labelMatch

labelMatch is a graphical corresponding point annotation tool. In computer vision, normally we decide correspondences of points automatically by a feature point extraction algorithm (e.g. SIFT) and a feature matching algorithm (e.g. Brute Force). But when you want to do it manually, you can use this tool!

Requirements

• Python3
• PyQT5

How to Start

with Binary (Windows only)

You can use binary file. Please download from the Release.

with git-clone (Any OS)

git clone https://github.com/daisatojp/labelMatch.git --recursive
cd labelMatch

# I like virtualenv
python3 -m venv .venv
## Linux
source .venv/bin/activate
## Windows
.\.venv\Scripts\activate

python -m pip install --upgrade pip
# Linux
pip install loguru \
            numpy \
            opencv-python-headless \
            tqdm \
            PyQt5 \
            PyQt5-stubs
# Windows
pip install loguru `
            numpy `
            opencv-python-headless `
            tqdm `
            PyQt5 `
            PyQt5-stubs

# Linux
export PYTHONPATH="."
# Windows
$Env:PYTHONPATH = "."

python labelMatch/__main__.py

Tutorial

• Click Open Workspace.
  • Select annotation directory where in you save annotation files.
  • Select image directory where in images are located.
  • Click OK
• Select Edit > Edit Keypoint or just press v. You can create a keypoint by clicking images. You can delete a keypoint by double clicking it.
• Select Edit > Edit Match or just press e. You can match two keypoints of each two images by clicking the two keypoints in order. You can delete a match by double clicking a keypoint which is connected to the match.
• You can save annotation file by pushing Save File button or select File > Save File. If you want to save automatically, select View > Auto Save Mode.

Annotation format

You can export annotated data to one json file in the following format.

{
  "views": [
    {
      "id_view": 0,
      "filename": ["dirname", "dirname", ..., "filename"],
      "keypoints": [
        [<x1>, <y1>],
        [<x2>, <y2>],
        ...
      ]
    }
    ...
  ],
  "pairs": [
    {
      "id_view_i": <id of first view>,
      "id_view_j": <id of second view>,
      "matches": [
        [<index of keypoint of first view>, <index of keypoint of second view>],
        [<index of keypoint of first view>, <index of keypoint of second view>],
        ...
      ]
    }
    ...
  ]
}

License

MIT license

Make executable for Windows

pip install pyinstaller
pyinstaller labelMatch.spec

Acknowledgment

• labelImg as a reference
• Chateau de Sceaux Image Dataset to test software