Merge pull request #27228 from utibenkei:fix_java_enum_wrapper

utibenkei · web-flow · commit 97f73ba0b544 · 2025-04-21T20:51:38.000+03:00
Explicitly specify enum type scopes to improve Java wrapper generation #27228 Changed DataLayout and ImagePaddingMode to dnn::DataLayout and dnn::ImagePaddingMode to explicitly specify their scopes. This allows gen_java.py to correctly register disc_type, preventing constructors and methods using these enum types from being skipped during Java wrapper generation. Similarly updated QRCodeEncoder::CorrectionLevel and QRCodeEncoder::EncodeMode with explicit scope declarations. Also added a new Java test class `DnnBlobFromImageWithParamsTest` based on: https://github.com/opencv/opencv/blob/4.x/modules/dnn/test/test_misc.cpp#L133-L243 Related issues #23753 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
diff --git a/modules/dnn/include/opencv2/dnn/dnn.hpp b/modules/dnn/include/opencv2/dnn/dnn.hpp
@@ -1219,16 +1219,16 @@ CV__DNN_INLINE_NS_BEGIN
     {
         CV_WRAP Image2BlobParams();
         CV_WRAP Image2BlobParams(const Scalar& scalefactor, const Size& size = Size(), const Scalar& mean = Scalar(),
-                            bool swapRB = false, int ddepth = CV_32F, DataLayout datalayout = DNN_LAYOUT_NCHW,
-                            ImagePaddingMode mode = DNN_PMODE_NULL, Scalar borderValue = 0.0);
+                            bool swapRB = false, int ddepth = CV_32F, dnn::DataLayout datalayout = DNN_LAYOUT_NCHW,
+                            ImagePaddingMode mode = dnn::DNN_PMODE_NULL, Scalar borderValue = 0.0);
 
         CV_PROP_RW Scalar scalefactor; //!< scalefactor multiplier for input image values.
         CV_PROP_RW Size size;    //!< Spatial size for output image.
         CV_PROP_RW Scalar mean;  //!< Scalar with mean values which are subtracted from channels.
         CV_PROP_RW bool swapRB;  //!< Flag which indicates that swap first and last channels
         CV_PROP_RW int ddepth;   //!< Depth of output blob. Choose CV_32F or CV_8U.
-        CV_PROP_RW DataLayout datalayout; //!< Order of output dimensions. Choose DNN_LAYOUT_NCHW or DNN_LAYOUT_NHWC.
-        CV_PROP_RW ImagePaddingMode paddingmode;   //!< Image padding mode. @see ImagePaddingMode.
+        CV_PROP_RW dnn::DataLayout datalayout; //!< Order of output dimensions. Choose DNN_LAYOUT_NCHW or DNN_LAYOUT_NHWC.
+        CV_PROP_RW dnn::ImagePaddingMode paddingmode;   //!< Image padding mode. @see ImagePaddingMode.
         CV_PROP_RW Scalar borderValue;   //!< Value used in padding mode for padding.
 
