Merge pull request #21692 from UnaNancyOwen:add_softmax

* add apply softmax option to ClassificationModel

* remove default arguments of ClassificationModel::setSoftMax()

* fix build for python

* fix docs warning for setSoftMax()

* add impl for ClassficationModel()

* fix failed build for docs by trailing whitespace

* move to implement classify() to ClassificationModel_Impl

* move to implement softmax() to ClassificationModel_Impl

* remove softmax from public method in ClassificationModel

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -1310,6 +1310,9 @@ CV__DNN_INLINE_NS_BEGIN
      class CV_EXPORTS_W_SIMPLE ClassificationModel : public Model
      {
      public:
+         CV_DEPRECATED_EXTERNAL  // avoid using in C++ code, will be moved to "protected" (need to fix bindings first)
+         ClassificationModel();
+
          /**
           * @brief Create classification model from network represented in one of the supported formats.
           * An order of @p model and @p config arguments does not matter.
@@ -1324,6 +1327,24 @@ CV__DNN_INLINE_NS_BEGIN
           */
          CV_WRAP ClassificationModel(const Net& network);
 
+         /**
+          * @brief Set enable/disable softmax post processing option.
+          *
+          * If this option is true, softmax is applied after forward inference within the classify() function
+          * to convert the confidences range to [0.0-1.0].
+          * This function allows you to toggle this behavior.
+          * Please turn true when not contain softmax layer in model.
+          * @param[in] enable Set enable softmax post processing within the classify() function.
+          */
+         CV_WRAP ClassificationModel& setEnableSoftmaxPostProcessing(bool enable);
+
+         /**
+          * @brief Get enable/disable softmax post processing option.
+          *
+          * This option defaults to false, softmax post processing is not applied within the classify() function.
+          */
+         CV_WRAP bool getEnableSoftmaxPostProcessing() const;
+
          /** @brief Given the @p input frame, create input blob, run net and return top-1 prediction.
           *  @param[in]  frame  The input image.
           */

modules/dnn/src/model.cpp

@@ -197,28 +197,95 @@ void Model::predict(InputArray frame, OutputArrayOfArrays outs) const
 }
 
 
+class ClassificationModel_Impl : public Model::Impl
+{
+public:
+    virtual ~ClassificationModel_Impl() {}
+    ClassificationModel_Impl() : Impl() {}
+    ClassificationModel_Impl(const ClassificationModel_Impl&) = delete;
+    ClassificationModel_Impl(ClassificationModel_Impl&&) = delete;
+
+    void setEnableSoftmaxPostProcessing(bool enable)
+    {
+        applySoftmax = enable;
+    }
+
+    bool getEnableSoftmaxPostProcessing() const
+    {
+        return applySoftmax;
+    }
+
+    std::pair<int, float> classify(InputArray frame)
+    {
+        std::vector<Mat> outs;
+        processFrame(frame, outs);
+        CV_Assert(outs.size() == 1);
+
+        Mat out = outs[0].reshape(1, 1);
+
+        if(getEnableSoftmaxPostProcessing())
+        {
+            softmax(out, out);
+        }
+
+        double conf;
+        Point maxLoc;
+        cv::minMaxLoc(out, nullptr, &conf, nullptr, &maxLoc);
+        return {maxLoc.x, static_cast<float>(conf)};
+    }
+
+protected:
+    void softmax(InputArray inblob, OutputArray outblob)
+    {
+        const Mat input = inblob.getMat();
+        outblob.create(inblob.size(), inblob.type());
+
+        Mat exp;
+        const float max = *std::max_element(input.begin<float>(), input.end<float>());
+        cv::exp((input - max), exp);
+        outblob.getMat() = exp / cv::sum(exp)[0];
+    }
+
+protected:
+    bool applySoftmax = false;
+};
+
+ClassificationModel::ClassificationModel()
+    : Model()
+{
+    // nothing
+}
+
 ClassificationModel::ClassificationModel(const String& model, const String& config)
-    : Model(model, config)
+    : ClassificationModel(readNet(model, config))
 {
     // nothing
 }
 
 ClassificationModel::ClassificationModel(const Net& network)
-    : Model(network)
+    : Model()
 {
-    // nothing
+    impl = makePtr<ClassificationModel_Impl>();
+    impl->initNet(network);
 }
 
-std::pair<int, float> ClassificationModel::classify(InputArray frame)
+ClassificationModel& ClassificationModel::setEnableSoftmaxPostProcessing(bool enable)
 {
-    std::vector<Mat> outs;
-    impl->processFrame(frame, outs);
-    CV_Assert(outs.size() == 1);
+    CV_Assert(impl != nullptr && impl.dynamicCast<ClassificationModel_Impl>() != nullptr);
+    impl.dynamicCast<ClassificationModel_Impl>()->setEnableSoftmaxPostProcessing(enable);
+    return *this;
+}
 
-    double conf;
-    cv::Point maxLoc;
-    minMaxLoc(outs[0].reshape(1, 1), nullptr, &conf, nullptr, &maxLoc);
-    return {maxLoc.x, static_cast<float>(conf)};
+bool ClassificationModel::getEnableSoftmaxPostProcessing() const
+{
+    CV_Assert(impl != nullptr && impl.dynamicCast<ClassificationModel_Impl>() != nullptr);
+    return impl.dynamicCast<ClassificationModel_Impl>()->getEnableSoftmaxPostProcessing();
+}
+
+std::pair<int, float> ClassificationModel::classify(InputArray frame)
+{
+    CV_Assert(impl != nullptr && impl.dynamicCast<ClassificationModel_Impl>() != nullptr);
+    return impl.dynamicCast<ClassificationModel_Impl>()->classify(frame);
 }
 
 void ClassificationModel::classify(InputArray frame, int& classId, float& conf)