/img2bw

Command-line application to binarize images

Primary LanguagePythonMIT LicenseMIT

img2bw

img2bw is a simple command-line application to binarize images.

Requirements

  • Python3.7 or more

Installation

pip install img2bw

Usage

To binarize a single image, type:

img2bw image.jpg --method otsu

To binarize all the images in a directory, type:

img2bw input_dir/ --output output_dir/

If no method is specified, otsu will be used.

You can try all the methods using --method try-all

Thresholding algorithms

These are the available algorithms along with common names and their corresponding papers. You can use them with the option: --method (name)

  • Global thresholding:

    • otsu: Otsu's method
      • Otsu, Nobuyuki. "A threshold selection method from gray-level histograms." IEEE transactions on systems, man, and cybernetics 9.1 (1979): 62-66.
    • p-tile: p-tile threshold algorithm
      • Parker, J. R. (2010). Algorithms for image processing and computer vision. John Wiley & Sons. (p-tile)
    • two-peaks: Selects two peaks from the histogram and return the index of the minimum value between them.
      • Parker, J. R. (2010). Algorithms for image processing and computer vision. John Wiley & Sons. (Two peaks)
    • multiotsu: Generates classes - 1 threshold values to divide gray levels
      • Liao, P-S., Chen, T-S. and Chung, P-C., "A fast algorithm for multilevel thresholding", Journal of Information Science and Engineering 17 (5): 713-727, 2001.
    • isodata: Histogram-based threshold, Ridler-Calvard method or inter-means
      • Ridler, TW & Calvard, S (1978), "Picture thresholding using an iterative selection method" IEEE Transactions on Systems, Man and Cybernetics 8: 630-632, :DOI:10.1109/TSMC.1978.4310039
    • min-error: Minimum error thresholding algorithm.
      • Kittler, J. and J. Illingworth. ‘‘On Threshold Selection Using Clustering Criteria,’’ IEEE Transactions on Systems, Man, and Cybernetics 15, no. 5 (1985): 652–655.
    • minimum: Minimum method. The histogram of the input image is computed and smoothed until there are only two maxima.
      • C. A. Glasbey, "An analysis of histogram-based thresholding algorithms," CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993.
    • triangle: Threshold value based on the triangle algorithm
      • Zack, G. W., Rogers, W. E. and Latt, S. A., 1977, Automatic Measurement of Sister Chromatid Exchange Frequency, Journal of Histochemistry and Cytochemistry 25 (7), pp. 741-753 :DOI:10.1177/25.7.70454
    • yen: Yen's method
      • Yen J.C., Chang F.J., and Chang S. (1995) "A New Criterion for Automatic Multilevel Thresholding" IEEE Trans. on Image Processing, 4(3): 370-378. :DOI:10.1109/83.366472
    • mean: Threshold value based on the mean of grayscale values.
      • C. A. Glasbey, "An analysis of histogram-based thresholding algorithms," CVGIP: Graphical Models and Image Processing,vol. 55, pp. 532-537, 1993. :DOI:10.1006/cgip.1993.1040

  • Entropy thresholding:

    • pun: Pun's threshold algorithm.
      • Pun, T. "A New Method for Grey-Level Picture Thresholding Using the Entropy of the Histogram,"" Signal Processing 2, no. 3 (1980): 223–237.
    • kapur: Kapur's threshold algorithm
      • Kapur, J. N., P. K. Sahoo, and A. K. C.Wong. "A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram,"" Computer Vision, Graphics, and Image Processing 29, no. 3 (1985): 273–285.
    • johannsen: Johannsen's threshold algorithm.
      • Johannsen, G., and J. Bille "A Threshold Selection Method Using Information Measures,"" Proceedings of the Sixth International Conference on Pattern Recognition, Munich, Germany (1982): 140–143.
    • li: Li's iterative Minimum Cross Entropy method
      • Li C.H. and Lee C.K. (1993) "Minimum Cross Entropy Thresholding" Pattern Recognition, 26(4): 617-625 :DOI:10.1016/0031-3203(93)90115-D

  • Local thresholding:

