effect of increasing sigma Condensed code: Here we don’t have much room to play with, except with the sigma parameter while blurring.Īs sigma increases, the pic becomes clearer but run time also increases. We can save the image using: plt.imsave(‘img2.png’, final_img, cmap=’gray’, vmin=0, vmax=255) Final result Entire code in action Each stage of the algorithm import matplotlib.pyplot as pltplt.imshow(final_img, cmap=”gray”) Note that we need to keep the cmap argument equal to “gray”. We can plot our final image using plt.imgshow. def dodge(front,back): result=front*255/(255-back) result=255 result=255 return result.astype(‘uint8’) final_img= dodge(blur_img,gray_img) Final image We have the blurred image, which highlights the boldest edges.Īs all our images are read using Numpy, all the matrix calculations are super fast. This lightens the bottom layer depending on the value of the top layer. The Colour Dodge blend mode divides the bottom layer by the inverted top layer. import scipy.ndimageblur_img = _filter(inverted_img,sigma=5) More blurring on increasing sigma 5. Sigma controls the extent of the variance and thus, the degree of blurring. The key here is the variance of the Gaussian function or sigma.Īs sigma increases, the image becomes more blurred. Blurring is done by applying a Gaussian filter to the inverted image. inverted_img = 255-gray_img Inverted Image 4. We can invert images simply by subtracting from 255, as grayscale images are 8 bit images or have a maximum of 256 tones. So our function will look like: import numpy as npdef grayscale(rgb): return np.dot(rgb, )Īpplying grayscale: gray_img = grayscale(start_img) Grayscaled Image 3. You can learn why this formula works right here. Numpy doesn’t have any in-built function for grayscaling, but we can easily convert the image using the formula. So this is a three channel image of size 196x160. You can see how Python sees this image with the shape attribute: start_img.shape(196, 160, 30) Load image import imageioimg=""start_img = imageio.imread(img) Initial image I will go with this image of Indian cricketer Virat Kohli: 1.
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