Yolo v4 (Darknet) Mac M2 安装与运行
源代码（有官方教程，大家有兴趣也可直接去里面看部署流程）：AlexeyAB/darknet: YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
环境安装
brew
Homebrew — The Missing Package Manager for macOS (or Linux)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
PyTorch
由于是 Mac，这里的PyTorch是CPU版本
pip3 install torch torchvision torchaudio
安装慢的话可以尝试添加镜像
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torch torchvision torchaudio
OpenCV
brew install opencv
如果以上命令报错，可以尝试先upgrade一下brew，命令如下：
brew upgrade
或者一些版本相对旧brew也可以尝试
brew install opencv@2
Darknet 部署
git clone https://github.com/AlexeyAB/darknet
cd darknet
mkdir build_release
cd build_release
cmake .. -DENABLE_CUDA=OFF
cmake --build . --target install --parallel 8
Weights下载：yolov4.weights，复制到darknet即可
测试运行
darknet项目目录下，终端运行
./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights data/dog.jpg -i 0 -thresh 0.25
终端输出类似
GPU isn't used
OpenCV version: 4.11.0
mini_batch = 1, batch = 8, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF
1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF
2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
3 route 1 -> 304 x 304 x 64
4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF
6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF
8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF
9 route 8 2 -> 304 x 304 x 128
10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF
11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF
12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
13 route 11 -> 152 x 152 x 128
14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF
15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF
18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF
19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF
....
并弹出预测图像
则部署成功！！
可能遇到的问题
…
opencv.pc…
当使用brew install opencv可能会出现这个问题
解决方法：
- 进入文件夹
/opt/homebrew/Cellar/opencv/[opencv版本号]/lib/pkgconfig - 重命名文件夹内的文件为
opencv.pc
OpenCV 4.x+ requires enabled C++11 support
解决方法：
打开项目文件下的
Makefile修改
Makefile内容
# 删除原来 CPP=.... 的内容, 替换为下面这一句
CPP=g++ -std=c++11
…
./src/image_opencv.cpp:12:1: fatal error: unknown type name IplImage…
解决方法：
- 修改项目文件下的
src/image_opencv.cpp，建议备份一下
#ifdef OPENCV
#include "stdio.h"
#include "stdlib.h"
#include "opencv2/opencv.hpp"
#include "image.h"
using namespace cv;
extern "C" {
/*IplImage *image_to_ipl(image im)
{
int x,y,c;
IplImage *disp = cvCreateImage(cvSize(im.w,im.h), IPL_DEPTH_8U, im.c);
int step = disp->widthStep;
for(y = 0; y < im.h; ++y){
for(x = 0; x < im.w; ++x){
for(c= 0; c < im.c; ++c){
float val = im.data[c*im.h*im.w + y*im.w + x];
disp->imageData[y*step + x*im.c + c] = (unsigned char)(val*255);
}
}
}
return disp;
}
image ipl_to_image(IplImage* src)
{
int h = src->height;
int w = src->width;
int c = src->nChannels;
image im = make_image(w, h, c);
unsigned char *data = (unsigned char *)src->imageData;
int step = src->widthStep;
int i, j, k;
for(i = 0; i < h; ++i){
for(k= 0; k < c; ++k){
for(j = 0; j < w; ++j){
im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
}
}
}
return im;
}*/
Mat image_to_mat(image im)
{
image copy = copy_image(im);
constrain_image(copy);
if(im.c == 3) rgbgr_image(copy);
int x, y, c;
Mat m = Mat(Size(im.w, im.h), CV_8UC(im.c));
int step = m.step;
for (y = 0; y < im.h; ++y) {
for (x = 0; x < im.w; ++x) {
for (c = 0; c < im.c; ++c) {
float val = copy.data[c*im.h*im.w + y * im.w + x];
m.data[y*step + x * im.c + c] = (unsigned char)(val * 255);
}
}
}
/* IplImage *ipl = image_to_ipl(copy);
Mat m = cvarrToMat(ipl, true);
cvReleaseImage(&ipl);*/
free_image(copy);
return m;
}
image mat_to_image(Mat m)
{
/* IplImage ipl = m;
image im = ipl_to_image(&ipl);
rgbgr_image(im);*/
int h = m.rows;
int w = m.cols;
int c = m.channels();
image im = make_image(w, h, c);
unsigned char *data = (unsigned char *)m.data;
int step = m.step;
int i, j, k;
for (i = 0; i < h; ++i) {
for (k = 0; k < c; ++k) {
for (j = 0; j < w; ++j) {
im.data[k*w*h + i * w + j] = data[i*step + j * c + k] / 255.;
}
}
}
if (im.c == 3) rgbgr_image(im);
return im;
}
void *open_video_stream(const char *f, int c, int w, int h, int fps)
{
VideoCapture *cap;
if(f) cap = new VideoCapture(f);
else cap = new VideoCapture(c);
if(!cap->isOpened()) return 0;
if(w) cap->set(CAP_PROP_FRAME_WIDTH, w);
if(h) cap->set(CAP_PROP_FRAME_HEIGHT, w);
if(fps) cap->set(CAP_PROP_FPS, w);
return (void *) cap;
}
image get_image_from_stream(void *p)
{
VideoCapture *cap = (VideoCapture *)p;
Mat m;
*cap >> m;
if(m.empty()) return make_empty_image(0,0,0);
return mat_to_image(m);
}
image load_image_cv(char *filename, int channels)
{
int flag = -1;
if (channels == 0) flag = -1;
else if (channels == 1) flag = 0;
else if (channels == 3) flag = 1;
else {
fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
}
Mat m;
m = imread(filename, flag);
if(!m.data){
fprintf(stderr, "Cannot load image \"%s\"\n", filename);
char buff[256];
sprintf(buff, "echo %s >> bad.list", filename);
system(buff);
return make_image(10,10,3);
//exit(0);
}
image im = mat_to_image(m);
return im;
}
int show_image_cv(image im, const char* name, int ms)
{
Mat m = image_to_mat(im);
imshow(name, m);
int c = waitKey(ms);
if (c != -1) c = c%256;
return c;
}
void make_window(char *name, int w, int h, int fullscreen)
{
namedWindow(name, WINDOW_NORMAL);
if (fullscreen) {
setWindowProperty(name, WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN);
} else {
resizeWindow(name, w, h);
if(strcmp(name, "Demo") == 0) moveWindow(name, 0, 0);
}
}
}
#endif