Pre

一定要知道，yolov8的输出与Yolov5 7.0 实例分割的输出不一样，

output0: float32[1,116,8400]。 116是4个box坐标信息+80个类别概率+32个mask系数

output1: float32[1,32,160,160]。一张图片一组mask原型。这个mask原型是相对于整个图像的。

一、OpenCV DNN C++ 部署

所以output0需要装置一下，117->116,其他的与yolov5实例分割没啥区别：

net.setInput(blob);
    std::vector<cv::Mat> net_output_img;
    vector<string> output_layer_names{ "output0","output1" };
    net.forward(net_output_img, output_layer_names); //get outputs
    std::vector<int> class_ids;// res-class_id
    std::vector<float> confidences;// res-conf
    std::vector<cv::Rect> boxes;// res-box
    std::vector<vector<float>> picked_proposals;  //output0[:,:, 4 + _className.size():net_width]===> for mask
    int net_width = _className.size() + 4 + _segChannels;

//转置
    Mat output0=Mat( Size(net_output_img[0].size[2], net_output_img[0].size[1]), CV_32F, (float*)net_output_img[0].data).t();  //[bs,116,8400]=>[bs,8400,116]

    int rows = output0.rows;
    float* pdata = (float*)output0.data;
    for (int r = 0; r < rows; ++r) {
            cv::Mat scores(1, _className.size(), CV_32FC1, pdata + 4);
            Point classIdPoint;
            double max_class_socre;
            minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
            max_class_socre = (float)max_class_socre;
            if (max_class_socre >= _classThreshold) {
                vector<float> temp_proto(pdata + 4 + _className.size(), pdata + net_width);
                picked_proposals.push_back(temp_proto);
                //rect [x,y,w,h]
                float x = (pdata[0] - params[2]) / params[0];
                float y = (pdata[1] - params[3]) / params[1];
                float w = pdata[2] / params[0];
                float h = pdata[3] / params[1];
                int left = MAX(int(x - 0.5 * w + 0.5), 0);
                int top = MAX(int(y - 0.5 * h + 0.5), 0);
                class_ids.push_back(classIdPoint.x);
                confidences.push_back(max_class_socre);
                boxes.push_back(Rect(left, top, int(w + 0.5), int(h + 0.5)));
            }
            pdata += net_width;//next line
    }
    //NMS
    vector<int> nms_result;
    NMSBoxes(boxes, confidences, _classThreshold, _nmsThreshold, nms_result);
    std::vector<vector<float>> temp_mask_proposals;
    Rect holeImgRect(0, 0, srcImg.cols, srcImg.rows);
    for (int i = 0; i < nms_result.size(); ++i) {

        int idx = nms_result[i];
        OutputSeg result;
        result.id = class_ids[idx];
        result.confidence = confidences[idx];
        result.box = boxes[idx] & holeImgRect;
        temp_mask_proposals.push_back(picked_proposals[idx]);
        output.push_back(result);
    }
    MaskParams mask_params;
    mask_params.params = params;
    mask_params.srcImgShape = srcImg.size();
    for (int i = 0; i < temp_mask_proposals.size(); ++i) {
        GetMask2(Mat(temp_mask_proposals[i]).t(), net_output_img[1], output[i], mask_params);
    }

但是我read模型的时候报错：ERROR during processing node with 2 inputs and 1 outputs: [Reshape]:(/model.22/dfl/Reshape_output_0) from domain='ai.onnx'

应该是OpenCV版本需要升级到4.7。之前部署检测的时候也报这个错：yolov8 OpenCV DNN 部署推理报错_爱钓鱼的歪猴的博客-CSDN博客

我不想升级OpenCV了，所以选择用ONNX RUNTIME 部署