From 1272b2287763c2eef1adce07f3bba72fc1a80cd5 Mon Sep 17 00:00:00 2001
From: LAGNEAU Romain <romain.lagneau@inria.fr>
Date: Wed, 16 Oct 2024 15:21:14 +0200
Subject: [PATCH 1/4] [CORE] Minor changes to handle YoloV11

---
 .../visp3/detection/vpDetectorDNNOpenCV.h      |  8 +++++---
 .../detection/src/dnn/vpDetectorDNNOpenCV.cpp  | 18 +++++++++++-------
 2 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/modules/detection/include/visp3/detection/vpDetectorDNNOpenCV.h b/modules/detection/include/visp3/detection/vpDetectorDNNOpenCV.h
index 2da8aafe2d..23e2e7e3af 100644
--- a/modules/detection/include/visp3/detection/vpDetectorDNNOpenCV.h
+++ b/modules/detection/include/visp3/detection/vpDetectorDNNOpenCV.h
@@ -74,6 +74,7 @@ BEGIN_VISP_NAMESPACE
  * - Yolo v5, see usage to detect objects belonging to the COCO dataset using \ref dnn_supported_yolov5 network
  * - Yolo v7, see usage to detect objects belonging to the COCO dataset using \ref dnn_supported_yolov7 network
  * - Yolo v8, see usage to detect objects belonging to the COCO dataset using \ref dnn_supported_yolov8 network
+ * - Yolo v11, see usage to detect objects belonging to the COCO dataset using \ref dnn_supported_yolov11 network
  *
  * This class can be initialized from a JSON file if ViSP has been compiled with NLOHMANN JSON (see \ref soft_tool_json to see how to do it).
  * Examples of such JSON files can be found in the tutorial folder.
@@ -98,8 +99,9 @@ class VISP_EXPORT vpDetectorDNNOpenCV
     YOLO_V4 = 5, /*!< The \b vpDetectorDNNOpenCV object will use the parsing method corresponding to a YoloV4 DNN. See \b vpDetectorDNNOpenCV::postProcess_YoloV3_V4 for more information.*/
     YOLO_V5 = 6, /*!< The \b vpDetectorDNNOpenCV object will use the parsing method corresponding to a YoloV5 DNN. See \b vpDetectorDNNOpenCV::postProcess_YoloV5_V7 for more information.*/
     YOLO_V7 = 7, /*!< The \b vpDetectorDNNOpenCV object will use the parsing method corresponding to a YoloV7 DNN. See \b vpDetectorDNNOpenCV::postProcess_YoloV5_V7 for more information.*/
-    YOLO_V8 = 8, /*!< The \b vpDetectorDNNOpenCV object will use the parsing method corresponding to a YoloV8 DNN. See \b vpDetectorDNNOpenCV::postProcess_YoloV8 for more information.*/
-    COUNT = 9 /*!< The number of parsing method that come along with the \b vpDetectorDNNOpenCV class.*/
+    YOLO_V8 = 8, /*!< The \b vpDetectorDNNOpenCV object will use the parsing method corresponding to a YoloV8 DNN. See \b vpDetectorDNNOpenCV::postProcess_YoloV8_V11 for more information.*/
+    YOLO_V11 = 9, /*!< The \b vpDetectorDNNOpenCV object will use the parsing method corresponding to a YoloV11 DNN. See \b vpDetectorDNNOpenCV::postProcess_YoloV8_V11 for more information.*/
+    COUNT = 10 /*!< The number of parsing method that come along with the \b vpDetectorDNNOpenCV class.*/
   } DNNResultsParsingType;
 
   typedef struct DetectionCandidates
@@ -560,7 +562,7 @@ class VISP_EXPORT vpDetectorDNNOpenCV
 
   void postProcess_YoloV5_V7(DetectionCandidates &proposals, std::vector<cv::Mat> &dnnRes, const NetConfig &netConfig);
 
-  void postProcess_YoloV8(DetectionCandidates &proposals, std::vector<cv::Mat> &dnnRes, const NetConfig &netConfig);
+  void postProcess_YoloV8_V11(DetectionCandidates &proposals, std::vector<cv::Mat> &dnnRes, const NetConfig &netConfig);
 
   void postProcess_FasterRCNN(DetectionCandidates &proposals, std::vector<cv::Mat> &dnnRes, const NetConfig &netConfig);
 
