Merge pull request #28752 from abhishek-gola:net_profiling

Added net profiling support #28752

### 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
- [x] The PR is proposed to the proper branch
- [x] 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

samples/dnn/segmentation.py

@@ -73,6 +73,7 @@ def main(func_args=None):
         help()
         exit(1)
 
+    cv.utils.logging.setLogLevel(cv.utils.logging.LOG_LEVEL_INFO)
     args.model = findModel(args.model, args.sha1)
     if args.labels is not None:
         args.labels = findFile(args.labels)
@@ -105,6 +106,8 @@ def main(func_args=None):
     net = cv.dnn.readNetFromONNX(args.model, engine)
     net.setPreferableBackend(get_backend_id(args.backend))
     net.setPreferableTarget(get_target_id(args.target))
+    if hasattr(cv.dnn, 'DNN_PROFILE_SUMMARY'):
+        net.setProfilingMode(cv.dnn.DNN_PROFILE_SUMMARY)
 
     winName = 'Deep learning semantic segmentation in OpenCV'
     cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
@@ -138,6 +141,7 @@ def main(func_args=None):
         t0 = cv.getTickCount()
         if args.alias == 'u2netp':
             output = net.forward(net.getUnconnectedOutLayersNames())
+            net.printPerfProfile()
             pred = output[0][0, 0, :, :]
             mask = (pred * 255).astype(np.uint8)
             mask = cv.resize(mask, (frame.shape[1], frame.shape[0]), interpolation=cv.INTER_AREA)
@@ -149,6 +153,7 @@ def main(func_args=None):
             frame = cv.addWeighted(frame, 0.25, foreground_overlay, 0.75, 0)
         else:
             score = net.forward()
+            net.printPerfProfile()
 
             numClasses = score.shape[1]
             height = score.shape[2]
@@ -176,4 +181,4 @@ def main(func_args=None):
         cv.imshow(winName, frame)
 
 if __name__ == "__main__":
-    main()
+    main()