    • bradley-roth: Bradley-Roth thresholding algorithm
      • Bradley, D., & Roth, G. (2007). Adaptive thresholding using the integral image. Journal of Graphics Tools, 12(2), 13-21.
    • bernsen: Bernsen thresholding algorithm
      • Bernsen, J (1986), "Dynamic Thresholding of Grey-Level Images", Proc. of the 8th Int. Conf. on Pattern Recognition
    • contrast: Contrast thresholding algorithm
      • Parker, J. R. (2010). Algorithms for image processing and computer vision. John Wiley & Sons. (Contrast thresholding)
    • feng: Feng's thresholding algorithm
      • Meng-Ling Feng and Yap-Peng Tan, "Contrast adaptive thresholding of low quality document images”, IEICE Electron. Express, Vol. 1, No. 16, pp.501-506, (2004).
    • local-mean: Adaptive or dynamic thresholding. The threshold value is the mean of the neighbourhood area minus the constant C
    • local-median: Adaptive or dynamic thresholding. The threshold value is the median of the neighbourhood area minus the constant C
    • local-gaussian: Adaptive or dynamic thresholding. The threshold value is a gaussian-weighted sum of the neighbourhood values minus the constant
    • niblack: Niblack local threshold
      • Niblack, W.: "An introduction to digital image processing" (Prentice- Hall, Englewood Cliffs, NJ, 1986), pp. 115–116
    • sauvola: Sauvola local threshold
      • Sauvola, J., Seppanen, T., Haapakoski, S., and Pietikainen, M.: "Adaptive document thresholding". Proc. 4th Int. Conf. on Document Analysis and Recognition, Ulm Germany, 1997, pp. 147–152.
    • wolf: Wolf's thresholding algorithm.
      • C. Wolf, J-M. Jolion, "Extraction and Recognition of Artificial Text in Multimedia Documents", Pattern Analysis and Applications, 6(4):309-326, (2003).
    • nick: NICK thresholding algorithm.
      • Khurshid, K., Siddiqi, I., Faure, C., & Vincent, N. (2009, January). Comparison of Niblack inspired Binarization methods for ancient documents. In IS&T/SPIE Electronic Imaging (pp. 72470U-72470U). International Society for Optics and Photonics.
    • singh: Singh thresholding algorithm
      • Singh, O. I., Sinam, T., James, O., & Singh, T. R. (2012). Local contrast and mean based thresholding technique in image binarization. International Journal of Computer Applications, 51, 5-10.

More options

To view all the available options, type img2bw --help in the terminal:

usage: img2bw [-h] [-o OUTPUT] [-e {jpg,jpeg,jfif,png,tiff,bmp,pnm}]
              [-m {otsu,isodata,li,local,mean,minimum,multiotsu,niblack,sauvola,triangle,yen,p-tile,two-peaks,min-error,pun,kapur,johannsen,wolf,nick,bradley-roth,bernsen,contrast,singh,feng,try-all}]
              [-b BLOCK_SIZE] [-c NUM_CLASSES]
              input

positional arguments:
  input                 Input file or directory

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output directory
  -e {jpg,jpeg,jfif,png,tiff,bmp,pnm}, --output-ext {jpg,jpeg,jfif,png,tiff,bmp,pnm}
                        Output file extension
  -m {otsu,isodata,li,local,mean,minimum,multiotsu,niblack,sauvola,triangle,yen,p-tile,two-peaks,min-error,pun,kapur,johannsen,wolf,nick,bradley-roth,bernsen,contrast,singh,feng,try-all}, --method {otsu,isodata,li,local,mean,minimum,multiotsu,niblack,sauvola,triangle,yen,p-tile,two-peaks,min-error,pun,kapur,johannsen,wolf,nick,bradley-roth,bernsen,contrast,singh,feng,try-all}
                        Method used to perform the binarization
  -b BLOCK_SIZE, --block-size BLOCK_SIZE
                        Odd size of pixel neighborhood which is used to
                        calculate the threshold value (local threshold)
  -c NUM_CLASSES, --num-classes NUM_CLASSES
                        Number of classes to be thresholded (multiotsu)

Additional information

This package is simply wrapper to easily apply multiple threshold algorithms to an image (or the images in a directory). I didn't code the algorithms so I send a big thank you to all the authors of libraries that made this wrapper possible: