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 implement the filters:
- Download the faces data first:
dataset = fetch_olivetti_faces(shuffle=True, random_state=rng)original = img_as_float(dataset.data)faces = original.copy()print(faces.shape)# (400, 4096)
- Add random Gaussian noise to the image with a variance of 0.005:
image_shape = (64, 64)rng = RandomState(0)n_samples, n_features = faces.shapefaces = random_noise(faces, var=0.005)
- Use PCA and reconstruct the images using only 50 dominant principal components:
n_components = 50 # 256estimator = decomposition.PCA(n_components=n_components, svd_solver='randomized', whiten=True)print("Extracting the top %d PCs..." % (n_components))t0 = time()faces_recons = estimator.inverse_transform(estimator.fit_transform(faces)) ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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