vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

2026-08-22 00:10:33 +08:00
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+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+#include "npy_blob.hpp"
+#include <opencv2/dnn/shape_utils.hpp>
+#include <set>
+
+namespace opencv_test { namespace {
+
+template<typename TString>
+static std::string _tf(TString filename)
+{
+    return findDataFile(std::string("dnn/") + filename);
+}
+
+class Test_Caffe_nets : public DNNTestLayer
+{
+public:
+    void testFaster(const std::string& proto, const std::string& model, const Mat& ref,
+                    double scoreDiff = 0.0, double iouDiff = 0.0)
+    {
+        checkBackend();
+        Net net = readNet(findDataFile("dnn/" + proto),
+                          findDataFile("dnn/" + model, false));
+        net.setPreferableBackend(backend);
+        net.setPreferableTarget(target);
+
+        if (target == DNN_TARGET_CPU_FP16)
+            net.enableWinograd(false);
+
+        Mat img = imread(findDataFile("dnn/dog416.png"));
+        resize(img, img, Size(800, 600));
+        Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
+        Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);
+
+        net.setInput(blob);
+        net.setInput(imInfo, "im_info");
+        // Output has shape 1x1xNx7 where N - number of detections.
+        // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
+        Mat out = net.forward();
+        scoreDiff = scoreDiff ? scoreDiff : default_l1;
+        iouDiff = iouDiff ? iouDiff : default_lInf;
+        normAssertDetections(ref, out, ("model name: " + model).c_str(), 0.8, scoreDiff, iouDiff);
+    }
+};
+
+TEST(Reproducibility_SSD, Accuracy)
+{
+    applyTestTag(
+        CV_TEST_TAG_MEMORY_512MB,
+        CV_TEST_TAG_DEBUG_VERYLONG
+    );
+
+    // The classic engine importer no longer carries the Caffe-SSD specific
+    // handling (LpNormalization/DetectionOutput); this model is supported on
+    // the new engine only.
+    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;
+    }
+
+    Net net = readNetFromONNX(findDataFile("dnn/onnx/models/ssd_vgg16.onnx", false));
+    ASSERT_FALSE(net.empty());
+    net.setPreferableBackend(DNN_BACKEND_OPENCV);
+
+    Mat sample = imread(_tf("street.png"));
+    ASSERT_TRUE(!sample.empty());
+
+    if (sample.channels() == 4)
+        cvtColor(sample, sample, COLOR_BGRA2BGR);
+
+    Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
+    net.setInput(in_blob);
+
+    Mat out = net.forward();
+
+    Mat ref = blobFromNPY(_tf("ssd_out.npy"));
+    normAssertDetections(ref, out, "", 0.06, 1e-4, 0.18);
+}
+
+TEST(Test_Caffe, multiple_inputs)
+{
+    const string model = findDataFile("dnn/layers/net_input.onnx");
+    Net net = readNetFromONNX(model);
+    net.setPreferableBackend(DNN_BACKEND_OPENCV);
+
+    Mat first_image(10, 11, CV_32FC3);
+    Mat second_image(10, 11, CV_32FC3);
+    randu(first_image, -1, 1);
+    randu(second_image, -1, 1);
+
+    first_image = blobFromImage(first_image);
+    second_image = blobFromImage(second_image);
+
+    Mat first_image_blue_green = slice(first_image, Range::all(), Range(0, 2), Range::all(), Range::all());
+    Mat first_image_red = slice(first_image, Range::all(), Range(2, 3), Range::all(), Range::all());
+    Mat second_image_blue_green = slice(second_image, Range::all(), Range(0, 2), Range::all(), Range::all());
+    Mat second_image_red = slice(second_image, Range::all(), Range(2, 3), Range::all(), Range::all());
+
+    net.setInput(first_image_blue_green, "old_style_input_blue_green");
+    net.setInput(first_image_red, "different_name_for_red");
+    net.setInput(second_image_blue_green, "input_layer_blue_green");
+    net.setInput(second_image_red, "old_style_input_red");
+    Mat out = net.forward();
+
+    normAssert(out, first_image + second_image);
+}
+
+INSTANTIATE_TEST_CASE_P(/**/, Test_Caffe_nets, dnnBackendsAndTargets());
+
+}} // namespace