301 lines
12 KiB
C++
301 lines
12 KiB
C++
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#include "postprocess.h"
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std::tuple<uint8_t, uint8_t, uint8_t> hsv2bgr(float h, float s, float v)
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{
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const int h_i = static_cast<int>(h * 6);
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const float f = h * 6 - h_i;
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const float p = v * (1 - s);
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const float q = v * (1 - f*s);
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const float t = v * (1 - (1 - f) * s);
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float r, g, b;
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switch (h_i) {
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case 0:r = v; g = t; b = p;break;
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case 1:r = q; g = v; b = p;break;
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case 2:r = p; g = v; b = t;break;
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case 3:r = p; g = q; b = v;break;
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case 4:r = t; g = p; b = v;break;
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case 5:r = v; g = p; b = q;break;
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default:r = 1; g = 1; b = 1;break;}
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return std::make_tuple(static_cast<uint8_t>(b * 255), static_cast<uint8_t>(g * 255), static_cast<uint8_t>(r * 255));
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}
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std::tuple<uint8_t, uint8_t, uint8_t> hsv2rgb(float h, float s, float v)
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{
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const int h_i = static_cast<int>(h * 6);
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const float f = h * 6 - h_i;
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const float p = v * (1 - s);
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const float q = v * (1 - f*s);
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const float t = v * (1 - (1 - f) * s);
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float r, g, b;
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switch (h_i) {
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case 0:r = v; g = t; b = p;break;
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case 1:r = q; g = v; b = p;break;
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case 2:r = p; g = v; b = t;break;
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case 3:r = p; g = q; b = v;break;
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case 4:r = t; g = p; b = v;break;
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case 5:r = v; g = p; b = q;break;
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default:r = 1; g = 1; b = 1;break;}
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return std::make_tuple(static_cast<uint8_t>(r * 255), static_cast<uint8_t>(g * 255), static_cast<uint8_t>(b * 255));
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}
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std::tuple<uint8_t, uint8_t, uint8_t> randomColor(int id)
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{
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float h_plane = ((((unsigned int)id << 2) ^ 0x937151) % 100) / 100.0f;;
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float s_plane = ((((unsigned int)id << 3) ^ 0x315793) % 100) / 100.0f;
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// return hsv2bgr(h_plane, s_plane, 1);
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return hsv2rgb(h_plane, s_plane, 1);
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}
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//坐标转换
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void xywh2xyxy(float *xywh, float * xyxy)
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{
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xyxy[0] = (float)(xywh[0] - xywh[2] / 2);
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xyxy[1] = (float)(xywh[1] - xywh[3] / 2);
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xyxy[2] = (float)(xywh[0] + xywh[2] / 2);
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xyxy[3] = (float)(xywh[1] + xywh[3] / 2);
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}
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//获取区域框1
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cv::Rect getRect(cv::Mat& img, unsigned int uiModelWidth, unsigned int uiModelHeight, float fBbox[4])
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{
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float l, r, t, b;
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float r_w = uiModelWidth / (img.cols * 1.0);
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float r_h = uiModelHeight / (img.rows * 1.0);
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if(r_h > r_w){
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l = fBbox[0] - fBbox[2] / 2.f;
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r = fBbox[0] + fBbox[2] / 2.f;
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t = fBbox[1] - fBbox[3] / 2.f - (uiModelHeight - r_w * img.rows) / 2;
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b = fBbox[1] + fBbox[3] / 2.f - (uiModelHeight - r_w * img.rows) / 2;
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l = l / r_w;
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r = r / r_w;
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t = t / r_w;
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b = b / r_w;
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}else{
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l = fBbox[0] - fBbox[2] / 2.f - (uiModelWidth - r_h * img.cols) / 2;
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r = fBbox[0] + fBbox[2] / 2.f - (uiModelWidth - r_h * img.cols) / 2;
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t = fBbox[1] - fBbox[3] / 2.f;
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b = fBbox[1] + fBbox[3] / 2.f;
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l = l / r_h;
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r = r / r_h;
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t = t / r_h;
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b = b / r_h;
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}
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return cv::Rect(round(l), round(t), round(r - l), round(b - t));
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}
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//获取区域框2
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cv::Rect getRectangle(cv::Mat& img, float fBbox[4])
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{
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return cv::Rect(round(fBbox[0]), round(fBbox[1]), round(fBbox[2]-fBbox[0]), round(fBbox[3]-fBbox[1]));
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}
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//计算IOU
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float iou(float fLbox[4], float fRbox[4])
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{
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float interBox[] = {
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(std::max)(fLbox[0] - fLbox[2] / 2.f , fRbox[0] - fRbox[2] / 2.f), //left
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(std::min)(fLbox[0] + fLbox[2] / 2.f , fRbox[0] + fRbox[2] / 2.f), //right
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(std::max)(fLbox[1] - fLbox[3] / 2.f , fRbox[1] - fRbox[3] / 2.f), //top
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(std::min)(fLbox[1] + fLbox[3] / 2.f , fRbox[1] + fRbox[3] / 2.f), //bottom
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};
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if (interBox[2] > interBox[3] || interBox[0] > interBox[1])
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return 0.0f;
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float fInterBoxS = (interBox[1] - interBox[0])*(interBox[3] - interBox[2]);
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return fInterBoxS / (fLbox[2] * fLbox[3] + fRbox[2] * fRbox[3] - fInterBoxS);
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}
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//比较两者置信度
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bool confCmp(const Detection& a, const Detection& b)
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{
