YOLOv3移植部署瑞芯微RV1109开发板项目源码(适用RV1126可编译运行)+项目使用说明+CV库.zip

#include <stdio.h> #include <stdint.h> #include <stdlib.h> #include <fstream> #include <iostream> #include <fstream> #include <atomic> #include <mutex> #include <chrono> #include <sys/time.h> #include <sys/stat.h> #include <sys/syscall.h> #include <dirent.h> #include <unistd.h> #include <signal.h> #include "rknn_api.h" #include "opencv2/core/core.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/opencv.hpp" #include "yolov3_post_process.h" using namespace std; using namespace cv; Scalar colorArray[10]={ Scalar(139,0,0,255), Scalar(139,0,139,255), Scalar(0,0,139,255), Scalar(0,100,0,255), Scalar(139,139,0,255), Scalar(209,206,0,255), Scalar(0,127,255,255), Scalar(139,61,72,255), Scalar(0,255,0,255), Scalar(255,0,0,255), }; static string labels[80]={"person", "bicycle", "car","motorbike ","aeroplane ","bus ","train","truck ","boat", "traffic light","fire hydrant","stop sign ","parking meter","bench","bird","cat","dog ","horse ","sheep","cow", "elephant","bear","zebra ","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis", "snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket", "bottle","wine glass","cup","fork","knife ","spoon","bowl","banana","apple","sandwich","orange","broccoli", "carrot","hot dog","pizza ","donut","cake","chair","sofa","pottedplant","bed","diningtable","toilet ","tvmonitor", "laptop","mouse","remote ","keyboard ","cell phone","microwave ","oven ","toaster","sink","refrigerator ", "book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush "}; static int nboxes_0=GRID0*GRID0*nanchor; static int nboxes_1=GRID1*GRID1*nanchor; static int nboxes_2=GRID2*GRID2*nanchor; static int nboxes_total=nboxes_0+nboxes_1+nboxes_2; void free_detections(detection *dets, int n) { int i; for(i = 0; i < n; ++i){ free(dets[i].prob); } free(dets); } box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int netw, int neth, int stride) { box b; b.x = (i + x[index + 0*stride]) / lw; b.y = (j + x[index + 1*stride]) / lh; b.w = exp(x[index + 2*stride]) * biases[2*n] / netw; b.h = exp(x[index + 3*stride]) * biases[2*n+1] / neth; return b; } void get_network_boxes(float *predictions, int netw,int neth,int GRID,int* masks, float* anchors, int box_off, detection* dets) { int lw=GRID; int lh=GRID; int nboxes=GRID*GRID*nanchor; int LISTSIZE=1+4+nclasses; //darkent output排列格式: box顺序为先grid再anchor //1个anchor: 7*7*x+7*7*y+7*7*w+7*7*w+7*7*obj+7*7*classes1+7*7*classes2..+7*7*classes80,共3个anchor //x和y(先做,或者放在后面转换到实际坐标这一步中也行),以及obj和classes做logisic //xy做logistic for(int n=0;n<nanchor;n++){ int index=n*lw*lh*LISTSIZE; int index_end=index+2*lw*lh; for(int i=index;i<index_end;i++) predictions[i]=1./(1.+exp(-predictions[i])); } //类别和obj做logistic for(int n=0;n<nanchor;n++){ int index=n*lw*lh*LISTSIZE+4*lw*lh; int index_end=index+(1+nclasses)*lw*lh; for(int i=index;i<index_end;i++){ predictions[i]=1./(1.+exp(-predictions[i])); } } //dets将outpus重排列,dets[i]为第i个框,box顺序为先anchor再grid int count=box_off; for(int i=0;i<lw*lh;i++){ int row=i/lw; int col=i%lw; for(int n=0;n<nanchor;n++){ int box_loc=n*lw*lh+i; int box_index=n*lw*lh*LISTSIZE+i; //box的x索引,ywh索引只要依次加上lw*lh int obj_index=box_index+4*lw*lh; float objectness=predictions[obj_index]; if(objectness<OBJ_THRESH) continue; dets[count].objectness=objectness; dets[count].classes=nclasses; dets[count].bbox=get_yolo_box(predictions,anchors,masks[n],box_index,col,row,lw,lh,netw,neth,lw*lh); for(int j=0;j<nclasses;j++){ //for(int j=0;j<1;j++){ int