diff --git a/modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp b/modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp
index d3846019e1..6696e8c83b 100644
--- a/modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp
+++ b/modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp
@@ -50,7 +50,7 @@ BEGIN_VISP_NAMESPACE
  *
  * \return std::string The list of the supported parsing methods / types of DNNs.
  */
-std::string vpDetectorDNNOpenCV::getAvailableDnnResultsParsingTypes()
+  std::string vpDetectorDNNOpenCV::getAvailableDnnResultsParsingTypes()
 {
   std::string list = "[";
   for (unsigned int i = 0; i < vpDetectorDNNOpenCV::COUNT - 1; i++) {
@@ -88,6 +88,9 @@ std::string vpDetectorDNNOpenCV::dnnResultsParsingTypeToString(const DNNResultsP
   case YOLO_V8:
     name = "yolov8";
     break;
+  case YOLO_V11:
+    name = "yolov11";
+    break;
   case FASTER_RCNN:
     name = "faster-rcnn";
     break;
@@ -474,7 +477,7 @@ std::vector<cv::String> vpDetectorDNNOpenCV::getOutputsNames()
     names.resize(outLayers.size());
     for (size_t i = 0; i < outLayers.size(); ++i)
       names[i] = layersNames[outLayers[i] - 1];
-  }
+}
   return names;
 }
 #endif
@@ -499,7 +502,8 @@ void vpDetectorDNNOpenCV::postProcess(DetectionCandidates &proposals)
     postProcess_YoloV5_V7(proposals, m_dnnRes, m_netConfig);
     break;
   case YOLO_V8:
-    postProcess_YoloV8(proposals, m_dnnRes, m_netConfig);
+  case YOLO_V11:
+    postProcess_YoloV8_V11(proposals, m_dnnRes, m_netConfig);
     break;
   case FASTER_RCNN:
     postProcess_FasterRCNN(proposals, m_dnnRes, m_netConfig);
@@ -815,7 +819,7 @@ void vpDetectorDNNOpenCV::postProcess_YoloV5_V7(DetectionCandidates &proposals,
   \param dnnRes: raw results of the \b vpDetectorDNNOpenCV::detect step.
   \param netConfig: the configuration of the network, to know for instance the DNN input size.
 */
-void vpDetectorDNNOpenCV::postProcess_YoloV8(DetectionCandidates &proposals, std::vector<cv::Mat> &dnnRes, const NetConfig &netConfig)
+void vpDetectorDNNOpenCV::postProcess_YoloV8_V11(DetectionCandidates &proposals, std::vector<cv::Mat> &dnnRes, const NetConfig &netConfig)
 {
   // Code adapted from here: https://github.com/JustasBart/yolov8_CPP_Inference_OpenCV_ONNX/blob/minimalistic/inference.cpp
   // Compute the ratio between the original size of the image and the network size to translate network coordinates into
@@ -965,7 +969,7 @@ void vpDetectorDNNOpenCV::postProcess_SSD_MobileNet(DetectionCandidates &proposa
       proposals.m_confidences.push_back(maxScore);
       proposals.m_boxes.push_back(cv::Rect(left, top, width, height));
       proposals.m_classIds.push_back(classId);
-    }
+}
   }
 }
 #endif
@@ -1146,7 +1150,7 @@ void vpDetectorDNNOpenCV::setPreferableTarget(const int &targetId) { m_net.setPr
 void vpDetectorDNNOpenCV::setScaleFactor(const double &scaleFactor)
 {
   m_netConfig.m_scaleFactor = scaleFactor;
-  if ((m_netConfig.m_parsingMethodType == YOLO_V7 || m_netConfig.m_parsingMethodType == YOLO_V8) && m_netConfig.m_scaleFactor != 1 / 255.) {
+  if ((m_netConfig.m_parsingMethodType == YOLO_V7 || m_netConfig.m_parsingMethodType == YOLO_V8 || m_netConfig.m_parsingMethodType == YOLO_V11) && m_netConfig.m_scaleFactor != 1 / 255.) {
     std::cout << "[vpDetectorDNNOpenCV::setParsingMethod] WARNING: scale factor should be 1/255. to normalize pixels value." << std::endl;
   }
 }
@@ -1169,7 +1173,7 @@ void vpDetectorDNNOpenCV::setParsingMethod(const DNNResultsParsingType &typePars
 {
   m_netConfig.m_parsingMethodType = typeParsingMethod;
   m_parsingMethod = parsingMethod;
-  if ((m_netConfig.m_parsingMethodType == YOLO_V7 || m_netConfig.m_parsingMethodType == YOLO_V8) && m_netConfig.m_scaleFactor != 1 / 255.) {
+  if ((m_netConfig.m_parsingMethodType == YOLO_V7 || m_netConfig.m_parsingMethodType == YOLO_V8 || m_netConfig.m_parsingMethodType == YOLO_V11) && m_netConfig.m_scaleFactor != 1 / 255.) {
     m_netConfig.m_scaleFactor = 1 / 255.;
     std::cout << "[vpDetectorDNNOpenCV::setParsingMethod] NB: scale factor changed to 1/255. to normalize pixels value." << std::endl;
   }

From f8eb2a1a43f75f21f794b7c9e1890402cb3e8b70 Mon Sep 17 00:00:00 2001
From: LAGNEAU Romain <romain.lagneau@inria.fr>
Date: Wed, 16 Oct 2024 15:45:14 +0200
Subject: [PATCH 2/4] [DOC] Updated doc to explain how to use a Yolo v11 with
 ViSP

---
 .../detection_dnn/tutorial-detection-dnn.dox  | 93 +++++++++++++++++++
 1 file changed, 93 insertions(+)

diff --git a/doc/tutorial/detection_dnn/tutorial-detection-dnn.dox b/doc/tutorial/detection_dnn/tutorial-detection-dnn.dox
index c5897fb046..2608ad5b08 100644
--- a/doc/tutorial/detection_dnn/tutorial-detection-dnn.dox
+++ b/doc/tutorial/detection_dnn/tutorial-detection-dnn.dox
@@ -508,6 +508,42 @@ $ ./tutorial-dnn-object-detection-live --input-json ./default_yolov8.json
 
 If you want to train your own YoloV8 model, please refer to the [official documentation](https://docs.ultralytics.com/modes/train/).
 
+\subsubsection dnn_supported_yolov11 Yolo v11
+
+Please follow the [official documentation](https://docs.ultralytics.com/quickstart/#install-ultralytics)
+to install Ultralytics' tools in order to be able to train or export a model. The installation using Docker has been tested for
+the sake of this tutorial.
+
+You can get the pre-trained YoloV11 models [here](https://docs.ultralytics.com/models/yolo11/#performance-metrics) . For
+this tutorial, we tested the [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt)
+pre-trained model.
+
+To export a model stored in Pytorch format into an ONNX format, you can use the Ultralytics' tool:
+```
+$ sudo docker run -it --ipc=host --gpus all ultralytics/ultralytics:latest
+root@8efe0fdbe196:/ultralytics#yolo export model=/path/to/yolo11s.pt format=onnx imgsz=640 opset=12
+```
+
+\note The `opset` option permits to set the version of ONNX to use to export the model. If you use OpenCV 4.10.0 this
+option does not seem to be required.
+
+\note It seems that OpenCV 4.7.0 is not compatible with Yolo v11. To upgrade OpenCV please follow the instructions in
+the section \ref dnn_model_upgrade_opencv below.
+
+Please use the following commands to run the tutorial program:
+```
+$ DNN_PATH=/path/to/my/dnn/folder \
+  CONFIG=none \
+  MODEL=${DNN_PATH}/yolov11/weights/yolov11s.onnx \
+  LABELS=${DNN_PATH}/yolov11/cfg/coco_classes.txt \
+  TYPE=yolov11 \
+  FRAMEWORK=onnx \
+  WIDTH=640 HEIGHT=640
+$ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
+    --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
+    --filterThresh -0.25 --scale 0.0039
+```
+
 \section dnn_model_other Other dnn models
 \subsection dnn_model_other_zoo OpenCV model zoo
 
@@ -556,6 +592,63 @@ Aborted (core dumped)
 
 You may have been missing the onnxsim library or forgotten to remove the `--end2end` option during the export of the network.
 
+\subsection dnn_error_yolov11 Yolo v11: several issues possible
+
+You may face the following error:
+```
+what():  OpenCV(4.7.0) /root/3rdparty/opencv/modules/dnn/src/onnx/onnx_importer.cpp:1073: error: (-2:Unspecified error) in function 'handleNode'
+> Node [Split@ai.onnx]:(onnx_node!/model.10/m/m.0/attn/Split) parse error: OpenCV(4.7.0) /root/3rdparty/opencv/modules/dnn/src/layers/slice_layer.cpp:274: error: (-215:Assertion failed) splits > 0 && inpShape[axis_rw] % splits == 0 in function 'getMemoryShapes'
+```
+It is because the version of ONNX used to export the model does not match the one that OpenCV uses. Please be sure that you used the `opset` option in the export command, such as follow:
+```
+yolo export model=/path/to/yolo11s.pt format=onnx imgsz=640 opset=12
+```
+\note  The `opset` option does not seem to be needed with OpenCV 4.10.0 .
+
+You may face the following error when trying to run the tutorial with a Yolo v11 model:
+```
+terminate called after throwing an instance of 'cv::Exception'
+  what():  OpenCV(4.7.0) /root/3rdparty/opencv/modules/dnn/src/net_impl_fuse.cpp:252: error: (-215:Assertion failed) biasLayerData->outputBlobs.size() == 1 in function 'fuseLayers'
+```
+It is because the OpenCV version that you use is too old. Please update OpenCV following the instructions presented in
+the \ref dnn_model_upgrade_opencv below.
+
+\subsubsection dnn_model_upgrade_opencv Upgrading OpenCV from source
+
+We suppose that OpenCV has been installed from source as described in the section \ref build_opencv_with_cuda
+above.
+
+To upgrade OpenCV, please follow the steps below:
+
+```
+$ cd $VISP_WS/3rdparty/opencv
+$ git fecth
+$ git checkout 4.10.0
+$ cd build
+$ cmake .. \
+-DCMAKE_BUILD_TYPE=RELEASE \
+-DCMAKE_INSTALL_PREFIX=/usr \
+-DCMAKE_INSTALL_LIBDIR=lib \
+-DWITH_CUDA=ON \
+-DWITH_CUDNN=ON \
+-DOPENCV_DNN_CUDA=ON \
+-DENABLE_FAST_MATH=1 \
+-DCUDA_FAST_MATH=1 \
+-DCUDA_ARCH_BIN=${GPU_CAPABILITIES} \
+-DWITH_CUBLAS=1 \
+-DOPENCV_EXTRA_MODULES_PATH=${HOME}/visp_ws/3rdparty/opencv_contrib/modules \
+-DBUILD_PERF_TESTS=Off \
+-DBUILD_TESTS=Off \
+-DBUILD_EXAMPLES=Off \
+-DBUILD_opencv_apps=Off \
+-DBUILD_opencv_java_bindings_generator=Off \
+-DBUILD_opencv_js=Off
+$ make -j$(nproc) install
+$ cd $VISP_WS/visp-build
+$ cmake ../visp
+$ make -j$(nproc)
+```
+
 \section dnn_next Next tutorial
 
 You may continue following \ref tutorial-detection-tensorrt.

From 044644153175e04ce5d4dd13ebceb3a04e0f4fde Mon Sep 17 00:00:00 2001
From: Fabien Spindler <Fabien.Spindler@inria.fr>
Date: Fri, 1 Nov 2024 11:26:32 +0100
Subject: [PATCH 3/4] Update copyright header and fix code indentation

---
 .../include/visp3/detection/vpDetectorDNNOpenCV.h   |  7 ++++---
 modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp   | 13 ++++++-------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/modules/detection/include/visp3/detection/vpDetectorDNNOpenCV.h b/modules/detection/include/visp3/detection/vpDetectorDNNOpenCV.h
index 23e2e7e3af..08f33f4dbd 100644
--- a/modules/detection/include/visp3/detection/vpDetectorDNNOpenCV.h
+++ b/modules/detection/include/visp3/detection/vpDetectorDNNOpenCV.h
@@ -1,6 +1,6 @@
 /*
  * ViSP, open source Visual Servoing Platform software.
- * Copyright (C) 2005 - 2023 by Inria. All rights reserved.
+ * Copyright (C) 2005 - 2024 by Inria. All rights reserved.
  *
  * This software is free software; you can redistribute it and/or modify
  * it under the terms of the GNU General Public License as published by
@@ -30,8 +30,9 @@
  * Description:
  * DNN object detection using OpenCV DNN module.
  */
-#ifndef _vpDetectorDNN_h_
-#define _vpDetectorDNN_h_
+
+#ifndef VP_DETECTOR_DNN_OPENCV_H
+#define VP_DETECTOR_DNN_OPENCV_H
 
