.gitignore
New file @@ -0,0 +1,2 @@ *.avi track go.mod
@@ -2,4 +2,4 @@ go 1.12 require gocv.io/x/gocv v0.21.0 require basic.com/valib/goffmpeg.git v0.0.0-20191129092141-7363d175bd62 go.sum
@@ -1,2 +1,2 @@ gocv.io/x/gocv v0.21.0 h1:dVjagrupZrfCRY0qPEaYWgoNMRpBel6GYDH4mvQOK8Y= gocv.io/x/gocv v0.21.0/go.mod h1:Rar2PS6DV+T4FL+PM535EImD/h13hGVaHhnCu1xarBs= basic.com/valib/goffmpeg.git v0.0.0-20191129092141-7363d175bd62 h1:KzkPzJE76RkHeYBgAMfSiz1vzJaQRKkRDCXnw2XmxqA= basic.com/valib/goffmpeg.git v0.0.0-20191129092141-7363d175bd62/go.mod h1:1x75Hawh/BjgPsQtuJ24px89gzk3uAslD8e0Xs6Z7GQ= gohumantrack/TensorRT-5.1.2.2/lib/libnvcaffe_parser.soBinary files differ
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gohumantrack/gohumantrack.go
@@ -1,39 +1,45 @@ package gohumantrack /* #cgo CFLAGS: -I${SRCDIR}/sdk/include -I/usr/local/cuda/include -w -g #cgo CFLAGS: -I${SRCDIR}/sdk -w -g #cgo LDFLAGS: -L/usr/local/cuda/lib64 -L${SRCDIR}/sdk/libs #cgo LDFLAGS: -Wl,-rpath,${SRCDIR}/sdk/libs #cgo LDFLAGS: -lHumanTracker -lutools -ldl -lpthread -lm #cgo LDFLAGS: -L${SRCDIR}/opencv-2.4.13/lib -L${SRCDIR}/TensorRT-5.1.2.2/lib #cgo LDFLAGS: -Wl,-rpath,${SRCDIR}/sdk/libs:${SRCDIR}/TensorRT-5.1.2.2:${SRCDIR}/opencv-2.4.13 #cgo LDFLAGS: -lmidHumanTrack -lHumanTracker -lutools #cgo LDFLAGS: -lcudart -lcublas -lcurand -lcusolver -lcudnn #cgo LDFLAGS: -lnppc -lnppicom #cgo LDFLAGS: -lnvrtc -lnvinfer -lnvinfer_plugin -lnvparsers -lnvonnxparser #cgo LDFLAGS: -lopencv_video -lopencv_highgui #cgo LDFLAGS: -ldl -lpthread -lm #include <stdlib.h> #include "sy_human_tracker.h" typedef void* hdl_human_tracker; #include "c_include/c_human_tracker.h" void *create_batch_image(const int size){ sy_img *imgs = (sy_img*)malloc(size * sizeof(sy_img)); c_img *imgs = (c_img*)malloc(size * sizeof(c_img)); return imgs; } int fill_images(void *imgs, const int size, const int index, void *data, const int w, const int h, const int c){ if(!imgs || !data || size <= index) return -1; sy_img *images = (sy_img*)imgs; c_img *images = (c_img*)imgs; images[index].data_ = data; images[index].w_ = w; images[index].h_ = h; images[index].c_ = c; return index; } void *init_fgres(const int size){ fgRet *ret = (fgRet*)malloc(size * sizeof(fgRet)); c_fgRet *ret = (c_fgRet*)malloc(size * sizeof(c_fgRet)); for(int i = 0; i < size; i++){ ret[i].fgNum = 0; } return ret; } void *process(void *handle, void *imgs, const int size){ sy_img *images = (sy_img*)imgs; fgRet *res = init_fgres(size); int ret = human_tracker_process(handle, images, size, res); c_img *images = (c_img*)imgs; c_fgRet *res = init_fgres(size); int ret = c_human_tracker_process(handle, images, size, res); if (ret != 0) return NULL; return res; @@ -64,21 +70,17 @@ // int ID;//行人跟踪ID // float feature[FEATURESIZE];//行人专属特征，可用来做ReID const ( MAX_BG_NUM = 2000 FEATURESIZE = 128 ) // FgInfo info type FgInfo struct { Left int Right int Top int Bottom int Left int32 Right int32 Top int32 Bottom