Objection-Detection-and-location-RealsenseD435
Requirements
C++ Version
Ubuntu18.04 OR 16.04
Opencv 4.x
C++ 11_std At least,I used the C++ 17 std
Eigen3 :in absolutely Path /usr/local/eigen3
Cmake>= 3.17
PCL lib>=1.7.1
Intel Realsense SDK >=2.0
Yolov3 by Darknet
Python Version
pyrealsense2.x
Opencv-python
numpy
with same as C++ version,the SDK of realsense D435 must have been installed
How to use
C++
git clone https://github.com/Mazhichaoruya/Objection-Detection-and-location-RealsenseD435.git
cd Objection-Detection-and-location-RealsenseD435/Yolo_model
wget https://pjreddie.com/media/files/yolov3.weights ;wget https://pjreddie.com/media/files/yolov3-tiny.weights
For avoiding unreasonable troubles,I used the absolute path,so you have to change the path in src/main.cpp
on line 25-27
String yolo_tiny_model ="/home/mzc/code/CLionProjects/DNN435/Yolo_model/yolov3.weights";
String yolo_tiny_cfg = "/home/mzc/code/CLionProjects/DNN435/Yolo_model/yolov3.cfg";
String classname_path="/home/mzc/code/CLionProjects/DNN435/Yolo_model/object_detection_classes_yolov3.txt";
You can use your weight by Darknet or others supported by DNN too
cd ..
mkdir bulid; cd build
cmake ..
make
./DNN_Yolo
Attention:Default parameter on line 228 in src/main.cpp
net.setPreferableBackend(DNN_BACKEND_OPENCV);// DNN_BACKEND_INFERENCE_ENGINE
if you have IntelCore CPU you can chose "DNN_BACKEND_INFERENCE_ENGINE"to accelerate youe model--Openvino;
But you should make sure your CPU is Intel and the Contrib of Opencv has been installed.
Python
When you install the essential job ,just run the main.py,the model will start to work.
cd Python
python3 main.py
Example
The windows show the Classname and Position in Camera coordinate system of Objection
To be continue
I will continue to slove the problems of loss targets' depth information when one Box have two or more targets
Find some faster Models of Objection and Try to use cuda acceleration
Take it to rospack and run it on ROS robot with slam