 #include <visp3/core/vpConfig.h>
 
diff --git a/modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp b/modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp
index 6696e8c83b..39b6bdbb35 100644
--- a/modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp
+++ b/modules/detection/src/dnn/vpDetectorDNNOpenCV.cpp
@@ -1,7 +1,6 @@
-/****************************************************************************
- *
+/*
  * ViSP, open source Visual Servoing Platform software.
- * Copyright (C) 2005 - 2023 by Inria. All rights reserved.
+ * Copyright (C) 2005 - 2024 by Inria. All rights reserved.
  *
  * This software is free software; you can redistribute it and/or modify
  * it under the terms of the GNU General Public License as published by
@@ -30,8 +29,8 @@
  *
  * Description:
  * DNN object detection using OpenCV DNN module.
- *
-*****************************************************************************/
+ */
+
 #include <visp3/core/vpConfig.h>
 
 // Check if std:c++17 or higher
@@ -477,7 +476,7 @@ std::vector<cv::String> vpDetectorDNNOpenCV::getOutputsNames()
     names.resize(outLayers.size());
     for (size_t i = 0; i < outLayers.size(); ++i)
       names[i] = layersNames[outLayers[i] - 1];
-}
+  }
   return names;
 }
 #endif
@@ -969,7 +968,7 @@ void vpDetectorDNNOpenCV::postProcess_SSD_MobileNet(DetectionCandidates &proposa
       proposals.m_confidences.push_back(maxScore);
       proposals.m_boxes.push_back(cv::Rect(left, top, width, height));
       proposals.m_classIds.push_back(classId);
-}
+    }
   }
 }
 #endif

From 066bed427b5a946bc0a112d30c0e37ab1b02620d Mon Sep 17 00:00:00 2001
From: Fabien Spindler <Fabien.Spindler@inria.fr>
Date: Fri, 1 Nov 2024 12:01:26 +0100
Subject: [PATCH 4/4] Update dnn tutorial doc

---
 .../detection_dnn/tutorial-detection-dnn.dox  | 262 +++++++++---------
 1 file changed, 135 insertions(+), 127 deletions(-)

diff --git a/doc/tutorial/detection_dnn/tutorial-detection-dnn.dox b/doc/tutorial/detection_dnn/tutorial-detection-dnn.dox
index 2608ad5b08..d97c73a9ae 100644
--- a/doc/tutorial/detection_dnn/tutorial-detection-dnn.dox
+++ b/doc/tutorial/detection_dnn/tutorial-detection-dnn.dox
@@ -46,9 +46,9 @@ Please ensure to install a CuDNN version that is compatible with your version of
 
 3. Then, you need to determine the Compute capability of your GPU either from the [NVidia website](https://developer.nvidia.com/cuda-gpus)
 or using the [nvidia-smi tool](https://developer.nvidia.com/nvidia-system-management-interface). On a Debian distribution, you would run:
-  ```
-  $ export GPU_CAPABILITIES=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader)
-  ```
+\code{.sh}
+$ export GPU_CAPABILITIES=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader)
+\endcode
 4. Check if the package already installed on your computer. On a Debian distribution, you would run:
    ```
    $ apt list --installed | grep -i opencv
@@ -61,51 +61,50 @@ or using the [nvidia-smi tool](https://developer.nvidia.com/nvidia-system-manage
    ```
 
 5. Install OpenCV dependencies. On a Debian distribution, you would run:
-  ```
-  ## libx11-dev is a recommended ViSP 3rd parties
-  # If you installed another version of CUDA, please install the version of CuDNN which is compatible with your version
-  $ sudo apt update
-  $ sudo apt install libgtk-3-dev \
-    cmake \
-    git \
-    pip \
-    cmake-curses-gui \
-    locate \
-    libx11-dev
-  ```
+\code{.sh}
+# libx11-dev is a recommended ViSP 3rd parties
+# If you installed another version of CUDA, please install the version of CuDNN which is compatible with your version
+$ sudo apt update
+$ sudo apt install libgtk-3-dev \
+  cmake \
+  git \
+  pip \
+  cmake-curses-gui \
+  locate \
+  libx11-dev
+\endcode
 
 6. Get the sources. The \b vpDetectorDNNOpenCV has been tested with **OpenCV 4.7**. First, get the OpenCV_contrib
 sources, that contain the Cuda DNN module.
 On a Debian distribution, you would run:
-  ```
-  $ cd ${HOME}/visp_ws/3rdparty/
-  $ git clone --branch 4.7.0 https://github.com/opencv/opencv_contrib
-  $ git clone --branch 4.7.0 https://github.com/opencv/opencv
-  ```
+\code{.sh}
+$ cd ${HOME}/visp_ws/3rdparty/
+$ git clone --branch 4.7.0 https://github.com/opencv/opencv_contrib
+$ git clone --branch 4.7.0 https://github.com/opencv/opencv
+\endcode
 
