Metadata-Version: 2.1
Name: im2dhisteq
Version: 0.0.10
Summary: This module attempts to enhance contrast of a given image by equalizing its two dimensional histogram.
Home-page: https://github.com/Mamdasn/im2dhisteq
Author: mamdasn s
Author-email: <mamdassn@gmail.com>
License: UNKNOWN
Description: 
        # im2dhisteq
        This module attempts to enhance contrast of a given image by equalizing its two dimensional histogram. An easy way to enhance quality of a given image is to just equalize its histogram, but despite using minimum resources and a very short process time, there are a lot of drawbacks to it.
        One of the ways to tackle drawbacks of `histogram equalization method` is to instead equalize the image's `two dimensional histogram`, as one dimensional histogram of a given image does not contain the image's contextual information. Tests on a multitude of images has shown, by taking contextual information of an image in addition to the image's histogram into account when attempting to enhance contrast, output images are significantly better in quality in compare to histogram equalizaion and a handful of other known methods.  
          
        You can access the article that came up with this method [here](https://www.researchgate.net/publication/256822485_Two-dimensional_histogram_equalization_and_contrast_enhancement).
        
        ## Two Dimensional Histogram 
        [Here](https://github.com/Mamdasn/im2dhist) is the source code for the im2dhist python library with a short description on how it's done. 
        
        ## Installation
        
        Run the following to install:
        
        ```python
        pip install im2dhisteq
        ```
        
        ## Usage
        
        ```python
        import numpy as np
        import cv2
        from im2dhisteq import im2dhisteq
        
        def imresize(img, wr=500, hr=None): # This is just for imshow-ing images with titles
            [ h, w] = img.shape
            hr = (h*wr)//w if not hr else hr
            img_resized = cv2.resize(img, dsize=(wr, hr))
            return img_resized
        
        def main():
            fullname = 'cloudy-day.jpg'
            image = cv2.imread(fullname)
            gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            
            # w_neighboring=6 is generally an adequate value, drived by a lot of experimenting.
            # w_neighboring=6 corresponds to a 13*13 square
            gray_image_2DHisteq = im2dhisteq(gray_image, w_neighboring=6)
            
            # This is just for imshow-ing images with titles
            gray_Image_resized = imresize(gray_image)
            gray_Image_2DHisteq_resized = imresize(gray_image_2DHisteq)
        
            cv2.imshow('Original Image', gray_Image_resized)
            cv2.imshow('2DHeq Image', gray_Image_2DHisteq_resized)
            cv2.waitKey(0)
        
        if __name__ == '__main__': main()
        ```
        
        ## Showcase
        * A one minute comparative video: https://youtu.be/tX1KbJ2ugdE
        * This is a sample image and its corresponding 2d-histogram equalized image.  
        ![cloudy-day-original-im2dhisteq.jpg Image](https://raw.githubusercontent.com/Mamdasn/im2dhisteq/main/assets/cloudy-day-original-im2dhisteq.jpg "cloudy-day-original-im2dhisteq.jpg Image")
        
        
Keywords: python,histogram,imhist,2dhist,hist2d,im2dhisteq,histogram equalization,two dimensional histogram
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
