JS Segment Annotator

Javascript image annotation tool based on image segmentation.

• Label image regions with mouse.
• Written in vanilla Javascript, with require.js dependency (packaged).
• Pure client-side implementation of image segmentation.

A browser must support HTML canvas to use this tool.

There is an online demo.

Importing data

Prepare a JSON file that looks like the following. The required fields are labels and imageURLs. The annotationURLs are for existing data and can be omitted. Place the JSON file inside the data/ directory.

{
  "labels": [
    "background",
    "skin",
    "hair",
    "dress",
    "glasses",
    "jacket",
    "skirt"
  ],
  "imageURLs": [
    "data/images/1.jpg",
    "data/images/2.jpg"
  ],
  "annotationURLs": [
    "data/annotations/1.png",
    "data/annotations/2.png"
  ]
}

Then edit main.js to point to this JSON file. Open a Web browser and visit index.html.

Known issues

Browser incompatibility

A segmentation result can greatly differ due to the difference in Javascript implementation across Web browsers. The difference stems from numerical precision of floating point numbers, and there is no easy way to produce the exact same result across browsers.

Python tips

Annotation PNG

The annotation PNG file contains label map encoded in RGB value. Do the following to encode an index map.

import numpy as np
from PIL import Image

# Decode
encoded = np.array(Image.open('data/annotations/1.png'))
annotation = np.bitwise_or(np.bitwise_or(
    encoded[:, :, 0].astype(np.uint32),
    encoded[:, :, 1].astype(np.uint32) << 8),
    encoded[:, :, 2].astype(np.uint32) << 16)

print(np.unique(annotation))

# Encode
Image.fromarray(np.stack([
    np.bitwise_and(annotation, 255),
    np.bitwise_and(annotation >> 8, 255),
    np.bitwise_and(annotation >> 16, 255),
    ], axis=2).astype(np.uint8)).save('encoded.png')

JSON

Use JSON module.

import json

with open('data/example.json', 'r') as f:
    dataset = json.load(f)

Using dataURL

Do the following to convert between dataURL and NumPy format.

from PIL import Image
import base64
import io

# Encode
with io.BytesIO() as buffer:
    encoded.save(buffer, format='png')
    data_url = b'data:image/png;base64,' + base64.b64encode(buffer.getvalue())

# Decode
binary = base64.b64decode(data_url.replace(b'data:image/png;base64,', b''))
encoded = Image.open(io.BytesIO(binary))

Matlab tips

Annotation PNG

The annotation PNG file contains label map encoded in RGB value. Do the following to encode an index map.

% Decode

X = imread('data/annotations/0.png');
annotation = X(:, :, 1);
annotation = bitor(annotation, bitshift(X(:, :, 2), 8));
annotation = bitor(annotation, bitshift(X(:, :, 3), 16));

% Encode

X = cat(3, bitand(annotation, 255), ...
           bitand(bitshift(annotation, -8), 255), ...
           bitand(bitshift(annotation, -16)), 255));
imwrite(uint8(X), 'data/annotations/0.png');

JSON

Use the matlab-json package.

• https://github.com/kyamagu/matlab-json

Using dataURL

Get the byte encoding tools.

• https://www.mathworks.com/matlabcentral/fileexchange/39526-byte-encoding-utilities

Do the following to convert between dataURL and Matlab format.

% Decode

dataURL = 'data:image/png;base64,...';
png_data = base64decode(strrep(dataURL, 'data:image/png;base64,', ''));
annotation = imdecode(png_data, 'png');

% Encode

png_data = imencode(annotation, 'png');
dataURL = ['data:image/png;base64,', base64encode(png_data)];

Citation

We appreciate if you cite the following article in an academic paper. The tool was originally developed for this work.

@article{tangseng2017looking,
Author        = {Pongsate Tangseng and Zhipeng Wu and Kota Yamaguchi},
Title         = {Looking at Outfit to Parse Clothing},
Eprint        = {1703.01386v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.CV},
Year          = {2017},
Month         = {Mar},
Url           = {http://arxiv.org/abs/1703.01386v1}
}