zyy-cn/darknet_interface

Darknet Interface

This repository is an INTERFACE for darknet, which allow you to use darknet detector in your own program(C, C++, Python, etc...) to do something interesting (like object detect using YOLO in your PC, Raspberry PI, Nvidia TX1, etc...) by linking with "libdarknet.so" and "libdetector.so".

Demo GIF

Click here to view demo

Update

2020.07.16 Yolo_v4 is available.

Interface Functions Discription

void detector_init(char cfgfile, char weightfile)

brief: initiate an detector into the memory by loading specific network configuration and pretrained weight file.
param[in1]: path to the network configuration file
param[in2]: path to the network pretrained weight file
retval: void

float* test_detector_file(char filename, float thresh, float hier_thresh, int num_output_class)

brief: detect objects in a specific image file.
param[in1]: path to the image file which want to be detected
param[in2]: detect thresh
param[in3]: set 0.5 as default and don't care in yolov3 (maybe used only for yolo9000?)
param[in4]: the number of objects being detected in image
retval: information about boundary box according to all objects detected in image. Each object is described as a set of six float data in sequence:
- class index
- confidence score
- top-left x-coordinate of bbox
- top-left y-coordinate of bbox
- width of bbox
- height of bbox

float* test_detector_uchar(unsigned char* data, int w, int h, int c, float thresh, float hier_thresh, int* num_output_class);

brief: detect objects in an image data buffer
param[in1]: a pointer to the image data buffer. In such buffer an image was arranged as bgrbgr...bgr by rows (the same as cv::Mat::data or IplImage.imageData) with format unsigned char.
param[in2]: width of image
param[in3]: height of image
param[in4]: channels of image
param[in5~7]: the same as "test_detector_file" described before
retval: the same as "test_detector_file" described before

void detector_uninit()

brief: free memory when all detect task finished.
retval: void

double what_is_the_time_now()

brief: grab the current time
retval: an double representing the current time(In seconds)

How to use

Step 1. Compile your darknet

Note that this interface can be used both for the original darknet and AlexeyAB version, you can choose any one you like to compile.
Btw, I perfer to use AlexeyAB version because I think its faster than the original one, especially in Raspberry PI(OpenBLAS is highly recommended!!).

If you choose to use the original darknet:

$ git clone https://github.com/pjreddie/darknet.git darknet
$ cd darknet

Or you can choose AlexeyAB version:

$ git clone https://github.com/AlexeyAB/darknet.git darknet_AlexeyAB
$ cd darknet_AlexeyAB

modify those variants listed below:

$ vim Makefile
    GPU=1           (optional)
    CUDNN=1         (optional)
    CUDNN_HALF=1    (optional, AlexeyAB version only)
    LIBSO=1         (AlexeyAB version only)

and then:

$ make -j4
$ cd ..

after do this you can find "libdarknet.so" in darknet repository folder.If the name of this shared library is "darknet.so", you should change it to "libdarknet.so" manually:

$ mv darknet.so libdarknet.so
or
$ ln -s darknet.so libdarknet.so

Step 2. Clone and compile this repository beside the darknet repository you just cloned

$ git clone https://github.com/zyy-cn/darknet_interface.git darknet_interface

After do this your directory structure should be looks like this:

|-- darknet (or darknet_AlexeyAB)
    |-- cfg
        |-- ...
    |-- src
        |-- ...
    |-- ...
    |-- Makefile
|-- darknet_interface
    |-- src
        |-- ...
    |-- script
        |-- ...
    |-- README.md

go on:

$ cd darknet_interface/src
$ chmod 777 *
$ vim gcc.sh

Modify those variants listed on the top of gcc.sh according to your own environment, and then:

$ ./gcc.sh
$ cd ..

after do this you can get "libdetector.so"(for CPP) and "libdetector_c.so"(for C) in darknet_interface/lib.
Note that your must compile and install OPENCV(version 3.3.1 or before) firstly if you want to use it, and macro definition "-DOPENCV" should be added by setting "IS_USE_OPENCV" to "1" in gcc.sh. If you decide to use GPU and CUDNN on step 1, don't forget to add "-DGPU" and "-DCUDNN" correspondly by setting "IS_USE_GPU" and "IS_USE_CUDNN" to "1", And "0" if you don't mean to.

step 3. Run demo

you can run c++ demo, which allow you to do detect in webcam, video or single image file:

$ cd bin
$ export LD_LIBRARY_PATH=../lib:$LD_LIBRARY_PATH
$ ./demo ${detect_type} ${cfg} ${weights} ${thresh} ${image_path|video_path|webcam_index}

in which ${detect_type} can be 'image', 'video' or 'webcam'. For instance, you can do detect object using model trained on MSCOCO(can be downloaded here) with thresh 0.5 through camera 0 by running:

$ ./demo webcam ../../darknet/cfg/yolov3.cfg ../../darknet/weights/yolov3.weights 0.5 0

AI camera is also provided, here is the usage:

$ ./AI_camera ${cfg_path} ${weight_path} ${thresh} ${capture_interval} ${webcam_index} ${target_class_index_list}

${target_class_index_list} is an integer vector in which class index of targets you want to detected is stored. Captured image is stored in bin/cap renamed by time. For instance, you can detect person, cat and dog(class_id is 0, 15 and 16) using model trained on MSCOCO with thresh 0.5 and capture interval 10s through camera 1 by running:

$ ./AI_camera ../../darknet/cfg/yolov3.cfg ../../darknet/weights/yolov3.weights 0.5 10 1 0 15 16

you can also run python demo which is similar to its c++ counterpart:

$ cd script
$ export LD_LIBRARY_PATH=../lib:$LD_LIBRARY_PATH
$ python demo.py ${detect_type} ${cfg} ${weights} ${thresh} ${video_path|webcam_index}

in which ${detect_type} can be 'video' or 'webcam'. If you want to detect multi images contanted in a directory:

$ python detect_dir_img.py ${cfg} ${weights} ${thresh} ${input directory} ${output directory}

step 4. use this interface in your own programs

in C/CPP:
- include "detector.h";
- link with "libdarknet.so", "libdetector.so"(or "libdetector_c.so") compiled in step 1 and 2;
- add macro definition ("-DOPENCV", "-DGPU" and "-DCUDNN") and set path to the libraries and headers if you want to use them;
- make sure your "libdarknet.so" is compiled with GPU and CUDNN in step 1 before add "-DGPU" and "-DCUDNN" when your program compiled;
- view "demo.cpp" and "gcc.sh" for more details.
in python:
- view "demo.py" and "detect_dir_img.py" for more details

Darknet 接口

本项目是一个用于darknet的接口, 可以让你在你自己的程序中（C，C++，Python等）通过链接“libdarknet.so”和“libdetector.so”这两个库，就可以使用darknet检测器来做一些很酷的事情（比如在你的个人电脑，树莓派，英伟达TX1等设备上运行YOLO目标检测算法）。

演示动画

点击这里观看演示

更新

2020.07.16 支持YOLO_V4.

接口函数描述

void detector_init(char cfgfile, char weightfile)

简介: 通过载入指定模型配置文件和预训练权重文件，将一个检测器变量在内存中初始化。
参数[in1]: 网络配置文件路径
参数[in2]: 网络预训练权重文件路径
返回值: 无

float* test_detector_file(char filename, float thresh, float hier_thresh, int num_output_class)

简介: 在指定图像文件中进行目标检测
参数[输入1]: 你想要进行检测的图像文件的路径
参数[输入2]: 检测阈值
参数[输入3]: 默认设置为0.5就行，在yolo第3版中用不到（好像只在yolo9000中有用？）
参数[输入4]: 在图像中检测到的物体的总数量
返回值: 所有在图像中检测到的物体的包围盒信息。每个物体用一组由六个浮点数组成的集合来依次描述：
- 类别号
- 置信度分数
- 包围盒左上角x坐标
- 包围盒左上角y坐标
- 包围盒宽度
- 包围盒高度

float* test_detector_uchar(unsigned char* data, int w, int h, int c, float thresh, float hier_thresh, int* num_output_class);

简介: 在一个图像数据缓存里进行目标检测
参数[输入1]: 指向图像数据缓存的指针。在这样一个缓存里，图像数据以bgrbgr...bgr的顺序按行排列 (跟Opencv中的cv::Mat::data或IplImage.imageData的排列顺序相同)，数据类型为unsigned char。
参数[输入2]: 图像宽度
参数[输入3]: 图像高度
参数[输入4]: 图像通道数
参数[输入5~7]: 跟之前在“test_detector_file”中描述的相同
返回值: 跟之前在“test_detector_file”中描述的相同

void detector_uninit()

