vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

modules/dnn/test/test_nms.cpp

@@ -0,0 +1,136 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2017, Intel Corporation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#include "test_precomp.hpp"
+#include "npy_blob.hpp"
+
+namespace opencv_test { namespace {
+
+TEST(NMS, Accuracy)
+{
+    //reference results obtained using tf.image.non_max_suppression with iou_threshold=0.5
+    std::string dataPath = findDataFile("dnn/nms_reference.yml");
+    FileStorage fs(dataPath, FileStorage::READ);
+
+    std::vector<Rect> bboxes;
+    std::vector<float> scores;
+    std::vector<int> ref_indices;
+
+    fs["boxes"] >> bboxes;
+    fs["probs"] >> scores;
+    fs["output"] >> ref_indices;
+
+    const float nms_thresh = .5f;
+    const float score_thresh = .01f;
+    std::vector<int> indices;
+    cv::dnn::NMSBoxes(bboxes, scores, score_thresh, nms_thresh, indices);
+
+    ASSERT_EQ(ref_indices.size(), indices.size());
+
+    std::sort(indices.begin(), indices.end());
+    std::sort(ref_indices.begin(), ref_indices.end());
+
+    for(size_t i = 0; i < indices.size(); i++)
+        ASSERT_EQ(indices[i], ref_indices[i]);
+}
+
+TEST(BatchedNMS, Accuracy)
+{
+    //reference results obtained using tf.image.non_max_suppression with iou_threshold=0.5
+    std::string dataPath = findDataFile("dnn/batched_nms_reference.yml");
+    FileStorage fs(dataPath, FileStorage::READ);
+
+    std::vector<Rect> bboxes;
+    std::vector<float> scores;
+    std::vector<int> idxs;
+    std::vector<int> ref_indices;
+
+    fs["boxes"] >> bboxes;
+    fs["probs"] >> scores;
+    fs["idxs"] >> idxs;
+    fs["output"] >> ref_indices;
+
+    const float nms_thresh = .5f;
+    const float score_thresh = .05f;
+    std::vector<int> indices;
+    cv::dnn::NMSBoxesBatched(bboxes, scores, idxs, score_thresh, nms_thresh, indices);
+
+    ASSERT_EQ(ref_indices.size(), indices.size());
+
+    std::sort(indices.begin(), indices.end());
+    std::sort(ref_indices.begin(), ref_indices.end());
+
+    for(size_t i = 0; i < indices.size(); i++)
+        ASSERT_EQ(indices[i], ref_indices[i]);
+}
+
+TEST(SoftNMS, Accuracy)
+{
+    //reference results are obtained using TF v2.7 tf.image.non_max_suppression_with_scores
+    std::string dataPath = findDataFile("dnn/soft_nms_reference.yml");
+    FileStorage fs(dataPath, FileStorage::READ);
+
+    std::vector<Rect> bboxes;
+    std::vector<float> scores;
+    std::vector<int> ref_indices;
+    std::vector<float> ref_updated_scores;
+
+    fs["boxes"] >> bboxes;
+    fs["probs"] >> scores;
+    fs["indices"] >> ref_indices;
+    fs["updated_scores"] >> ref_updated_scores;
+
+    std::vector<float> updated_scores;
+    const float score_thresh = .01f;
+    const float nms_thresh = .5f;
+    std::vector<int> indices;
+    const size_t top_k = 0;
+    const float sigma = 1.; // sigma in TF is being multiplied by 2, so 0.5 should be passed there
+    cv::dnn::softNMSBoxes(bboxes, scores, updated_scores, score_thresh, nms_thresh, indices, top_k, sigma);
+
+    ASSERT_EQ(ref_indices.size(), indices.size());
+    for(size_t i = 0; i < indices.size(); i++)
+    {
+        ASSERT_EQ(indices[i], ref_indices[i]);
+    }
+
+    ASSERT_EQ(ref_updated_scores.size(), updated_scores.size());
+    for(size_t i = 0; i < updated_scores.size(); i++)
+    {
+        EXPECT_NEAR(updated_scores[i], ref_updated_scores[i], 1e-7);
+    }
+}
+
+// Test NMS -> Reshape with zero detections using ONNX model.
+// NMS with dynamic output shapes is only supported by the new engine.
+TEST(NMS, ZeroDetections_Reshape)
+{
+    auto engine_forced = static_cast<cv::dnn::EngineType>(
+        cv::utils::getConfigurationParameterSizeT("OPENCV_FORCE_DNN_ENGINE", cv::dnn::ENGINE_AUTO));
+    if (engine_forced == cv::dnn::ENGINE_CLASSIC)
+    {
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_PARSER);
+        return;
+    }
+
+    std::string onnxmodel = findDataFile("dnn/onnx/models/nms_reshape_empty.onnx");
+    cv::dnn::Net net = cv::dnn::readNetFromONNX(onnxmodel);
+    ASSERT_FALSE(net.empty());
+
+    Mat boxes = blobFromNPY(findDataFile("dnn/onnx/data/input_nms_reshape_empty_0.npy"));
+    Mat scores = blobFromNPY(findDataFile("dnn/onnx/data/input_nms_reshape_empty_1.npy"));
+    net.setInput(boxes, "boxes");
+    net.setInput(scores, "scores");
+
+    std::vector<Mat> outs;
+    net.forward(outs, std::vector<String>{"output"});
+    ASSERT_EQ(outs.size(), (size_t)1);
+    Mat ref = blobFromNPY(findDataFile("dnn/onnx/data/output_nms_reshape_empty.npy"));
+    normAssert(ref, outs[0], "NMS_ZeroDetections_Reshape");
+}
+
+}} // namespace