, green, blue). Since each of these values is an 8-bit unsigned char, the total number of colors is 256x256x256, which is more than 16 million colors. Consequently, to reduce the complexity of an analysis, it is sometimes useful to reduce the number of colors in an image. One simple way to achieve this goal is to simply subdivide the RGB space into cubes of equal sizes. For example, if you reduce the number of colors in each dimension by 8, then you would obtain a total of 32x32x32 colors. Each color in the original image is then assigned a new color value in the color-reduced image that corresponds to the value in the center of the cube to which it belongs.

這個(gè)例子就是通過操作每一個(gè)像素點(diǎn)來減少色彩的位數(shù)，基本內(nèi)容在以上的英文引文中已經(jīng)有了介紹，代碼的實(shí)現(xiàn)也比較直接。在彩色圖像中，3個(gè)通道的數(shù)據(jù)是依次排列的，每一行的像素三個(gè)通道的值依次排列，cv::Mat中的通道排列順序?yàn)锽GR，那么一個(gè)圖像需要的地址塊空間為uchar 寬×高×3.但是需要注意的是，有些處理器針對(duì)行數(shù)為4或8的圖像處理更有效率，因此為了提高效率就會(huì)填充一些額外的像素，這些額外的像素不被顯示和保存，值是忽略的。

實(shí)現(xiàn)這個(gè)功能的代碼如下：

// using .ptr and [] void Widget::colorReduce0(cv::Mat &image, int div) {int nl= image.rows; // number of linesint nc= image.cols * image.channels(); // total number of elements per linefor (int j=0; j<nl; j++){uchar* data= image.ptr<uchar>(j);for (int i=0; i<nc; i++){// process each pixel ---------------------data[i]= data[i]/div*div+div/2;// end of pixel processing ----------------} // end of line} }

data[i]= data[i]/div*div+div/2; 通過整除的方式，就像素位數(shù)進(jìn)行減少，這里沒明白的是為啥后面還要加上div/2。

效果如下：

程序源代碼：

#include "widget.h" #include "ui_widget.h" #include <QDebug> Widget::Widget(QWidget *parent) :QWidget(parent),ui(new Ui::Widget) {ui->setupUi(this);}Widget::~Widget() {delete ui; }void Widget::on_openButton_clicked() {QString fileName = QFileDialog::getOpenFileName(this,tr("Open Image"),".",tr("Image Files (*.png *.jpg *.bmp)"));qDebug()<<"filenames:"<<fileName;image = cv::imread(fileName.toAscii().data());ui->imgfilelabel->setText(fileName);//here use 2 ways to make a copy // image.copyTo(originalimg); //make a copyoriginalimg = image.clone(); //clone the imgqimg = Widget::Mat2QImage(image);display(qimg); //display by the labelif(image.data){ui->saltButton->setEnabled(true);ui->originalButton->setEnabled(true);ui->reduceButton->setEnabled(true);} }QImage Widget::Mat2QImage(const cv::Mat &mat) {QImage img;if(mat.channels()==3){//cvt Mat BGR 2 QImage RGBcvtColor(mat,rgb,CV_BGR2RGB);img =QImage((const unsigned char*)(rgb.data),rgb.cols,rgb.rows,rgb.cols*rgb.channels(),QImage::Format_RGB888);}else{img =QImage((const unsigned char*)(mat.data),mat.cols,mat.rows,mat.cols*mat.channels(),QImage::Format_RGB888);}return img; }void Widget::display(QImage img) {QImage imgScaled;imgScaled = img.scaled(ui->imagelabel->size(),Qt::KeepAspectRatio); // imgScaled = img.QImage::scaled(ui->imagelabel->width(),ui->imagelabel->height(),Qt::KeepAspectRatio);ui->imagelabel->setPixmap(QPixmap::fromImage(imgScaled)); }void Widget::on_originalButton_clicked() {qimg = Widget::Mat2QImage(originalimg);display(qimg); }void Widget::on_saltButton_clicked() {salt(image,3000);qimg = Widget::Mat2QImage(image);display(qimg); } void Widget::on_reduceButton_clicked() {colorReduce0(image,64);qimg = Widget::Mat2QImage(image);display(qimg); } void Widget::salt(cv::Mat &image, int n) {int i,j;for (int k=0; k<n; k++){i= qrand()%image.cols;j= qrand()%image.rows;if (image.channels() == 1){ // gray-level imageimage.at<uchar>(j,i)= 255;}else if (image.channels() == 3){ // color imageimage.at<cv::Vec3b>(j,i)[0]= 255;image.at<cv::Vec3b>(j,i)[1]= 255;image.at<cv::Vec3b>(j,i)[2]= 255;}} }// using .ptr and [] void Widget::colorReduce0(cv::Mat &image, int div) {int nl= image.rows; // number of linesint nc= image.cols * image.channels(); // total number of elements per linefor (int j=0; j<nl; j++){uchar* data= image.ptr<uchar>(j);for (int i=0; i<nc; i++){// process each pixel ---------------------data[i]= data[i]/div*div+div/2;// end of pixel processing ----------------} // end of line} }復(fù)制代碼復(fù)制代碼#ifndef WIDGET_H #define WIDGET_H#include <QWidget> #include <QImage> #include <QFileDialog> #include <QTimer> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp>using namespace cv;namespace Ui { class Widget; }class Widget : public QWidget {Q_OBJECTpublic:explicit Widget(QWidget *parent = 0);~Widget();private slots:void on_openButton_clicked();QImage Mat2QImage(const cv::Mat &mat);void display(QImage image);void salt(cv::Mat &image, int n);void on_saltButton_clicked();void on_reduceButton_clicked();void colorReduce0(cv::Mat &image, int div);void on_originalButton_clicked();private:Ui::Widget *ui;cv::Mat image;cv::Mat originalimg; //store the original imgQImage qimg;QImage imgScaled;cv::Mat rgb; };#endif // WIDGET_H復(fù)制代碼書中還給了其他十余種操作的方法： 復(fù)制代碼#include <iostream>#include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp>// using .ptr and [] void colorReduce0(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint nc= image.cols * image.channels(); // total number of elements per linefor (int j=0; j<nl; j++) {uchar* data= image.ptr<uchar>(j);for (int i=0; i<nc; i++) {// process each pixel ---------------------data[i]= data[i]/div*div + div/2;// end of pixel processing ----------------} // end of line } }// using .ptr and * ++ void colorReduce1(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint nc= image.cols * image.channels(); // total number of elements per linefor (int j=0; j<nl; j++) {uchar* data= image.ptr<uchar>(j);for (int i=0; i<nc; i++) {// process each pixel ---------------------*data++= *data/div*div + div/2;// end of pixel processing ----------------} // end of line } }// using .ptr and * ++ and modulo void colorReduce2(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint nc= image.cols * image.channels(); // total number of elements per linefor (int j=0; j<nl; j++) {uchar* data= image.ptr<uchar>(j);for (int i=0; i<nc; i++) {// process each pixel ---------------------int v= *data;*data++= v - v%div + div/2;// end of pixel processing ----------------} // end of line } }// using .ptr and * ++ and bitwise void colorReduce3(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint nc= image.cols * image.channels(); // total number of elements per lineint n= static_cast<int>(log(static_cast<double>(div))/log(2.0));// mask used to round the pixel valueuchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0for (int j=0; j<nl; j++) {uchar* data= image.ptr<uchar>(j);for (int i=0; i<nc; i++) {// process each pixel ---------------------*data++= *data&mask + div/2;// end of pixel processing ----------------} // end of line } }// direct pointer arithmetic void colorReduce4(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint nc= image.cols * image.channels(); // total number of elements per lineint n= static_cast<int>(log(static_cast<double>(div))/log(2.0));int step= image.step; // effective width// mask used to round the pixel valueuchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0// get