![]() ![]() Before down-sampling apply a Gaussian filter (to smooth the image) for anti-aliasing.Select every 4th pixel in the x and the y direction from the original image to compute the values of the pixels in the smaller image.Start with a large gray-scale image and reduce the image size 16 times, by reducing both height and width by 4 times.The output image obtained after applying the Gotham filter is shown below:ĭown-sampling with anti-aliasing using Gaussian Filter Plt.hist(np.array(b2).ravel(), normed=True) Plt.hist(np.array(b1).ravel(), normed=True) Plt.title('with blue channel interpolation', size=20) A decrease in blue channel for upper mid-tonesīlue_levels = ī2 = omarray((np.reshape(np.interp(np.array(b1).ravel(), np.linspace(0,255,len(blue_levels)), blue_levels), (im.height, im.width))).astype(np.uint8), mode='L').A boost in blue channel for lower mid-tones.Plt.title('with transformation', size=20) Plt.hist(np.array(r).ravel(), normed=True) Plt.title('with red channel interpolation', size=20) Im = Image.open('./images/city.jpg') # pixel values in The Gotham filter is computed as follows (the steps taken from here), applying the following operations on an image, the corresponding python code, input and output images are shown along with the operations (with the following input image): Creating Instagram-like Gotham Filter The Gotham filter ![]() There are more sophisticated techniques to improve the quality of morphing, but this is the simplest one. The next animation shows the simple face morphing: Im2 = mpimg.imread("./images/ronaldo.jpg") / 255 Im1 = mpimg.imread("./images/messi.jpg") / 255 # scale RGB values in The following code block shows how to implement it using matplotlib’s image and pylab modules.
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