简介: 在所有检测任务完成后释放内存。
返回值: 无

double what_is_the_time_now()

简介: 抓取当前时间
返回值: 一个代表当前时间的双精度浮点型数据（精确到秒）

用法

步骤 1. 编译你的darknet

注意这个接口在原版darknet和AlexeyAB版本的darknet上都管用，你可以任意选择你喜欢的版本来编译.
顺便说一句，我比较喜欢用AlexeyAB的那个版本，因为我觉得它比原版快一点，特别是在树莓派上（强烈推荐把OpenBLAS一起编进去！！！）。

如果你选择使用原版的darknet：

$ git clone https://github.com/pjreddie/darknet.git darknet
$ cd darknet

或者你可以用AlexeyAB的版本：

$ git clone https://github.com/AlexeyAB/darknet.git darknet_AlexeyAB
$ cd darknet_AlexeyAB

在Makefile中修改这下面列出的这些变量：

$ vim Makefile
    GPU=1           (可选)
    CUDNN=1         (可选)
    CUDNN_HALF=1    (可选, 只有AlexeyAB版本才需要修改)
    LIBSO=1         (只有AlexeyAB版本才需要修改)

然后:

$ make -j4
$ cd ..

做完这些以后你会在darknet目录文件夹下找到“libdarknet.so”这个库文件。要是这个共享库文件的名字是“darknet.so”，你得手动把它改成“libdarknet.so”：

$ mv darknet.so libdarknet.so
或者
$ ln -s darknet.so libdarknet.so

步骤 2. 在你刚刚克隆下来的darknet项目文件夹旁边，克隆这个接口项目

$ git clone https://github.com/zyy-cn/darknet_interface.git darknet_interface

干完这步以后，你的目录结构应该长这样：

|-- darknet (or darknet_AlexeyAB)
    |-- cfg
        |-- ...
    |-- src
        |-- ...
    |-- ...
    |-- Makefile
|-- darknet_interface
    |-- src
        |-- ...
    |-- script
        |-- ...
    |-- README.md

继续：

$ cd darknet_interface/src
$ chmod 777 *
$ vim gcc.sh

根据你自己的系统环境，修改列在gcc.sh最上面的那些变量，然后：

$ ./gcc.sh
$ cd ..

完成以后，你可以在darknet_interface/lib目录下找到“libdetector.so”（用于C++）和“libdetector_c.so”（用于C）这两个库文件。
注意你如果想用opencv的话，你得先编译并安装好它（3.3.1或更低的版本），并且通过在“gcc.sh”中设置“IS_USE_OPENCV”为1来添加宏定义“-DOPENCV”。如果在步骤1里，你设置了使用GPU和CUDNN，那别忘了把“IS_USE_GPU”和“IS_USE_CUDNN”相应地设置成“1”来添加宏定义“-DGPU”和“-DCUDNN”，要是你不想用它们，就设置成“0”。

步骤 3. 运行示例

你可以运行C++示例, 这个示例可以让你在摄像头画面，视频文件或图像文件中进行目标检测：

$ cd bin
$ export LD_LIBRARY_PATH=../lib:$LD_LIBRARY_PATH
$ ./demo ${detect_type} ${cfg} ${weights} ${thresh} ${image_path|video_path|webcam_index}

其中${detect_type}可以是'image','video'或'webcam'。举个例子，你可以用在MSCOCO上预训练好的模型(从这里可以下载到)以阈值0.5在摄像头0的画面里进行目标检测，命令如下：

$ ./demo webcam ../../darknet/cfg/yolov3.cfg ../../darknet/weights/yolov3.weights 0.5 0

AI相机也提供了，用法为:

$ ./AI_camera ${cfg_path} ${weight_path} ${thresh} ${webcam_index} ${target_class_index_list}

其中${target_class_index_list}是一个整型数组，其中存的是你想要检测的类的类别号。拍摄下来的图像以拍摄时间为文件名，依次存储在bin/cap文件夹中。比如，你可以用在MSCOCO上预训练好的模型，在摄像头1的画面中，以阈值0.5和抓拍间隔10秒，对人、猫和狗这三个类别（类别号分别是0，15，16）进行抓拍，命令如下：

