Updated internal calls to linear resize to use bit-exact version

modules/bgsegm/test/test_backgroundsubtractor_lsbp.cpp

@@ -91,7 +91,7 @@ template<typename T>
 static double evaluateBGSAlgorithm(Ptr<T> bgs) {
     Mat background = imread(getDataDir() + "shared/fruits.png");
     Mat object = imread(getDataDir() + "shared/baboon.png");
-    cv::resize(object, object, Size(100, 100));
+    cv::resize(object, object, Size(100, 100), 0, 0, INTER_LINEAR_EXACT);
     Ptr<bgsegm::SyntheticSequenceGenerator> generator = bgsegm::createSyntheticSequenceGenerator(background, object);
 
     double f1_mean = 0;

modules/ccalib/src/randpattern.cpp

@@ -371,7 +371,7 @@ void RandomPatternGenerator::generatePattern()
 
         Mat r = Mat(n, m, CV_32F);
         cv::randn(r, Scalar::all(0), Scalar::all(1));
-        cv::resize(r, r, Size(_imageWidth ,_imageHeight));
+        cv::resize(r, r, Size(_imageWidth ,_imageHeight), 0, 0, INTER_LINEAR_EXACT);
         double min_r, max_r;
         minMaxLoc(r, &min_r, &max_r);
 

modules/dpm/src/dpm_feature.cpp

@@ -87,7 +87,7 @@ void Feature::computeFeaturePyramid(const Mat &imageM, vector< Mat > &pyramid)
     {
         const double scale = (double)(1.0f/pow(params.sfactor, i));
         Mat imScaled;
-        resize(imageM, imScaled, imSize * scale);
+        resize(imageM, imScaled, imSize * scale, 0, 0, INTER_LINEAR_EXACT);
         // First octave at twice the image resolution
         computeHOG32D(imScaled, pyramid[i], params.binSize/2,
                 params.padx + 1, params.pady + 1);
@@ -106,7 +106,7 @@ void Feature::computeFeaturePyramid(const Mat &imageM, vector< Mat > &pyramid)
         {
             Mat imScaled2;
             Size_<double> imScaledSize = imScaled.size();
-            resize(imScaled, imScaled2, imScaledSize*0.5);
+            resize(imScaled, imScaled2, imScaledSize*0.5, 0, 0, INTER_LINEAR_EXACT);
             imScaled = imScaled2;
             computeHOG32D(imScaled2, pyramid[j+params.interval],
                     params.binSize, params.padx + 1, params.pady + 1);

modules/face/samples/facemark_lbf_fitting.cpp

@@ -104,7 +104,7 @@ int main(int argc, char** argv ){
         double __time__ = (double)getTickCount();
 
         float scale = (float)(400.0/frame.cols);
-        resize(frame, img, Size((int)(frame.cols*scale), (int)(frame.rows*scale)));
+        resize(frame, img, Size((int)(frame.cols*scale), (int)(frame.rows*scale)), 0, 0, INTER_LINEAR_EXACT);
 
         facemark->getFaces(img, rects);
         rects_scaled.clear();

modules/face/samples/sampleDetectLandmarks.cpp

@@ -72,7 +72,7 @@ int main(int argc,char** argv){
     facemark->loadModel(filename);
     cout<<"Loaded model"<<endl;
     vector<Rect> faces;
-    resize(img,img,Size(460,460));
+    resize(img,img,Size(460,460), 0, 0, INTER_LINEAR_EXACT);
     facemark->getFaces(img,faces);
     vector< vector<Point2f> > shapes;
     if(facemark->fit(img,faces,shapes))

modules/face/samples/sampleDetectLandmarksvideo.cpp

@@ -83,7 +83,7 @@ int main(int argc,char** argv){
         shapes.clear();
         cap>>img;
         //Detect faces in the current image
-        resize(img,img,Size(600,600));
+        resize(img,img,Size(600,600), 0, 0, INTER_LINEAR_EXACT);
         facemark->getFaces(img,faces);
         if(faces.size()==0){
             cout<<"No faces found in this frame"<<endl;

modules/face/samples/sample_face_swapping.cpp

@@ -139,8 +139,8 @@ int main( int argc, char** argv)
     //Detect faces in the current image
     float ratio1 = (float)img1.cols/(float)img1.rows;
     float ratio2 = (float)img2.cols/(float)img2.rows;
-    resize(img1,img1,Size((int)(640*ratio1),(int)(640*ratio1)));
-    resize(img2,img2,Size((int)(640*ratio2),(int)(640*ratio2)));
+    resize(img1,img1,Size((int)(640*ratio1),(int)(640*ratio1)), 0, 0, INTER_LINEAR_EXACT);
+    resize(img2,img2,Size((int)(640*ratio2),(int)(640*ratio2)), 0, 0, INTER_LINEAR_EXACT);
     Mat img1Warped = img2.clone();
     facemark->getFaces(img1,faces1);
     facemark->getFaces(img2,faces2);