int32 Confidence float32 CenterX int CenterY int Feature [FEATURESIZE]float32 X int32 Y int32 ID int32 Feature [128]float32 } // fgInfo fginfo[MAX_BG_NUM];//上述结构体，代表一幅图中所有的框，最大支持2000个框，暂不可修改 @@ -86,13 +88,13 @@ // FgResult result type FgResult struct { Infos [MAX_BG_NUM]FgInfo FgNum int Fginfo [2000]FgInfo FgNum int32 } // HumanTracker struct type HumanTracker struct { handle C.hdl_human_tracker handle unsafe.Pointer batchSize int } @@ -101,7 +103,7 @@ if gpu == -1 { return nil } p := C.human_tracker_create(C.int(gpu), C.int()) p := C.c_human_tracker_create(C.int(gpu), C.int(batchSize), C.int(flag)) if p != nil { return &HumanTracker{p, batchSize} } @@ -111,7 +113,7 @@ // Free free func (h *HumanTracker) Free() { if h.handle != nil { C.human_tracker_release(&h.handle) C.c_human_tracker_release(&h.handle) } } @@ -120,11 +122,11 @@ if h.handle == nil { return "human tracker handle null" } ver := C.getVersion() ver := C.c_getVersion() if ver == nil { return "get version string null" } return C.CString(ver) return C.GoString(ver) } // Process process @@ -132,40 +134,40 @@ if len(imgs) != h.batchSize { return nil, errors.New("input images count doesn't equalize to batchsize") } cImgs := C.create_batch_image(h.batchSize) cImgs := C.create_batch_image(C.int(h.batchSize)) if cImgs == nil { return nil, errors.New("create C images error") } defer C.free(cImgs) for k, v := range imgs { ret := C.fill_images(cImgs, h.batchSize, k, unsafe.Pointer(&v.Data[0]), C.int(v.Width), C.int(v.Height), C.int(v.Channel)) if ret != k { ret := C.fill_images(cImgs, C.int(h.batchSize), C.int(k), unsafe.Pointer(&v.Data[0]), C.int(v.Width), C.int(v.Height), C.int(v.Channel)) if int(ret) != k { return nil, errors.New("fill C images error") } } cRet := C.process(h.handle, cImgs, h.batchSize) cRet := C.process(h.handle, cImgs, C.int(h.batchSize)) if cRet == nil { return nil, errors.New("create C results error") } defer C.free(cRet) defer C.free(unsafe.Pointer(cRet)) var result []FgResult p := uintptr(cRet) for i := 0; i < h.batchSize; i++ { j := *(*FgResult)(unsafe.Pointer(p)) FgResult = append(FgResult, j) result = append(result, j) p += unsafe.Sizeof(j) } return return result, nil } // FFSimilarity similarity func FFSimilarity(feaA, feaB [FEATURESIZE]float32) float64 { func FFSimilarity(feaA, feaB [128]float32) float64 { var norm1, norm2 float64 var score float64 for i := 0; i < FEATURESIZE; i++ { for i := 0; i < 128; i++ { norm1 += float64(feaA[i]) * float64(feaA[i]) norm2 += float64(feaB[i]) * float64(feaB[i]) score += float64(feaA[i]) * float64(feaB[i]) @@ -182,3 +184,16 @@ return score } // CFFSimilarity use c code func CFFSimilarity(feaA, feaB []float32) (float64, error) { if len(feaA) != len(feaB) { return 0, errors.New("Must be same length") } if len(feaA) != 128 || len(feaB) != 128 { return 0, errors.New("Must be 128 size") } s := C.c_FF_Similarity((*C.float)(&feaA[0]), (*C.float)(&feaB[0])) return float64(s), nil } gohumantrack/opencv-2.4.13/lib/libopencv_core.soBinary files differ
