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[導讀]點擊上方藍字關注我哦～ 01 前言在前面兩篇關于火焰檢測的文章中，最終的效果不是很好，為了提高火焰檢測的效果，又搜集了一些火焰數(shù)據(jù)，訓練的網(wǎng)絡由之前的yolov3-tiny改為mobilev2-yolov3，最終在樹莓派上利用NCNN推算框架，比之前的效果要好很多，如圖：

點擊上方藍字關注我哦～

前言

在前面兩篇關于火焰檢測的文章中，最終的效果不是很好，為了提高火焰檢測的效果，又搜集了一些火焰數(shù)據(jù)，訓練的網(wǎng)絡由之前的yolov3-tiny改為mobilev2-yolov3，最終在樹莓派上利用NCNN推算框架，比之前的效果要好很多，如圖：

實現(xiàn)步驟

在darknet下訓練

訓練的cfg和model文件如果需要聯(lián)系筆者。

在樹莓派上部署NCNN

官方提供了在樹莓派上的編譯說明，按照這個說明是可以編譯起來的。這里可以參考這篇文章來安裝依賴：

sudo apt-get install git cmakesudo apt-get install -y gfortransudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compilersudo apt-get install --no-install-recommends libboost-all-devsudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev libatlas-base-dev

然后下載NCNN：

git clone https://github.com/Tencent/ncnn.gitcd ncnn

編輯CMakeList.txt文件，添加examples和benchmark：

add_subdirectory(examples)add_subdirectory(benchmark)add_subdirectory(tools)

然后就可以按照官方文檔進行編譯了，官方提供的pi3 toolchain在4代Raspbian上可以直接使用，最新版的NCNN會自動使用OpenMP：

cd <ncnn-root-dir>mkdir -p buildcd buildcmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/pi3.toolchain.cmake -DPI3=ON ..make -j4

模型轉換

cd <ncnn-root-dir>cd buildcd tools/darknet./darknet2ncnn mobilenetV2-yolov3.cfg mobilenetV2-yolov3.weights mobilenetV2-yolov3.param mobilenetV2-yolov3.bin 1

運行

cd <ncnn-root-dir>cd buildcd example./mobilenetV2-yolov3

部分代碼

#include "net.h"
#include "platform.h"

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <stdio.h>
#include <vector>
#include <sys/time.h>
#include <stdio.h>
#include <string.h>
#if NCNN_VULKAN
#include "gpu.h"
#endif // NCNN_VULKAN

#define MobileNetV2-yolov3_TINY 1 //0 or undef for MobileNetV2-yolov3

struct Object
{
 cv::Rect_<float> rect;
 int label;
 float prob;
};

double what_time_is_it_now(){
 struct timeval time;
 if (gettimeofday(&time,NULL)){
 return 0;
 }
 return (double)time.tv_sec + (double)time.tv_usec * .000001;
}
ncnn::Net MobileNetV2-yolov3;
static int detect_MobileNetV2-yolov3(const cv::Mat& bgr, std::vector<Object>& objects)
{
 double time;
#if NCNN_VULKAN
 MobileNetV2-yolov3.opt.use_vulkan_compute = true;
#endif // NCNN_VULKAN

 const int target_size = 320;
 time = what_time_is_it_now();
 int img_w = bgr.cols;
 int img_h = bgr.rows;
 //PIXEL_BGR
 ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, bgr.cols, bgr.rows, target_size, target_size);

 const float mean_vals[3] = {0, 0, 0};
 const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
 in.substract_mean_normalize(mean_vals, norm_vals);

 ncnn::Extractor ex = MobileNetV2-yolov3.create_extractor();
 ex.set_num_threads(4);

 ex.input("data", in);

 ncnn::Mat out;
 ex.extract("output", out);
 printf("Predicted in %f seconds.11\n", what_time_is_it_now()-time);
 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;
}

void draw_objects(cv::Mat& image, const std::vector<Object>& objects){
 static const char* class_names[] = {"background", "fire"};

 //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));
 }
}

int main(int argc, char** argv){
 MobileNetV2-yolov3.load_param("MobileNetV2-YOLOv3-Lite.param");
 MobileNetV2-yolov3.load_model("MobileNetV2-YOLOv3-Lite.bin");
 cv::VideoCapture cap(0);
 if(!cap.isOpened()){
 printf("capture err");
 return -1;
 }
 cv::Mat cv_img;
 std::vector<Object> objects;
 while(true){
 if(!cap.read(cv_img)){
 printf("cv_img err");
 return -1;
 }
 detect_MobileNetV2-yolov3(cv_img, objects);
 draw_objects(cv_img, objects);
 cv::imshow("video", cv_img);
 cv::waitKey(1);
 }
 cap.release();
 return 0;
}