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opencv/main.cpp
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174
opencv/main.cpp
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#include <fstream>
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#include <sstream>
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#include <iostream>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/dnn.hpp>
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using namespace std;
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using namespace cv;
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using namespace dnn;
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struct Net_config
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{
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float confThreshold; // Confidence threshold
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float nmsThreshold; // Non-maximum suppression threshold
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string modelpath;
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string datatype;
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};
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class FreeYOLO
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{
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public:
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FreeYOLO(Net_config config);
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void detect(Mat& frame);
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private:
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int inpWidth;
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int inpHeight;
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int nout;
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int num_proposal;
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vector<string> class_names;
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int num_class;
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const int num_stride = 3;
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int strides[3] = { 8,16,32 };
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float confThreshold;
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float nmsThreshold;
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Net net;
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};
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FreeYOLO::FreeYOLO(Net_config config)
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{
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this->confThreshold = config.confThreshold;
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this->nmsThreshold = config.nmsThreshold;
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this->net = readNet(config.modelpath);
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size_t pos = config.modelpath.rfind("_");
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size_t pos_ = config.modelpath.rfind(".");
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int len = pos_ - pos - 1;
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string hxw = config.modelpath.substr(pos + 1, len);
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pos = hxw.rfind("x");
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string h = hxw.substr(0, pos);
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len = hxw.length() - pos;
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string w = hxw.substr(pos + 1, len);
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this->inpHeight = stoi(h);
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this->inpWidth = stoi(w);
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if (config.datatype == "coco")
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{
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string classesFile = "coco.names";
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ifstream ifs(classesFile.c_str());
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string line;
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while (getline(ifs, line)) this->class_names.push_back(line);
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}
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else if (config.datatype == "face")
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{
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this->class_names.push_back("face");
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}
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else
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{
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this->class_names.push_back("person");
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}
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this->num_class = class_names.size();
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}
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void FreeYOLO::detect(Mat& frame)
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{
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const float ratio = std::min(float(this->inpHeight) / float(frame.rows), float(this->inpWidth) / float(frame.cols));
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const int neww = int(frame.cols * ratio);
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const int newh = int(frame.rows * ratio);
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Mat dstimg;
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resize(frame, dstimg, Size(neww, newh));
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copyMakeBorder(dstimg, dstimg, 0, this->inpHeight - newh, 0, this->inpWidth - neww, BORDER_CONSTANT, 114);
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Mat blob = blobFromImage(dstimg);
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this->net.setInput(blob);
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vector<Mat> outs;
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this->net.forward(outs, this->net.getUnconnectedOutLayersNames()); // <20><>ʼ<EFBFBD><CABC><EFBFBD><EFBFBD>
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num_proposal = outs[0].size[1];
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nout = outs[0].size[2];
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const float* pdata = (float*)outs[0].data;
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int n = 0, i = 0, j = 0, k = 0; ///cx, cy, w, h, box_score, class_score
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vector<float> confidences;
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vector<Rect> boxes;
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vector<int> classIds;
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for (n = 0; n < this->num_stride; n++) ///<2F><><EFBFBD><EFBFBD>ͼ<EFBFBD>߶<EFBFBD>
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{
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int num_grid_x = (int)ceil((this->inpWidth / strides[n]));
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int num_grid_y = (int)ceil((this->inpHeight / strides[n]));
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for (i = 0; i < num_grid_y; i++)
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{
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for (j = 0; j < num_grid_x; j++)
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{
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const float box_score = pdata[4];
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int max_ind = 0;
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float max_class_socre = 0;
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for (k = 0; k < num_class; k++)
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{
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if (pdata[k + 5] > max_class_socre)
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{
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max_class_socre = pdata[k + 5];
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max_ind = k;
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}
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}
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max_class_socre *= box_score;
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max_class_socre = sqrt(max_class_socre);
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if (max_class_socre > this->confThreshold)
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{
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float cx = (0.5f + j + pdata[0]) * strides[n]; ///cx
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float cy = (0.5f + i + pdata[1]) * strides[n]; ///cy
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float w = expf(pdata[2]) * strides[n]; ///w
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float h = expf(pdata[3]) * strides[n]; ///h
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float xmin = (cx - 0.5 * w) / ratio;
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float ymin = (cy - 0.5 * h) / ratio;
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float xmax = (cx + 0.5 * w) / ratio;
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float ymax = (cy + 0.5 * h) / ratio;
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int left = int((cx - 0.5 * w) / ratio);
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int top = int((cy - 0.5 * h) / ratio);
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int width = int(w / ratio);
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int height = int(h / ratio);
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confidences.push_back(max_class_socre);
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boxes.push_back(Rect(left, top, width, height));
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classIds.push_back(max_ind);
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}
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pdata += nout;
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}
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}
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}
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vector<int> indices;
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dnn::NMSBoxes(boxes, confidences, this->confThreshold, this->nmsThreshold, indices);
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for (size_t i = 0; i < indices.size(); ++i)
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{
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int idx = indices[i];
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Rect box = boxes[idx];
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rectangle(frame, Point(box.x, box.y), Point(box.x + box.width, box.y + box.height), Scalar(0, 0, 255), 3);
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string label = format("%.2f", confidences[idx]);
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label = this->class_names[classIds[idx]] + ":" + label;
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putText(frame, label, Point(box.x, box.y - 10), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 255, 0), 2);
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}
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}
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int main()
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{
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Net_config cfg = { 0.8, 0.5, "weights/face/yolo_free_huge_widerface_192x320.onnx", "face" };
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FreeYOLO net(cfg);
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string imgpath = "images/face/1.jpg";
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Mat srcimg = imread(imgpath);
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net.detect(srcimg);
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static const string kWinName = "Deep learning object detection in OpenCV";
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namedWindow(kWinName, WINDOW_NORMAL);
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imshow(kWinName, srcimg);
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waitKey(0);
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destroyAllWindows();
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}
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