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f21bde4d9f
* core: disable invalid constructors in C API by default - C API objects will lose their default initializers through constructors * samples: stop using of C API
135 lines
5.3 KiB
C++
135 lines
5.3 KiB
C++
#include <stdio.h>
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#include <iostream>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/core/utility.hpp>
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using namespace cv; // all the new API is put into "cv" namespace. Export its content
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using namespace std;
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static void help()
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{
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cout <<
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"\nThis program shows how to use cv::Mat and IplImages converting back and forth.\n"
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"It shows reading of images, converting to planes and merging back, color conversion\n"
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"and also iterating through pixels.\n"
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"Call:\n"
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"./image [image-name Default: lena.jpg]\n" << endl;
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}
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//#define USE_LEGACY_C_API 1 // not working with modern OpenCV
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#ifdef USE_LEGACY_C_API
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# include <opencv2/highgui/highgui_c.h>
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# include <opencv2/imgcodecs/imgcodecs_c.h>
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#endif
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int main( int argc, char** argv )
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{
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cv::CommandLineParser parser(argc, argv, "{help h | |}{@image|lena.jpg|}");
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if (parser.has("help"))
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{
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help();
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return 0;
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}
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string imagename = parser.get<string>("@image");
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#ifdef USE_LEGACY_C_API
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//! [iplimage]
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Ptr<IplImage> iplimg(cvLoadImage(imagename.c_str())); // Ptr<T> is safe ref-counting pointer class
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if(!iplimg)
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{
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fprintf(stderr, "Can not load image %s\n", imagename.c_str());
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return -1;
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}
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Mat img = cv::cvarrToMat(iplimg); // cv::Mat replaces the CvMat and IplImage, but it's easy to convert
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// between the old and the new data structures (by default, only the header
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// is converted, while the data is shared)
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//! [iplimage]
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#else
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Mat img = imread(samples::findFile(imagename)); // the newer cvLoadImage alternative, MATLAB-style function
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if(img.empty())
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{
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fprintf(stderr, "Can not load image %s\n", imagename.c_str());
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return -1;
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}
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#endif
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if( img.empty() ) // check if the image has been loaded properly
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return -1;
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Mat img_yuv;
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cvtColor(img, img_yuv, COLOR_BGR2YCrCb); // convert image to YUV color space. The output image will be created automatically
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vector<Mat> planes; // Vector is template vector class, similar to STL's vector. It can store matrices too.
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split(img_yuv, planes); // split the image into separate color planes
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#if 1
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// method 1. process Y plane using an iterator
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MatIterator_<uchar> it = planes[0].begin<uchar>(), it_end = planes[0].end<uchar>();
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for(; it != it_end; ++it)
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{
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double v = *it*1.7 + rand()%21-10;
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*it = saturate_cast<uchar>(v*v/255.);
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}
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// method 2. process the first chroma plane using pre-stored row pointer.
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// method 3. process the second chroma plane using individual element access
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for( int y = 0; y < img_yuv.rows; y++ )
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{
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uchar* Uptr = planes[1].ptr<uchar>(y);
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for( int x = 0; x < img_yuv.cols; x++ )
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{
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Uptr[x] = saturate_cast<uchar>((Uptr[x]-128)/2 + 128);
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uchar& Vxy = planes[2].at<uchar>(y, x);
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Vxy = saturate_cast<uchar>((Vxy-128)/2 + 128);
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}
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}
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#else
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Mat noise(img.size(), CV_8U); // another Mat constructor; allocates a matrix of the specified size and type
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randn(noise, Scalar::all(128), Scalar::all(20)); // fills the matrix with normally distributed random values;
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// there is also randu() for uniformly distributed random number generation
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GaussianBlur(noise, noise, Size(3, 3), 0.5, 0.5); // blur the noise a bit, kernel size is 3x3 and both sigma's are set to 0.5
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const double brightness_gain = 0;
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const double contrast_gain = 1.7;
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#ifdef USE_LEGACY_C_API
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// it's easy to pass the new matrices to the functions that only work with IplImage or CvMat:
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// step 1) - convert the headers, data will not be copied
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IplImage cv_planes_0 = planes[0], cv_noise = noise;
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// step 2) call the function; do not forget unary "&" to form pointers
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cvAddWeighted(&cv_planes_0, contrast_gain, &cv_noise, 1, -128 + brightness_gain, &cv_planes_0);
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#else
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addWeighted(planes[0], contrast_gain, noise, 1, -128 + brightness_gain, planes[0]);
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#endif
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const double color_scale = 0.5;
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// Mat::convertTo() replaces cvConvertScale. One must explicitly specify the output matrix type (we keep it intact - planes[1].type())
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planes[1].convertTo(planes[1], planes[1].type(), color_scale, 128*(1-color_scale));
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// alternative form of cv::convertScale if we know the datatype at compile time ("uchar" here).
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// This expression will not create any temporary arrays and should be almost as fast as the above variant
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planes[2] = Mat_<uchar>(planes[2]*color_scale + 128*(1-color_scale));
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// Mat::mul replaces cvMul(). Again, no temporary arrays are created in case of simple expressions.
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planes[0] = planes[0].mul(planes[0], 1./255);
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#endif
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// now merge the results back
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merge(planes, img_yuv);
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// and produce the output RGB image
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cvtColor(img_yuv, img, COLOR_YCrCb2BGR);
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// this is counterpart for cvNamedWindow
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namedWindow("image with grain", WINDOW_AUTOSIZE);
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#ifdef USE_LEGACY_C_API
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// this is to demonstrate that img and iplimg really share the data - the result of the above
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// processing is stored in img and thus in iplimg too.
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cvShowImage("image with grain", iplimg);
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#else
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imshow("image with grain", img);
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#endif
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waitKey();
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return 0;
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// all the memory will automatically be released by Vector<>, Mat and Ptr<> destructors.
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}
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