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Disclaimer

This is a modified repository from dataset-CFPD. Please refer to the original repository for more details.

CFPD | Colorful Fashion Parsing Data

This dataset is used in the paper, (S. Liu, J. Feng, C. Domokos, H. Xu, J. Huang, Z. Hu, & S. Yan. 2014) CFPD | Fashion parsing with weak color-category labels.

Details

  • 2,682 images
  • 600 x 400 (height, width)
  • pixel-level annotated (segmentation map)
    • 23 categories
    • 13 colors
  • make_label.py makes the followings from fashon_parsing_data.mat.
    • label/bbox.json: bounding box (for object detection not semantic segmentation).
    • label/categories.tsv
      • category_id
      • category
  • label/main_categories.tsv is selected from categories.tsv for object detection.

Setup Dataset

1. install the requirements
pip install -r requirements.txt

2. download zip file (name) from author's GoogleDrive
download.sh

3. rename "data" file manually to "data.zip" or run the command below:
rename data data.zip

4. unzip data.zip manually or follow instructions from here (http://stahlworks.com/dev/index.php?tool=zipunzip) to unzip from cmd

5. make label/categories.tsv, label/bbox.json from fashon_parsing_data.mat
python make_label.py

6. Convert .mat to hdf5(.h5), run convert_tmm_to_hdf5.m with matlab

7. Convert .hdf5 files into images with annotations and lists
python export.py

fashon_parsing_data.mat

structure

  • #refs#
    • 0, 0A~zz: 2,719 groups (each record may be a image info)
      • category_label
        • np.array, float64(actually int), (1, 425)
        • map: super-pix id (1425) -> fine category id (1117)
      • color_label
        • np.array, float64(actually int), (1,425)
        • map: super-pix id (1425) -> fine color id (160)
      • ?img_name
        • np.array, uint16, (2~9 etc, 1)
        • range: 59~108 etc
        • ???
      • segmentation
        • np.array, float32, (400, 600)
        • map: img pix loc (w, h) -> super-pix id (1~425)
        • (height, width) (transposing this) is the right orientation.
    • b~x: 23 datasets (each record means category id)
      • np.array, uint16, (1, #fine_category)
      • range: 1~117
      • fine categories's ids under the cateogry.
    • y,z,A~K: 13 datasets (each record means color id)
      • np.array, uint16, (1, #fine_color)
      • range: 1~60
      • fine color's ids under the color.
  • all_category_name
    • np.array, h5py.h5r.Reference, (1, 23)
    • Each reference correspods the above cateogory keys under #refs#.
  • all_color_name
    • np.array, h5py.h5r.Reference, (1, 13)
    • Each reference correspods the above color keys under #refs#.

Dataset Problem