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124 lines
5.6 KiB
Markdown
124 lines
5.6 KiB
Markdown
# Example (JKQTPlotter): Simple math image plot, showin a 1-channel OpenCV cv::Mat {#JKQTPlotterImagePlotOpenCV}
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This project (see `./examples/simpletest_imageplot_opencv/`) simply creates a JKQTPlotter widget (as a new window) and adds a color-coded image plot of a mathematical function (here the Airy disk). The image is generated as an OpenCV cv::Mat image and then copied into a single column of the internal datasdtore (JKQTPMathImage could be directly used without the internal datastore).
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To copy the data a special OpenCV Interface function `JKQTPCopyCvMatToColumn()` is used, that copies the data from a cv::Mat directly into a column.
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The function `JKQTPCopyCvMatToColumn()` is available from the (non-default) header-only extension from `jkqtplotter/jkqtpopencvinterface.h`. This header provides facilities to interface JKQTPlotter with OPenCV.
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The source code of the main application is (see [`jkqtplotter_simpletest_imageplot_opencv.cpp`](https://github.com/jkriege2/JKQtPlotter/tree/master/examples/simpletest_imageplot_opencv/jkqtplotter_simpletest_imageplot_opencv.cpp):
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```.cpp
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#include <QApplication>
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#include <cmath>
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#include "jkqtplotter/jkqtplotter.h"
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#include "jkqtplotter/jkqtpgraphsimage.h"
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#include "jkqtplotter/jkqtpopencvinterface.h"
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#include <opencv/cv.h>
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#ifndef M_PI
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#define M_PI 3.14159265358979323846
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#endif
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int main(int argc, char* argv[])
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{
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QApplication app(argc, argv);
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JKQTPlotter plot;
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// 1. create a plotter window and get a pointer to the internal datastore (for convenience)
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plot.getPlotter()->setUseAntiAliasingForGraphs(true); // nicer (but slower) plotting
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plot.getPlotter()->setUseAntiAliasingForSystem(true); // nicer (but slower) plotting
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plot.getPlotter()->setUseAntiAliasingForText(true); // nicer (but slower) text rendering
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JKQTPDatastore* ds=plot.getDatastore();
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// 2. now we create data for the charts (taken from https://commons.wikimedia.org/wiki/File:Energiemix_Deutschland.svg)
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cv::Mat airydisk(150, 150, CV_64FC1); // OpenCV-Image for the data
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const double dx=1e-2; // size of a pixel in x-direction [micrometers]
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const double dy=1e-2; // size of a pixel in x-direction [micrometers]
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const double w=static_cast<double>(airydisk.cols)*dx;
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const double h=static_cast<double>(airydisk.rows)*dy;
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// 2.1 Parameters for airy disk plot (see https://en.wikipedia.org/wiki/Airy_disk)
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double NA=1.1; // numerical aperture of lens
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double wavelength=488e-3; // wavelength of the light [micrometers]
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// 2.2 calculate image of airy disk in a row-major array
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double x, y=-h/2.0;
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for (int iy=0; iy<airydisk.rows; iy++ ) {
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x=-w/2.0;
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for (int ix=0; ix<airydisk.cols; ix++ ) {
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const double r=sqrt(x*x+y*y);
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const double v=2.0*M_PI*NA*r/wavelength;
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airydisk.at<double>(iy,ix) = pow(2.0*j1(v)/v, 2);
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x+=dx;
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}
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y+=dy;
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}
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// 3. make data available to JKQTPlotter by adding it to the internal datastore.
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// In this step the contents of one channel of the openCV cv::Mat is copied into a column
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// of the datastore in row-major order
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size_t cAiryDisk=JKQTPCopyCvMatToColumn(ds, airydisk, "imagedata");
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// 4. create a graph (JKQTPColumnMathImage) with the column created above as data
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// The data is color-coded with the color-palette JKQTPMathImageMATLAB
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// the converted range of data is determined automatically because setAutoImageRange(true)
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JKQTPColumnMathImage* graph=new JKQTPColumnMathImage(&plot);
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graph->setTitle("");
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// image column with the data
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graph->setImageColumn(cAiryDisk);
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// set size of the data (the datastore does not contain this info, as it only manages 1D columns of data and this is used to assume a row-major ordering
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graph->setNx(airydisk.cols);
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graph->setNy(airydisk.rows);
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// where does the image start in the plot, given in plot-axis-coordinates (bottom-left corner)
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graph->setX(-w/2.0);
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graph->setY(-h/2.0);
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// width and height of the image in plot-axis-coordinates
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graph->setWidth(w);
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graph->setHeight(h);
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// color-map is "MATLAB"
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graph->setPalette(JKQTPMathImageMATLAB);
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// get coordinate axis of color-bar and set its label
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graph->getColorBarRightAxis()->setAxisLabel("light intensity [A.U.]");
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// determine min/max of data automatically and use it to set the range of the color-scale
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graph->setAutoImageRange(true);
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// you can set the color-scale range manually by using:
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// graph->setAutoImageRange(false);
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// graph->setImageMin(0);
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// graph->setImageMax(10);
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// 5. add the graphs to the plot, so it is actually displayed
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plot.addGraph(graph);
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// 6. set axis labels
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plot.getXAxis()->setAxisLabel("x [{\\mu}m]");
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plot.getYAxis()->setAxisLabel("y [{\\mu}m]");
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// 7. fix axis and plot aspect ratio to 1
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plot.getPlotter()->setMaintainAspectRatio(true);
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plot.getPlotter()->setMaintainAxisAspectRatio(true);
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// 8 autoscale the plot so the graph is contained
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plot.zoomToFit();
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// show plotter and make it a decent size
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plot.show();
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plot.resize(600,600);
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plot.setWindowTitle("JKQTPColumnMathImage");
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return app.exec();
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}
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```
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The result looks like this:
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See [`test/simpletest_imageplot`](https://github.com/jkriege2/JKQtPlotter/tree/master/examples/simpletest_imageplot) for a detailed description of the other possibilities that the class JKQTPColumnMathImage (and also JKQTPMathImage) offer with respect to determining how an image is plottet.
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