corrected tests for the new segmentation way of inference (initialize and segment anything interfaces changed) and added one more test to check the image dimensions W,H

Author: IasonTheodorou

test/sam_test.cpp

@@ -28,13 +28,14 @@ class SamInferenceTest : public ::testing::Test
         NonSquareImgSize = { testImage_800x600.cols, testImage_800x600.rows };
 
         // Use package helpers to build default params and SAM objects.
-        std::tie(samSegmentors, params_encoder, params_decoder) = Initializer();
+        std::tie(samSegmentors, params_encoder, params_decoder, res, resSam) = Initializer();
 
 #ifdef USE_CUDA
         params_encoder.cudaEnable = true;  // Enable CUDA if compiled with it
 #else
         params_encoder.cudaEnable = false; // Otherwise run on CPU
 #endif
+
     }
 
     // Clean up the SAM objects after each test.
@@ -46,6 +47,8 @@ class SamInferenceTest : public ::testing::Test
     std::vector<int> NonSquareImgSize;
     std::vector<std::unique_ptr<SAM>> samSegmentors;
     SEG::DL_INIT_PARAM params_encoder, params_decoder;
+    SEG::DL_RESULT res;
+    std::vector<SEG::DL_RESULT> resSam;
 };
 
 // Simple smoke test: we can construct a SAM object without throwing.
@@ -84,8 +87,8 @@ TEST_F(SamInferenceTest, FullInferencePipeline)
         GTEST_SKIP() << "Models not found in build dir";
     }
 
-    auto masks = SegmentAnything(samSegmentors, params_encoder, params_decoder, testImage_realistic);
+    SegmentAnything(samSegmentors, params_encoder, params_decoder, testImage_realistic, resSam, res);
 
     // We only check that a vector is returned. (You can strengthen this to EXPECT_FALSE(masks.empty()).)
-    EXPECT_TRUE(masks.size() >= 0) << "Masks should be a valid output vector";
+    EXPECT_TRUE(res.masks.size() >= 0) << "Masks should be a valid output vector";
 }

test/test_utils.cpp

@@ -66,6 +66,22 @@ TEST_F(UtilsTest, PreprocessTopLeftPaddingAndAspect) {
     }
 }
 
+// Explicitly ensure imgSize is interpreted as [W, H] in PreProcess for non-square targets.
+TEST_F(UtilsTest, PreprocessNonSquareWidthHeightOrder) {
+    // Input image: H=300, W=500
+    cv::Mat img(300, 500, CV_8UC3, cv::Scalar(5, 6, 7));
+
+    // Target canvas (W,H) with non-square dims
+    std::vector<int> target{640, 480};
+    cv::Mat out;
+
+    ASSERT_EQ(u.PreProcess(img, target, out), nullptr);
+    // cols = width, rows = height
+    EXPECT_EQ(out.cols, target[0]);
+    EXPECT_EQ(out.rows, target[1]);
+    EXPECT_EQ(out.size(), cv::Size(target[0], target[1]));
+}
+
 // Parameterized fixture: used with TEST_P to run the same test body
 // for many (input size, target size) pairs.
 class UtilsPreprocessParamTest