April 2020
Intermediate to advanced
438 pages
12h 2m
English
Content preview from Python Image Processing Cookbook
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How to do it...
Perform the following steps to apply an affine transformation to an image using the scipy.ndimage module functions:
- Read the color image, convert it into grayscale, and obtain the grayscale image shape:
img = rgb2gray(imread('images/humming.png'))w, h = img.shape
- Apply identity transform:
mat_identity = np.array([[1,0,0],[0,1,0],[0,0,1]])img1 = ndi.affine_transform(img, mat_identity)
- Apply reflection transform (along the x axis):
mat_reflect = np.array([[1,0,0],[0,-1,0],[0,0,1]]) @ np.array([[1,0,0],[0,1,-h],[0,0,1]])img1 = ndi.affine_transform(img, mat_reflect) # offset=(0,h)
- Scale the image (0.75 times along the x axis and 1.25 times along the y axis):
s_x, s_y = 0.75, 1.25mat_scale = np.array([[s_x,0,0],[0,s_y,0],[0,0,1]]) ...
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