modules/face/src/face_alignment.cpp

@@ -173,7 +173,7 @@ bool FacemarkKazemiImpl::scaleData( vector< vector<Point2f> > & trainlandmarks,
         //calculating scale for x and y axis
         scalex=float(s.width)/float(trainimages[i].cols);
         scaley=float(s.height)/float(trainimages[i].rows);
-        resize(trainimages[i],trainimages[i],s);
+        resize(trainimages[i],trainimages[i],s,0,0,INTER_LINEAR_EXACT);
         for (vector<Point2f>::iterator it = trainlandmarks[i].begin(); it != trainlandmarks[i].end(); it++) {
             Point2f pt = (*it);
             pt.x = pt.x*scalex;

modules/face/src/facemarkAAM.cpp

@@ -374,7 +374,7 @@ bool FacemarkAAMImpl::fitImpl( const Mat image, std::vector<Point2f>& landmarks,
         imgray = image;
     }
 
-    resize(imgray,img,Size(int(image.cols/scale),int(image.rows/scale)));// matlab use bicubic interpolation, the result is float numbers
+    resize(imgray,img,Size(int(image.cols/scale),int(image.rows/scale)), 0, 0, INTER_LINEAR_EXACT);// matlab use bicubic interpolation, the result is float numbers
 
     /*chop the textures model*/
     int maxCol = param_m;

modules/face/tutorials/face_landmark/face_landmark_detection.markdown

@@ -63,7 +63,7 @@ in which landmarks have to be detected.
 facemark->loadModel(filename);
 cout<<"Loaded model"<<endl;
 vector<Rect> faces;
-resize(img,img,Size(460,460));
+resize(img,img,Size(460,460),0,0,INTER_LINEAR_EXACT);
 facemark->getFaces(img,faces);
 vector< vector<Point2f> > shapes;
 

modules/face/tutorials/face_landmark/face_landmark_video.markdown

@@ -74,7 +74,7 @@ while(1){
 	faces.clear();
 	shapes.clear();
 	cap>>img;
-	resize(img,img,Size(600,600));
+	resize(img,img,Size(600,600),0,0,INTER_LINEAR_EXACT);
 	facemark->getFaces(img,faces);
 	if(faces.size()==0){
 	    cout<<"No faces found in this frame"<<endl;

modules/face/tutorials/face_landmark/sample_face_swapping.markdown

@@ -54,8 +54,8 @@ vector<Rect> faces1,faces2;
 vector< vector<Point2f> > shape1,shape2;
 float ratio1 = (float)img1.cols/(float)img1.rows;
 float ratio2 = (float)img2.cols/(float)img2.rows;
-resize(img1,img1,Size(640*ratio1,640*ratio1));
-resize(img2,img2,Size(640*ratio2,640*ratio2));
+resize(img1,img1,Size(640*ratio1,640*ratio1),0,0,INTER_LINEAR_EXACT);
+resize(img2,img2,Size(640*ratio2,640*ratio2),0,0,INTER_LINEAR_EXACT);
 Mat img1Warped = img2.clone();
 facemark->getFaces(img1,faces1);
 facemark->getFaces(img2,faces2);

modules/img_hash/src/average_hash.cpp

@@ -26,7 +26,7 @@ public:
                   input.type() == CV_8UC3 ||
                   input.type() == CV_8U);
 
-        cv::resize(input, resizeImg, cv::Size(8,8));
+        cv::resize(input, resizeImg, cv::Size(8,8), 0, 0, INTER_LINEAR_EXACT);
         if(input.type() == CV_8UC3)
         {
             cv::cvtColor(resizeImg, grayImg, CV_BGR2GRAY);

modules/img_hash/src/block_mean_hash.cpp

@@ -39,7 +39,7 @@ public:
                   input.type() == CV_8UC3 ||
                   input.type() == CV_8U);
 
-        cv::resize(input, resizeImg_, cv::Size(imgWidth,imgHeight));
+        cv::resize(input, resizeImg_, cv::Size(imgWidth,imgHeight), 0, 0, INTER_LINEAR_EXACT);
         if(input.type() == CV_8UC3)
         {
             cv::cvtColor(resizeImg_, grayImg_, CV_BGR2GRAY);

modules/img_hash/src/phash.cpp

@@ -20,7 +20,7 @@ public:
                   input.type() == CV_8UC3 ||
                   input.type() == CV_8U);
 
-        cv::resize(input, resizeImg, cv::Size(32,32));
+        cv::resize(input, resizeImg, cv::Size(32,32), 0, 0, INTER_LINEAR_EXACT);
         if(input.type() == CV_8UC3)
         {
             cv::cvtColor(resizeImg, grayImg, CV_BGR2GRAY);

modules/line_descriptor/samples/matching.cpp

@@ -197,8 +197,8 @@ int main( int argc, char** argv )
 
