Why do I write "OpenCV Android Development Combat" this book

Why do I write "OpenCV Android Development Combat" this book

2015 I published my first personal book on image processing, Java image processing-programming techniques and application practice, which is based on a detailed description of the basic algorithms of image processing and their techniques in coding implementation from the above theory and coding. A blink of an eye has been three years, in this three years of time I have been concerned about the development and future of image processing and computer vision technology, while gradually germination of another image processing related technical books, mainly because the "Java image processing-programming skills and Application Practice" book is not aimed at engineering application scenarios , the reader is very difficult to start directly after learning to do the project, so the second book as a Project REAL-world book type, can help you solve engineering and project practical technical problems. OPENCV is Intel's Open source computer vision framework, with a very powerful library of image and video analytics processing algorithms. With the OPENCV framework, Android programmers can solve face detection, OCR recognition, AR application development, image and video analysis and processing, text processing, etc. androd developers often encounter problems, considering these real needs, in the light of the principle of the ground, from easy to difficult, Outlined the outline, get the mechanical industry press Yang Shu editor Affirmation and strong support, so only "OpenCV Android Development Combat" a book of writing and publishing.

Google in October 2008 released the first mobile phone with Android system, from this Android system in mobile and embedded terminal big, occupy the mobile operating system half of the market, the market demand explosion growth, a large number of Android developers appear, A huge Android developer ecosystem has evolved over the past decade, and the Android operating system and developer teams have shifted from the initial focus to quality. Android developers also face technical stack aging, work seven-year itch, technology direction choice, career development bottleneck and other problems. Computer vision as one of the branches of artificial intelligence, in line with the future development direction of science and technology, OPENCV is open source commercial application of the most popular computer vision framework, including more than 3,000 algorithm implementations, its SDK support Java, C + +, Python and other programming languages, Support for Windows systems, Linux systems, Android systems, Mac systems. In the future with the release and popularization of 5G mobile phone, mobile applications will inevitably rely on mobile phone camera and real-time video content analysis and processing, OPENCV in these two aspects have unique technical advantages, Android developers only have to continue to learn, keep up with the pace of the times, can get a career further development. OPENCV Computer vision is the "sky-high-flying, sea-wide diving" broad world, is the Android developer Technology direction, the first step toward AI technology. Especially look forward to become friends with the vast majority of Android developers, look forward to your readers reading feedback and questions to exchange, read the same book, write different technical life, get its skills, know its way.

Attached Book catalogue:
1th Chapter OpenCV for Android frame
What is 1.1 OpenCV?
The history and development status of 1.1.1 OpenCV frame
1.1.2 Core module and function introduction
1.1.3 Opencv4android SDK Introduction
1.2 Opencv4android Development Environment Construction
1.2.1 opencv4android SDK Download and import
1.2.2 Environment Construction
1.2.3 Code Test
1.3 Building a OPENCV demo app
1.4 Taking photos and selecting pictures
1.5 Summary

2nd Chapter mat and Bitmap Object
2.1 Mat Object
2.2 Bitmap objects in Android
2.3 Basic shape drawing and filling
2.2.1 Mat-based drawing and padding
Drawing and padding on the 2.2.2 bitmap
2.4 Mat and bitmap conversion and use
2.5 Summary

3rd Mat Pixel operation
How to manipulate pixels in 3.1 OpenCV mat
3.1.1 Mat type and get, put method
3.1.2 How to properly cycle through each pixel point
3.2 Image channel and mean value variance calculation
3.3.1– channel separation and merging, calculating mean and standard equations, using mean and standard equations to filter blank images
3.3 Pixel Operation Classic example-adjust the brightness and contrast of the image
3.4 Two image blends
3.5.1-Direct Pixel addition
3.5.2-weighting-based pixel addition
3.5 Mat Other various pixel operations (including reverse, logical operation, square root, etc.)
3.6 Summary

The 4th Chapter image operation
4.1 Blur
4.2 Statistical Sorting filter
4.2.1-Median Filter
4.2.2-Maximum Minimum value filter
4.3 Edge retention Filter
4.3.1-Bilateral filtering
4.3.2-Mean migration filter
4.4 Custom Filtering
4.5 Morphological operations
4.6 Threshold and adaptive threshold values
4.6.1 Threshold (Introduction of 5 threshold methods)
4.6.2 Adaptive Thresholds (introduction of two adaptive threshold methods)
4.7 Summary