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return a.fClassConf > b.fClassConf;
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}
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//获取缩放比例
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float GetResizeRatio(unsigned int uiImgWidth, unsigned int uiImgHeight, unsigned int uiModelWidth, unsigned int uiModelHeight)
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{
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float fRatioW = static_cast<float>(uiImgWidth) / uiModelWidth;
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float fRatioH = static_cast<float>(uiImgHeight) / uiModelHeight;
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return (fRatioW - fRatioH > 1e-5) ? fRatioW : fRatioH;
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}
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//左上顶点补边方式坐标还原
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void UpperVertexResetLocation(float fResizeRatio, unsigned int uiOrigWidth, unsigned int uiOrigHeight, Detection &detection)
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{
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for(int i=0; i<4; i++){
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detection.fBbox[i] = detection.fBbox[i] * fResizeRatio;
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}
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detection.fBbox[0] = (detection.fBbox[0] < uiOrigWidth) ? detection.fBbox[0] : uiOrigWidth;
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detection.fBbox[1] = (detection.fBbox[1] < uiOrigHeight) ? detection.fBbox[1] : uiOrigHeight;
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detection.fBbox[2] = (detection.fBbox[2] < uiOrigWidth) ? detection.fBbox[2] : uiOrigWidth;
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detection.fBbox[3] = (detection.fBbox[3] < uiOrigHeight) ? detection.fBbox[3] : uiOrigHeight;
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}
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//中心补边方式坐标还原
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void CenterResetLocation(float fResizeRatio, unsigned int uiOrigWidth, unsigned int uiOrigHeight, unsigned int uiInputWidth, unsigned int uiInputHeight, Detection &detection)
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{
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int w, h, x, y;
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float r_w = uiInputWidth / (uiOrigWidth*1.0);
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float r_h = uiInputHeight / (uiOrigHeight*1.0);
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if (r_h > r_w) {
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w = uiInputWidth;
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h = r_w * uiOrigHeight;
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x = 0;
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y = (uiInputHeight - h) / 2;
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detection.fBbox[1] -= y;
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detection.fBbox[3] -= y;
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} else {
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w = r_h * uiOrigWidth;
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h = uiInputHeight;
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x = (uiInputWidth - w) / 2;
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y = 0;
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detection.fBbox[0] -= x;
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detection.fBbox[2] -= x;
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}
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for(int i=0; i<4; i++){
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detection.fBbox[i] = detection.fBbox[i] * fResizeRatio;
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}
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detection.fBbox[0] = (detection.fBbox[0] < uiOrigWidth) ? detection.fBbox[0] : uiOrigWidth;
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detection.fBbox[1] = (detection.fBbox[1] < uiOrigHeight) ? detection.fBbox[1] : uiOrigHeight;
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detection.fBbox[2] = (detection.fBbox[2] < uiOrigWidth) ? detection.fBbox[2] : uiOrigWidth;
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detection.fBbox[3] = (detection.fBbox[3] < uiOrigHeight) ? detection.fBbox[3] : uiOrigHeight;
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}
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//非极大值抑制,默认0.5
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void yolov5ClearDecodeOpenCVNms(std::vector<ClearDetection>& vecRes, float *fOutput, unsigned int uiOutSize, unsigned int uiDetSize, unsigned int uiClassNum, unsigned int uiClearNum, float fConfThresh = 0.5, float fNmsThresh = 0.4)
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{
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//1.筛选出第一轮结果，根据conf > conf_thresh
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std::vector<int> vecFilterList;
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for(int i = 0; i < uiOutSize /uiDetSize ; ++i){ //锚框数量(遍历锚框筛去置信度过于低的)
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if(fOutput[uiDetSize * i + 4] > fConfThresh){
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vecFilterList.emplace_back(i); // 记录下所有置信度大于conf_thresh的锚框
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}
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}
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if(vecFilterList.size() == 0) return;
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//2.查找剩余锚框中每个类得分最高值并记录标签
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std::vector<ClearDetection> vecResult;
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std::vector<cv::Rect> vecBoxes;
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std::vector<float> vecScores;
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for(int i : vecFilterList){
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float* pClassConfidence = &fOutput[uiDetSize * i + 5]; //类别
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float fClassConf = *pClassConfidence++;
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int iClassLabel = 0;
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for(int j = 1; j <uiClassNum; ++j, ++pClassConfidence){ //N个类别中查找置信度最高的,并记录标签
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if(*pClassConfidence > fClassConf){
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fClassConf = *pClassConfidence;
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iClassLabel = j;
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}
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}
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float* pClearConfidence = &fOutput[uiDetSize * i + 5 + uiClassNum]; //清晰度
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float fClearConf = *pClearConfidence++;
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int iClearLabel = 0;
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for(int n = 1; n <uiClearNum; ++n, ++pClearConfidence){ //N个清晰度中查找置信度最高的,并记录标签
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if(*pClearConfidence > fClearConf){
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fClearConf = *pClearConfidence;
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iClearLabel = n;
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}
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}
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float xywh[4] = {fOutput[i*uiDetSize + 0], fOutput[i*uiDetSize + 1], fOutput[i*uiDetSize + 2], fOutput[i*uiDetSize + 3]};
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float xyxy[4];
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xywh2xyxy(xywh, xyxy); //转换后的坐标
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ClearDetection clearDetection;
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for(int n = 0; n<4; n++){
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clearDetection.detection.fBbox[n] = xyxy[n];
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}
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clearDetection.detection.fClassConf = fOutput[uiDetSize * i + 4] * fClassConf;
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clearDetection.detection.iClassId = iClassLabel;
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clearDetection.fClearConf = fOutput[uiDetSize * i + 4] * fClearConf;
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clearDetection.iClearId = iClearLabel;
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//总得分大于某阈值才进行NMS,此处阈值可以乘某系数!
