__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_topic
parameter to match your camera, and the weights_name
, config_name
and classes_name
parameters 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.cfg
for YOLOv3, yolov3-tiny.cfg
for tiny YOLOv3, and yolov3-voc.cfg
for YOLOv3-VOC.
classes_name
(string)
The name of the file for the detected classes in the classes folder. Use coco.names
for COCO, and voc.names
for 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_image
is 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
1) catkin workspace
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src/
2) 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 ..
3) __Download ddynamic_reconfigure__
cd src
git clone https://github.com/pal-robotics/ddynamic_reconfigure/tree/kinetic-devel
cd ..
4) 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
5) Run D435 node
roslaunch realsense2_camera rs_camera.launch
6) 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...
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*2000
but not less than 4000
), f.e. max_batches=6000
if you train for 3 classes
change line steps to 80% and 90% of max_batches, f.e. steps=4800,5400
change line classes=80
to your number of objects in each of 3
[yolo]
-layers:
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
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)
obj.names
in the directory path_to/yolov3/cfg/
, with objects names - each in new lineperson
car
cat
dog
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/
path_to/yolov3/JPEGImages
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 from 0
to (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]
<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
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
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