   /* plot matches */
   cv::Mat lsd_outImg;
-  resize( imageMat1, imageMat1, Size( imageMat1.cols / 2, imageMat1.rows / 2 ) );
-  resize( imageMat2, imageMat2, Size( imageMat2.cols / 2, imageMat2.rows / 2 ) );
+  resize( imageMat1, imageMat1, Size( imageMat1.cols / 2, imageMat1.rows / 2 ), 0, 0, INTER_LINEAR_EXACT );
+  resize( imageMat2, imageMat2, Size( imageMat2.cols / 2, imageMat2.rows / 2 ), 0, 0, INTER_LINEAR_EXACT );
   std::vector<char> lsd_mask( matches.size(), 1 );
   drawLineMatches( imageMat1, octave0_1, imageMat2, octave0_2, good_matches, lsd_outImg, Scalar::all( -1 ), Scalar::all( -1 ), lsd_mask,
                    DrawLinesMatchesFlags::DEFAULT );

modules/line_descriptor/src/binary_descriptor.cpp

@@ -723,7 +723,7 @@ int BinaryDescriptor::OctaveKeyLines( cv::Mat& image, ScaleLines &keyLines )
     numOfFinalLine += edLineVec_[octaveCount]->lines_.numOfLines;
 
     /* resize image for next level of pyramid */
-    cv::resize( blur, image, cv::Size(), ( 1.f / factor ), ( 1.f / factor ) );
+    cv::resize( blur, image, cv::Size(), ( 1.f / factor ), ( 1.f / factor ), INTER_LINEAR_EXACT );
 
     /* update sigma values */
     preSigma2 = curSigma2;

modules/optflow/perf/opencl/perf_dis_optflow.cpp

@@ -94,8 +94,8 @@ void MakeArtificialExample(UMat &dst_frame1, UMat &dst_frame2)
 
     UMat tmp(Size(dst_frame1.cols / (1 << src_scale), dst_frame1.rows / (1 << src_scale)), CV_8U);
     randu(tmp, 0, 255);
-    resize(tmp, dst_frame1, dst_frame1.size(), 0.0, 0.0, INTER_LINEAR);
-    resize(tmp, dst_frame2, dst_frame2.size(), 0.0, 0.0, INTER_LINEAR);
+    resize(tmp, dst_frame1, dst_frame1.size(), 0.0, 0.0, INTER_LINEAR_EXACT);
+    resize(tmp, dst_frame2, dst_frame2.size(), 0.0, 0.0, INTER_LINEAR_EXACT);
 
     Mat displacement_field(Size(dst_frame1.cols / (1 << OF_scale), dst_frame1.rows / (1 << OF_scale)),
                            CV_32FC2);

modules/optflow/perf/perf_disflow.cpp

@@ -88,8 +88,8 @@ void MakeArtificialExample(Mat &dst_frame1, Mat &dst_frame2)
 
     Mat tmp(Size(dst_frame1.cols / (1 << src_scale), dst_frame1.rows / (1 << src_scale)), CV_8U);
     randu(tmp, 0, 255);
-    resize(tmp, dst_frame1, dst_frame1.size(), 0.0, 0.0, INTER_LINEAR);
-    resize(tmp, dst_frame2, dst_frame2.size(), 0.0, 0.0, INTER_LINEAR);
+    resize(tmp, dst_frame1, dst_frame1.size(), 0.0, 0.0, INTER_LINEAR_EXACT);
+    resize(tmp, dst_frame2, dst_frame2.size(), 0.0, 0.0, INTER_LINEAR_EXACT);
 
     Mat displacement_field(Size(dst_frame1.cols / (1 << OF_scale), dst_frame1.rows / (1 << OF_scale)),
                            CV_32FC2);

modules/saliency/src/BING/objectnessBING.cpp

@@ -138,7 +138,7 @@ void ObjectnessBING::predictBBoxSI( Mat &img3u, ValStructVec<float, Vec4i> &valB
 
     height = min( height, imgH ), width = min( width, imgW );
     Mat im3u, matchCost1f, mag1u;
-    resize( img3u, im3u, Size( cvRound( _W * imgW * 1.0 / width ), cvRound( _W * imgH * 1.0 / height ) ) );
+    resize( img3u, im3u, Size( cvRound( _W * imgW * 1.0 / width ), cvRound( _W * imgH * 1.0 / height ) ), 0, 0, INTER_LINEAR_EXACT );
     gradientMag( im3u, mag1u );
 
     matchCost1f = _tigF.matchTemplate( mag1u );

