- Ubuntu 16.04 / 18.04
- ROS Kinetic / Melodic
- OpenCV
Gazebo Real-time Screw Rotation Detection - [Link]
Gazebo Real-time Grasp Detection - [Link]
Parts-Arrangement-Robot - [Link]
Gazebo Real-time Screw Grasp Detection - [Link]
To download the prerequisites for this package (except for ROS itself), navigate to the package folder and run:
$ cd yolov3_pytorch_ros
$ sudo pip install -r requirements.txt
Navigate to your catkin workspace and run:
$ catkin_make yolov3_pytorch_ros
- First, make sure to put your weights in the models folder. For the training process in order to use custom objects, please refer to the original YOLO page. As an example, to download pre-trained weights from the COCO data set, go into the models folder and run:
wget http://pjreddie.com/media/files/yolov3.weights
- Modify the parameters in the launch file and launch it. You will need to change the
image_topicparameter to match your camera, and theweights_name,config_nameandclasses_nameparameters depending on what you are trying to do.
$ roslaunch yolov3_pytorch_ros detector.launch
-
image_topic(string)Subscribed camera topic.
-
weights_name(string)Weights to be used from the models folder.
-
config_name(string)The name of the configuration file in the config folder. Use
yolov3.cfgfor YOLOv3,yolov3-tiny.cfgfor tiny YOLOv3, andyolov3-voc.cfgfor YOLOv3-VOC. -
classes_name(string)The name of the file for the detected classes in the classes folder. Use
coco.namesfor COCO, andvoc.namesfor VOC. -
publish_image(bool)Set to true to get the camera image along with the detected bounding boxes, or false otherwise.
-
detected_objects_topic(string)Published topic with the detected bounding boxes.
-
detections_image_topic(string)Published topic with the detected bounding boxes on top of the image.
-
confidence(float)Confidence threshold for detected objects.
-
image_topic(sensor_msgs::Image)Subscribed camera topic.
-
detected_objects_topic(yolov3_pytorch_ros::BoundingBoxes)Published topic with the detected bounding boxes.
-
detections_image_topic(sensor_msgs::Image)Published topic with the detected bounding boxes on top of the image (only published if
publish_imageis set to true).
-
[IntelRealSense -Linux Distribution]
sudo apt-key adv --keyserver keys.gnupg.net --recv-key F6E65AC044F831AC80A06380C8B3A55A6F3EFCDE || sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key sudo add-apt-repository "deb http://realsense-hw-public.s3.amazonaws.com/Debian/apt-repo bionic main" -u sudo apt-get install librealsense2-dkms sudo apt-get install librealsense2-utils sudo apt-get install librealsense2-dev sudo apt-get install librealsense2-dbg #(리얼센스 패키지 설치 확인하기) realsense-viewer -
- catkin workspace
mkdir -p ~/catkin_ws/src cd ~/catkin_ws/src/- Download realsense-ros pkg
git clone https://github.com/IntelRealSense/realsense-ros.git cd realsense-ros/ git checkout `git tag | sort -V | grep -P "^\d+\.\d+\.\d+" | tail -1` cd ..- Download ddynamic_reconfigure
cd src git clone https://github.com/pal-robotics/ddynamic_reconfigure/tree/kinetic-devel cd ..- Pkg installation
catkin_init_workspace cd .. catkin_make clean catkin_make -DCATKIN_ENABLE_TESTING=False -DCMAKE_BUILD_TYPE=Release catkin_make install echo "source ~/catkin_ws/devel/setup.bash" >> ~/.bashrc source ~/.bashrc- Run D435 node
roslaunch realsense2_camera rs_camera.launch- Run rviz testing
rosrun rviz rvzi Add > Image to view the raw RGB image
Training YOlOv3:
git clone https://github.com/pjreddie/darknet
cd darknet
vim Makefile
...
GPU=1 # if no using GPU 0
CUDNN=1 # if no 0
OPENCV=0
OPENMP=0
DEBUG=0
make
mkdir yolov3
cd yolov3
mkdir JPEGImages labels backup cfg
├── JPEGImages
│ ├── object-00001.jpg
│ └── object-00002.jpg
│ ...
