MACHINE LEARNING FOR OPENCV 4 : intelligent algorithms for building image processing apps... using opencv 4, python, and scikit-learn, 2nd edit.

Description

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 learning What'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 point Using 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 Features Technical 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 Representing text features Representing images Using color spaces Encoding images in the RGB space Encoding images in the HSV and HLS space Detecting corners in images Using the star detector and BRIEF descriptor Using Oriented FAST and Rotated BRIEF (ORB) Summary Section 2: Operations with OpenCV Chapter 5: Using Decision Trees to Make a Medical Diagnosis Technical requirements Understanding decision trees Building our first decision tree Generating new data Understanding the task by understanding the data Preprocessing the data Constructing the tree Visualizing a trained decision tree Investigating the inner workings of a decision tree Rating the importance of features Understanding the decision rules Controlling the complexity of decision trees Using decision trees to diagnose breast cancer Loading the dataset Building the decision tree Using decision trees for regression Summary Chapter 6: Detecting Pedestrians with Support Vector Machines Technical requirement Understanding linear SVMs Learning optimal decision boundaries Implementing our first SVM Generating the dataset Visualizing the dataset Preprocessing the dataset Building the support vector machine Visualizing the decision boundary Dealing with nonlinear decision boundaries Understanding the kernel trick Knowing our kernels Implementing nonlinear SVMs Detecting pedestrians in the wild Obtaining the dataset Taking a glimpse at the histogram of oriented gradients (HOG) Generating negatives Implementing the SVM Bootstrapping the model Detecting pedestrians in a larger image Further improving the model Multiclass classification using SVMs About the data Attribute information Summary Chapter 7: Implementing a Spam Filter with Bayesian Learning Technical requirements Understanding Bayesian inference Taking a short detour through probability theory Understanding Bayes' theorem Understanding the Naive Bayes classifier Implementing your first Bayesian classifier Creating a toy dataset Classifying the data with a normal Bayes classifier Classifying the data with a Naive Bayes classifier Visualizing conditional probabilities Classifying emails using the Naive Bayes classifier Loading the dataset Building a data matrix using pandas Preprocessing the data Training a normal Bayes classifier Training on the full dataset Using n-grams to improve the result Using TF-IDF to improve the result Summary Chapter 8: Discovering Hidden Structures with Unsupervised Learning Technical requirements Understanding unsupervised learning Understanding k-means clustering Implementing our first k-means example Understanding expectation-maximization Implementing our expectation-maximization solution Knowing the limitations of expectation-maximization The first caveat – no guarantee of finding the global optimum The second caveat – we must select the number of clusters beforehand The third caveat – cluster boundaries are linear The fourth caveat – k-means is slow for a large number of samples Compressing color spaces using k-means Visualizing the true-color palette Reducing the color palette using k-means Classifying handwritten digits using k-means Loading the dataset Running k-means Organizing clusters as a hierarchical tree Understanding hierarchical clustering Implementing agglomerative hierarchical clustering Comparing clustering algorithms Summary Section 3: Advanced Machine Learning with OpenCV Chapter 9: Using Deep Learning to Classify Handwritten Digits Technical requirements Understanding the McCulloch-Pitts neuron Understanding the perceptron Implementing your first perceptron Generating a toy dataset Fitting the perceptron to data Evaluating the perceptron classifier Applying the perceptron to data that is not linearly separable Understanding multilayer perceptrons Understanding gradient descent Training MLPs with backpropagation Implementing a MLP in OpenCV Preprocessing the data Creating an MLP classifier in OpenCV Customizing the MLP classifier Training and testing the MLP classifier Getting acquainted with deep learning Getting acquainted with Keras Classifying handwritten digits Loading the MNIST dataset Preprocessing the MNIST dataset Training an MLP using OpenCV Training a deep neural network using Keras Preprocessing the MNIST dataset Creating a convolutional neural network Model summary Fitting the model Summary Chapter 10: Ensemble Methods for Classification Technical requirements Understanding ensemble methods Understanding averaging ensembles Implementing a bagging classifier Implementing a bagging regressor Understanding boosting ensembles Weak learners Implementing a boosting classifier Implementing a boosting regressor Understanding stacking ensembles Combining decision trees into a random forest Understanding the shortcomings of decision trees Implementing our first random forest Implementing a random forest with scikit-learn Implementing extremely randomized trees Using random forests for face recognition Loading the dataset Preprocessing the dataset Training and testing the random forest Implementing AdaBoost Implementing AdaBoost in OpenCV Implementing AdaBoost in scikit-learn Combining different models into a voting classifier Understanding different voting schemes Implementing a voting classifier Plurality Summary Chapter 11: Selecting the Right Model with Hyperparameter Tuning Technical requirements Evaluating a model Evaluating a model the wrong way Evaluating a model in the right way Selecting the best model Understanding cross-validation Manually implementing cross-validation in OpenCV Using scikit-learn for k-fold cross-validation Implementing leave-one-out cross-validation Estimating robustness using bootstrapping Manually implementing bootstrapping in OpenCV Assessing the significance of our results Implementing Student's t-test Implementing McNemar's test Tuning hyperparameters with grid search Implementing a simple grid search Understanding the value of a validation set Combining grid search with cross-validation Combining grid search with nested cross-validation Scoring models using different evaluation metrics Choosing the right classification metric Choosing the right regression metric Chaining algorithms together to form a pipeline Implementing pipelines in scikit-learn Using pipelines in grid searches Summary Chapter 12: Using OpenVINO with OpenCV Technical requirements Introduction to OpenVINO OpenVINO toolkit installation OpenVINO components Interactive face detection demo Using OpenVINO Inference Engine with OpenCV Using OpenVINO Model Zoo with OpenCV Image classification using OpenCV with OpenVINO Inference Engine Image classification using OpenVINO Image classification using OpenCV with OpenVINO Summary Chapter 13: Conclusion Technical requirements Approaching a machine learning problem Building your own estimator Writing your own OpenCV-based classifier in C++ Writing your own scikit-learn-based classifier in Python Where to go from here Summary Other Books You May Enjoy Index