 7. Compile OpenCV and install it from source. On a Debian distribution, you would run:
-  ```
-  $ mkdir -p ${HOME}/visp_ws/3rdparty/opencv/build &&\
-  cd ${HOME}/visp_ws/3rdparty/opencv/build
-  $ cmake .. \
-    -DCMAKE_BUILD_TYPE=RELEASE \
-    -DCMAKE_INSTALL_PREFIX=/usr \
-    -DCMAKE_INSTALL_LIBDIR=lib \
-    -DWITH_CUDA=ON \
-    -DWITH_CUDNN=ON \
-    -DOPENCV_DNN_CUDA=ON \
-    -DENABLE_FAST_MATH=1 \
-    -DCUDA_FAST_MATH=1 \
-    -DCUDA_ARCH_BIN=${GPU_CAPABILITIES} \
-    -DWITH_CUBLAS=1 \
-    -DOPENCV_EXTRA_MODULES_PATH=${HOME}/visp_ws/3rdparty/opencv_contrib/modules \
-    -DBUILD_PERF_TESTS=Off \
-    -DBUILD_TESTS=Off \
-    -DBUILD_EXAMPLES=Off \
-    -DBUILD_opencv_apps=Off \
-    -DBUILD_opencv_java_bindings_generator=Off \
-    -DBUILD_opencv_js=Off
-  ```
+\code{.sh}
+$ mkdir -p ${HOME}/visp_ws/3rdparty/opencv/build && cd ${HOME}/visp_ws/3rdparty/opencv/build
+$ cmake .. \
+  -DCMAKE_BUILD_TYPE=RELEASE \
+  -DCMAKE_INSTALL_PREFIX=/usr \
+  -DCMAKE_INSTALL_LIBDIR=lib \
+  -DWITH_CUDA=ON \
+  -DWITH_CUDNN=ON \
+  -DOPENCV_DNN_CUDA=ON \
+  -DENABLE_FAST_MATH=1 \
+  -DCUDA_FAST_MATH=1 \
+  -DCUDA_ARCH_BIN=${GPU_CAPABILITIES} \
+  -DWITH_CUBLAS=1 \
+  -DOPENCV_EXTRA_MODULES_PATH=${HOME}/visp_ws/3rdparty/opencv_contrib/modules \
+  -DBUILD_PERF_TESTS=Off \
+  -DBUILD_TESTS=Off \
+  -DBUILD_EXAMPLES=Off \
+  -DBUILD_opencv_apps=Off \
+  -DBUILD_opencv_java_bindings_generator=Off \
+  -DBUILD_opencv_js=Off
+\endcode
 