modules/saliency/src/motionSaliencyBinWangApr2014.cpp

@@ -515,8 +515,8 @@ bool MotionSaliencyBinWangApr2014::templateReplacement( const Mat& finalBFMask,
               /* Check if the value of current pixel BA in potentialBackground model is already contained in at least one of its neighbors'
                * background model
                */
-              resize( replicateCurrentBAMat, replicateCurrentBAMat, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR );
-              resize( diffResult, diffResult, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR );
+              resize( replicateCurrentBAMat, replicateCurrentBAMat, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR_EXACT );
+              resize( diffResult, diffResult, Size( backgroundModelROI.cols, backgroundModelROI.rows ), 0, 0, INTER_LINEAR_EXACT );
 
               backgroundModelROI.convertTo( backgroundModelROI, CV_8U );
 

modules/saliency/src/staticSaliencySpectralResidual.cpp

@@ -92,11 +92,11 @@ bool StaticSaliencySpectralResidual::computeSaliencyImpl( InputArray image, Outp
   if( image.channels() == 3 )
   {
     cvtColor( image, imageGR, COLOR_BGR2GRAY );
-    resize( imageGR, grayDown, resizedImageSize, 0, 0, INTER_LINEAR );
+    resize( imageGR, grayDown, resizedImageSize, 0, 0, INTER_LINEAR_EXACT );
   }
   else
   {
-    resize( image, grayDown, resizedImageSize, 0, 0, INTER_LINEAR );
+    resize( image, grayDown, resizedImageSize, 0, 0, INTER_LINEAR_EXACT );
   }
 
   grayDown.convertTo( realImage, CV_64F );
@@ -130,7 +130,7 @@ bool StaticSaliencySpectralResidual::computeSaliencyImpl( InputArray image, Outp
   magnitude = magnitude / maxVal;
   magnitude.convertTo( magnitude, CV_32F );
 
-  resize( magnitude, saliencyMap, image.size(), 0, 0, INTER_LINEAR );
+  resize( magnitude, saliencyMap, image.size(), 0, 0, INTER_LINEAR_EXACT );
 
 #ifdef SALIENCY_DEBUG
   // visualize saliency map

modules/structured_light/samples/cap_pattern.cpp

@@ -167,10 +167,10 @@ int main( int argc, char** argv )
       moveWindow( "cam2", 640 + 75, 0 );
 
       // Resizing images to avoid issues for high resolution images, visualizing them as grayscale
-      resize( frame1, tmp, Size( 640, 480 ) );
+      resize( frame1, tmp, Size( 640, 480 ), 0, 0, INTER_LINEAR_EXACT);
       cvtColor( tmp, tmp, COLOR_RGB2GRAY );
       imshow( "cam1", tmp );
-      resize( frame2, tmp, Size( 640, 480 ) );
+      resize( frame2, tmp, Size( 640, 480 ), 0, 0, INTER_LINEAR_EXACT);
       cvtColor( tmp, tmp, COLOR_RGB2GRAY );
       imshow( "cam2", tmp );
 

modules/structured_light/samples/pointcloud.cpp

@@ -262,7 +262,7 @@ int main( int argc, char** argv )
     applyColorMap( scaledDisparityMap, cm_disp, COLORMAP_JET );
 
     // Show the result
-    resize( cm_disp, cm_disp, Size( 640, 480 ) );
+    resize( cm_disp, cm_disp, Size( 640, 480 ), 0, 0, INTER_LINEAR_EXACT );
     imshow( "cm disparity m", cm_disp );
 
     // Compute the point cloud
@@ -273,7 +273,7 @@ int main( int argc, char** argv )
     // Compute a mask to remove background
     Mat dst, thresholded_disp;
     threshold( scaledDisparityMap, thresholded_disp, 0, 255, THRESH_OTSU + THRESH_BINARY );
-    resize( thresholded_disp, dst, Size( 640, 480 ) );
+    resize( thresholded_disp, dst, Size( 640, 480 ), 0, 0, INTER_LINEAR_EXACT );
     imshow( "threshold disp otsu", dst );
 
 #ifdef HAVE_OPENCV_VIZ

modules/text/samples/end_to_end_recognition.cpp

@@ -263,10 +263,10 @@ int main(int argc, char* argv[])
 
 
 
-    //resize(out_img_detection,out_img_detection,Size(image.cols*scale_img,image.rows*scale_img));
+    //resize(out_img_detection,out_img_detection,Size(image.cols*scale_img,image.rows*scale_img),0,0,INTER_LINEAR_EXACT);
     //imshow("detection", out_img_detection);
     //imwrite("detection.jpg", out_img_detection);
-    //resize(out_img,out_img,Size(image.cols*scale_img,image.rows*scale_img));
+    //resize(out_img,out_img,Size(image.cols*scale_img,image.rows*scale_img),0,0,INTER_LINEAR_EXACT);
     namedWindow("recognition",WINDOW_NORMAL);
     imshow("recognition", out_img);
     waitKey(0);

modules/text/samples/webcam_demo.cpp

@@ -193,7 +193,7 @@ int main(int argc, char* argv[])
         double t_all = (double)getTickCount();
 
         if (downsize)
-            resize(frame,frame,Size(320,240));
+            resize(frame,frame,Size(320,240),0,0,INTER_LINEAR_EXACT);
 