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gohumantrack/sdk/c_include/c_human_tracker.h
New file @@ -0,0 +1,89 @@ #ifndef C_HUMAN_TRACKER #define C_HUMAN_TRACKER #include <string.h> #define MAX_BG_NUM 2000 #define FEATURESIZE 128 #ifdef __cplusplus extern "C" { #endif //ImgData #ifndef __C_IMG__ #define __C_IMG__ typedef struct c_img { unsigned char * data_;//按opencv Mat格式存储的图像数据(bgrbgrbgr…) int w_;//图像宽度 int h_;//图像高度 int c_;//图像通道数，目前仅支持3通道 } c_img; void set_data(c_img img, int m_w, int m_h, int m_c, unsigned char * m_data); #endif typedef struct c_fgInfo { int left;//行人包围框左上角横坐标 int right;//行人包围框右下角横坐标 int top;//行人包围框左上角纵坐标 int bottom;//行人包围框右下角纵坐标 float confidence;//行人检测框置信度得分 int center_x;//行人包围框中心点x int center_y;//行人包围框中心点y int ID;//行人跟踪ID float feature[FEATURESIZE];//行人专属特征，可用来做ReID }c_fgInfo; typedef struct c_fgRet { c_fgInfo fginfo[MAX_BG_NUM];//上述结构体，代表一幅图中所有的框，最大支持2000个框，暂不可修改 int fgNum;//一幅图中框的数量（fginfo中的有效框数） }c_fgRet; /*功能:整个检测跟踪程序初始化，创建句柄 参数: Gpuid: 显卡ID， batch_size: 批处理数量，可以设置为视频路数 wh_flag: 视频帧的图像宽大于高时，设置为0，否则设置为1。 返回值:句柄 */ void * c_human_tracker_create( int gpuid, int batch_size, int wh_flag); /* 功能:资源释放 参数:句柄的地址 返回值:void */ void c_human_tracker_release(void **handle); /* 功能:对多张图像进行检测跟踪 参数: handle：句柄 batch_mat: sy_img类型的数组，存放多张图像。 batch_img_length：图像数量 trackresult: 每张图像的检测和跟踪结果，提前声明好的数组，数量为图像数，以fgRet类型存储。 返回值:-1 失败或其他错误码 0 成功 */ int c_human_tracker_process(void * handle, c_img batch_mat[], int batch_img_length, c_fgRet trackresult[]); /* 功能:获得版本信息 参数:void 返回值:版本信息char* */ const char* c_getVersion(); /* 功能:特征值比对 参数:特征值1和特征值2 返回值:得分 */ double c_FF_Similarity(float * feaA, float * feaB); #ifdef __cplusplus } #endif #endif gohumantrack/sdk/cpp_include/sy_human_tracker.h
File was renamed from gohumantrack/sdk/include/sy_human_tracker.h @@ -1,10 +1,7 @@ #ifndef SY_HUMAN_TRACKER #define SY_HUMAN_TRACKER #include <math.h> #include<vector> #define MAX_BG_NUM 2000 #define FEATURESIZE 128 //ImgData #ifndef __SY_IMG__ #define __SY_IMG__ @@ -14,13 +11,13 @@ int w_;//图像宽度 int h_;//图像高度 int c_;//图像通道数，目前仅支持3通道 // void set_data(int m_w, int m_h, int m_c, unsigned char * m_data) // { // w_ = m_w; // h_ = m_h; // c_ = m_c; // data_ = m_data; //Shallow copy // } void set_data(int m_w, int m_h, int m_c, unsigned char * m_data) { w_ = m_w; h_ = m_h; c_ = m_c; data_ = m_data; //Shallow copy } } sy_img; #endif @@ -33,7 +30,7 @@ int center_x;//行人包围框中心点x int center_y;//行人包围框中心点y int ID;//行人跟踪ID float feature[FEATURESIZE];//行人专属特征，可用来做ReID float feature[128];//行人专属特征，可用来做ReID }fgInfo; typedef struct fgRet { @@ -75,33 +72,5 @@ 返回值:版本信息char* */ const char* getVersion(); /* 功能:特征值比对 参数:特征值1和特征值2 返回值:得分 */ double FF_Similarity(float * feaA, float * feaB) { double norm1 = 0, norm2 = 0; int i = 0; double score = 0; for (i = 0; i < FEATURESIZE; i++) { norm1 += feaA[i] * feaA[i]; norm2 += feaB[i] * feaB[i]; score += feaA[i] * feaB[i]; } norm1 = sqrt(norm1); norm2 = sqrt(norm2); score = score / (norm1 * norm2); if (score < 0) // 制去除小数部分 score = 0; return score; } #endif gohumantrack/sdk/libs/libmidHumanTrack.soBinary files differ