$ ./AI_camera ../../darknet/cfg/yolov3.cfg ../../darknet/weights/yolov3.weights 0.5 10 1 0 15 16

你也可以运行python示例，跟上面的C++版本的示例用法很相似：

$ cd script
$ export LD_LIBRARY_PATH=../lib:$LD_LIBRARY_PATH
$ python demo.py ${detect_type} ${cfg} ${weights} ${thresh} ${video_path|webcam_index}

其中${detect_type}可以是'video'或'webcam'。如果你想要批量检测存储在一个目录之下的多张图片：

$ python detect_dir_img.py ${cfg} ${weights} ${thresh} ${input directory} ${output directory}

步骤 4. 在你自己的程序中使用darknet接口

在C/C++中:
- 包含头文件"detector.h";
- 链接在步骤1和2中编译出来的库文件“libdarknet.so”和“libdetector.so”(或“libdetector_c.so”);
- 如果你想要用OpenCV、GPU或CUDNN的话，就添加宏定义(“-DOPENCV”，“-DGPU”和“-DCUDNN”)并添加相应的库文件和头文件的路径，。
- 你在编译自己的程序时，添加宏定义“-DGPU”和“-DCUDNN”之前，得先确保在步骤1中编译出的“libdarknet.so”本身是可以使用GPU和CNDUU的;
- 更多细节请查看“demo.cpp”和“gcc.sh”。
在python中:
- 更多细节请查看“demo.py”和“detect_dir_img.py”。

TODO

License

This project is released under the WTFPL LICENSE.

zyy-cn / darknet_interface

Darknet Interface

Demo GIF

Update

Interface Functions Discription

void detector_init(char cfgfile, char weightfile)

float* test_detector_file(char filename, float thresh, float hier_thresh, int num_output_class)

float* test_detector_uchar(unsigned char* data, int w, int h, int c, float thresh, float hier_thresh, int* num_output_class);

void detector_uninit()

double what_is_the_time_now()

How to use

Step 1. Compile your darknet

Step 2. Clone and compile this repository beside the darknet repository you just cloned

step 3. Run demo

step 4. use this interface in your own programs

Darknet 接口

演示动画

更新

接口函数描述

void detector_init(char cfgfile, char weightfile)

float* test_detector_file(char filename, float thresh, float hier_thresh, int num_output_class)

float* test_detector_uchar(unsigned char* data, int w, int h, int c, float thresh, float hier_thresh, int* num_output_class);

void detector_uninit()

double what_is_the_time_now()

用法

步骤 1. 编译你的darknet

步骤 2. 在你刚刚克隆下来的darknet项目文件夹旁边，克隆这个接口项目

步骤 3. 运行示例

步骤 4. 在你自己的程序中使用darknet接口

TODO

License

About

Languages

Darknet Interface

Demo GIF

Update

Interface Functions Discription

void detector_init(char *cfgfile, char *weightfile)

float* test_detector_file(char filename, float thresh, float hier_thresh, int num_output_class)

float* test_detector_uchar(unsigned char* data, int w, int h, int c, float thresh, float hier_thresh, int* num_output_class);

void detector_uninit()

double what_is_the_time_now()

How to use

Step 1. Compile your darknet

Step 2. Clone and compile this repository beside the darknet repository you just cloned

step 3. Run demo

step 4. use this interface in your own programs

Darknet 接口

演示动画

更新

接口函数描述

void detector_init(char *cfgfile, char *weightfile)

float* test_detector_file(char filename, float thresh, float hier_thresh, int num_output_class)

float* test_detector_uchar(unsigned char* data, int w, int h, int c, float thresh, float hier_thresh, int* num_output_class);

void detector_uninit()

double what_is_the_time_now()

用法

步骤 1. 编译你的darknet

步骤 2. 在你刚刚克隆下来的darknet项目文件夹旁边，克隆这个接口项目

步骤 3. 运行示例

步骤 4. 在你自己的程序中使用darknet接口

TODO

License

About

Languages

void detector_init(char cfgfile, char weightfile)

void detector_init(char cfgfile, char weightfile)