Skip to content

Latest commit

 

History

History
96 lines (76 loc) · 4.08 KB

README.md

File metadata and controls

96 lines (76 loc) · 4.08 KB
Raw

Project

Our research lab is working on building a drone to identify damage on aircraft via an autonomous visual inspection

Parrot Instructions

  1. run source /opt/ros/indigo/setup.bash
  2. Connect SLAMDUNK with your laptop through USB interface.
  3. It will show Wired connection 1 probably, which is the name of the connection. (Use the newest connection)
  4. Go to edit connections, click on the wired connection, and then click on edit.
  5. Go to Ethernet tab and select MAC address of the SLAMDUNK.
  6. Go to IPv4 Setting tab, select change method to shared to other computers in the dropdown menu.
  7. use ifconfig to find the name of the usb port of the parrot
  8. run sudo arp-scan --localnet --interface=enp0s20u2 (the last part is the name of the usb port for parrot) to find the ip address.
  9. run export ROS_MASTER_URI=http://192.168.45.1:11311 instead of 192.168.45.1 add the ip of the parrot
  10. run export ROS_HOSTNAME= (insert ip of you your computer after the equals)
  11. run rosnode list you should have the out puts Output:
/rosout
/slamdunk_node
  1. run $ rosparam get /properties/ro_parrot_build_version it should output 1.0.0 or the version number of the firmware the page with instructions to set up the stuff and other features as well: http://developer.parrot.com/docs/slamdunk/#overview

Training instructions

The following are a modified version of the instructions found on the yolo website

  1. Strat by collecting images. the goal is to collect close to 500 img/class
  2. create a new folder in BBox-Label-Tool/Images We are currently trying to divide up our data set into 500 ish image chuncks
  3. run main.py and annotate images.
  4. run the convert.py script to format the annotations in yolo form
  5. Set up the darknet config
  6. run ./darknet detector train cfg/lockheed.data cfg/yolov3.cfg <weights(if needed)>

Forked from puzzledqs/BBox-Label-Tool

Improvements

  1. Add multi-class support
  2. Change some of the color-candidates for better display
  3. Fix the 'Example' filepath for convenience
  4. Change the image format from '.JPEG' to '.JPG'

New Usage

For multi-class task, modify 'class.txt' with your own class-candidates and before labeling bbox, choose the 'Current Class' in the Combobox and make sure you click 'ComfirmClass' button.

The remaining usage is the same as the origin one.

Contact info: jxgu1016@gmail.com

BBox-Label-Tool

A simple tool for labeling object bounding boxes in images, implemented with Python Tkinter.

Updates:

  • 2017.5.21 Check out the multi-class branch for a multi-class version implemented by @jxgu1016

Screenshot: Label Tool

Data Organization

LabelTool
|
|--main.py # source code for the tool
|
|--Images/ # direcotry containing the images to be labeled
|
|--Labels/ # direcotry for the labeling results
|
|--Examples/ # direcotry for the example bboxes

Environment

  • python 2.7
  • python PIL (Pillow)

Run

$ python main.py

Usage

  1. The current tool requires that the images to be labeled reside in /Images/001, /Images/002, etc. You will need to modify the code if you want to label images elsewhere.
  2. Input a folder number (e.g, 1, 2, 5...), and click Load. The images in the folder, along with a few example results will be loaded.
  3. To create a new bounding box, left-click to select the first vertex. Moving the mouse to draw a rectangle, and left-click again to select the second vertex.
  • To cancel the bounding box while drawing, just press <Esc>.
  • To delete a existing bounding box, select it from the listbox, and click Delete.
  • To delete all existing bounding boxes in the image, simply click ClearAll.
  1. After finishing one image, click Next to advance. Likewise, click Prev to reverse. Or, input an image id and click Go to navigate to the speficied image.
  • Be sure to click Next after finishing a image, or the result won't be saved.