         /** @brief Get rectangle coordinates in original image system from rectangle in blob coordinates.
diff --git a/modules/dnn/misc/java/test/DnnBlobFromImageWithParamsTest.java b/modules/dnn/misc/java/test/DnnBlobFromImageWithParamsTest.java
@@ -0,0 +1,134 @@
+package org.opencv.test.dnn;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.Scalar;
+import org.opencv.core.Size;
+import org.opencv.core.Range;
+import org.opencv.dnn.Dnn;
+import org.opencv.dnn.Image2BlobParams;
+import org.opencv.test.OpenCVTestCase;
+
+public class DnnBlobFromImageWithParamsTest extends OpenCVTestCase {
+
+        public void testBlobFromImageWithParamsNHWCScalarScale()
+        {
+            Mat img = new Mat(10, 10, CvType.CV_8UC4, new Scalar(0, 1, 2, 3));
+            Scalar scalefactor = new Scalar(0.1, 0.2, 0.3, 0.4);
+
+            Image2BlobParams params = new Image2BlobParams();
+            params.set_scalefactor(scalefactor);
+            params.set_datalayout(Dnn.DNN_LAYOUT_NHWC);
+
+            Mat blob = Dnn.blobFromImageWithParams(img, params); // [1, 10, 10, 4]
+
+            float[] expectedValues = { (float)scalefactor.val[0] * 0, (float)scalefactor.val[1] * 1, (float)scalefactor.val[2] * 2, (float)scalefactor.val[3] * 3 }; // Target Value.
+            for (int h = 0; h < 10; h++)
+            {
+                for (int w = 0; w < 10; w++)
+                {
+                      float[] actualValues = new float[4];
+                      blob.get(new int[]{0, h, w, 0}, actualValues);
+                      for (int c = 0; c < 4; c++)
+                      {
+                          // Check equal
+                          assertEquals(expectedValues[c], actualValues[c]);
+                      }
+                }
+            }
+        }
+
+        public void testBlobFromImageWithParamsCustomPaddingLetterBox()
+        {
+            Mat img = new Mat(40, 20, CvType.CV_8UC4, new Scalar(0, 1, 2, 3));
+
+            // Custom padding value that you have added
+            Scalar customPaddingValue = new Scalar(5, 6, 7, 8); // Example padding value
+            Size targetSize = new Size(20, 20);
+
+            Mat targetImg = img.clone();
+            Core.copyMakeBorder(targetImg, targetImg, 0, 0, (int)targetSize.width / 2, (int)targetSize.width / 2, Core.BORDER_CONSTANT, customPaddingValue);
+
+            // Set up Image2BlobParams with your new functionality
+            Image2BlobParams params = new Image2BlobParams();
+            params.set_size(targetSize);
+            params.set_paddingmode(Dnn.DNN_PMODE_LETTERBOX);
+            params.set_borderValue(customPaddingValue); // Use your new feature here
+
+            // Create blob with custom padding
+            Mat blob = Dnn.blobFromImageWithParams(img, params);
+
+            // Create target blob for comparison
+            Mat targetBlob = Dnn.blobFromImage(targetImg, 1.0, targetSize);
+
+            assertEquals(0, Core.norm(targetBlob, blob, Core.NORM_INF), EPS);
+        }
+
+        public void testBlobFromImageWithParams4chLetterBox()
+        {
+            Mat img = new Mat(40, 20, CvType.CV_8UC4, new Scalar(0, 1, 2, 3));
+
+            // Construct target mat.
+            Mat[] targetChannels = new Mat[4];
+
+            // The letterbox will add zero at the left and right of output blob.
+            // After the letterbox, every row data would have same value showing as valVec.
+            byte[] valVec = { 0,0,0,0,0, 1,1,1,1,1,1,1,1,1,1, 0,0,0,0,0};
+
+            Mat rowM = new Mat(1, 20, CvType.CV_8UC1);
+            rowM.put(0, 0, valVec);
+            for (int i = 0; i < 4; i++) {
+                Core.multiply(rowM, new Scalar(i), targetChannels[i] = new Mat());
+            }
+
+            Mat targetImg = new Mat();
+            Core.merge(Arrays.asList(targetChannels), targetImg);
+            Size targetSize = new Size(20, 20);
+
+            Image2BlobParams params = new Image2BlobParams();
+            params.set_size(targetSize);
+            params.set_paddingmode(Dnn.DNN_PMODE_LETTERBOX);
+            Mat blob = Dnn.blobFromImageWithParams(img, params);
+            Mat targetBlob = Dnn.blobFromImage(targetImg, 1.0, targetSize); // only convert data from uint8 to float32.
+
+            assertEquals(0, Core.norm(targetBlob, blob, Core.NORM_INF), EPS);
+        }
+
+        public void testBlobFromImageWithParams4chMultiImage()
+        {
+            Mat img = new Mat(10, 10, CvType.CV_8UC4, new Scalar(0, 1, 2, 3));
+
+            Scalar scalefactor = new Scalar(0.1, 0.2, 0.3, 0.4);
+
+            Image2BlobParams param = new Image2BlobParams();
+            param.set_scalefactor(scalefactor);
+            param.set_datalayout(Dnn.DNN_LAYOUT_NHWC);
+
+            List<Mat> images = new ArrayList<>();
+            images.add(img);
+            Mat img2 = new Mat();
+            Core.multiply(img, Scalar.all(2), img2);
+            images.add(img2);
+
+            Mat blobs = Dnn.blobFromImagesWithParams(images, param);
+
+            Range[] ranges = new Range[4];
+            ranges[0] = new Range(0, 1);
+            ranges[1] = new Range(0, blobs.size(1));
+            ranges[2] = new Range(0, blobs.size(2));
+            ranges[3] = new Range(0, blobs.size(3));
+
+            Mat blob0 = blobs.submat(ranges).clone();
+
+            ranges[0] = new Range(1, 2);
+            Mat blob1 = blobs.submat(ranges).clone();
+
+            Core.multiply(blob0, Scalar.all(2), blob0);
+
+            assertEquals(0, Core.norm(blob0, blob1, Core.NORM_INF), EPS);
+        }
+}
diff --git a/modules/objdetect/include/opencv2/objdetect.hpp b/modules/objdetect/include/opencv2/objdetect.hpp
@@ -741,10 +741,10 @@ class CV_EXPORTS_W QRCodeEncoder {
         CV_PROP_RW int version;
 
         //! The optional level of error correction (by default - the lowest).
-        CV_PROP_RW CorrectionLevel correction_level;
+        CV_PROP_RW QRCodeEncoder::CorrectionLevel correction_level;
 
         //! The optional encoding mode - Numeric, Alphanumeric, Byte, Kanji, ECI or Structured Append.
-        CV_PROP_RW EncodeMode mode;
+        CV_PROP_RW QRCodeEncoder::EncodeMode mode;
 
         //! The optional number of QR codes to generate in Structured Append mode.
         CV_PROP_RW int structure_number;
