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Commit c14aacc

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Image contrast adjusting filter (avinashkranjan#1105)
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import numpy as np
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import matplotlib.pyplot as plt
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from PIL import Image
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import os.path
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img_path = input("Enter the path here: ") #example -> C:\Users\xyz\OneDrive\Desktop\project\image.jpg
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img = Image.open(img_path)
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# convert our image into a numpy array
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img = np.asarray(img)
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#print(img.shape)
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# put pixels in a 1D array by flattening out img array
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flat = img.flatten()
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# create our own histogram function
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def get_histogram(image, bins):
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# array with size of bins, set to zeros
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histogram = np.zeros(bins)
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# loop through pixels and sum up counts of pixels
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for pixel in image:
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histogram[pixel] += 1
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# return our final result
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return histogram
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# execute our histogram function
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hist = get_histogram(flat, 256)
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# execute the fn
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cs = np.cumsum(hist)
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# numerator & denomenator
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nj = (cs - cs.min()) * 255
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N = cs.max() - cs.min()
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# re-normalize the cumsum
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cs = nj / N
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# cast it back to uint8 since we can't use floating point values in images
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cs = cs.astype('uint8')
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# get the value from cumulative sum for every index in flat, and set that as img_new
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img_new = cs[flat]
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# put array back into original shape since we flattened it
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img_new = np.reshape(img_new, img.shape)
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# set up side-by-side image display
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fig = plt.figure()
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fig.set_figheight(15)
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fig.set_figwidth(15)
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# display the real image
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fig.add_subplot(1,2,1)
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plt.imshow(img, cmap='gray')
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plt.title("Image 'Before' Contrast Adjustment")
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# display the new image
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fig.add_subplot(1,2,2)
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plt.imshow(img_new, cmap='gray')
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plt.title("Image 'After' Contrast Adjustment")
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filename = os.path.basename(img_path)
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plt.savefig("./Image Contrast Adjusting Filter/(Contrast Adjusted)"+filename)
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plt.show()
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# Image Contrast Adjusting Filter
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Images captured in dim light mostly not have a much better contrast so an (Image Contrast Adjusting filter) filter helps accordingly to adjusts and increase its contrast and hence we are having a better version of the image. Hence this filter helps to get a high contrast image.
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## Libraries used
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Firstly import the following python libraries
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* Matplotlib,
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* NumPy
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* PIL
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Save the image and your code in the same folder
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Run the python code
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## Detailed explanation of the method used
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* Imported the required libraries ( Numpy, Matplotlib, PIL, IPython.display)
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* Read the input image using Image from PIL library
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* Converted the image into an array and then flatten it making it a 1d array
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* Count the number of occurance of each pixel in the image and hence getting an array of frequency count of each pixels
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* Then making cdf from the frequency count list
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* Normalizing the cdf
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* Converting the cdf shape into the shape of given image
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* Finally converted the image into contrast adjusted image
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## Comparison With The Result
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<img src="Images/Result.jpg" height="300px">
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## Author(s)
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[Akriti](https://github.com/A-kriti)
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numpy==1.18.5
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matplotlib==3.2.2
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Pillow==8.2.0

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