Merge pull request #1479 from rolalaro/doc_misc

Improvements of the tutorial documentation

doc/tutorial/detection_dnn/tutorial-detection-dnn.dox

@@ -474,6 +474,10 @@ If you want to train your own YoloV7 model, please refer to the [official docume
 If your dataset is rather small (only hundreds of pictures), you may want to consider to base your training on
 `yolov7-tiny` network, as it tends to get better results.
 
+\warning If you train your own model, be sure to use the same image size in the `python train.py`,
+`python export.py` and `./tutorial-dnn-object-detection-live` commands. Otherwise, it can lead to either an error thrown
+by OpenCV or the absence of detection by the ONNX model while the Pytorch model works perfectly using the `python detect.py` command.
+
 \subsubsection dnn_supported_yolov8 Yolo v8
 
 You can find the weights (`yolov8s.onnx`) in ONNX format

doc/tutorial/misc/tutorial-synthetic-blenderproc.dox

@@ -715,6 +715,10 @@ We also download the pretrained yolo model, that we will finetune on our own dat
 (yolov7) ~/yolov7 $ wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
 ```
 
+\warning While creating the yolov7 virtual environment, you may face the following error:
+"requirements to build wheel did not run successfully". It can be solved by fixing the version of the Python of
+the virtual environment: `conda create --name yolov7 pip python=3.10`
+
 To fine-tune a YoloV7, we should create two new files: the network configuration and the hyperparameters.
 We will reuse the ones provided for the tiny model.
 ```

tutorial/detection/dnn/tutorial-dnn-object-detection-live.cpp

@@ -338,6 +338,11 @@ int main(int argc, const char *argv[])
     cv::Mat frame;
     while (true) {
       capture >> frame;
+      if (frame.type() == CV_8UC4) {
+        // RGBa format is not supported by the class, converting to BGR format
+        cv::Mat cpy = frame;
+        cv::cvtColor(cpy, frame, cv::COLOR_RGBA2BGR);
+      }
       if (frame.empty())
         break;