#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-
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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 【项目资源介绍】 在瑞芯微rockchip的AI芯片rv1109上,利用rknn和opencv库,修改了官方yolov3后处理部分代码Bug, Ubuntu18.04系统,瑞芯微交叉编译工具链,yolov3-demo示例后可成功上板部署运行。 同时,对在瑞芯微rockchip的其他AI芯片上利用NPU rknn部署CNN模型提供参考和借鉴。 以下为demo工程说明: libopencv_api 交叉编译时所依赖的opencv库,本工程opencv版本为4.3.0,已经打包好了; 亦可根据自己项目需要选择不同版本/不同阉割程度的opencv依赖库,需自行编译。 librknn_api 交叉编译时所依赖的rknn库,瑞芯微官方提供,rknn_api.h/librknn_api.so; 使用过程详见yolov3检测模型的预处理/加载/推断过程,main.cpp文件。 demo使用步骤如下: 工程编译生成test可执行文件; 命令make 工程编译生成libyolo库文件; 命
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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 (254个子文件)
yolov3_post_process.cpp 8KB
main.cpp 7KB
core_c.h 126KB
msa_macros.h 81KB
types_c.h 70KB
imgproc_c.h 50KB
kmeans_index.h 36KB
cvdef.h 33KB
constants_c.h 30KB
dist.h 27KB
cv_cpu_helper.h 26KB
hierarchical_clustering_index.h 26KB
autotuned_index.h 21KB
kdtree_single_index.h 20KB
kdtree_index.h 19KB
lsh_table.h 18KB
rknn_api.h 18KB
types_c.h 18KB
lsh_index.h 15KB
result_set.h 15KB
index_testing.h 11KB
highgui_c.h 10KB
any.h 8KB
cv_cpu_dispatch.h 8KB
hdf5.h 7KB
allocator.h 6KB
composite_index.h 6KB
nn_index.h 6KB
all_indices.h 6KB
saving.h 6KB
simplex_downhill.h 6KB
videoio_c.h 6KB
calib3d_c.h 5KB
cap_ios.h 5KB
interface.h 5KB
dynamic_bitset.h 4KB
defines.h 4KB
random.h 4KB
heap.h 4KB
logger.h 4KB
linear_index.h 4KB
cvconfig.h 3KB
ground_truth.h 3KB
matrix.h 3KB
params.h 3KB
object_factory.h 3KB
sampling.h 3KB
timer.h 3KB
ios.h 2KB
general.h 2KB
config.h 2KB
constants_c.h 2KB
interface.h 1KB
yolov3_post_process.h 1001B
interface.h 551B
constants_c.h 462B
constants_c.h 398B
dummy.h 197B
imgcodecs_c.h 145B
imgproc.hpp 226KB
color_detail.hpp 219KB
calib3d.hpp 195KB
mat.hpp 157KB
intrin_avx512.hpp 156KB
intrin_wasm.hpp 152KB
core.hpp 148KB
intrin_sse.hpp 131KB
intrin_avx.hpp 130KB
mat.inl.hpp 100KB
ml.hpp 90KB
intrin_neon.hpp 86KB
intrin_cpp.hpp 81KB
opencl_clamdblas.hpp 80KB
intrin_msa.hpp 71KB
types.hpp 71KB
intrin_vsx.hpp 66KB
dnn.hpp 65KB
features2d.hpp 64KB
videoio.hpp 55KB
vsx_utils.hpp 50KB
vec_math.hpp 49KB
matx.hpp 46KB
persistence.hpp 46KB
sse_utils.hpp 41KB
cuda.hpp 38KB
utility.hpp 37KB
photo.hpp 37KB
objdetect.hpp 37KB
highgui.hpp 35KB
tracking.hpp 34KB
functional.hpp 32KB
simd_functions.hpp 30KB
base.hpp 26KB
flann.hpp 26KB
ocl.hpp 24KB
border_interpolate.hpp 24KB
opengl.hpp 23KB
all_layers.hpp 23KB
reduce_key_val.hpp 23KB
opencl_core.hpp 22KB
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