Add preprocessing warps for separate parameters

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

@@ -999,9 +999,14 @@ CV__DNN_INLINE_NS_BEGIN
       * Model creates net from file with trained weights and config,
       * sets preprocessing input and runs forward pass.
       */
-     class CV_EXPORTS_W Model : public Net
+     class CV_EXPORTS_W_SIMPLE Model : public Net
      {
      public:
+         /**
+          * @brief Default constructor.
+          */
+         Model();
+
          /**
           * @brief Create model from deep learning network represented in one of the supported formats.
           * An order of @p model and @p config arguments does not matter.
@@ -1020,7 +1025,7 @@ CV__DNN_INLINE_NS_BEGIN
           *  @param[in] size New input size.
           *  @note If shape of the new blob less than 0, then frame size not change.
          */
-         Model& setInputSize(const Size& size);
+         CV_WRAP Model& setInputSize(const Size& size);
 
          /** @brief Set input size for frame.
          *  @param[in] width New input width.
@@ -1028,27 +1033,27 @@ CV__DNN_INLINE_NS_BEGIN
          *  @note If shape of the new blob less than 0,
          *  then frame size not change.
          */
-         Model& setInputSize(int width, int height);
+         CV_WRAP Model& setInputSize(int width, int height);
 
          /** @brief Set mean value for frame.
           *  @param[in] mean Scalar with mean values which are subtracted from channels.
          */
-         Model& setInputMean(const Scalar& mean);
+         CV_WRAP Model& setInputMean(const Scalar& mean);
 
          /** @brief Set scalefactor value for frame.
           *  @param[in] scale Multiplier for frame values.
          */
-         Model& setInputScale(double scale);
+         CV_WRAP Model& setInputScale(double scale);
 
          /** @brief Set flag crop for frame.
           *  @param[in] crop Flag which indicates whether image will be cropped after resize or not.
          */
-         Model& setInputCrop(bool crop);
+         CV_WRAP Model& setInputCrop(bool crop);
 
          /** @brief Set flag swapRB for frame.
           *  @param[in] swapRB Flag which indicates that swap first and last channels.
          */
-         Model& setInputSwapRB(bool swapRB);
+         CV_WRAP Model& setInputSwapRB(bool swapRB);
 
          /** @brief Set preprocessing parameters for frame.
          *  @param[in] size New input size.
@@ -1078,7 +1083,7 @@ CV__DNN_INLINE_NS_BEGIN
       * ClassificationModel creates net from file with trained weights and config,
       * sets preprocessing input, runs forward pass and return top-1 prediction.
       */
-     class CV_EXPORTS_W ClassificationModel : public Model
+     class CV_EXPORTS_W_SIMPLE ClassificationModel : public Model
      {
      public:
          /**
@@ -1111,7 +1116,7 @@ CV__DNN_INLINE_NS_BEGIN
       * sets preprocessing input, runs forward pass and return result detections.
       * For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported.
       */
-     class CV_EXPORTS_W DetectionModel : public Model
+     class CV_EXPORTS_W_SIMPLE DetectionModel : public Model
      {
      public:
          /**

modules/dnn/misc/python/test/test_dnn.py

@@ -138,10 +138,7 @@ class dnn_test(NewOpenCVTests):
         config = self.find_dnn_file("dnn/MobileNetSSD_deploy.prototxt")
         frame = cv.imread(img_path)
         model = cv.dnn_DetectionModel(weights, config)
-        size = (300, 300)
+        model.setInputParams(size=(300, 300), mean=(127.5, 127.5, 127.5), scale=1.0/127.5)
-        mean = (127.5, 127.5, 127.5)
-        scale = 1.0 / 127.5
-        model.setInputParams(size=size, mean=mean, scale=scale)
 
         iouDiff = 0.05
         confThreshold = 0.0001
@@ -164,6 +161,21 @@ class dnn_test(NewOpenCVTests):
             cv.rectangle(frame, list(box), (0, 255, 0))
 
 
+    def test_classification_model(self):
+        img_path = self.find_dnn_file("dnn/googlenet_0.png")
+        weights = self.find_dnn_file("dnn/squeezenet_v1.1.caffemodel")
+        config = self.find_dnn_file("dnn/squeezenet_v1.1.prototxt")
+        ref = np.load(self.find_dnn_file("dnn/squeezenet_v1.1_prob.npy"))
+
+        frame = cv.imread(img_path)
+        model = cv.dnn_ClassificationModel(config, weights)
+        model.setInputSize(227, 227)
+        model.setInputCrop(True)
+
+        out = model.predict(frame)
+        normAssert(self, out, ref)
+
+
     def test_face_detection(self):
         testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
         proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt', required=testdata_required)

modules/dnn/src/model.cpp

@@ -23,7 +23,7 @@ struct Model::Impl
     Mat    blob;
     std::vector<String> outNames;
 
-    void predict(Net& net, const Mat& frame, std::vector<Mat>& outs)
+    void predict(Net& net, const Mat& frame, OutputArrayOfArrays outs)
     {
         if (size.empty())
             CV_Error(Error::StsBadSize, "Input size not specified");
@@ -41,16 +41,18 @@ struct Model::Impl
     }
 };
 
+Model::Model() : impl(new Impl) {}
+
 Model::Model(const String& model, const String& config)
     : Net(readNet(model, config)), impl(new Impl)
 {
     impl->outNames = getUnconnectedOutLayersNames();
-};
+}
 
 Model::Model(const Net& network) : Net(network), impl(new Impl)
 {
     impl->outNames = getUnconnectedOutLayersNames();
-};
+}
 
 Model& Model::setInputSize(const Size& size)
 {
@@ -100,9 +102,7 @@ void Model::setInputParams(double scale, const Size& size, const Scalar& mean,
 
 void Model::predict(InputArray frame, OutputArrayOfArrays outs)
 {
-    std::vector<Mat> outputs;
+    impl->predict(*this, frame.getMat(), outs);
-    outs.getMatVector(outputs);
-    impl->predict(*this, frame.getMat(), outputs);
 }
 
 ClassificationModel::ClassificationModel(const String& model, const String& config)