 8. Compile and install OpenCV. On a Debian distribution, you would run:
   ```
@@ -230,22 +229,22 @@ or for a non-sorted vector with:
 
 The default behavior is to detect human faces, but you can input another model to detect the objects you want. To see
 which are the options, run:
-```
+\code{.sh}
 $ cd $VISP_WS/visp-build/tutorial/detection/dnn
 $ ./tutorial-dnn-object-detection-live --help
-```
+\endcode
 
 \subsection dnn_usecase_face_detection Face detection
 
 The default behavior is to detect human faces using a model provided by OpenCV and learned over a ResNet 10 network.
 If you have a laptop, simply run:
-```
+\code{.sh}
 $ cd $VISP_WS/visp-build/tutorial/detection/dnn
 $ ./tutorial-dnn-object-detection-live
-```
+\endcode
 
 The previous command is similar to the next one:
-```
+\code{.sh}
 $ CONFIG=opencv_face_detector.pbtxt \
   MODEL=opencv_face_detector_uint8.pb \
   LABELS=class.txt \
@@ -255,7 +254,7 @@ $ CONFIG=opencv_face_detector.pbtxt \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --confThresh 0.35 --filterThresh -0.25 --scale 1
-```
+\endcode
 
 \subsection dnn_models_coco COCO dataset objects detection
 
@@ -271,7 +270,7 @@ the weights (`frozen_inference_graph.pb`) [there](http://download.tensorflow.org
 and the labels (`coco_classes.txt`) [here](https://github.com/lagadic/visp/blob/master/tutorial/detection/dnn/coco_classes.txt).
 
 To run the tutorial with the Faster-RCNN network, please run the following commands:
-```
+\code{.sh}
 $ cd $VISP_WS/visp-build/tutorial/detection/dnn
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=${DNN_PATH}/Faster-RCNN/cfg/config.pbtxt \
@@ -283,13 +282,13 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --confThresh 0.35 --filterThresh -0.25 --scale 1
-```
+\endcode
 
 Alternatively, if you have installed the NLOHMANN JSON library and you are using the weights quoted above,
 you can use the following command line:
-```
+\code{.sh}
 $ ./tutorial-dnn-object-detection-live --input-json ./default_faster-rcnn.json
-```
+\endcode
 
 If you want to train your own Faster-RCNN model, please refer to this [tutorial](https://debuggercafe.com/how-to-train-faster-rcnn-resnet50-fpn-v2-on-custom-dataset/).
 
@@ -303,7 +302,7 @@ and the labels (`coco_classes.txt`) [here](https://github.com/lagadic/visp/blob/
 The parameters to use with this network were found [there](https://github.com/opencv/opencv/blob/0052d46b8e33c7bfe0e1450e4bff28b88f455570/samples/dnn/models.yml#L68).
 
 To run the tutorial with the `Mobilenet V1` network, please run the following commands:
-```
+\code{.sh}
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=${DNN_PATH}/MobileNet-SSD/cfg/ssd_mobilenet_v1_coco_2017_11_17.pbtxt \
   MODEL=${DNN_PATH}/MobileNet-SSD/weights/frozen_inference_graph.pb \
@@ -314,13 +313,13 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --filterThresh -0.25 --scale 1
-```
+\endcode
 
 Alternatively, if you have installed the NLOHMANN JSON library and you are using the weights quoted above,
 you can use the following command line:
-```
+\code{.sh}
 $ ./tutorial-dnn-object-detection-live --input-json ./default_ssd-mobilenet_v1.json
-```
+\endcode
 
 If you would rather use the v3 of Mobilenet-SSD, please download the config file (`ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt`)
 [here](https://gist.github.com/dkurt/54a8e8b51beb3bd3f770b79e56927bd7),
@@ -328,7 +327,7 @@ the weights (`frozen_inference_graph.pb`) [there](http://download.tensorflow.org
 and the labels (`coco_classes.txt`) [here](https://github.com/lagadic/visp/blob/master/tutorial/detection/dnn/coco_classes.txt).
 
 Then, to run the tutorial with the `Mobilenet V3` network, please run the following commands:
-```
+\code{.sh}
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=${DNN_PATH}/MobileNet-SSD/cfg/ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt \
   MODEL=${DNN_PATH}/MobileNet-SSD/weights/frozen_inference_graph.pb \
@@ -339,13 +338,13 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0.0019 0.0019 0.0019 \
     --filterThresh -0.25 --scale 0.00389
-```
+\endcode
 
 Alternatively, if you have installed the NLOHMANN JSON library and you are using the weights quoted above,
 you can use the following command line:
-```
+\code{.sh}
 $ ./tutorial-dnn-object-detection-live --input-json ./default_ssd-mobilenet_v3.json
-```
+\endcode
 
 If you want to train your own MobileNet SSD model, please refer to this
 [tutorial](https://www.forecr.io/blogs/ai-algorithms/how-to-train-ssd-mobilenet-model-for-object-detection-using-pytorch)
@@ -358,7 +357,7 @@ the weights (`yolov3.weights`) [there](https://pjreddie.com/media/files/yolov3.w
 and the labels (`coco_classes.txt`) [here](https://github.com/lagadic/visp/blob/master/tutorial/detection/dnn/coco_classes.txt).
 
 To run the tutorial program `tutorial-dnn-object-detection-live.cpp`, use the following commands:
-```
+\code{.sh}
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=${DNN_PATH}/yolov3/cfg/yolov3.cfg \
   MODEL=${DNN_PATH}/yolov3/weights/yolov3.weights \
@@ -369,13 +368,13 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --filterThresh -0.25 --scale 0.0039
-```
+\endcode
 
 Alternatively, if you have installed the NLOHMANN JSON library and you are using the weights quoted above,
 you can use the following command line:
-```
+\code{.sh}
 $ ./tutorial-dnn-object-detection-live --input-json ./default_yolov3.json
-```
+\endcode
 
 If you want to train your own YoloV3 model, please refer to the [official documentation](https://github.com/ultralytics/yolov3).
 
@@ -386,7 +385,7 @@ the weights (`yolov4-tiny.weights`) [there](https://github.com/AlexeyAB/darknet/
 and the labels (`coco_classes.txt`) [here](https://github.com/lagadic/visp/blob/master/tutorial/detection/dnn/coco_classes.txt).
 
 To run the tutorial program `tutorial-dnn-object-detection-live.cpp`, use the following commands:
-```
+\code{.sh}