         /*Text Detection*/
         cvtColor(frame,gray,COLOR_BGR2GRAY);

modules/text/src/ocr_beamsearch_decoder.cpp

@@ -603,7 +603,7 @@ void OCRBeamSearchClassifierCNN::eval( InputArray _src, vector< vector<double> >
         cvtColor(src,src,COLOR_RGB2GRAY);
     }
 
-    resize(src,src,Size(window_size*src.cols/src.rows,window_size));
+    resize(src,src,Size(window_size*src.cols/src.rows,window_size),0,0,INTER_LINEAR_EXACT);
 
     int seg_points = 0;
 

modules/text/src/ocr_hmm_decoder.cpp

@@ -779,14 +779,14 @@ void OCRHMMClassifierKNN::eval( InputArray _mask, vector<int>& out_class, vector
     {
         int height = image_width*tmp.rows/tmp.cols;
         if(height == 0) height = 1;
-        resize(tmp,tmp,Size(image_width,height));
+        resize(tmp,tmp,Size(image_width,height),0,0,INTER_LINEAR_EXACT);
         tmp.copyTo(mask(Rect(0,(image_height-height)/2,image_width,height)));
     }
     else
     {
         int width = image_height*tmp.cols/tmp.rows;
         if(width == 0) width = 1;
-        resize(tmp,tmp,Size(width,image_height));
+        resize(tmp,tmp,Size(width,image_height),0,0,INTER_LINEAR_EXACT);
         tmp.copyTo(mask(Rect((image_width-width)/2,0,width,image_height)));
     }
 
@@ -837,7 +837,7 @@ void OCRHMMClassifierKNN::eval( InputArray _mask, vector<int>& out_class, vector
         copyMakeBorder(maps[i],maps[i],7,7,7,7,BORDER_CONSTANT,Scalar(0));
         GaussianBlur(maps[i], maps[i], Size(7,7), 2, 2);
         normalize(maps[i],maps[i],0,255,NORM_MINMAX);
-        resize(maps[i],maps[i],Size(image_width,image_height));
+        resize(maps[i],maps[i],Size(image_width,image_height),0,0,INTER_LINEAR_EXACT);
     }
 
     //Generate features for each bitmap
@@ -1032,7 +1032,7 @@ void OCRHMMClassifierCNN::eval( InputArray _src, vector<int>& out_class, vector<
     }
 
     // shall we resize the input image or make a copy ?
-    resize(img,img,Size(window_size,window_size));
+    resize(img,img,Size(window_size,window_size),0,0,INTER_LINEAR_EXACT);
 
     Mat quad;
     Mat tmp;

modules/text/src/ocr_holistic.cpp

@@ -85,7 +85,7 @@ protected:
     {
         CV_Assert(image.channels() == 1 && image.depth() == CV_8U);
         Mat resized;
-        resize(image, resized, getPerceptiveField());
+        resize(image, resized, getPerceptiveField(), 0, 0, INTER_LINEAR_EXACT);
         Mat blob = dnn::blobFromImage(resized);
         net.setInput(blob, "data");
         Mat prob = net.forward("prob");

modules/tracking/src/gtrTracker.cpp

@@ -145,8 +145,8 @@ bool TrackerGOTURNImpl::updateImpl(const Mat& image, Rect2d& boundingBox)
 
     //Preprocess
     //Resize
-    resize(targetPatch, targetPatch, Size(INPUT_SIZE, INPUT_SIZE));
-    resize(searchPatch, searchPatch, Size(INPUT_SIZE, INPUT_SIZE));
+    resize(targetPatch, targetPatch, Size(INPUT_SIZE, INPUT_SIZE), 0, 0, INTER_LINEAR_EXACT);
+    resize(searchPatch, searchPatch, Size(INPUT_SIZE, INPUT_SIZE), 0, 0, INTER_LINEAR_EXACT);
 
     //Mean Subtract
     targetPatch = targetPatch - 128;

modules/tracking/src/tldDetector.hpp

@@ -56,7 +56,7 @@ namespace cv
 		const int MAX_EXAMPLES_IN_MODEL = 500;
 		const int MEASURES_PER_CLASSIFIER = 13;
 		const int GRIDSIZE = 15;
-		const int DOWNSCALE_MODE = cv::INTER_LINEAR;
+		const int DOWNSCALE_MODE = cv::INTER_LINEAR_EXACT;
         const double THETA_NN = 0.5;
 		const double CORE_THRESHOLD = 0.5;
         const double CLASSIFIER_MARGIN = 0.1;

modules/tracking/src/tldModel.cpp

@@ -114,7 +114,7 @@ namespace cv
 
 #ifdef BLUR_AS_VADIM
 					GaussianBlur(standardPatch, blurredPatch, GaussBlurKernelSize, 0.0);
-					resize(blurredPatch, blurredPatch, minSize);
+					resize(blurredPatch, blurredPatch, minSize, 0, 0, INTER_LINEAR_EXACT);
 #else
 					resample(blurredImg, RotatedRect(center, size, angle), blurredPatch);
 #endif

modules/tracking/src/tldTracker.cpp

@@ -287,7 +287,7 @@ int TrackerTLDImpl::Pexpert::additionalExamples(std::vector<Mat_<uchar> >& examp
 
 #if defined BLUR_AS_VADIM
             GaussianBlur(standardPatch, blurredPatch, GaussBlurKernelSize, 0.0);
-            resize(blurredPatch, blurredPatch, initSize_);
+            resize(blurredPatch, blurredPatch, initSize_, 0, 0, INTER_LINEAR_EXACT);
 #else
             resample(blurredImg, RotatedRect(center, size, angle), blurredPatch);
 #endif

modules/tracking/src/tldUtils.cpp

@@ -103,7 +103,7 @@ double scaleAndBlur(const Mat& originalImg, int scale, Mat& scaledImg, Mat& blur
     for( int i = 0; i < scale; i++, dScale *= scaleStep );
     Size2d size = originalImg.size();
     size.height /= dScale; size.width /= dScale;
-    resize(originalImg, scaledImg, size);
+    resize(originalImg, scaledImg, size, 0, 0, INTER_LINEAR_EXACT);
     GaussianBlur(scaledImg, blurredImg, GaussBlurKernelSize, 0.0);
     return dScale;
 }

modules/tracking/src/trackerKCF.cpp

@@ -306,7 +306,7 @@ namespace cv{
     CV_Assert(img.channels() == 1 || img.channels() == 3);
 
     // resize the image whenever needed
-    if(resizeImage)resize(img,img,Size(img.cols/2,img.rows/2));
+    if(resizeImage)resize(img,img,Size(img.cols/2,img.rows/2),0,0,INTER_LINEAR_EXACT);
 
     // detection part
     if(frame>0){

modules/xfeatures2d/src/sift.cpp

@@ -197,7 +197,8 @@ static const float SIFT_DESCR_MAG_THR = 0.2f;
 // factor used to convert floating-point descriptor to unsigned char
 static const float SIFT_INT_DESCR_FCTR = 512.f;
 
-#if 0
+#define DoG_TYPE_SHORT 0
+#if DoG_TYPE_SHORT
 // intermediate type used for DoG pyramids
 typedef short sift_wt;
 static const int SIFT_FIXPT_SCALE = 48;
@@ -233,7 +234,11 @@ static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma
     {
         sig_diff = sqrtf( std::max(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA * 4, 0.01f) );
         Mat dbl;
+#if DoG_TYPE_SHORT
+        resize(gray_fpt, dbl, Size(gray_fpt.cols*2, gray_fpt.rows*2), 0, 0, INTER_LINEAR_EXACT);
+#else
         resize(gray_fpt, dbl, Size(gray_fpt.cols*2, gray_fpt.rows*2), 0, 0, INTER_LINEAR);
+#endif
         GaussianBlur(dbl, dbl, Size(), sig_diff, sig_diff);
         return dbl;
     }

modules/xfeatures2d/test/test_rotation_and_scale_invariance.cpp

@@ -438,7 +438,7 @@ protected:
         {
             float scale = 1.f + scaleIdx * 0.5f;
             Mat image1;
-            resize(image0, image1, Size(), 1./scale, 1./scale);
+            resize(image0, image1, Size(), 1./scale, 1./scale, INTER_LINEAR_EXACT);
 
             vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
             featureDetector->detect(image1, keypoints1);
@@ -563,7 +563,7 @@ protected:
             float scale = 1.f + scaleIdx * 0.5f;
 