the pointer to the image bufferuchar *data= image.data;for (int j=0; j<nl; j++) {for (int i=0; i<nc; i++) {// process each pixel ---------------------*(data+i)= *data&mask + div/2;// end of pixel processing ----------------} // end of line data+= step; // next line} }// using .ptr and * ++ and bitwise with image.cols * image.channels() void colorReduce5(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint n= static_cast<int>(log(static_cast<double>(div))/log(2.0));// mask used to round the pixel valueuchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0for (int j=0; j<nl; j++) {uchar* data= image.ptr<uchar>(j);for (int i=0; i<image.cols * image.channels(); i++) {// process each pixel ---------------------*data++= *data&mask + div/2;// end of pixel processing ----------------} // end of line } }// using .ptr and * ++ and bitwise (continuous) void colorReduce6(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint nc= image.cols * image.channels(); // total number of elements per lineif (image.isContinuous()) {// then no padded pixelsnc= nc*nl; nl= 1; // it is now a 1D array}int n= static_cast<int>(log(static_cast<double>(div))/log(2.0));// mask used to round the pixel valueuchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0for (int j=0; j<nl; j++) {uchar* data= image.ptr<uchar>(j);for (int i=0; i<nc; i++) {// process each pixel ---------------------*data++= *data&mask + div/2;// end of pixel processing ----------------} // end of line } }// using .ptr and * ++ and bitwise (continuous+channels) void colorReduce7(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint nc= image.cols ; // number of columnsif (image.isContinuous()) {// then no padded pixelsnc= nc*nl; nl= 1; // it is now a 1D array}int n= static_cast<int>(log(static_cast<double>(div))/log(2.0));// mask used to round the pixel valueuchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0for (int j=0; j<nl; j++) {uchar* data= image.ptr<uchar>(j);for (int i=0; i<nc; i++) {// process each pixel ---------------------*data++= *data&mask + div/2;*data++= *data&mask + div/2;*data++= *data&mask + div/2;// end of pixel processing ----------------} // end of line } }// using Mat_ iterator void colorReduce8(cv::Mat &image, int div=64) {// get iteratorscv::Mat_<cv::Vec3b>::iterator it= image.begin<cv::Vec3b>();cv::Mat_<cv::Vec3b>::iterator itend= image.end<cv::Vec3b>();for ( ; it!= itend; ++it) {// process each pixel ---------------------(*it)[0]= (*it)[0]/div*div + div/2;(*it)[1]= (*it)[1]/div*div + div/2;(*it)[2]= (*it)[2]/div*div + div/2;// end of pixel processing ----------------} }// using Mat_ iterator and bitwise void colorReduce9(cv::Mat &image, int div=64) {// div must be a power of 2int n= static_cast<int>(log(static_cast<double>(div))/log(2.0));// mask used to round the pixel valueuchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0// get iteratorscv::Mat_<cv::Vec3b>::iterator it= image.begin<cv::Vec3b>();cv::Mat_<cv::Vec3b>::iterator itend= image.end<cv::Vec3b>();// scan all pixelsfor ( ; it!= itend; ++it) {// process each pixel ---------------------(*it)[0]= (*it)[0]&mask + div/2;(*it)[1]= (*it)[1]&mask + div/2;(*it)[2]= (*it)[2]&mask + div/2;// end of pixel processing ----------------} }// using MatIterator_ void colorReduce10(cv::Mat &image, int div=64) {// get iteratorscv::Mat_<cv::Vec3b> cimage= image;cv::Mat_<cv::Vec3b>::iterator it=cimage.begin();cv::Mat_<cv::Vec3b>::iterator itend=cimage.end();for ( ; it!= itend; it++) { // process each pixel ---------------------(*it)[0]= (*it)[0]/div*div + div/2;(*it)[1]= (*it)[1]/div*div + div/2;(*it)[2]= (*it)[2]/div*div + div/2;// end of pixel processing ----------------} }void colorReduce11(cv::Mat &image, int div=64) {int nl= image.rows; // number of linesint nc= image.cols; // number of columnsfor (int j=0; j<nl; j++) {for (int i=0; i<nc; i++) {// process each pixel ---------------------image.at<cv::Vec3b>(j,i)[0]= image.at<cv::Vec3b>(j,i)[0]/div*div + div/2;image.at<cv::Vec3b>(j,i)[1]= image.at<cv::Vec3b>(j,i)[1]/div*div + div/2;image.at<cv::Vec3b>(j,i)[2]= image.at<cv::Vec3b>(j,i)[2]/div*div + div/2;// end of pixel processing ----------------} // end of line } }// with input/ouput images void colorReduce12(const cv::Mat &image, // input image cv::Mat &result, // output imageint div=64) {int nl= image.rows; // number of linesint nc= image.cols ; // number of columns// allocate output image if necessaryresult.create(image.rows,image.cols,image.type());// created images have no padded pixelsnc= nc*nl; nl= 1; // it is now a 1D arrayint n= static_cast<int>(log(static_cast<double>(div))/log(2.0));// mask used to round the pixel valueuchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0for (int j=0; j<nl; j++) {uchar* data= result.ptr<uchar>(j);const uchar* idata= image.ptr<uchar>(j);for (int i=0; i<nc; i++) {// process each pixel ---------------------*data++= (*idata++)&mask + div/2;*data++= (*idata++)&mask + div/2;*data++= (*idata++)&mask + div/2;// end of pixel processing ----------------} // end of line } }// using overloaded operators void colorReduce13(cv::Mat &image, int div=64) {int n= static_cast<int>(log(static_cast<double>(div))/log(2.0));// mask used to round the pixel valueuchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0// perform color reductionimage=(image&cv::Scalar(mask,mask,mask))+cv::Scalar(div/2,div/2,div/2); }

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