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=${DNN_PATH}/yolov4/cfg/yolov4-tiny.cfg \
   MODEL=${DNN_PATH}/yolov4/weights/yolov4-tiny.weights \
@@ -397,13 +396,13 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --filterThresh -0.25 --scale 0.0039
-```
+\endcode
 
 Alternatively, if you have installed the NLOHMANN JSON library and you are using the weights quoted above,
 you can use the following command line:
-```
+\code{.sh}
 $ ./tutorial-dnn-object-detection-live --input-json ./default_yolov4.json
-```
+\endcode
 
 If you want to train your own YoloV4 model, please refer to the [official documentation](https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects).
 
@@ -415,7 +414,7 @@ and the labels (`coco_classes.txt`) [here](https://github.com/lagadic/visp/blob/
 \note You do not need a config file when using a network saved in ONNX format.
 
 To run the tutorial program `tutorial-dnn-object-detection-live.cpp`, use the following commands:
-```
+\code{.sh}
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=none \
   MODEL=${DNN_PATH}/yolov5/weights/yolov5n.onnx \
@@ -426,13 +425,13 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --filterThresh -0.25 --scale 0.0039
-```
+\endcode
 
 Alternatively, if you have installed the NLOHMANN JSON library and you are using the weights quoted above,
 you can use the following command line:
-```
+\code{.sh}
 $ ./tutorial-dnn-object-detection-live --input-json ./default_yolov5.json
-```
+\endcode
 
 If you want to train your own YoloV5 model, please refer to the
 [official documentation](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/#13-prepare-dataset-for-yolov5).
@@ -444,12 +443,12 @@ in the Pytorch format from [here](https://github.com/WongKinYiu/yolov7/releases/
 
 Then, convert it in ONNX format using the `export.py` script that you can find on the
 [YoloV7 repo](https://github.com/WongKinYiu/yolov7) with the following arguments:
-```
+\code{.sh}
 $ python3 export.py --weights ../weights/yolov7-tiny.pt --grid --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640  --max-wh 640
-```
+\endcode
 
 Finally, use the following commands to run the tutorial program:
-```
+\code{.sh}
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=none \
   MODEL=${DNN_PATH}/yolov7/weights/yolov7-tiny.onnx \
@@ -460,15 +459,15 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --filterThresh -0.25 --scale 0.0039
-```
+\endcode
 
 \note You do not need a config file when using a network saved in ONNX format.
 
 Alternatively, if you have installed the NLOHMANN JSON library and you are using the weights quoted above,
 you can use the following command line:
-```
+\code{.sh}
 $ ./tutorial-dnn-object-detection-live --input-json ./default_yolov7.json
-```
+\endcode
 
 If you want to train your own YoloV7 model, please refer to the [official documentation](https://github.com/WongKinYiu/yolov7#transfer-learning).
 If your dataset is rather small (only hundreds of pictures), you may want to consider to base your training on
@@ -485,7 +484,7 @@ You can find the weights (`yolov8s.onnx`) in ONNX format
 and the labels (`coco_classes.txt`) [here](https://github.com/lagadic/visp/blob/master/tutorial/detection/dnn/coco_classes.txt).
 
 Please use the following commands to run the tutorial program:
-```
+\code{.sh}
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=none \
   MODEL=${DNN_PATH}/yolov8/weights/yolov8s.onnx \
@@ -496,13 +495,13 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --filterThresh -0.25 --scale 0.0039
-```
+\endcode
 
 Alternatively, if you have installed the NLOHMANN JSON library and you are using the weights quoted above,
 you can use the following command line:
-```
+\code{.sh}
 $ ./tutorial-dnn-object-detection-live --input-json ./default_yolov8.json
-```
+\endcode
 
 \note You do not need a config file when using a network saved in ONNX format.
 
@@ -511,18 +510,18 @@ If you want to train your own YoloV8 model, please refer to the [official docume
 \subsubsection dnn_supported_yolov11 Yolo v11
 
 Please follow the [official documentation](https://docs.ultralytics.com/quickstart/#install-ultralytics)
-to install Ultralytics' tools in order to be able to train or export a model. The installation using Docker has been tested for
+to install Ultralytics tools in order to be able to train or export a model. The installation using Docker has been tested for
 the sake of this tutorial.
 
-You can get the pre-trained YoloV11 models [here](https://docs.ultralytics.com/models/yolo11/#performance-metrics) . For
+You can get the pre-trained YoloV11 models [here](https://docs.ultralytics.com/models/yolo11/#performance-metrics). For
 this tutorial, we tested the [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt)
 pre-trained model.
 
-To export a model stored in Pytorch format into an ONNX format, you can use the Ultralytics' tool:
-```
+To export a model stored in Pytorch format into an ONNX format, you can use the Ultralytics tool:
+\code{.sh}
 $ sudo docker run -it --ipc=host --gpus all ultralytics/ultralytics:latest
-root@8efe0fdbe196:/ultralytics#yolo export model=/path/to/yolo11s.pt format=onnx imgsz=640 opset=12
-```
+root@8efe0fdbe196:/ultralytics# yolo export model=/path/to/yolo11s.pt format=onnx imgsz=640 opset=12
+\endcode
 
 \note The `opset` option permits to set the version of ONNX to use to export the model. If you use OpenCV 4.10.0 this
 option does not seem to be required.
@@ -531,7 +530,7 @@ option does not seem to be required.
 the section \ref dnn_model_upgrade_opencv below.
 
 Please use the following commands to run the tutorial program:
-```
+\code{.sh}
 $ DNN_PATH=/path/to/my/dnn/folder \
   CONFIG=none \
   MODEL=${DNN_PATH}/yolov11/weights/yolov11s.onnx \
@@ -542,7 +541,7 @@ $ DNN_PATH=/path/to/my/dnn/folder \
 $ ./tutorial-dnn-object-detection-live --model $MODEL --labels $LABELS --config $CONFIG --type $TYPE \
