yolov8seg 瑞芯微RKNN部署

首先是把yolov8的onnx模型转成rknn模型，这里用的是yolov8n-seg.
转模型代码如下，这段是python代码：

if __name__ == '__main__':

    platform = 'rkXXXX' #写自己的型号
    exp = 'yolov8n_seg'
    Width = 640
    Height = 640
    MODEL_PATH = './onnx_models/yolov8n-seg.onnx'
    NEED_BUILD_MODEL = True
    # NEED_BUILD_MODEL = False
    im_file = './dog_bike_car_640x640.jpg'

    # Create RKNN object
    rknn = RKNN(verbose=False)

    OUT_DIR = "rknn_models"
    RKNN_MODEL_PATH = '{}/{}.rknn'.format(OUT_DIR,exp)
    if NEED_BUILD_MODEL:
        DATASET = './dataset.txt'
        rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform="rkXXXX")
        # Load model
        print('--> Loading model')
        ret = rknn.load_onnx(MODEL_PATH)
        if ret != 0:
            print('load model failed!')
            exit(ret)
        print('done')

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

        # Export rknn model
        if not os.path.exists(OUT_DIR):
            os.mkdir(OUT_DIR)
        print('--> Export RKNN model: {}'.format(RKNN_MODEL_PATH))
        ret = rknn.export_rknn(RKNN_MODEL_PATH)
        if ret != 0:
            print('Export rknn model failed.')
            exit(ret)
        print('done')
    else:
        ret = rknn.load_rknn(RKNN_MODEL_PATH)

    rknn.release()

运行成功之后会得到yolov8n_seg.rknn模型。

往下是cpp代码。
运行rknn模型，不是零copy的话用这段代码。

//设置outputs数组保存结果
rknn_output outputs[io_num.n_output];  //长度为2的数组
memset(outputs, 0, sizeof(outputs));
for (int i = 0; i < io_num.n_output; i++) {
   
   
  outputs[i].w