~~tag> YOLO NPU Edge2 RK3588~~

====== YOLOv7-tiny Edge2 Demo - 1 ======

{{indexmenu_n>1}}

===== Train Model =====

Download the YOLOv7 official code [[gh>WongKinYiu/yolov7]].

```shell
$ git clone https://github.com/WongKinYiu/yolov7.git
```

Refer ''README.md'' to create and train a YOLOv7 tiny model.

===== Convert Model =====

==== Build virtual environment ====

The SDK only supports **python3.6** or **python3.8**, here is an example of creating a virtual environment for **python3.8**.

Install python packages.

```shell
$ sudo apt update
$ sudo apt install python3-dev python3-numpy
```

Follow this docs to install [[https://conda.io/projects/conda/en/stable/user-guide/install/linux.html | conda]].

Then create a virtual environment.

```shell
$ conda create -n npu-env python=3.8
$ conda activate npu-env     #activate
$ conda deactivate           #deactivate
```

==== Get convert tool ====

Download Tool from [[gh>rockchip-linux/rknn-toolkit2]].

```shell
$ git clone https://github.com/rockchip-linux/rknn-toolkit2.git
$ git checkout 9ad79343fae625f4910242e370035fcbc40cc31a
```

Install dependences and RKNN toolkit2 packages.

```shell
$ cd rknn-toolkit2
$ sudo apt-get install python3 python3-dev python3-pip
$ sudo apt-get install libxslt1-dev zlib1g-dev libglib2.0 libsm6 libgl1-mesa-glx libprotobuf-dev gcc cmake
$ pip3 install -r doc/requirements_cp38-*.txt
$ pip3 install packages/rknn_toolkit2-*-cp38-cp38-linux_x86_64.whl
```

==== Convert ====

After training model, run ''export.py'' to convert model from **PT** to **ONNX**.

Enter ''rknn-toolkit2/examples/onnx/yolov5'' and modify ''test.py'' as follows.

```python test.py
# Create RKNN object
rknn = RKNN(verbose=True)

# pre-process config
print('--> Config model')
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
print('done')

# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model='./yolov7_tiny.onnx')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')

# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
    print('Build model failed!')
    exit(ret)
print('done')

# Export RKNN model
print('--> Export rknn model')
ret = rknn.export_rknn('./yolov7_tiny.rknn')
if ret != 0:
    print('Export rknn model failed!')
    exit(ret)
print('done')
```

Run ''test.py'' to generate RKNN model.

```shell
$ python3 test.py
```

===== Run NPU =====

==== Get source code ====

Clone the source code from our [[gh>khadas/edge2-npu]].

```shell
$ git clone https://github.com/khadas/edge2-npu
```

==== Install dependencies ====

```shell
$ sudo apt update
$ sudo apt install cmake libopencv-dev
```

==== Compile and run ====

=== Picture input demo ===

Put ''yolov7_tiny.rknn'' in ''edge2-npu/C++/yolov7_tiny/data/model''

```shell
# Compile
$ bash build.sh

# Run
$ cd install/yolov7_tiny
$ ./yolov7_tiny data/model/yolov7_tiny.rknn data/img/bus.jpg
```

=== Camera input demo ===

Put ''yolov7_tiny.rknn'' in ''edge2-npu/C++/yolov7_tiny_cap/data/model''

```shell
# Compile
$ bash build.sh

# Run
$ cd install/yolov7_tiny
$ ./yolov7_tiny data/model/yolov7_tiny.rknn 33
```

<WRAP info >
''33'' is camera device index.
</WRAP>


<WRAP tip >
If your **YOLOv7 tiny** model classes are not the same as **COCO**, please change ''data/coco_80_labels_list.txt'' and the ''OBJ_CLASS_NUM'' in ''include/postprocess.h''.
</WRAP>