     --framework $FRAMEWORK --width $WIDTH --height $HEIGHT --nmsThresh 0.5 --mean 0 0 0 \
     --filterThresh -0.25 --scale 0.0039
-```
+\endcode
 
 \section dnn_model_other Other dnn models
 \subsection dnn_model_other_zoo OpenCV model zoo
@@ -570,11 +569,11 @@ that does not match the one expected by the DNN).
 
 \subsection dnn_error_unimplemented YoloV3: transpose weights is not functionNotImplementedError
 
-```
+\code{.sh}
 terminate called after throwing an instance of 'cv::Exception'
 what(): OpenCV(4.7.0) error: (-213:The function/feature is not implemented) Transpose the weights (except for convolutional) is not
 implemented in function 'ReadDarknetFromWeightsStream'
-```
+\endcode
 
 Following the proposition found [here](https://github.com/opencv/opencv/issues/15502#issuecomment-531755462) to download once
 again the weights from [here](https://pjreddie.com/media/files/yolov3.weights) permitted to solve this error.
@@ -582,72 +581,81 @@ again the weights from [here](https://pjreddie.com/media/files/yolov3.weights) p
 \subsection dnn_error_nonmaxsuppr YoloV7: can't create NonMaxSuppression layer
 
 When using a YoloV7 model exported in `onnx` format, one can face the following error:
-```
+\code{.sh}
 [ERROR:0@0.335] global onnx_importer.cpp:1054 handleNode DNN/ONNX: ERROR during processing node with 5 inputs and 1 outputs: [NonMaxSuppression]onnx_node!/end2end/NonMaxSuppression) from domain='ai.onnx'
 terminate called after throwing an instance of 'cv::Exception'
 what():  OpenCV(4.7.0) opencv/modules/dnn/src/onnx/onnx_importer.cpp:1073: error: (-2:Unspecified error) in function 'handleNode'
 Node [NonMaxSuppression@ai.onnx]onnx_node!/end2end/NonMaxSuppression) parse error: OpenCV(4.7.0) opencv/modules/dnn/src/net_impl.hpp:108: error: (-2:Unspecified error) Can't create layer "onnx_node!/end2end/NonMaxSuppression" of type "NonMaxSuppression" in function 'getLayerInstance'
 Aborted (core dumped)
-```
+\endcode
 
 You may have been missing the onnxsim library or forgotten to remove the `--end2end` option during the export of the network.
 
-\subsection dnn_error_yolov11 Yolo v11: several issues possible
+\subsection dnn_error_yolov11 Yolo v11: Known issues
+
+\subsubsection dnn_model_onnx_mismatch ONNX version mismatch
 
 You may face the following error:
-```
+\code{.sh}
 what():  OpenCV(4.7.0) /root/3rdparty/opencv/modules/dnn/src/onnx/onnx_importer.cpp:1073: error: (-2:Unspecified error) in function 'handleNode'
 > Node [Split@ai.onnx]:(onnx_node!/model.10/m/m.0/attn/Split) parse error: OpenCV(4.7.0) /root/3rdparty/opencv/modules/dnn/src/layers/slice_layer.cpp:274: error: (-215:Assertion failed) splits > 0 && inpShape[axis_rw] % splits == 0 in function 'getMemoryShapes'
-```
-It is because the version of ONNX used to export the model does not match the one that OpenCV uses. Please be sure that you used the `opset` option in the export command, such as follow:
-```
-yolo export model=/path/to/yolo11s.pt format=onnx imgsz=640 opset=12
-```
+\endcode
+It is because the version of ONNX used to export the model does not match the one that OpenCV uses.
+Please be sure that you used the `opset` option in the export command as stated in \ref dnn_supported_yolov11 section, such as follow:
+\code{.sh}
+$ yolo export model=/path/to/yolo11s.pt format=onnx imgsz=640 opset=12
+\endcode
 \note  The `opset` option does not seem to be needed with OpenCV 4.10.0 .
 
-You may face the following error when trying to run the tutorial with a Yolo v11 model:
-```
+\subsubsection dnn_model_opencv_deprecated OpenCV version too old
+
+You may also face the following error when trying to run the tutorial with a Yolo v11 model:
+\code{.sh}
 terminate called after throwing an instance of 'cv::Exception'
   what():  OpenCV(4.7.0) /root/3rdparty/opencv/modules/dnn/src/net_impl_fuse.cpp:252: error: (-215:Assertion failed) biasLayerData->outputBlobs.size() == 1 in function 'fuseLayers'
-```
+\endcode
 It is because the OpenCV version that you use is too old. Please update OpenCV following the instructions presented in
 the \ref dnn_model_upgrade_opencv below.
 
-\subsubsection dnn_model_upgrade_opencv Upgrading OpenCV from source
+\subsubsection dnn_model_upgrade_opencv Fix by upgrading OpenCV from source
 
 We suppose that OpenCV has been installed from source as described in the section \ref build_opencv_with_cuda
 above.
 
-To upgrade OpenCV, please follow the steps below:
+To upgrade OpenCV to version 4.10.0, please follow the steps below:
 
-```
+\code{.sh}
 $ cd $VISP_WS/3rdparty/opencv
-$ git fecth
+$ git fetch
 $ git checkout 4.10.0
 $ cd build
 $ cmake .. \
--DCMAKE_BUILD_TYPE=RELEASE \
--DCMAKE_INSTALL_PREFIX=/usr \
--DCMAKE_INSTALL_LIBDIR=lib \
--DWITH_CUDA=ON \
--DWITH_CUDNN=ON \
--DOPENCV_DNN_CUDA=ON \
--DENABLE_FAST_MATH=1 \
--DCUDA_FAST_MATH=1 \
--DCUDA_ARCH_BIN=${GPU_CAPABILITIES} \
--DWITH_CUBLAS=1 \
--DOPENCV_EXTRA_MODULES_PATH=${HOME}/visp_ws/3rdparty/opencv_contrib/modules \
--DBUILD_PERF_TESTS=Off \
--DBUILD_TESTS=Off \
--DBUILD_EXAMPLES=Off \
--DBUILD_opencv_apps=Off \
--DBUILD_opencv_java_bindings_generator=Off \
--DBUILD_opencv_js=Off
-$ make -j$(nproc) install
+  -DCMAKE_BUILD_TYPE=RELEASE \
+  -DCMAKE_INSTALL_PREFIX=/usr \
+  -DCMAKE_INSTALL_LIBDIR=lib \
+  -DWITH_CUDA=ON \
+  -DWITH_CUDNN=ON \
+  -DOPENCV_DNN_CUDA=ON \
+  -DENABLE_FAST_MATH=1 \
+  -DCUDA_FAST_MATH=1 \
+  -DCUDA_ARCH_BIN=${GPU_CAPABILITIES} \
+  -DWITH_CUBLAS=1 \
+  -DOPENCV_EXTRA_MODULES_PATH=${HOME}/visp_ws/3rdparty/opencv_contrib/modules \
+  -DBUILD_PERF_TESTS=Off \
+  -DBUILD_TESTS=Off \
+  -DBUILD_EXAMPLES=Off \
+  -DBUILD_opencv_apps=Off \
+  -DBUILD_opencv_java_bindings_generator=Off \
+  -DBUILD_opencv_js=Off
+$ make -j$(nproc)
+$ sudo make install
+\endcode
+Once OpenCV is build and installed, you need to rebuild ViSP:
+\code{.sh}
 $ cd $VISP_WS/visp-build
 $ cmake ../visp
 $ make -j$(nproc)
-```
+\endcode
 
 \section dnn_next Next tutorial