├── labels
│ ├── object-00001.txt
│ └── object-00002.txt
│ ...
├── backup
│ ├── yolov3-object.backup
│ └── yolov3-object_20000.weights
│ ...
├── cfg
│ ├── obj.data
│ ├── yolo-obj.cfg
│ └── obj.names
└── obj_test.txt...
1. Create file yolo-obj.cfg with the same content as in yolov3.cfg (or copy yolov3.cfg to yolo-obj.cfg) and:
-
change line batch to
batch=64 -
change line subdivisions to
subdivisions=8 -
change line max_batches to (
classes*2000but not less than4000), f.e.max_batches=6000if you train for 3 classes -
change line steps to 80% and 90% of max_batches, f.e.
steps=4800,5400 -
change line
classes=80to your number of objects in each of 3[yolo]-layers:-
cfg/yolov3.cfg#L610
-
cfg/yolov3.cfg#L696
-
cfg/yolov3.cfg#L783
[convolutional] ... filters = 24 #3*(classes + 5) [yolo] ... classes=3
-
-
change [
filters=255] to filters=3x(classes + 5)in the 3[convolutional]before each[yolo]layer- cfg/yolov3.cfg#L603
- cfg/yolov3.cfg#L689
- cfg/yolov3.cfg#L776
So if classes=1 then should be filters=18. If classes=2 then write filters=21.
(Do not write in the cfg-file: filters=(classes + 5)x3)
2. Create file obj.names in the directory path_to/yolov3/cfg/, with objects names - each in new line
person
car
cat
dog
3. Create file obj.data in the directory path_to/yolov3/cfg/, containing (where classes = number of objects):
classes= 3
train = /home/cai/workspace/yolov3/obj_train.txt
valid = /home/cai/workspace/yolov3/obj_test.txt
names = /home/cai/workspace/yolov3/cfg/obj.names
backup = /home/cai/workspace/yolov3/backup/
5. You should label each object on images from your dataset: [LabelImg] is a graphical image annotation tool
It will create .txt-file for each .jpg-image-file - in the same directory and with the same name, but with .txt-extension, and put to file: object number and object coordinates on this image, for each object in new line:
<object-class> <x_center> <y_center> <width> <height>
Where:
<object-class>- integer object number from0to(classes-1)<x_center> <y_center> <width> <height>- float values relative to width and height of image, it can be equal from(0.0 to 1.0]- for example:
<x> = <absolute_x> / <image_width>or<height> = <absolute_height> / <image_height> - atention:
<x_center> <y_center>- are center of rectangle (are not top-left corner)
For example for img1.jpg you will be created img1.txt containing:
1 0.716797 0.395833 0.216406 0.147222
0 0.687109 0.379167 0.255469 0.158333
1 0.420312 0.395833 0.140625 0.166667
6. Create file obj_train.txt & obj_test.txt in directory path_to/yolov3/, with filenames of your images, each filename in new line,for example containing:
path_to/yolov3/JPEGImages/img1.jpg
path_to/yolov3/JPEGImages/img2.jpg
path_to/yolov3/JPEGImages/img3.jpg
7. Download pre-trained weights for the convolutional layers (154 MB): https://pjreddie.com/media/files/darknet53.conv.74 and put to the directory path_to/darknet/
wget https://pjreddie.com/media/files/darknet53.conv.74
./darknet detector train [path to .data file] [path to .cfg file] [path to pre-taining weights-darknet53.conv.74]
[visualization]
./darknet detector train path_to/yolov3/cfg/obj.data path_to/yolov3/cfg/yolov3.cfg darknet53.conv.74 2>1 | tee visualization/train_yolov3.log
./darknet detector test path_to/yolov3/cfg/obj.data path_to/yolov3/cfg/yolov3.cfg path_to/yolov3/backup/yolov3_final.weights path_to/yolov3/test/test_img.jpg
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