YOLO DNNs  {#tutorial_dnn_yolo}
===============================

@tableofcontents

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|    |    |
| -: | :- |
| Original author | Alessandro de Oliveira Faria |
| Extended by     | Abduragim Shtanchaev |
| Compatibility   | OpenCV >= 4.9.0 |


Running pre-trained YOLO model in OpenCV
----------------------------------------

Deploying pre-trained models is a common task in machine learning, particularly when working with
hardware that does not support certain frameworks like PyTorch. This guide provides a comprehensive
overview of exporting pre-trained YOLO family models from PyTorch and deploying them using OpenCV's
DNN framework. For demonstration purposes, we will focus on the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/blob/main)
model, but the methodology applies to other supported models.

@note Currently, OpenCV supports the following YOLO models:
- [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/blob/main),
- [YOLONas](https://github.com/Deci-AI/super-gradients/tree/master),
- [YOLOv10](https://github.com/THU-MIG/yolov10/tree/main),
- [YOLOv9](https://github.com/WongKinYiu/yolov9),
- [YOLOv8](https://github.com/ultralytics/ultralytics/tree/main),
- [YOLOv7](https://github.com/WongKinYiu/yolov7/tree/main),
- [YOLOv6](https://github.com/meituan/YOLOv6/blob/main),
- [YOLOv5](https://github.com/ultralytics/yolov5),
- [YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4).

This support includes pre and post-processing routines specific to these models. Older versions of YOLO (v1–v3) are no longer supported, as Darknet format support was removed from OpenCV's DNN module.


Assuming that we have successfully trained YOLOX model, the subsequent step involves exporting and
running this model with OpenCV. There are several critical considerations to address before
proceeding with this process. Let's delve into these aspects.

### YOLO's Pre-processing & Output

Understanding the nature of inputs and outputs associated with YOLO family detectors is pivotal.
These detectors, akin to most Deep Neural Networks (DNN), typically exhibit variation in input
sizes contingent upon the model's scale.

| Model Scale  | Input Size   |
|--------------|--------------|
| Small Models <sup>[1](https://github.com/Megvii-BaseDetection/YOLOX/tree/main#standard-models)</sup>| 416x416      |
| Midsize Models <sup>[2](https://github.com/Megvii-BaseDetection/YOLOX/tree/main#standard-models)</sup>| 640x640    |
| Large Models <sup>[3](https://github.com/meituan/YOLOv6/tree/main#benchmark)</sup>| 1280x1280    |

This table provides a quick reference to understand the different input dimensions commonly used in
various YOLO models inputs. These are standard input shapes. Make sure you use input size that you
trained model with, if it is differed from from the size mentioned in the table.

The next critical element in the process involves understanding the specifics of image pre-processing
for YOLO detectors. While the fundamental pre-processing approach remains consistent across the YOLO
family, there are subtle yet crucial differences that must be accounted for to avoid any degradation
in performance. Key among these are the `resize type` and the `padding value` applied post-resize.
For instance, the [YOLOX model](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/yolox/data/data_augment.py#L142)
utilizes a `LetterBox` resize method and a padding value of `114.0`. It is imperative to ensure that
these parameters, along with the normalization constants, are appropriately matched to the model being
exported.

Regarding the model's output, it typically takes the form of a tensor with dimensions [BxNxC+5] or
[BxNxC+4], where 'B' represents the batch size, 'N' denotes the number of anchors, and 'C' signifies
the number of classes (for instance, 80 classes if the model is trained on the COCO dataset).
The additional 5 in the former tensor structure corresponds to the objectness score (obj), confidence
score (conf), and the bounding box coordinates (cx, cy, w, h). Notably, the YOLOv8 model's output
is shaped as [BxNxC+4], where there is no explicit objectness score, and the object score is directly
inferred from the class score. For the YOLOX model, specifically, it is also necessary to incorporate
anchor points to rescale predictions back to the image domain. This step will be integrated into
the ONNX graph, a process that we will detail further in the subsequent sections.


### PyTorch Model Export

Now that we know know the parameters of the pre-precessing we can go on and export the model from
Pytorch to ONNX graph. Since in this tutorial we are using YOLOX as our sample model, lets use its
export for demonstration purposes (the process is  identical for the rest of the YOLO detectors except `YOLOv10` model, see details on how to export it later in the post).
To exporting YOLOX we can just use [export script](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/tools/export_onnx.py). Particularly we need following commands:

@code{.bash}
git clone https://github.com/Megvii-BaseDetection/YOLOX.git
cd YOLOX
wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth # download pre-trained weights
python3 -m tools.export_onnx --output-name yolox_s.onnx -n yolox-s -c yolox_s.pth --decode_in_inference
@endcode

**NOTE:** Here `--decode_in_inference` is to include anchor box creation in the ONNX graph itself.
It sets [this value](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/yolox/models/yolo_head.py#L210C16-L210C39)
to `True`, which subsequently includes anchor generation function.

