opencv/modules/dnn/test/test_nms.cpp

// 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