Machine Learning for OpenCV 4 intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn
Machine Learning for OpenCV 4, Second Edition will help the readers to implement and train machine learning algorithms with OpenCV 4 and scikit-learn in Python. By the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your ma...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt Publishing
2019
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | DE-1050 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Inhaltsangabe:
- Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Fundamentals of Machine Learning and OpenCV; Chapter 1: A Taste of Machine Learning; Technical requirements; Getting started with machine learning; Problems that machine learning can solve; Getting started with Python; Getting started with OpenCV; Installation; Getting the latest code for this book; Getting to grips with Python's Anaconda distribution; Installing OpenCV in a conda environment; Verifying the installation; Getting a glimpse of OpenCV's ml module
- Applications of machine learningWhat's new in OpenCV 4.0?; Summary; Chapter 2: Working with Data in OpenCV; Technical requirements; Understanding the machine learning workflow; Dealing with data using OpenCV and Python; Starting a new IPython or Jupyter session; Dealing with data using Python's NumPy package; Importing NumPy; Understanding NumPy arrays; Accessing single array elements by indexing; Creating multidimensional arrays; Loading external datasets in Python; Visualizing the data using Matplotlib; Importing Matplotlib; Producing a simple plot; Visualizing data from an external dataset
- Dealing with data using OpenCV's TrainData container in C++Summary; Chapter 3: First Steps in Supervised Learning; Technical requirements; Understanding supervised learning; Having a look at supervised learning in OpenCV; Measuring model performance with scoring functions; Scoring classifiers using accuracy, precision, and recall; Scoring regressors using mean squared error, explained variance, and R squared; Using classification models to predict class labels; Understanding the k-NN algorithm; Implementing k-NN in OpenCV; Generating the training data; Training the classifier
- Predicting the label of a new data pointUsing regression models to predict continuous outcomes; Understanding linear regression; Linear regression in OpenCV; Using linear regression to predict Boston housing prices; Loading the dataset; Training the model; Testing the model; Applying Lasso and ridge regression; Classifying iris species using logistic regression; Understanding logistic regression; Loading the training data; Making it a binary classification problem; Inspecting the data; Splitting data into training and test sets; Training the classifier; Testing the classifier; Summary
- Chapter 4: Representing Data and Engineering FeaturesTechnical requirements; Understanding feature engineering; Preprocessing data; Standardizing features; Normalizing features; Scaling features to a range; Binarizing features; Handling the missing data; Understanding dimensionality reduction; Implementing Principal Component Analysis (PCA) in OpenCV; Implementing independent component analysis (ICA); Implementing non-negative matrix factorization (NMF); Visualizing the dimensionality reduction using t-Distributed Stochastic Neighbor Embedding (t-SNE); Representing categorical variables