Below we demonstrated the minimal version of the export script (which could be used for models other
than YOLOX) in case it is needed. However, usually each YOLO repository has predefined export script.

@code{.py}
    import onnx
    import torch
    from onnxsim import simplify

    # load the model state dict
    ckpt = torch.load(ckpt_file, map_location="cpu")
    model.load_state_dict(ckpt)

    # prepare dummy input
    dummy_input = torch.randn(args.batch_size, 3, exp.test_size[0], exp.test_size[1])

    #export the model
    torch.onnx._export(
        model,
        dummy_input,
        "yolox.onnx",
        input_names=["input"],
        output_names=["output"],
        dynamic_axes={"input": {0: 'batch'},
                      "output": {0: 'batch'}})

    # use onnx-simplifier to reduce reduent model.
    onnx_model = onnx.load(args.output_name)
    model_simp, check = simplify(onnx_model)
    assert check, "Simplified ONNX model could not be validated"
    onnx.save(model_simp, args.output_name)
@endcode

#### Exporting YOLOv10 model

In oder to run YOLOv10 one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for on how to cut off the postprocessing, there is this [forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. The forked branch cuts of the postprocessing by [returning output](https://github.com/Abdurrahheem/yolov10/blob/4fdaafd912c8891642bfbe85751ea66ec20f05ad/ultralytics/nn/modules/head.py#L522) of the model before postprocessing procedure itself. To convert torch model to ONNX follow this proceduce.

@code{.bash}
git clone git@github.com:Abdurrahheem/yolov10.git
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
python export_opencv.py --model=<model-name> --imgsz=<input-img-size>
@endcode

By default `--model="yolov10s"` and `--imgsz=(480,640)`. This will generate file `yolov10s.onnx`, which can be use for inference in OpenCV

### Running Yolo ONNX detector with OpenCV Sample

Once we have our ONNX graph of the model, we just simply can run with OpenCV's sample. To that we need to make sure:

1. OpenCV is build with -DBUILD_EXAMLES=ON flag.
2. Navigate to the OpenCV's `build` directory
3. Run the following command:

@code{.cpp}
./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> \
                                --padvalue=<padding_value> \
                                --paddingmode=<padding_mode> \
                                --backend=<computation_backend> \
                                --target=<target_computation_device> \
                                --width=<model_input_width> \
                                --height=<model_input_height> \
@endcode

- --input: File path to your input image or video. If omitted, it will capture frames from a camera.
- --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).
- --scale: Scale factor for input normalization (e.g., 1.0, 1/255.0, etc).
- --yolo: YOLO model version (e.g., YOLOv8, YOLOX, YOLOv10, etc.).
- --padvalue: Padding value used in pre-processing (e.g., 114.0).
- --paddingmode: Method for handling image resizing and padding. Options: 0 (resize without extra processing), 1 (crop after resize), 2 (resize with aspect ratio preservation).
- --backend: Selection of computation backend (0 for automatic, 1 for Halide, 2 for OpenVINO, etc.).
- --target: Selection of target computation device (0 for CPU, 1 for OpenCL, etc.).
- --device: Camera device number (0 for default camera). If `--input` is not provided camera with index 0 will used by default.
- --width: Model input width. Not to be confused with the image width. (e.g., 416, 480, 640, 1280, etc).
- --height: Model input height. Not to be confused with the image height. (e.g., 416, 480, 640, 1280, etc).

Here `mean`, `scale`, `padvalue`, `paddingmode` should exactly match those that we discussed
in pre-processing section in order for the model to match result in PyTorch

To demonstrate how to run OpenCV YOLO samples without your own pretrained model, follow these instructions:

1. Ensure Python is installed on your platform.
2. Confirm that OpenCV is built with the `-DBUILD_EXAMPLES=ON` flag.

Run the YOLOX detector(with default values):

@code{.sh}
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_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/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_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

VIDEO DEMO:
@youtube{NHtRlndE2cg}

### Building a Custom Pipeline

Sometimes there is a need to make some custom adjustments in the inference pipeline. With OpenCV DNN
module this is also quite easy to achieve. Below we will outline the sample implementation details:

- Import required libraries

@snippet samples/dnn/object_detection.cpp includes

- Read ONNX graph and create neural network model:

@snippet samples/dnn/object_detection.cpp read_net

- Read image and pre-process it:

@snippet samples/dnn/object_detection.cpp preprocess_params
@snippet samples/dnn/object_detection.cpp preprocess_call

- Inference:

@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/object_detection.cpp postprocess

- Draw predicted boxes

@snippet samples/dnn/object_detection.cpp draw_boxes
