NCNN环境搭建
获取ncnn
https://github.com/Tencent/ncnn
编译ncnn
1 | cd ncnn |
安装onnx-simplifier
1 | pip install onnx-simplifier |
使用
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Opencv与ncnn开发环境配置模板
opencv编译详细步骤：
https://www.cnblogs.com/raina/p/11365854.html
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/opt/Qt5.13.0/5.13.0/gcc_64/lib/cmake/Qt5是我Qt的Qt5Config.cmake所在路径, 需要改成你自己的, 如果不需要opencv支持Qt用户界面, 可以把-D WITH_QT=ON \和-D CMAKE_PREFIX_PATH=/opt/Qt5.13.0/5.13.0/gcc_64/lib/cmake/Qt5 \两行删掉.
另外, 不指定”Qt5Config.cmake”所在路径, 在cmake编译的时候可能会报如下错误:
CMake Error at cmake/OpenCVFindLibsGUI.cmake:18 (find_package):
Could not find a package configuration file provided by “Qt5” with any of
the following names:
Qt5Config.cmake
qt5-config.cmake
Add the installation prefix of “Qt5” to CMAKE_PREFIX_PATH or set “Qt5_DIR”
to a directory containing one of the above files. If “Qt5” provides a
separate development package or SDK, be sure it has been installed.
配置OpenCV环境
sudo gedit /etc/ld.so.conf.d/opencv.conf
在文件最后添加
/usr/local/lib
生效配置:
sudo ldconfig
CMakeLists.txt
# 最低版本要求
cmake_minimum_required(VERSION 3.4.1)
project(ncnnOpencv)
# 设置C++编译版本
set(CMAKE_CXX_STANDARD 11)
# ncnn项目所在路径，需要替换
set(NCNN_DIR /home/ray/ncnn)
# 分别设置ncnn的链接库和头文件
set(NCNN_LIBS ${NCNN_DIR}/build/install/lib/libncnn.a)
set(NCNN_INCLUDE_DIRS ${NCNN_DIR}/build/install/include/ncnn)
# 配置OpenCV
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
include_directories(${NCNN_INCLUDE_DIRS})
# 配置OpenMP
FIND_PACKAGE( OpenMP REQUIRED)
if(OPENMP_FOUND)
message("OPENMP FOUND")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()
# 建立链接依赖
add_executable(ncnnOpencv Main.cpp)
target_link_libraries(ncnnOpencv ${NCNN_LIBS})
target_link_libraries(ncnnOpencv ${OpenCV_LIBS})Main.cpp
#include <iostream>
#include <fstream>
#include <algorithm>
#include <opencv2/opencv.hpp>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/videoio.hpp>
#include <stdio.h>
#include <vector>
#include "platform.h"
#include "net.h"
#if NCNN_VULKAN
#include "gpu.h"
#endif // NCNN_VULKAN
using namespace std;
using namespace cv;
struct Object
{
cv::Rect_<float> rect;
int label;
float prob;
};
static int detect_yolov3(const cv::Mat& bgr, std::vector<Object>& objects)
{
ncnn::Net yolov3;
#if NCNN_VULKAN
yolov3.opt.use_vulkan_compute = true;
#endif // NCNN_VULKAN
// original pretrained model from https://github.com/eric612/MobileNet-YOLO
// param : https://drive.google.com/open?id=1V9oKHP6G6XvXZqhZbzNKL6FI_clRWdC-
// bin : https://drive.google.com/open?id=1DBcuFCr-856z3FRQznWL_S5h-Aj3RawA
// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
yolov3.load_param("mobilenet_yolo.param");
yolov3.load_model("mobilenet_yolo.bin");
const int target_size = 352;
int img_w = bgr.cols;
int img_h = bgr.rows;
ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, target_size, target_size);
const float mean_vals[3] = {127.5f, 127.5f, 127.5f};
const float norm_vals[3] = {0.007843f, 0.007843f, 0.007843f};
in.substract_mean_normalize(mean_vals, norm_vals);
ncnn::Extractor ex = yolov3.create_extractor();
ex.set_num_threads(4);
ex.input("data", in);
ncnn::Mat out;
ex.extract("detection_out", out);
// printf("%d %d %d\n", out.w, out.h, out.c);
objects.clear();
for (int i=0; i<out.h; i++)
{
const float* values = out.row(i);
Object object;
object.label = values[0];
object.prob = values[1];
object.rect.x = values[2] * img_w;
object.rect.y = values[3] * img_h;
object.rect.width = values[4] * img_w - object.rect.x;
object.rect.height = values[5] * img_h - object.rect.y;
objects.push_back(object);
}
return 0;
}
static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
static 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 image = bgr.clone();
for (size_t i = 0; i < objects.size(); i++)
{
const Object& obj = objects[i];
fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0));
char text[256];
sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);
int baseLine = 0;
cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
int x = obj.rect.x;
int y = obj.rect.y - label_size.height - baseLine;
if (y < 0)
y = 0;
if (x + label_size.width > image.cols)
x = image.cols - label_size.width;
cv::rectangle(image, cv::Rect(cv::Point(x, y),
cv::Size(label_size.width, label_size.height + baseLine)),
cv::Scalar(255, 255, 255), -1);
cv::putText(image, text, cv::Point(x, y + label_size.height),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
}
cv::imshow("image", image);
// cv::waitKey(0);
}
void drawText(Mat & image)
{
putText(image, "Hello OpenCV",
Point(20, 50),
FONT_HERSHEY_COMPLEX, 1, // font face and scale
Scalar(255, 255, 255), // white
1, LINE_AA); // line thickness and type
}
int main()
{
Mat image;
VideoCapture capture;
capture.open(0);
if(capture.isOpened())
{
cout << "Capture is opened" << endl;
for(;;)
{
capture >> image;
if(image.empty())
break;
// drawText(image);
std::vector<Object> objects;
detect_yolov3(image, objects);
draw_objects(image, objects);
// imshow("Sample", image);
if(waitKey(10) >= 0)
break;
}
}
else
{
cout << "No capture" << endl;
image = Mat::zeros(480, 640, CV_8UC1);
drawText(image);
imshow("Sample", image);
waitKey(0);
}
return 0;
}执行cmake和make:
mkdir build
cd build
cmake ..
makec_cpp_properties.json
{
"configurations": [
{
"name": "Linux",
"includePath": [
"${workspaceFolder}/**",
"/usr/local/include/opencv4",
"/home/ray/ncnn/build/install/include/ncnn"
],
"defines": [],
"compilerPath": "/usr/bin/gcc",
"cStandard": "c11",
"cppStandard": "c++17",
"intelliSenseMode": "clang-x64"
}
],
"version": 4
}