gohumantrack/sdk/libs/libutools.so
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main.go
@@ -7,7 +7,7 @@ "track/gohumantrack" "gocv.io/x/gocv" "basic.com/valib/goffmpeg.git" ) var ( @@ -20,55 +20,71 @@ flag.StringVar(&url2, "cam2", "", "url") } func main() { flag.Parse() fmt.Println("url1: ", url1, " url2: ", url2) if len(url1) == 0 || len(url2) == 0 { fmt.Println("url null") } cam1, err := gocv.OpenVideoCapture(url1) if err != nil { fmt.Println("cv open url1 error: ", err) return } cam2, err := gocv.OpenVideoCapture(url2) if err != nil { fmt.Println("cv open url2 error: ", err) return } goffmpeg.InitFFmpeg("./runtime/libcffmpeg.so") img1 := gocv.NewMat() defer img1.Close() img2 := gocv.NewMat() defer img2.Close() cam1 := goffmpeg.New(false, false) cam1.Run(url1) cam1.BuildDecoder() cam1.CloseStream() cam2 := goffmpeg.New(false, false) cam2.Run(url2) cam2.BuildDecoder() cam2.CloseStream() tracker := gohumantrack.NewHumanTracker(0, 2, 0) fmt.Println("version: ", tracker.GetVersion()) for { if ok := cam1.Read(&img1); !ok || img1.Empty() { fmt.Println("read url1 image error") time.Sleep(40 * time.Millisecond) continue } if ok := cam2.Read(&img2); !ok || img2.Empty() { fmt.Println("read url2 image error") time.Sleep(40 * time.Millisecond) continue } data1, ow1, oh1, _ := cam1.GetYUV() data2, ow2, oh2, _ := cam2.GetYUV() if ow1 > 0 && oh1 > 0 && ow2 > 0 && oh2 > 0 { var images []gohumantrack.ImageHumanTracker img := gohumantrack.ImageHumanTracker{ Data: img1.ToBytes(), Width: img1.Cols, Height: img1.Rows, Channel: img1.Channels, var images []gohumantrack.ImageHumanTracker img := gohumantrack.ImageHumanTracker{ Data: data1, Width: ow1, Height: oh1, Channel: 3, } images = append(images, img) img = gohumantrack.ImageHumanTracker{ Data: data2, Width: ow2, Height: oh2, Channel: 3, } images = append(images, img) res, err := tracker.Process(images) if err == nil { for _, v := range res { fmt.Printf("result size: %d\n", v.FgNum) for i := 0; i < int(v.FgNum); i++ { r := v.Fginfo[i] if r.Confidence > 0 { fmt.Printf(" Index %d Rect: %dx%dx%dx%d", i, r.Left, r.Top, r.Right, r.Bottom) fmt.Printf(" Confidence: %f", r.Confidence*100) fmt.Printf(" Center: %dx%d", r.X, r.Y) fmt.Printf(" ID: %d\n", r.ID) } } } } else { fmt.Println("process error: ", err) } } else { // fmt.Println("cam1 size: ", ow1, "x", oh1, " cam2 size: ", ow2, "x", oh2) time.Sleep(38 * time.Millisecond) } images = append(images, img) img = gohumantrack.ImageHumanTracker{ Data: img2.ToBytes(), Width: img2.Cols, Height: img2.Rows, Channel: img2.Channels, } images = append(images, img) } tracker.Free() goffmpeg.FreeFFmpeg() } runtime/libHumanTracker.soBinary files differ
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