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2 Gaussian blurring is used to define an image scale to work in, for interpolation, for computing interest points, and in many more applications. SciPy comes with a module for filtering called scipy.ndimage.filters that can be used to compute these convolutions using a fast 1D separation. All you need to do is: from PIL import Image from numpy import * numpy from scipy.ndimage import filters im = array(Image.open('empire.jpg').convert('L')) = im2 filters.gaussian_filter(im,5) 001-08 T التعليل Here the last parameter of gaussian_filter() is the standard deviation. sagino O 80 (a) q Langla (b) Bia oltes (c) gooliage (d) toteu Figure 1.9: An example of Gaussian blurring using the scipy.ndimage.filters module. (a) original image in grayscale, (b) Gaussian filter with o=2, (c) with σ = 5, (d) with σ = 10. Figure 1.9 shows examples of an image blurred with increasing segma. Larger values gives less details. To blur color images, simply apply Gaussian blurring to each color channel. Exercises 1. F amankay 2. Take an image and apply Gaussian blur like in Figure 1.9. Plot the image contours for increasing values of segma. What happens? Can you explain why? تأخذ صورة الك NOOR FALEH 8

2 Gaussian blurring is used to define an image scale to work in, for interpolation, for computing interest points, and in many more applications. SciPy comes with a module for filtering called scipy.ndimage.filters that can be used to compute these convolutions using a fast 1D separation. All you need to do is: from PIL import Image from numpy import * numpy from scipy.ndimage import filters im = array(Image.open('empire.jpg').convert('L')) = im2 filters.gaussian_filter(im,5) 001-08 T التعليل Here the last parameter of gaussian_filter() is the standard deviation. sagino O 80 (a) q Langla (b) Bia oltes (c) gooliage (d) toteu Figure 1.9: An example of Gaussian blurring using the scipy.ndimage.filters module. (a) original image in grayscale, (b) Gaussian filter with o=2, (c) with σ = 5, (d) with σ = 10. Figure 1.9 shows examples of an image blurred with increasing segma. Larger values gives less details. To blur color images, simply apply Gaussian blurring to each color channel. Exercises 1. F amankay 2. Take an image and apply Gaussian blur like in Figure 1.9. Plot the image contours for increasing values of segma. What happens? Can you explain why? تأخذ صورة الك NOOR FALEH 8

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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2 Gaussian blurring is used to define an image scale to work in, for interpolation, for computing interest points, and in many more applications. SciPy comes with a module for filtering called scipy.ndimage.filters that can be used to compute these convolutions using a fast 1D separation. All you need to do is: from PIL import Image from numpy import * numpy from scipy.ndimage import filters im = array(Image.open('empire.jpg').convert('L')) = im2 filters.gaussian_filter(im,5) 001-08 T التعليل Here the last parameter of gaussian_filter() is the standard deviation. sagino O 80 (a) q Langla (b) Bia oltes (c) gooliage (d) toteu Figure 1.9: An example of Gaussian blurring using the scipy.ndimage.filters module. (a) original image in grayscale, (b) Gaussian filter with o=2, (c) with σ = 5, (d) with σ = 10. Figure 1.9 shows examples of an image blurred with increasing segma. Larger values gives less details. To blur color images, simply apply Gaussian blurring to each color channel. Exercises 1. F amankay 2. Take an image and apply Gaussian blur like in Figure 1.9. Plot the image contours for increasing values of segma. What happens? Can you explain why? تأخذ صورة الك NOOR FALEH 8
Transcribed Image Text:2 Gaussian blurring is used to define an image scale to work in, for interpolation, for computing interest points, and in many more applications. SciPy comes with a module for filtering called scipy.ndimage.filters that can be used to compute these convolutions using a fast 1D separation. All you need to do is: from PIL import Image from numpy import * numpy from scipy.ndimage import filters im = array(Image.open('empire.jpg').convert('L')) = im2 filters.gaussian_filter(im,5) 001-08 T التعليل Here the last parameter of gaussian_filter() is the standard deviation. sagino O 80 (a) q Langla (b) Bia oltes (c) gooliage (d) toteu Figure 1.9: An example of Gaussian blurring using the scipy.ndimage.filters module. (a) original image in grayscale, (b) Gaussian filter with o=2, (c) with σ = 5, (d) with σ = 10. Figure 1.9 shows examples of an image blurred with increasing segma. Larger values gives less details. To blur color images, simply apply Gaussian blurring to each color channel. Exercises 1. F amankay 2. Take an image and apply Gaussian blur like in Figure 1.9. Plot the image contours for increasing values of segma. What happens? Can you explain why? تأخذ صورة الك NOOR FALEH 8
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