Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 10 This is still not perfect as there are a few bright objects in the parking lot that are not picked up. We will improve it a bit later in the chapter. The Otsu threshold was able to identify the region of the sky as brighter than the building. An alternative thresholding method is the Ridley-Calvard method (also named after its inventors): thresh = mh.thresholding.rc(image) print(thresh) This method returns a smaller threshold, 137.7, and tells apart the building details. Whether this is better or worse depends on what you are trying to distinguish. Gaussian blurring Blurring your image may seem odd, but it often serves to reduce noise, which helps with further processing. With mahotas, it is just a function call: image = mh.colors.rgb2gray(image) im8 = mh.gaussian_filter(image,8) [ 205 ]
Computer Vision – Pattern Recognition Notice how we did not convert the gray screenshot to unsigned integers; we just made use of the floating point result as it is. The second argument to the gaussian_filter function is the size of the filter (the standard deviation of the filter). Larger values result in more blurring, as can be seen in the following screenshot (shown are filtering with sizes 8, 16, and 32): We can use the screenshot on the left and threshold it with Otsu (using the same code seen previously). Now the result is a perfect separation of the building region and the sky. While some of the details have been smoothed over, the bright regions in the parking lot have also been smoothed over. The result is an approximate outline of the sky without any artifacts. By blurring, we got rid of the detail that didn't matter to the broad picture. Have a look at the following screenshot: [ 206 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
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Topic Modeling Topic modeling was f
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Classification - Detecting Poor Ans
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Classification II - Sentiment Analy
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Big(ger) Data Keys, keys, and more
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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Building Machine Learning Systems with Python - Richert, Coelho

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