Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 10 However, some specialized equipment (mostly in scientific fields) can take images with more bit resolution. 12 or 16 bits are common. Mahotas can deal with all these types, including floating point images (not all operations make sense with floating point numbers, but when they do, mahotas supports them). In many computations, even if the original data is composed of unsigned integers, it is advantageous to convert to floating point numbers in order to simplify handling of rounding and overflow issues. Mahotas can use a variety of different input/output backends. Unfortunately, none of them can load all existing image formats (there are hundreds, with several variations of each). However, loading PNG and JPEG images is supported by all of them. We will focus on these common formats and refer you to the mahotas documentation on how to read uncommon formats. The return value of mh.imread is a NumPy array. This means that you can use standard NumPy functionalities to work with images. For example, it is often useful to subtract the mean value of the image from it. This can help to normalize images taken under different lighting conditions and can be accomplished with the standard mean method: image = image – image.mean() We can display the image on screen using maplotlib, the plotting library we have already used several times: from matplotlib import pyplot as plt plt.imshow(image) plt.show() This shows the image using the convention that the first dimension is the height and the second the width. It correctly handles color images as well. When using Python for numerical computation, we benefit from the whole ecosystem working well together. Basic image processing We will start with a small dataset that was collected especially for this book. It has three classes: buildings, natural scenes (landscapes), and pictures of texts. There are 30 images in each category, and they were all taken using a cell phone camera with minimal composition, so the images are similar to those that would be uploaded to a modern website. This dataset is available from the book's website. Later in the chapter, we will look at a harder dataset with more images and more categories. [ 201 ]
Computer Vision – Pattern Recognition This screenshot of a building is one of the images in the dataset. We will use this screenshot as an example. As you may be aware, image processing is a large field. Here we will only be looking at some very basic operations we can perform on our images. Some of the most basic operations can be performed using NumPy only, but otherwise we will use mahotas. Thresholding Thresholding is a very simple operation: we transform all pixel values above a certain threshold to 1 and all those below to 0 (or by using Booleans, transform it to True and False): binarized = (image > threshold_value) The value of the threshold width (threshold_value in the code) needs to be chosen. If the images are all very similar, we can pick one statically and use it for all images. Otherwise, we must compute a different threshold for each image based on its pixel values. Mahotas implements a few methods for choosing a threshold value. One is called Otsu, after its inventor. The first necessary step is to convert the image to grayscale with rgb2gray. [ 202 ]
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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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Big(ger) Data In EC2 parlance, a ru
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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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