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if(clearDetection.detection.fClassConf>fConfThresh) {
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cv::Rect tempRect;
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tempRect.x = xyxy[0];
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tempRect.y = xyxy[1];
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tempRect.width = std::abs(xyxy[2] - xyxy[0]);
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tempRect.height = std::abs(xyxy[3] - xyxy[1]);
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vecBoxes.push_back(tempRect);
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vecScores.push_back(clearDetection.detection.fClassConf);
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vecResult.push_back(clearDetection);
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}
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}
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std::vector<int> vecIdx; //保留nms后框的索引
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cv::dnn::NMSBoxes(vecBoxes, vecScores, fConfThresh, fNmsThresh, vecIdx);
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for (std::size_t i = 0; i < vecIdx.size(); i++){
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vecRes.push_back(vecResult[vecIdx[i]]);
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}
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}
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//非极大值抑制,默认0.5
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void yolov5DecodeOpenCVNms(std::vector<Detection>& vecRes, float *fOutput, unsigned int uiOutSize, unsigned int uiDetSize, unsigned int uiClassNum, float fConfThresh = 0.5, float fNmsThresh = 0.4)
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{
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//1.筛选出第一轮结果，根据conf > conf_thresh
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std::vector<int> vecFilterList;
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for(int i = 0; i < uiOutSize /uiDetSize ; ++i){ //锚框数量(遍历锚框筛去置信度过于低的)
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if(fOutput[uiDetSize * i + 4] > fConfThresh){
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vecFilterList.emplace_back(i); // 记录下所有置信度大于conf_thresh的锚框
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}
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}
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if(vecFilterList.size() == 0) return;
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//2.查找剩余锚框中每个类得分最高值并记录标签
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std::vector<Detection> vecResult;
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std::vector<cv::Rect> vecBoxes;
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std::vector<float> vecScores;
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for(int i : vecFilterList){
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float* pClassConfidence = &fOutput[uiDetSize * i + 5]; //类别
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float fClassConf = *pClassConfidence++;
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int iClassLabel = 0;
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for(int j = 1; j <uiClassNum; ++j, ++pClassConfidence){ //N个类别中查找置信度最高的,并记录标签
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if(*pClassConfidence > fClassConf){
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fClassConf = *pClassConfidence;
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iClassLabel = j;
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}
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}
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float xywh[4] = {fOutput[i*uiDetSize + 0], fOutput[i*uiDetSize + 1], fOutput[i*uiDetSize + 2], fOutput[i*uiDetSize + 3]};
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float xyxy[4];
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xywh2xyxy(xywh, xyxy); //转换后的坐标
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Detection detection;
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for(int n = 0; n<4; n++){
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detection.fBbox[n] = xyxy[n];
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}
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detection.fClassConf = fOutput[uiDetSize * i + 4] * fClassConf;
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detection.iClassId = iClassLabel;
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//总得分大于某阈值才进行NMS,此处阈值可以乘某系数!
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|
if(detection.fClassConf>fConfThresh) {
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|
cv::Rect tempRect;
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|
tempRect.x = xyxy[0];
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|
tempRect.y = xyxy[1];
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|
tempRect.width = std::abs(xyxy[2] - xyxy[0]);
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|
tempRect.height = std::abs(xyxy[3] - xyxy[1]);
|
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|
|
|
|||
|
|
vecBoxes.push_back(tempRect);
|
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|
|
vecScores.push_back(detection.fClassConf);
|
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|
|
vecResult.push_back(detection);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
std::vector<int> vecIdx; //保留nms后框的索引
|
|||
|
|
cv::dnn::NMSBoxes(vecBoxes, vecScores, fConfThresh, fNmsThresh, vecIdx);
|
|||
|
|
|
|||
|
|
for (std::size_t i = 0; i < vecIdx.size(); i++){
|
|||
|
|
vecRes.push_back(vecResult[vecIdx[i]]);
|
|||
|
|
}
|
|||
|
|
}
|