通过Tengine ssd 处理usb camera 实时图像
开发环境：
Rock960开发板
Ubuntu16.04
deltavision usb camera
一 Tengine安装
下载Tengine代码： https://github.com/OAID/Tengine
安装文档：doc/install.md
1. 依赖库
- caffe依赖库
sudo apt install libprotobuf-dev protobuf-compiler libboost-all-dev libgoogle-glog-dev- opencv
sudo apt install libopencv-dev2.配置文件
cd ~/tengine
cp makefile.config.example makefile.config因为直接在rock960上开发，直接可以使用默认配置
3. 编译
cd ~/tengine
make4. 验证
./build/tests/bin/bench_mobilenet -r1二. 运行自带的ssd example
1. 编译example
cd ~/tengine/examples
vim linux_build.sh修改cmake，“/home/usr/tengine”是tengine 所在目录，“/usr/lib/aarch64-linux-gnu” 是protobuf库的所在目录
修改 examples/mobilenet_ssd/CMakeLists.txt， 添加一句 set( TENGINE_DIR /home/rock/Tengine)
cmake -DPROTOBUF_DIR=/usr/lib/aarch64-linux-gnu -DTENGINE_DIR=/home/usr/tengine \
..编译
mkdir build
cd build
../linux_build.sh
make -j4 2. 运行ssd
把官方提供的模型，放在 ${Tengine_ROOT}/models/
- MobileNetSSD_deploy.caffemodel
- MobileNetSSD_deploy.prototxt
执行
cd example/build/mobilenet_ssd
./MSSD -p ../../../models/MobileNetSSD_deploy.prototxt -m ../../../models/MobileNetSSD_deploy.caffemodel -i img.jpg三 . 从usb camera 获取图像并处理
读取usb camera 的方法参考：https://mp.csdn.net/postedit/85252440
1. 复制 mobilenet_ssd目录，命名为mobilenet_ssd_camera
修改 examples/CMakeLists.txt文件， 添加一句
add_subdirectory(mobilenet_ssd_camera)2. 修改 mobilenet_ssd_camera/mssd.cpp
/*
* V4L2 video capture example
*
* This program can be used and distributed without restrictions.
*
* This program is provided with the V4L2 API
* see https://linuxtv.org/docs.php for more information
*/
#include
#include
#include
#include
#include
#include
#include /* getopt_long() */
#include /* low-level i/o */
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include "opencv2/imgproc/imgproc.hpp"
#include
#include "tengine_c_api.h"
#include "common.hpp"
#define DEF_PROTO "models/MobileNetSSD_deploy.prototxt"
#define DEF_MODEL "models/MobileNetSSD_deploy.caffemodel"
#define DEF_IMAGE "tests/images/ssd_dog.jpg"
#define CLEAR(x) memset(&(x), 0, sizeof(x))
#define COLS (640)
#define ROWS (480)
#define SSD_IMG_H (300)
#define SSD_IMG_W (300)
using namespace cv;
enum io_method {
IO_METHOD_READ,
IO_METHOD_MMAP,
IO_METHOD_USERPTR,
};
struct buffer {
void *start;
size_t length;
};
typedef struct buffer* PBUF;
static char *dev_name;
static enum io_method io = IO_METHOD_MMAP;
static int fd = -1;
struct buffer *buffers;
static unsigned int n_buffers;
static int out_buf;
static int force_format;
static int frame_count = 70;
static std::string proto_file;
static std::string model_file;
static std::string image_file;
static std::string save_name="save.jpg";
const char *model_name = "mssd_300";
cv::Mat yuvImg(ROWS , COLS, CV_8UC2);
cv::Mat rgbImg(ROWS, COLS,CV_8UC3);
cv::Mat resizeImg(SSD_IMG_W, SSD_IMG_H,CV_8UC3);
cv::Mat floatImg(SSD_IMG_W, SSD_IMG_H, CV_32FC3);
static int fpsTick();
struct Box
{
float x0;
float y0;
float x1;
float y1;
int class_idx;
float score;
};
void get_input_data_ssd(std::string& image_file, float* input_data, int img_h, int img_w)
{
cv::Mat img = cv::imread(image_file);
if (img.empty())
{
std::cerr << "Failed to read image file " << image_file << ".\n";
return;
}
cv::resize(img, img, cv::Size(img_h, img_w));
img.convertTo(img, CV_32FC3);
float *img_data = (float *)img.data;
int hw = img_h * img_w;
float mean[3]={127.5,127.5,127.5};
for (int h = 0; h < img_h; h++)
{
for (int w = 0; w < img_w; w++)
{
for (int c = 0; c < 3; c++)
{
input_data[c * hw + h * img_w + w] = 0.007843* (*img_data - mean[c]);
img_data++;
}
}
}
}
void post_process_ssd(std::string& image_file,float threshold,float* outdata,int num,std::string& save_name)
{
std::cout<<"post_process_ssd\n";
const char* class_names[] = {"background",
"aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair",
"cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant",
"sheep", "sofa", "train", "tvmonitor"};
//cv::Mat img = cv::imread(image_file);
int raw_h = rgbImg.size().height;
int raw_w = rgbImg.size().width;
std::vector boxes;
int line_width=raw_w*0.005;
printf("detect ruesult num: %d \n",num);
for (int i=0;i=threshold)
{
Box box;
box.class_idx=outdata[0];
box.score=outdata[1];
box.x0=outdata[2]*raw_w;
box.y0=outdata[3]*raw_h;
box.x1=outdata[4]*raw_w;
box.y1=outdata[5]*raw_h;
boxes.push_back(box);
printf("%s\t:%.0f%%\n", class_names[box.class_idx], box.score * 100);
printf("BOX:( %g , %g ),( %g , %g )\n",box.x0,box.y0,box.x1,box.y1);
}
outdata+=6;
}
for(int i=0;i<(int)boxes.size();i++)
{
Box box=boxes[i];
cv::rectangle(rgbImg, cv::Rect(box.x0, box.y0,(box.x1-box.x0),(box.y1-box.y0)),cv::Scalar(255, 255, 0),line_width);
std::ostringstream score_str;
score_str< 3. 重新make
回到 examples 目录，执行make\
正常的话， 会生成 examples/build/mobilenet_ssd_camera 目录
4. 执行
./MSSD -p ../../../models/MobileNetSSD_deploy.prototxt -m ../../../models/MobileNetSSD_deploy.caffemodel