Merge pull request #25710 from gursimarsingh:improved_object_detection_sample

Merged yolo_detector and object detection sample #25710

Relates to #25006

This pull request merges the yolo_detector.cpp sample with the object_detector.cpp sample. It also beautifies the bounding box display on the output images

### 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
- [ ] 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

doc/tutorials/dnn/dnn_yolo/dnn_yolo.markdown

@@ -150,13 +150,12 @@ Once we have our ONNX graph of the model, we just simply can run with OpenCV's s
 3. Run the following command:
 
 @code{.cpp}
-./bin/example_dnn_yolo_detector --input=<path_to_your_input_file> \
-                                --classes=<path_to_class_names_file> \
+./bin/example_dnn_object_detection <model_name> --input=<path_to_your_input_file> \
+                                --labels=<path_to_class_names_file> \
                                 --thr=<confidence_threshold> \
                                 --nms=<non_maximum_suppression_threshold> \
                                 --mean=<mean_normalization_value> \
                                 --scale=<scale_factor> \
-                                --yolo=<yolo_model_version> \
                                 --padvalue=<padding_value> \
                                 --paddingmode=<padding_mode> \
                                 --backend=<computation_backend> \
@@ -166,7 +165,7 @@ Once we have our ONNX graph of the model, we just simply can run with OpenCV's s
 @endcode
 
 - --input: File path to your input image or video. If omitted, it will capture frames from a camera.
-- --classes: File path to a text file containing class names for object detection.
+- --labels: File path to a text file containing class names for object detection.
 - --thr: Confidence threshold for detection (e.g., 0.5).
 - --nms: Non-maximum suppression threshold (e.g., 0.4).
 - --mean: Mean normalization value (e.g., 0.0 for no mean normalization).
@@ -191,43 +190,28 @@ To demonstrate how to run OpenCV YOLO samples without your own pretrained model,
 Run the YOLOX detector(with default values):
 
 @code{.sh}
-git clone https://github.com/opencv/opencv_extra.git
-cd opencv_extra/testdata/dnn
-python download_models.py yolox_s_inf_decoder
-cd ..
-export OPENCV_TEST_DATA_PATH=$(pwd)
+cd opencv/samples/dnn
+export OPENCV_DOWNLOAD_CACHE_DIR=<path to download the model>
+cd ../data
+export OPENCV_SAMPLES_DATA_PATH=$(pwd)
+python download_models.py yolov8x --save_dir=$OPENCV_DOWNLOAD_CACHE_DIR
 cd <build directory of OpenCV>
-./bin/example_dnn_yolo_detector
+./bin/example_dnn_object_detection yolov8x
 @endcode
 
 This will execute the YOLOX detector with your camera.
 For YOLOv8 (for instance), follow these additional steps:
 
 @code{.sh}
-cd opencv_extra/testdata/dnn
-python download_models.py yolov8
-cd ..
-export OPENCV_TEST_DATA_PATH=$(pwd)
+cd opencv/samples/dnn
+export OPENCV_DOWNLOAD_CACHE_DIR=<path to download the model>
+cd ../data
+export OPENCV_SAMPLES_DATA_PATH=$(pwd)
+python download_models.py yolov8n --save_dir=$OPENCV_DOWNLOAD_CACHE_DIR
 cd <build directory of OpenCV>
-
-./bin/example_dnn_yolo_detector --model=onnx/models/yolov8n.onnx --yolo=yolov8 --mean=0.0 --scale=0.003921568627 --paddingmode=2 --padvalue=144.0 --thr=0.5 --nms=0.4 --rgb=0
+./bin/example_dnn_object_detection yolov8n --model=onnx/models/yolov8n.onnx --mean=0.0 --scale=0.003921568627 --paddingmode=2 --padvalue=144.0 --thr=0.5 --nms=0.4 --rgb=0
 @endcode
 
-For YOLOv10, follow these steps:
-
-@code{.sh}
-cd opencv_extra/testdata/dnn
-python download_models.py yolov10
-cd ..
-export OPENCV_TEST_DATA_PATH=$(pwd)
-cd <build directory of OpenCV>
-
-./bin/example_dnn_yolo_detector --model=onnx/models/yolov10s.onnx --yolo=yolov10 --width=640 --height=480  --scale=0.003921568627 --padvalue=114
-@endcode
-
-This will run `YOLOv10` detector on first camera found on your system. If you want to run it on a image/video file, you can use `--input` option to specify the path to the file.
-
-
 VIDEO DEMO:
 @youtube{NHtRlndE2cg}
 
@@ -238,30 +222,30 @@ module this is also quite easy to achieve. Below we will outline the sample impl
 
 - Import required libraries
 
-@snippet samples/dnn/yolo_detector.cpp includes
+@snippet samples/dnn/object_detection.cpp includes
 
 - Read ONNX graph and create neural network model:
 
-@snippet samples/dnn/yolo_detector.cpp read_net
+@snippet samples/dnn/object_detection.cpp read_net
 
 - Read image and pre-process it:
 
-@snippet samples/dnn/yolo_detector.cpp preprocess_params
-@snippet samples/dnn/yolo_detector.cpp preprocess_call
-@snippet samples/dnn/yolo_detector.cpp preprocess_call_func
+@snippet samples/dnn/object_detection.cpp preprocess_params
+@snippet samples/dnn/object_detection.cpp preprocess_call
+@snippet samples/dnn/object_detection.cpp preprocess_call_func
 
 - Inference:
 
-@snippet samples/dnn/yolo_detector.cpp forward_buffers
-@snippet samples/dnn/yolo_detector.cpp forward
+@snippet samples/dnn/object_detection.cpp forward_buffers
+@snippet samples/dnn/object_detection.cpp forward
 
 - Post-Processing
 
 All post-processing steps are implemented in function `yoloPostProcess`. Please pay attention,
 that NMS step is not included into onnx graph. Sample uses OpenCV function for it.
 
-@snippet samples/dnn/yolo_detector.cpp postprocess
+@snippet samples/dnn/object_detection.cpp postprocess
 
 - Draw predicted boxes
 
-@snippet samples/dnn/yolo_detector.cpp draw_boxes
+@snippet samples/dnn/object_detection.cpp draw_boxes