The following code block shows how to to use the PIL point()function for thresholding with a fixed threshold:
im = Image.open('../images/swans.jpg').convert('L')pylab.hist(np.array(im).ravel(), bins=256, range=(0, 256), color='g')pylab.xlabel('Pixel values'), pylab.ylabel('Frequency'),pylab.title('Histogram of pixel values')pylab.show()pylab.figure(figsize=(12,18))pylab.gray()pylab.subplot(221), plot_image(im, 'original image'), pylab.axis('off')th = [0, 50, 100, 150, 200]for i in range(2, 5): im1 = im.point(lambda x: x > th[i]) pylab.subplot(2,2,i), plot_image(im1, 'binary image with threshold=' + str(th[i]))pylab.show()
The following screenshots show the output of the previous code. First, we can see the distribution ...
