瑞芯微：基于RKNN3568得yolov8seg部署

原创已于 2023-05-22 19:22:05 修改 · 1.8k 阅读

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#YOLO #rknn #yolov8nseg #深度学习 #目标检测

于 2023-05-22 18:35:08 首次发布

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该代码示例展示了如何将ONNX格式的yolov8seg模型转换并部署到RK3568平台，使用RKNN工具进行模型加载、构建、量化和导出。

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这段时间一直在搞rk3568，所以需要将yolov8seg部署在rk3568里面。。。。

python


import cv2
import numpy as np

from rknn.api import RKNN
import os

if __name__ == '__main__':

    platform = 'rk3568'
    exp = 'yolov8nseg'
    Width = 640
    Height = 640
    MODEL_PATH = './onnx_models/yolov8nseg.onnx'
    NEED_BUILD_MODEL = True
    # NEED_BUILD_MODEL = False
    im_file = './bus.jpg'

    # Create RKNN object
    rknn = RKNN()

    OUT_DIR = "rknn_models"
    RKNN_MODEL_PATH = './{}/{}_kk.rknn'.format(OUT_DIR,exp+'-'+str(Width)+'-'+str(Height))
    if NEED_BUILD_MODEL:
        DATASET = './pose.txt'
        rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform="rk3568")
        # 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()

python demo: