BuildingMachineLearningSystemsWithPython/ch10/features.py at master · luispedro/BuildingMachineLearningSystemsWithPython · GitHub

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# This code is supporting material for the book

# Building Machine Learning Systems with Python

# by Willi Richert and Luis Pedro Coelho

# published by PACKT Publishing

# It is made available under the MIT License

import numpy as np

import mahotas as mh

def edginess_sobel(image):

'''Measure the "edginess" of an image

image should be a 2d numpy array (an image)

Returns a floating point value which is higher the "edgier" the image is.

'''

edges = mh.sobel(image, just_filter=True)

edges = edges.ravel()

return np.sqrt(np.dot(edges, edges))

def texture(im):

'''Compute features for an image

Parameters

----------

im : ndarray

Returns

-------

fs : ndarray

1-D array of features

'''

im = im.astype(np.uint8)

return mh.features.haralick(im).ravel()

def chist(im):

'''Compute color histogram of input image

Parameters

----------

im : ndarray

should be an RGB image

Returns

-------

c : ndarray

1-D array of histogram values

'''

# Downsample pixel values:

im = im // 64

# We can also implement the following by using np.histogramdd

# im = im.reshape((-1,3))

# bins = [np.arange(5), np.arange(5), np.arange(5)]

# hist = np.histogramdd(im, bins=bins)[0]

# hist = hist.ravel()

# Separate RGB channels:

r,g,b = im.transpose((2,0,1))

pixels = 1 * r + 4 * g + 16 * b

hist = np.bincount(pixels.ravel(), minlength=64)

hist = hist.astype(float)

return np.log1p(hist)

BuildingMachineLearningSystemsWithPython/ch10/features.py at master · luispedro/BuildingMachineLearningSystemsWithPython · GitHub

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