Practical Machine Learning and Image Processing (eBook)

Himanshu Singh has more than five years of experience as a data science professional. Currently he is senior data scientist at Unify Technologies Private Limited. He gives corporate training on data science, ML, and DL. He's also a visiting faculty for analytics at the Narsee Monjee Institute of Management Studies, considered one of the premium management institutes in India. He is founder of Black Feathers Analytics and Rise of Literati Clubs.

Table of Contents 4
About the Author 9
About the Technical Reviewer 10
Acknowledgments 11
Introduction 12
Chapter 1: Setup Environment 13
Install Anaconda 13
Windows 14
macOS 16
Ubuntu 16
Install OpenCV 16
Install Keras 17
Test the Installations 17
Virtual Environments 18
Chapter 2: Introduction to Image Processing 19
Images 20
Pixels 20
Image Resolution 21
PPI and DPI 22
Bitmap Images 22
Lossless Compression 23
Lossy Compression 24
Image File Formats 24
Color Spaces 25
RGB 26
XYZ 27
HSV/HSL 29
LAB 30
LCH 30
YPbPr 31
YUV 32
YIQ 33
Advanced Image Concepts 33
Bezier Curve 34
Ellipsoid 35
Gamma Correction 36
Structural Similarity Index 37
Deconvolution 37
Homography 38
Convolution 39
Chapter 3: Basics of Python and Scikit Image 40
Basics of Python 41
Variables and Data Types 41
Data Structures 44
Lists 44
Dictionaries 44
Tuples 45
Control Flow Statements 45
Conditional Statements 48
Functions 49
Scikit Image 51
Uploading and Viewing an Image 52
Getting Image Resolution 53
Looking at Pixel Values 54
Converting Color Space 54
RGB to HSV and Vice Versa 55
RGB to XYZ and Vice Versa 56
RGB to LAB and Vice Versa 58
RGB to YUV and Vice Versa 59
RGB to YIQ and Vice Versa 61
RGB to YPbPr and Vice Versa 62
Saving an Image 64
Creating Basic Drawings 64
Lines 64
Rectangles 65
Circles 66
Bezier Curve 67
Doing Gamma Correction 68
Rotating, Shifting, and Scaling Images 70
Determining Structural Similarity 71
Chapter 4: Advanced Image Processing Using OpenCV 73
Blending Two Images 74
Changing Contrast and Brightness 76
Adding Text to Images 78
Smoothing Images 81
Median Filter 81
Gaussian Filter 81
Bilateral Filter 82
Changing the Shape of Images 85
Effecting Image Thresholding 90
Calculating Gradients 94
Performing Histogram Equalization 97
Chapter 5: Image Processing Using Machine Learning 99
Feature Mapping Using the SIFT Algorithm 100
Step 1: Space Construction 101
Step 2: Difference between the Gaussians 101
Step 3: Important Points 102
Step 4: Unimportant Key Points 102
Step 5: Orientation of Key Points 102
Step 6: Key Features 103
Image Registration Using the RANSAC Algorithm 108
estimate_affine 115
residual_lengths 115
Processing the Images 116
The Complete Code 116
Image Classification Using Artificial Neural Networks 120
Image Classification Using CNNs 128
Image Classification Using Machine Learning Approaches 135
Decision Trees 136
Support Vector Machines 137
Logistic Regression 137
Code 137
Important Terms 140
Chapter 6: Real-time Use Cases 143
Finding Palm Lines 143
Detecting Faces 145
Recognizing Faces 148
Tracking Movements 151
Detecting Lanes 153
Appendix: Important Concepts and Terminology 160
Adaboost 160
XGBoost 161
Pulse-coupled Neural Networks 162
Gradient Descent 163
Stochastic Gradient Descent 164
AdaDelta 165
Canny Edge Detector 165
Sobel Transformation 166
Haar Cascade 167
LBPH Face Recognition 167
Image Moments 167
Image Contours 168
Chessboard Corners Function 169
Calibrate Camera Function 170
Perspective Transformation Function 171
Index 173