C++ OpenCV调用onnx格式的NanoDet-plus

#include "BehaviorDetect.h" BehaviorDetect::BehaviorDetect() { ifstream ifs(classesFile.c_str()); string line; while (getline(ifs, line)) this->class_names.push_back(line); this->num_class = class_names.size(); this->inpHeight = imgsize; this->inpWidth = imgsize; this->net = readNet(model_path); this->net.setPreferableBackend(DNN_BACKEND_CUDA); this->net.setPreferableTarget(DNN_TARGET_CUDA); if (net.empty()) { isBehaviorDetectOK = false; return; } else { isBehaviorDetectOK = true; } } BehaviorDetect::~BehaviorDetect() { } void BehaviorDetect::detect(Mat srcimg, vector<struct_behavior> &res1) { res1.clear(); struct_behavior tempResult; int newh = 0, neww = 0, top = 0, left = 0; Mat cv_image = srcimg.clone(); Mat dst = this->resize_image(cv_image, &newh, &neww, &top, &left); this->normalize(dst); Mat blob = blobFromImage(dst); this->net.setInput(blob); vector<Mat> outs; this->net.forward(outs, this->net.getUnconnectedOutLayersNames()); //generate proposals vector<BoxInfo> generate_boxes; const float* preds = (float*)outs[0].data; generate_proposal(generate_boxes, preds); nms(generate_boxes); float ratioh = (float)cv_image.rows / newh; float ratiow = (float)cv_image.cols / neww; for (size_t i = 0; i < generate_boxes.size(); ++i) { int xmin = (int)max((generate_boxes[i].x1 - left)*ratiow, 0.f); int ymin = (int)max((generate_boxes[i].y1 - top)*ratioh, 0.f); int xmax = (int)min((generate_boxes[i].x2 - left)*ratiow, (float)cv_image.cols); int ymax = (int)min((generate_boxes[i].y2 - top)*ratioh, (float)cv_image.rows); rectangle(srcimg, Point(xmin, ymin), Point(xmax, ymax), Scalar(0, 0, 255), 2); string label = format("%.2f", generate_boxes[i].score); label = this->class_names[generate_boxes[i].label] + ":" + label; putText(srcimg, label, Point(xmin, ymin - 5), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1); tempResult.confidence = generate_boxes[i].score; tempResult.bbox = Rect(generate_boxes[i].x1, generate_boxes[i].y1, generate_boxes[i].x2- generate_boxes[i].x1, generate_boxes[i].y2- generate_boxes[i].y1); tempResult.obj_name = this->class_names[generate_boxes[i].label]; res1.push_back(tempResult); } } cv::Mat BehaviorDetect::resize_image(Mat srcimg, int *newh, int *neww, int *top, int *left) { int srch = srcimg.rows, srcw = srcimg.cols; *newh = this->inpHeight; *neww = this->inpWidth; Mat dstimg; if (this->keep_ratio && srch != srcw) { float hw_scale = (float)srch / srcw; if (hw_scale > 1) { *newh = this->inpHeight; *neww = int(this->inpWidth / hw_scale); resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA); *left = int((this->inpWidth - *neww) * 0.5); copyMakeBorder(dstimg, dstimg, 0, 0, *left, this->inpWidth - *neww - *left, BORDER_CONSTANT, 0); } else { *newh = (int)this->inpHeight * hw_scale; *neww = this->inpWidth; resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA); *top = (int)(this->inpHeight - *newh) * 0.5; copyMakeBorder(dstimg, dstimg, *top, this->inpHeight - *newh - *top, 0, 0, BORDER_CONSTANT, 0); } } else { resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA); } return dstimg; } void BehaviorDetect::normalize(Mat& img) { img.convertTo(img, CV_32F); int i = 0, j = 0; for (i = 0; i < img.rows; i++) { float* pdata = (float*)(img.data + i * img.step); for (j = 0; j < img.cols; j++) { pdata[0] = (pdata[0] - this->mean[0]) / this->std[0]; pdata[1] = (pdata[1] - this->mean[1]) / this->std[1]; pdata[2] = (pdata[2] - this->mean[2]) / this->std[2]; pdata += 3; } } } void BehaviorDetect::softmax_(const float* x, float* y, int length) { float sum = 0; int i = 0; for (i = 0; i < length; i++) { y[i] = exp(x[i]); sum += y[i]; } for (i = 0; i < length; i++) { y[i] /= sum; } } void BehaviorDetect::generate_proposal(vector<BoxInfo>& generate_boxes, const float* preds) { const int reg_1max = reg_max + 1; const int len = this->num_class + 4 * reg_1max; for (int n = 0; n < this->num_stages; n++) { const int stride_ = this->stride[n]; const int num_grid_y = (int)ceil((float)this->inpHeight / stride_); const int num_grid_x = (int)ceil((float)this->inpWidth / stride_); ////cout << "num_grid_x=" << num_grid_x << ",num_grid_y=" << num_grid_y << endl; for (int i = 0; i < num_grid_y; i++) { for (int j = 0; j < num_grid_x; j++) { int max_ind = 0; float max_score = 0; for (int k = 0; k < num_class; k++) { if (preds[k] > max_score) { max_score = preds[k]; max_ind = k; } } if (max_score >= score_threshold) { const float* pbox = preds + this->num_class; float dis_pred[4]; float* y = new float[reg_1max]; for (int k = 0; k < 4; k++) { softmax_(pbox + k * reg_1max, y, reg_1max); float dis = 0.f; for (int l = 0; l < reg_1max; l++) { dis += l * y[l]; } dis_pred[k] = dis * stride_; } delete[] y; /*float pb_cx = (j + 0.5f) * stride_ - 0.5; float pb_cy = (i + 0.5f) * stride_ - 0.5;*/ float pb_cx = j * stride_; float pb_cy = i * stride_; float x0 = pb_cx - dis_pred[0]; float y0 = pb_cy - dis_pred[1]; float x1 = pb_cx + dis_pred[2]; float y1 = pb_cy + dis_pred[3]; generate_boxes.push_back(BoxInfo{ x0, y0, x1, y1, max_score, max_ind }); } preds += len; } } } } void BehaviorDetect::nms(vector<BoxInfo>& input_boxes) { sort(input_boxes.begin(), input_boxes.end(), [](BoxInfo a, BoxInfo b) { return a.score > b.score; }); vector<float> vArea(input_boxes.size()); for (int i = 0; i < int(input_boxes.size()); ++i) { vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1) * (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 1); } vector<bool> isSuppressed(input_boxes.size(), false); for (int i = 0; i < int(input_boxes.size()); ++i) { if (isSuppressed[i]) { continue; } for (int j = i + 1; j < int(input_boxes.size()); ++j) { if (isSuppressed[j]) { continue; } float xx1 = (max)(input_boxes[i].x1, input_boxes[j].x1); float yy1 = (max)(input_boxes[i].y1, input_boxes[j].y1); float xx2 = (min)(input_boxes[i].x2, input_boxes[j].x2); float yy2 = (min)(input_boxes[i].y2, input_boxes[j].y2); float w = (max)(float(0), xx2 - xx1 + 1); float h = (max)(float(0), yy2 - yy1 + 1); float inter = w * h; float ovr = inter / (vArea[i] + vArea[j] - inter); if (ovr >= this->nms_threshold) { isSuppressed[j] = true; } } } // return post_nms; int idx_t = 0; input_boxes.erase(remove_if(input_boxes.begin(), input_boxes.end(), [&idx_t, &isSuppressed](const BoxInfo& f) { return isSuppressed[idx_t++]; }), input_boxes.end()); } vector<string> BehaviorDetect::getImages(string path) { vector<string> imagesList; intptr_t hFile = 0; struct _finddata_t fileinfo; string p; hFile = _findfirst(p.assign(path).append("\\*.jpg").c_str(), &fileinfo); if (hFile != -1) { do { //cout << fileinfo.name << endl; imagesList.push_back(fileinfo.name);//保存类名 } while (_findnext(hFile, &fileinfo) == 0); } return imagesList; }