fix legacy contants

modules/cvv/src/qtutil/filter/sobelfilterwidget.cpp

@@ -35,7 +35,7 @@ SobelFilterWidget::SobelFilterWidget(QWidget *parent)
 	ksize_->addItem("3");
 	ksize_->addItem("5");
 	ksize_->addItem("7");
-	ksize_->addItem("CV_SCHARR(-1)");
+	ksize_->addItem("FILTER_SCHARR(-1)");
 	ksize_->setCurrentIndex(1);
 
 	borderType_->addItem("BORDER_DEFAULT");
@@ -133,7 +133,7 @@ void SobelFilterWidget::applyFilter(InputArray in, OutputArray out) const
 		ksize = 7;
 		break;
 	case 4:
-		ksize = CV_SCHARR;
+		ksize = FILTER_SCHARR;
 		break;
 	}
 
@@ -258,15 +258,15 @@ std::pair<bool, QString> SobelFilterWidget::checkInput(InputArray in) const
 		ksize = 7;
 		break;
 	case 4:
-		ksize = CV_SCHARR;
+		ksize = FILTER_SCHARR;
 		break;
 	}
 
-	if (ksize == CV_SCHARR)
+	if (ksize == FILTER_SCHARR)
 	{
 		if (dx + dy != 1)
 		{
-			return { false, "ksize=CV_SCHARR but dx+dy != 1" };
+			return { false, "ksize=FILTER_SCHARR but dx+dy != 1" };
 		}
 	}
 	else

modules/face/include/opencv2/face.hpp

@@ -131,7 +131,7 @@ If you've set the threshold to 0.0 as we did above, then:
 
 @code
 //
-Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
+Mat img = imread("person1/3.jpg", IMREAD_GRAYSCALE);
 // Get a prediction from the model. Note: We've set a threshold of 0.0 above,
 // since the distance is almost always larger than 0.0, you'll get -1 as
 // label, which indicates, this face is unknown
@@ -176,13 +176,13 @@ public:
     vector<Mat> images;
     vector<int> labels;
     // images for first person
-    images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
-    images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
-    images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
+    images.push_back(imread("person0/0.jpg", IMREAD_GRAYSCALE)); labels.push_back(0);
+    images.push_back(imread("person0/1.jpg", IMREAD_GRAYSCALE)); labels.push_back(0);
+    images.push_back(imread("person0/2.jpg", IMREAD_GRAYSCALE)); labels.push_back(0);
     // images for second person
-    images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
-    images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
-    images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
+    images.push_back(imread("person1/0.jpg", IMREAD_GRAYSCALE)); labels.push_back(1);
+    images.push_back(imread("person1/1.jpg", IMREAD_GRAYSCALE)); labels.push_back(1);
+    images.push_back(imread("person1/2.jpg", IMREAD_GRAYSCALE)); labels.push_back(1);
     @endcode
 
     Now that you have read some images, we can create a new FaceRecognizer. In this example I'll create
@@ -275,7 +275,7 @@ public:
     // Do your initialization here (create the cv::FaceRecognizer model) ...
     // ...
     // Read in a sample image:
-    Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
+    Mat img = imread("person1/3.jpg", IMREAD_GRAYSCALE);
     // And get a prediction from the cv::FaceRecognizer:
     int predicted = model->predict(img);
     @endcode
@@ -286,7 +286,7 @@ public:
     using namespace cv;
     // Do your initialization here (create the cv::FaceRecognizer model) ...
     // ...
-    Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
+    Mat img = imread("person1/3.jpg", IMREAD_GRAYSCALE);
     // Some variables for the predicted label and associated confidence (e.g. distance):
     int predicted_label = -1;
     double predicted_confidence = 0.0;

modules/xobjdetect/src/wbdetector.cpp

@@ -63,7 +63,7 @@ static vector<Mat> sample_patches(
     vector<Mat> patches;
     size_t patch_count = 0;
     for (size_t i = 0; i < filenames.size(); ++i) {
-        Mat img = imread(filenames[i], CV_LOAD_IMAGE_GRAYSCALE);
+        Mat img = imread(filenames[i], IMREAD_GRAYSCALE);
         for (int row = 0; row + n_rows < img.rows; row += n_rows) {
             for (int col = 0; col + n_cols < img.cols; col += n_cols) {
                 patches.push_back(img(Rect(col, row, n_cols, n_rows)).clone());
@@ -84,7 +84,7 @@ static vector<Mat> read_imgs(const string& path)
     glob(path, filenames);
     vector<Mat> imgs;
     for (size_t i = 0; i < filenames.size(); ++i) {
-        imgs.push_back(imread(filenames[i], CV_LOAD_IMAGE_GRAYSCALE));
+        imgs.push_back(imread(filenames[i], IMREAD_GRAYSCALE));
     }
     return imgs;
 }
@@ -165,7 +165,7 @@ void WBDetectorImpl::train(
         for (; img_i < neg_filenames.size(); ++img_i) {
             cerr << "win " << bootstrap_count << "/" << stage_neg
                  << " img " << (img_i + 1) << "/" << neg_filenames.size() << "\r";
-            Mat img = imread(neg_filenames[img_i], CV_LOAD_IMAGE_GRAYSCALE);
+            Mat img = imread(neg_filenames[img_i], IMREAD_GRAYSCALE);
             vector<Rect> bboxes;
             Mat1f confidences;
             boost_.detect(eval, img, scales, bboxes, confidences);

modules/xobjdetect/tools/waldboost_detector/waldboost_detector.cpp

@@ -27,7 +27,7 @@ int main(int argc, char **argv)
         assert(argc == 6);
         vector<Rect> bboxes;
         vector<double> confidences;
-        Mat img = imread(argv[3], CV_LOAD_IMAGE_GRAYSCALE);
+        Mat img = imread(argv[3], IMREAD_GRAYSCALE);
         FileStorage fs(argv[2], FileStorage::READ);
         detector->read(fs.getFirstTopLevelNode());
         detector->detect(img, bboxes, confidences);