chore: import upstream snapshot with attribution

2026-07-13 12:06:04 +08:00
commit 86c9b1c39f
7743 changed files with 3316339 additions and 0 deletions
@@ -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);
+        }
+}