Merge pull request #26208 from alexlyulkov:al/new-engine-caffe-parser

Modified Caffe parser to support the new dnn engine #26208

Now the Caffe parser supports both the old and the new engine. It can be selected using newEngine argument in PopulateNet.

All cpu Caffe tests work fine except:

- Test_Caffe_nets.Colorization
- Test_Caffe_layers.FasterRCNN_Proposal

Both these tests doesn't work because of the bug in the new net.forward function. The function takes the name of the desired target last layer, but uses this name as the name of the desired output tensor.
Also Colorization test contains a strange model with a Silence layer in the end, so it doesn't have outputs. The old parser just ignored it. I think, the proper solution is to run this model until the (number_of_layers - 2) layer using proper net.forward arguments in the test.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

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

@@ -1051,17 +1051,24 @@ CV__DNN_INLINE_NS_BEGIN
     /** @brief Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
       * @param prototxt   path to the .prototxt file with text description of the network architecture.
       * @param caffeModel path to the .caffemodel file with learned network.
+      * @param engine select DNN engine to be used. With auto selection the new engine is used.
+      * Please pay attention that the new DNN does not support non-CPU back-ends for now.
       * @returns Net object.
       */
-    CV_EXPORTS_W Net readNetFromCaffe(CV_WRAP_FILE_PATH const String &prototxt, CV_WRAP_FILE_PATH const String &caffeModel = String());
+    CV_EXPORTS_W Net readNetFromCaffe(CV_WRAP_FILE_PATH const String &prototxt,
+                                      CV_WRAP_FILE_PATH const String &caffeModel = String(),
+                                      int engine = ENGINE_AUTO);
 
     /** @brief Reads a network model stored in Caffe model in memory.
       * @param bufferProto buffer containing the content of the .prototxt file
       * @param bufferModel buffer containing the content of the .caffemodel file
+      * @param engine select DNN engine to be used. With auto selection the new engine is used.
+      * Please pay attention that the new DNN does not support non-CPU back-ends for now.
       * @returns Net object.
       */
     CV_EXPORTS_W Net readNetFromCaffe(const std::vector<uchar>& bufferProto,
-                                      const std::vector<uchar>& bufferModel = std::vector<uchar>());
+                                      const std::vector<uchar>& bufferModel = std::vector<uchar>(),
+                                      int engine = ENGINE_AUTO);
 
     /** @brief Reads a network model stored in Caffe model in memory.
       * @details This is an overloaded member function, provided for convenience.
@@ -1070,10 +1077,13 @@ CV__DNN_INLINE_NS_BEGIN
       * @param lenProto length of bufferProto
       * @param bufferModel buffer containing the content of the .caffemodel file
       * @param lenModel length of bufferModel
+      * @param engine select DNN engine to be used. With auto selection the new engine is used.
+      * Please pay attention that the new DNN does not support non-CPU back-ends for now.
       * @returns Net object.
       */
     CV_EXPORTS Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
-                                    const char *bufferModel = NULL, size_t lenModel = 0);
+                                    const char *bufferModel = NULL, size_t lenModel = 0,
+                                    int engine = ENGINE_AUTO);
 
     /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
       * @param model  path to the .pb file with binary protobuf description of the network architecture