This fork is going to convert the project to ROS 2. All the text below is from the original repository.
- All image processing and face recognition functionalities are provided by utilizing the Shervin Emami's c++ source code for face recognition (http://www.shervinemami.info/faceRecognition.html).
- License: Attribution-NonCommercial 3.0 Unported (http://creativecommons.org/licenses/by-nc/3.0/)
The face_recognition ROS package provides an simple actionlib server interface for performing different face recognition functionalities in video stream.
This instalation process is for catkin (ROS Groovy or newer version) Assuming that your catkin workspace is under ~/catkin_ws, if not replace ~/catkin_ws with appropriate location. It also assumes you're running Bash shell, if you're running Zsh, source appropriate setup.zsh file.
cd ~/catkin_ws/src
git clone https://github.com/procrob/procrob_functional.git --branch catkin
cd ~/catkin_ws
catkin_make
source ~/catkin_ws/devel/setup.bash
This message includes 2 fields:
- int order_id
- string order_argument
-
order_id = 0
- (Recognize Once) Goal is to acknowledge the first face recognized in the video stream. When the first face is recognized with a confidence value higher than the desirable confidence threshold, the name of the person and the confidence value are sent back to the client as result.
-
order_id = 1
- (Recognize Continuously) Goal is to continuously recognize faces in the video stream. Every face recognized with confidence value higher than the desirable confidence threshold and its confidence value are sent back to the client as feedback. This goal is persuaded for infinite time until it is canceled or preempted by another goal.
-
order_id = 2 and order_argument = person_name
- (Add face images) Goal is to acquire training images for a NEW person. The video stream is processed for detecting a face which is saved and used as a training image for the new person. This process is continued until the desired number of training images for the new person is acquired. The name of the new person is provided as "order_argument"
-
order_id = 3
- (Train) The database is (re)trained from the training images
-
order_id = 4
- (Exit) The program exits.
- /camera/image_raw - video stream (standard ROS image transport)
-
confidence_value (double, default = 0.88)
- a face recognized with confidence value higher than the confidence_value threshold is accepted as valid.
-
show_screen_flag (boolean, default = true)
- if output screen is shown
-
add_face_number (int, default = 25)
- a parameter for the add_face_images goal which determines the number of training images for a new person to be acquired from the video stream
Details in http://www.shervinemami.info/faceRecognition.html
The name of persons and the images of their faces to train from are stored in the train.txt file. This file should be in the same directory as where you execute the program. See the example train.txt file for the desirable format of the train.txt (Note: the person numbers start from 1, spaces or special characters are not allowed in persons' names). The program trains from the train.txt file and stores the eigenfaces data in facedata.xml file.
When the face_recognition actionlib server is executed (Fserver), the program first tries to load data from the facedata.xml file if exists, if not it will try to learn from training images stored in train.txt if any. You can always add training data directly from the video stream using the add_face_images goal (order_id = 2). Note: when program retrains (order_id = 3), the content of facedata.xml is disregarded and the program trains only based on the train.txt file.
For demonstration purposes an actionlib client example for the face_recognition simple actionlib server has been provided. The client subscribes to face_recognition/FRClientGoal messages. Each FRClientGoal message contains an order_id and an order_argument which specify a goal to be executed by the face_recognition server. After receiving a message, the client sends the corresponding goal to the server. By registering relevant call back functions, the client receives feedback and result information from the execution of goals in the server and prints such information on the terminal.
- Launch terminal
- run
roscore - In separate terminal publish a video stream on topic /camera/image_raw.
- For example you can use usb_cam to publish images from your web cam as follows:
- Install usb_cam package
- Run
rosrun usb_cam usb_cam_node usb_cam_node/image_raw:=camera/image_raw _image_height:=<usb_cam_height> _image_width:=<usb_cam_width>
- run
- In separate terminals run the face recognition server and client as follows:
rosrun face_recognition Fserverrosrun face_recognition Fclient
- In another terminal publish messages on topic /fr_order to test different face recognition functionalities as follows: (notice the info which are printed on the client terminal after each command)
rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 2 "your_name"- to acquire training images for your face: you should try to appear in the video stream!
rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 3 "none"- to retrain and update the database, so that you can be recognized
rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 1 "none"- to recognize faces continuously. This would not stop until you preempt or cancel the goal. So lets preempt it by sending the next goal.
rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 2 "your\_friend's\_name"- add training images for a new person
rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 0 "none"- recognize once
rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 1 "none"- recognize continuous
rostopic pub -1 /fr_order face_recognition/FRClientGoal -- 4 "none"- exit