Amazing-Python-Scripts

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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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# example -> C:\Users\xyz\OneDrive\Desktop\project\image.jpg
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img_path = input("Enter the path here: ")
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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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