Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 10 Instead of rgb2gray, we can also have just the mean value of the red, green, and blue channels by calling image.mean(2). The result, however, will not be the same because rgb2gray uses different weights for the different colors to give a subjectively more pleasing result. Our eyes are not equally sensitive to the three basic colors. image = mh.colors.rgb2gray(image, dtype=np.uint8) plt.imshow(image) # Display the image By default, matplotlib will display this single-channel image as a false color image, using red for high values and blue for low. For natural images, grayscale is more appropriate. You can select it <strong>with</strong> the following: plt.gray() Now the screenshot is shown in grayscale. Note that only the way in which the pixel values are interpreted and shown has changed and the screenshot is untouched. We can continue our processing by computing the threshold value. thresh = mh.thresholding.otsu(image) print(thresh) imshow(image > thresh) When applied to the previous screenshot, this method finds the threshold 164 value, which separates the building and parked cars from the sky above. [ 203 ]
Computer Vision – Pattern Recognition The result may be useful on its own (if you are measuring some properties of the thresholded image) or it can be useful for further processing. The result is a binary image that can be used to select a region of interest. The result is still not very good. We can use operations on this screenshot to further refine it. For example, we can run the close operator to get rid of some of the noise in the upper corners. otsubin = (image thresh) otsubin = mh.open(otsubin, np.ones((15,15))) In either case, the operator takes a structuring element that defines the type of region we want to close. In our case, we used a 15x15 square. [ 204 ]
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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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Big(ger) Data In this system, pip i
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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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