hewumars

from keras.models import load_model
import tensorflow as tf
import os
from keras import backend as K
#路径设置
input_path = 'weights/'
weight_file = '****.h5'
weight_file_path = os.path.join(input_path,weight_file)
output_graph_name = weight_file[:-3]+'.pb'

hewumars

#include <gst/gst.h>
int main(int argc, char *argv[])
{
  gst_init(&argc,&argv);//命令行输入的参数传递给GStreamer函数库
  
  GstElement *pipeline, *filesrc, *decoder, *audiosink;//创建三个远见
  
  pipeline = gst_pipeline_new("pipeline");//创建用于容纳元件的新管道
  
  /*创建数据源元件*/

hewumars

#include <stdio.h>
#define HW_PAUSE()  do {\
        printf("---------------press Enter key to exit!---------------\n");\
        getchar();\
    } while (0)


#define HW_PRT(fmt...)   \
    do {\
        printf("[%s]-%d: ", __FUNCTION__, __LINE__);\

hewumars

//加载模型流程  //涉及的变量
HI_**_Param_S copParam[1];//自定义层参数结构体

static HI_RUNTIME_GROUP_INFO_S s_stGroupInfo;//模型组信息结构体，Load时用到
HI_RUNTIME_GROUP_HANDLE hGroupHandle;//句柄
HI_RUNTIME_WK_INFO_S astWkInfo[2];//wk模型信息，有几个模型就有多少元素
HI_RUNTIME_COP_ATTR_S stProposalAttr = {};  //custom operator自定义操作的属性 
HI_RUNTIME_CONNECTOR_ATTR_S stConnectorAttr = {0};//连接器的属性
HI_CHAR acConfig[1024] = {0};//modelgroup文件读入的数组
memset(&stProposalAttr, 0, sizeof(HI_RUNTIME_COP_ATTR_S));

hewumars

DeNet在COCO上实现mAP：33.6%@79Hz,41.8%@5Hz

hewumars

//permute
        u32Offset = 0;
        ps32InputBlob = pps32InputData[i];
        u32ChnOffset = au32GridNumHeight[i]*au32Stride[i]/sizeof(HI_S32);
        u32HeightOffset = au32Stride[i]/sizeof(HI_S32);
        for (h = 0; h < au32GridNumHeight[i]; h++)
        {
            for (w = 0; w < au32GridNumWidth[i]; w++)
            {
                for (c = 0; c < SAMPLE_SVP_NNIE_YOLOV3_EACH_BBOX_INFER_RESULT_NUM*u32EachGridBbox; c++)

hewumars

HI_U64 u64PhyAddrTmpBuf;
HI_S32 * pu32VirAddrTmpBuf;
HI_S32 *ps32TmpBuf=NULL;
//mmz名称，内存控制，物理地址，虚拟地址，size
s32Ret= SAMPLE_COMM_SVP_MallocMem("HIRTTmpBufSize",NULL,(HI_U64 *)&u64PhyAddrTmpBuf,(void**)&pu32VirAddrTmpBuf,u32TmpBufSize);
SAMPLE_CHECK_EXPR_RET(HI_SUCCESS!=s32Ret,s32Ret,"1SAMPLE_COMM_SVP_MallocMem:%x\n",pu32VirAddrTmpBuf);
ps32TmpBuf = pu32VirAddrTmpBuf;

hewumars

• 高质量数据优于花哨的算法

1. 准确性
2. 完整性
3. 一致性
4. 分布均匀性

• 数据清洗的工作流程

hewumars

说明

1. 所有库均来自opencv 4.1.1版本
2. 编译命令

mkdir build && cd build
cmake   -DBUILD_DOCS=off -DBUILD_SHARED_LIBS=off -DBUILD_FAT_JAVA_LIB=off -DBUILD_TESTS=off -DBUILD_TIFF=on -DBUILD_JASPER=off -DBUILD_JPEG=ON -DBUILD_ZLIB=on -DBUILD_OPENEXR=off -DBUILD_opencv_apps=off -DBUILD_opencv_calib3d=off -DBUILD_opencv_contrib=off -DBUILD_opencv_features2d=off -DBUILD_opencv_flann=off -DBUILD_opencv_gpu=off -DBUILD_opencv_java=off -DBUILD_opencv_legacy=off -DBUILD_opencv_ml=off -DBUILD_opencv_nonfree=off -DBUILD_opencv_objdetect=off -DBUILD_opencv_ocl=off -DBUILD_opencv_photo=off -DBUILD_opencv_python=off -DBUILD_opencv_stitching=off -DBUILD_opencv_superres=off -DBUILD_opencv_ts=off -DBUILD_opencv_video=off -DBUILD_opencv_videostab=off -DBUILD_opencv_world=off -DBUILD_opencv_lengcy=off -DBUILD_opencv_lengcy=off -DWITH_1394=off -DWITH_EIGEN=off -DWITH_FFMPEG=off -DWITH_GIGEAPI=off -DWITH_GSTREAMER=off -DWITH_GTK=off -DWITH_PVAPI=off  -DWITH_V4L=off -DWITH_LIBV4L=off -DWITH_CUDA=off -DWITH_CUFFT=off -DWITH_OPENCL=off -DWI

hewumars

void NV21_T_RGB( unsigned char *yuyv , unsigned char *rgb,unsigned int width , unsigned int height )
{
    const int nv_start = width * height ;
    HI_U32  i, j, index = 0, rgb_index = 0;
    HI_U8 y, u, v;
    int r, g, b, nv_index = 0;


    for(i = 0; i <  height ; i++)
    {