class_index=box_index+(5+j)*lw*lh; float prob=objectness*predictions[class_index]; dets[count].prob[j]=prob; //cout<<j<<"==>"<<dets[count].prob[j]<<"\n"; } ++count; } } //cout<<"count: "<<count-box_off<<"\n"; //return dets; } int outputs_transform(rknn_output rknn_outputs[], int net_width, int net_height, detection* dets){ float* output_0=(float*)rknn_outputs[0].buf; float* output_1=(float*)rknn_outputs[1].buf; float* output_2=(float*)rknn_outputs[2].buf; int masks_0[3] = {6, 7, 8}; int masks_1[3] = {3, 4, 5}; int masks_2[3] = {0, 1, 2}; //float anchors[12] = {10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319}; float anchors[18] = {10,13,16,30,33,23,30,61,62,45,59,119,116,90,156,198,373,326}; //输出xywh均在0-1范围内 get_network_boxes(output_0,net_width,net_height,GRID0,masks_0,anchors,0,dets); get_network_boxes(output_1,net_width,net_height,GRID1,masks_1,anchors,nboxes_0,dets); get_network_boxes(output_2,net_width,net_height,GRID2,masks_2,anchors,nboxes_0+nboxes_1,dets); return 0; } float overlap(float x1,float w1,float x2,float w2){ float l1=x1-w1/2; float l2=x2-w2/2; float left=l1>l2? l1:l2; float r1=x1+w1/2; float r2=x2+w2/2; float right=r1<r2? r1:r2; return right-left; } float box_intersection(box a, box b) { float w = overlap(a.x, a.w, b.x, b.w); float h = overlap(a.y, a.h, b.y, b.h); if(w < 0 || h < 0) return 0; float area = w*h; return area; } float box_union(box a, box b) { float i = box_intersection(a, b); float u = a.w*a.h + b.w*b.h - i; return u; } float box_iou(box a, box b) { return box_intersection(a, b)/box_union(a, b); } int nms_comparator(const void *pa, const void *pb) { detection a = *(detection *)pa; detection b = *(detection *)pb; float diff = 0; if(b.sort_class >= 0){ diff = a.prob[b.sort_class] - b.prob[b.sort_class]; } else { diff = a.objectness - b.objectness; } if(diff < 0) return 1; else if(diff > 0) return -1; return 0; } int do_nms_sort(detection *dets, int total, int classes, float thresh) { int i, j, k; k = total-1; for(i = 0; i <= k; ++i){ if(dets[i].objectness == 0){ detection swap = dets[i]; dets[i] = dets[k]; dets[k] = swap; --k; --i; } } total = k+1; //cout<<"total after OBJ_THRESH: "<<total<<"\n"; for(k = 0; k < classes; ++k){ for(i = 0; i < total; ++i){ dets[i].sort_class = k; } qsort(dets, total, sizeof(detection), nms_comparator); for(i = 0; i < total; ++i){ if(dets[i].prob[k] == 0) continue; box a = dets[i].bbox; for(j = i+1; j < total; ++j){ box b = dets[j].bbox; if (box_iou(a, b) > thresh){ dets[j].prob[k] = 0; } } } } return total; } int draw_image(cv::Mat img,detection* dets,int total,float thresh) { //::cvtColor(img, img, cv::COLOR_RGB2BGR); for(int i=0;i<total;i++){ char labelstr[4096]={0}; int class_=-1; int topclass=-1; float topclass_score=0; if(dets[i].objectness==0) continue; for(int j=0;j<nclasses;j++){ if(dets[i].prob[j]>thresh){ if(topclass_score<dets[i].prob[j]){ topclass_score=dets[i].prob[j]; topclass=j; } if(class_<0){ sprintf(labelstr,"%d",int(dets[i].prob[j]*100+0.5)); // strcat(labelstr,labels[j].data()); class_=j; } else{ strcat(labelstr,","); sprintf(labelstr,"%d",int(dets[i].prob[j]*100+0.5)); // strcat(labelstr,labels[j].data()); } //printf("%s: %.02f%%\n",labels[j].data(),dets[i].prob[j]*100); } } //如果class>0说明框中有物体,需画框 if(class_>=0){ box b=dets[i].bbox; int x1 =(b.x-b.w/2.)*img.cols; int x2=(b.x+b.w/2.)*img.cols; int y1=(b.y-b.h/2.)*img.rows; int y2=(b.y+b.h/2.)*img.rows; if(x1 < 0) x1 = 0; if(x2> img.cols-1) x2 = img.cols-1; if(y1 < 0) y1 = 0; if(y2 > img.rows-1) y2 = img.rows-