April 2020
Intermediate to advanced
438 pages
12h 2m
English
Content preview from Python Image Processing Cookbook
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How it works...
The decomposition.PCA() function from scikit-learn was used to instantiate a PCA object. The fit_transform() function was first used to project the images to a lower dimension (from 4096 to 50 dimensions), and then the inverse_transform() function was used to reconstruct the images from the lower-dimensional representations.
The fft2() function of scipy.fftpack was used to transform an image from the spatial to the frequency domain by forcing all the high-frequency basis vectors to be zero (except the lowest thirty basis vectors) and then the image was reconstructed with ifft2().
Finally, the pywt.Wavelet('haar') function was used to instantiate a Haar wavelet object. The pywt.wavedec2() function was used to get all the wavelet ...
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