The 5th Chapter Basic characteristic detection
5.1 Gradient operator
5.2 Laplace operator
5.3 Canny edge detection
5.4 Huffman Line Detection
5.5 Hoffman Circle Detection
5.6 Contour Detection and rendering
5.7.1-contour detection and contour mapping
5.7.2-Draw outline external rectangles and circles
5.7.3-Draw the minimum bounding rectangle
5.7 Contour Analysis
Draw an external rectangle, a minimum bounding rectangle, a cross-aspect ratio, an area, a contour perimeter, and so on
5.8 Image Histogram
5.8.1-Calculate histogram
5.8.2-Histogram equalization
5.8.3-Histogram comparison
5.8.4-Histogram Reverse projection
5.9 Template Matching (introduction of common image template matching algorithms)
5.10 Summary

The 6th chapter feature detection and matching
6.1 Harr Corner Point detection
(Harr Corner feature detection principle and related API usage introduction)
6.2 Shi-tomasi Corner Point detection
(Shi-tomasi angle Point detection principle and related API usage introduction)
6.3 Surf feature detection and matching
(Surf feature extraction steps and feature descriptors)
6.4 Sift feature detection and matching
(Sift feature extraction steps and feature descriptors)
6.5 Feature2d Detectors and descriptors
Brisk
Orb
AKAZE
6.6 Feature matching find known objects
(Match the result of the feature to find the known object in a picture and mark the object outline)
6.7 Cascade classifier and face detection
LBP Cascade Classifier
Harr cascading classifier
Application of cascade detector for face detection
6.8 Summary

7th. Using the camera
7.1 Using Javacameraview (Introduction opencv4android comes with the Call Camera feature component)
7.2 Horizontal screen and vertical screen display (discuss the problem of horizontal screen and vertical screen display)
7.3 Processing the camera preview frame image (to implement processing of the preview frame while knowing that excessive JNI mode calls the OpenCV API can cause performance issues)
7.4 Implement face Detection in preview frame (implement a real-time face detection example, technical thinking and coding implementation steps, Introduction NDK development method)
7.4.1–NDK Support Development Configuration
7.4.2– local method definition and OpenCV C + + code writing
Code implementation and running demo in 7.4.3–java
7.5 Summary

8th Chapter OCR Recognition
8.1 What is OCR
8.2 Open Source OCR framework Tesseract (Introduction to the use of the TESSERACT-OCR Framework on Android systems, complete the first Test case code)
8.3 Identifying the XXX number
8.3.1 UI Encoding (walkthrough call camera photo and display)
8.3.2 Location Search (how to achieve the location of XXX numbers by OPENCV, based on template matching technology and feature matching technology)
8.3.2 using TESSERACT-OCR API to identify
8.4 Improve OCR recognition rate
8.4.1 Training Custom data (telling how to train custom data in TESSERACT-OCR)
8.4.2 Image preprocessing (describes how to achieve skew correction through OPENCV, noise interference removal, edge removal, to reduce interference, improve recognition rate)
8.5 Summary (Summary of the contents of this chapter)

9th. Face Beauty
9.1 Integral Graph calculation (introduction of image integration graph algorithm)
9.2 Local mean-variance filtering based on integral image (details on how to implement your own algorithm in OPENCV)
9.3 Masking Layer Generation (detailed explanation and code demonstration implementation,)
9.4 Gaussian Weight fusion (detailed explanation and code demonstration implementation)
9.5 Edge Elevation (detailed explanation and code demonstration implementation,)
9.6 Beauty Implementation (NDK layer detailed explanation and code demonstration implementation,)
9.7 Summary (described in mobile applications common face grinding skin beauty algorithm implementation steps and details, complete the entire beauty algorithm, is the image processing knowledge of this application)

The 10th chapter real-time tracking and rendering of human eyes
10.1 Interface display with camera preview
10.2 Human face detection and tracking
10.3 Looking for the eye candidate area
10.4 Eye detection (eye detection with cascade classifier)
10.5 Looking for black eyeballs
10.6 Rendering and optimization
10.7 Summary

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Why do I write "OpenCV Android Development Combat" this book