             Mat image1;
-            resize(image0, image1, Size(), 1./scale, 1./scale);
+            resize(image0, image1, Size(), 1./scale, 1./scale, INTER_LINEAR_EXACT);
 
             vector<KeyPoint> keypoints1;
             scaleKeyPoints(keypoints0, keypoints1, 1.0f/scale);

modules/ximgproc/perf/perf_disparity_wls_filter.cpp

@@ -75,9 +75,9 @@ PERF_TEST_P( DisparityWLSFilterPerfTest, perf, Combine(GuideTypes::all(), SrcTyp
     MakeArtificialExample(rng,guide,disp_left,disp_right,ROI);
     if(use_downscale)
     {
-        resize(disp_left,disp_left,Size(),0.5,0.5);
+        resize(disp_left,disp_left,Size(),0.5,0.5, INTER_LINEAR_EXACT);
         disp_left/=2;
-        resize(disp_right,disp_right,Size(),0.5,0.5);
+        resize(disp_right,disp_right,Size(),0.5,0.5, INTER_LINEAR_EXACT);
         disp_right/=2;
         ROI = Rect(ROI.x/2,ROI.y/2,ROI.width/2,ROI.height/2);
     }

modules/ximgproc/samples/disparity_filtering.cpp

@@ -135,8 +135,8 @@ int main(int argc, char** argv)
             max_disp/=2;
             if(max_disp%16!=0)
                 max_disp += 16-(max_disp%16);
-            resize(left ,left_for_matcher ,Size(),0.5,0.5);
-            resize(right,right_for_matcher,Size(),0.5,0.5);
+            resize(left ,left_for_matcher ,Size(),0.5,0.5, INTER_LINEAR_EXACT);
+            resize(right,right_for_matcher,Size(),0.5,0.5, INTER_LINEAR_EXACT);
             //! [downscale]
         }
         else
@@ -196,7 +196,7 @@ int main(int argc, char** argv)
         if(!no_downscale)
         {
             // upscale raw disparity and ROI back for a proper comparison:
-            resize(left_disp,left_disp,Size(),2.0,2.0);
+            resize(left_disp,left_disp,Size(),2.0,2.0,INTER_LINEAR_EXACT);
             left_disp = left_disp*2.0;
             ROI = Rect(ROI.x*2,ROI.y*2,ROI.width*2,ROI.height*2);
         }

modules/ximgproc/samples/fast_hough_transform.cpp

@@ -233,7 +233,7 @@ static void rescale(Mat const &src, Mat &dst,
 {
     double scale = min(min(static_cast<double>(maxWidth) / src.cols,
                            static_cast<double>(maxHeight) / src.rows), 1.0);
-    resize(src, dst, Size(), scale, scale, INTER_LINEAR);
+    resize(src, dst, Size(), scale, scale, INTER_LINEAR_EXACT);
 }
 
 static void showHumanReadableImg(string const &name, Mat const &img)

modules/ximgproc/samples/filterdemo.cpp

@@ -87,7 +87,7 @@ int main( int argc, const char** argv)
         if( frame.rows <= rows0 )
             src = frame;
         else
-            resize(frame, src, Size(cvRound(480.*frame.cols/frame.rows), 480));
+            resize(frame, src, Size(cvRound(480.*frame.cols/frame.rows), 480), 0, 0, INTER_LINEAR_EXACT);
 
         float t = (float)getTickCount();
         ximgproc::anisotropicDiffusion(src, dst, alpha, sigma, niters);

modules/ximgproc/samples/thinning.cpp

@@ -11,7 +11,7 @@ using namespace cv;
 int main()
 {
     Mat img = imread("opencv-logo.png", IMREAD_COLOR);
-    resize(img, img, Size(), 0.5, 0.5);
+    resize(img, img, Size(), 0.5, 0.5, INTER_LINEAR_EXACT);
 
     /// Threshold the input image
     Mat img_grayscale, img_binary;

modules/ximgproc/test/test_adaptive_manifold.cpp

@@ -185,8 +185,8 @@ TEST_P(AdaptiveManifoldRefImplTest, RefImplAccuracy)
     //inconsistent downsample/upsample operations in reference implementation
     Size dstSize((guide.cols + 15) & ~15, (guide.rows + 15) & ~15);
 
-    resize(guide, guide, dstSize);
-    resize(src, src, dstSize);
+    resize(guide, guide, dstSize, 0, 0, INTER_LINEAR_EXACT);
+    resize(src, src, dstSize, 0, 0, INTER_LINEAR_EXACT);
 
     for (int iter = 0; iter < 4; iter++)
     {

modules/ximgproc/test/test_adaptive_manifold_ref_impl.cpp

@@ -818,7 +818,7 @@ namespace
         resize(buf.mul, buf.numerator, Size(), 1.0 / df, 1.0 / df);
 
         ensureSizeIsEnough(nsz, buf.denominator);
-        resize(buf.theta_masked, buf.denominator, Size(), 1.0 / df, 1.0 / df);        
+        resize(buf.theta_masked, buf.denominator, Size(), 1.0 / df, 1.0 / df);
         h_filter(buf.numerator, buf.numerator_filtered, sigma_s / df);
         h_filter(buf.denominator, buf.denominator_filtered, sigma_s / df);
 

modules/ximgproc/test/test_disparity_wls_filter.cpp

@@ -124,8 +124,8 @@ TEST_P(DisparityWLSFilterTest, MultiThreadReproducibility)
 
     if(use_downscale)
     {
-        resize(left_disp,left_disp,Size(),0.5,0.5);
-        resize(right_disp,right_disp,Size(),0.5,0.5);
+        resize(left_disp,left_disp,Size(),0.5,0.5, INTER_LINEAR_EXACT);
+        resize(right_disp,right_disp,Size(),0.5,0.5, INTER_LINEAR_EXACT);
         ROI = Rect(ROI.x/2,ROI.y/2,ROI.width/2,ROI.height/2);
     }
 

modules/ximgproc/test/test_domain_transform.cpp

@@ -85,7 +85,7 @@ Mat convertTypeAndSize(Mat src, int dstType, Size dstSize)
     }
 
     dst.convertTo(dst, dstType);
-    resize(dst, dst, dstSize);
+    resize(dst, dst, dstSize, 0, 0, dstType == CV_32FC1 ? INTER_LINEAR : INTER_LINEAR_EXACT);
 
     return dst;
 }

modules/ximgproc/test/test_guided_filter.cpp

@@ -99,7 +99,7 @@ static Mat convertTypeAndSize(Mat src, int dstType, Size dstSize)
     }
 
     dst.convertTo(dst, dstType);
-    resize(dst, dst, dstSize);
+    resize(dst, dst, dstSize, 0, 0, INTER_LINEAR_EXACT);
 
     return dst;
 }

modules/ximgproc/test/test_joint_bilateral_filter.cpp

@@ -83,7 +83,7 @@ static Mat convertTypeAndSize(Mat src, int dstType, Size dstSize)
     }
 
     dst.convertTo(dst, dstType);
-    resize(dst, dst, dstSize);
+    resize(dst, dst, dstSize, 0, 0, INTER_LINEAR_EXACT);
 
     return dst;
 }

modules/ximgproc/test/test_rolling_guidance_filter.cpp

@@ -81,7 +81,7 @@ static Mat convertTypeAndSize(Mat src, int dstType, Size dstSize)
     }
 
     dst.convertTo(dst, dstType);
-    resize(dst, dst, dstSize);
+    resize(dst, dst, dstSize, 0, 0, INTER_LINEAR_EXACT);
 
     return dst;
 }

modules/xobjdetect/src/waldboost.cpp

@@ -130,7 +130,7 @@ void WaldBoost::detect(Ptr<CvFeatureEvaluator> eval,
     float h;
     for (size_t i = 0; i < scales.size(); ++i) {
         float scale = scales[i];
-        resize(img, resized_img, Size(), scale, scale);
+        resize(img, resized_img, Size(), scale, scale, INTER_LINEAR_EXACT);
         eval->setImage(resized_img, 0, 0, feature_indices_);
         int n_rows = (int)(24 / scale);
         int n_cols = (int)(24 / scale);
@@ -162,7 +162,7 @@ void WaldBoost::detect(Ptr<CvFeatureEvaluator> eval,
     float h;
     for (size_t i = 0; i < scales.size(); ++i) {
         float scale = scales[i];
-        resize(img, resized_img, Size(), scale, scale);
+        resize(img, resized_img, Size(), scale, scale, INTER_LINEAR_EXACT);
         eval->setImage(resized_img, 0, 0, feature_indices_);
         int n_rows = (int)(24 / scale);
         int n_cols = (int)(24 / scale);

modules/xobjdetect/src/wbdetector.cpp

@@ -179,7 +179,7 @@ void WBDetectorImpl::train(
                 win_count = min(win_count, stage_neg - bootstrap_count);
                 Mat window;
                 for (int k = 0; k < win_count; ++k) {
-                    resize(img(bboxes[indices(k, 0)]), window, Size(24, 24));
+                    resize(img(bboxes[indices(k, 0)]), window, Size(24, 24), 0, 0, INTER_LINEAR_EXACT);
                     neg_imgs.push_back(window.clone());
                     bootstrap_count += 1;
                 }

modules/xphoto/perf/perf_learning_based_color_balance.cpp

@@ -64,7 +64,7 @@ PERF_TEST_P(learningBasedWBPerfTest, perf, Combine(SZ_ALL_HD, Values(CV_8UC3, CV
     Mat src_dscl(Size(size.width / 16, size.height / 16), t);
     RNG rng(1234);
     rng.fill(src_dscl, RNG::UNIFORM, 0, range_max_val);
-    resize(src_dscl, src, src.size());
+    resize(src_dscl, src, src.size(), 0, 0, INTER_LINEAR_EXACT);
     Ptr<xphoto::LearningBasedWB> wb = xphoto::createLearningBasedWB();
     wb->setRangeMaxVal(range_max_val);
     wb->setSaturationThreshold(0.98f);