I compared it with other books in relation to the translation about "OpenCV-Python Tutorials".
###** Introduction to OpenCV **
Introduction to OpenCV-Python Tutorial
Let's get started with OpenCV-Python
Install OpenCV-Python on Windows
Make OpenCV available on windows.
Install OpenCV-Python on Fedora
Make OpenCV available on Fedora.
###** GUI features in OpenCV **
Let's start image manipulation
画像を読み込むこと、表示すること、保存することを学びます。
Let's start video operation
動画の再生、カメラからの動画のキャプチャと動画としての保存を学びましょう。
OpenCV drawing functions
直線、矩形、楕円、円などをOpenCVを使って描画することを学びます。
Use mouse as paint brush
マウスを使って塗りつぶし
Use Trackbar as a color palette
パラメータを制御するためのtrackbarを作る
###** Core operation **
Basic operations on images
You will learn how to read and edit pixel values, operate images in the ROI (region of interest), and other basic operations.
"Practical machine learning system" Chapter 10 Computer vision pattern recognition
Loading images using mahotas is introduced.
Again, the data format after reading is numpy.array.
The function name is also imread ().
Arithmetic operations on images
Performs arithmetic operations on the image.
[Execution performance measurement and improvement method]
(http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_core/py_optimization/py_optimization.html#optimization-techniques)
It's important to get the answer. But getting the answer the fastest is even more important. Check the speed of your code, learn to optimize your code and more.
Mathematical methods in OpenCV
PCA(Principalcomponentsanalysis 主成分分析)、SVD(singularvaluedecomposition 特異値分解)などのOpenCVで提供されている数学的手法のいくつかを学びます。
###** Image processing with OpenCV **
Change color space
異なる色空間で画像を変換する方法を学びましょう。そして動画で色つきの物体を追跡させてみましょう。
Geometric transformation of images
回転、変形などのさまざまな幾何変換を施してみましょう。
Image Threshold Processing
画像をグローバルしきい値、適応的閾値処理 、大津の２値化などで2値化画像に変換してみましょう。
mahotas also includes the binarization of Otsu. Other binarization techniques Ridler-Calvard techniques are also implemented.
Image Smoothing
画像をぼかしたり、あつらえたカーネルを用いた画像にフィルタすることを学びます。
Morphological Transformation
Mol of contraction, expansion, Opening, Closing, etc.
Learn about phology transformations.
Image Gradients
Find image gradients, edges, etc. Let's do it.
Canny Edge Detection
Let's find the edge using Canny edge detection.
Image Pyramid
画像ピラミッドとそれを画像混合に使う方法を学びます。
####** Contour processing with OpenCV **
Outline: Let's get started
Find the outline and draw
Outline Features
Learn about finding various contour features, areas, perimeters, circumscribing rectangles, etc.
Contour Properties
Learn to find various contour characteristics, solidity, average strength and more.
Outline: Other Functions
Learn to find convexity defects, point Polygon Tests, and match with different shapes.
Contour Hierarchy
Learn about the hierarchy of contours.
Histogram in OpenCV
OpenCVにあるヒストグラムの全て。
OpenCV Histogram in OpenCV
Histograms-1: Find, plot and analyze! !! !!
Find the histogram and draw it.
Histograms-2: Histogram flattening
Learn to flatten the histogram to get a good contrast image.
Histograms --3: Two-dimensional Histogram
Learn to find and plot a 2D histogram.
Histogram-4: Histogram Backprojection
Learn to back-project a histogram on an object colored by region.
Image conversion with OpenCV
フーリエ変換、コサイン変換などOpenCVにある様々な画像変換に出会ってみましょう。
Template Matching
テンプレートマッチングを用いて画像中から物体を探してみましょう。
Hough Transform
画像の中から線を検出してみましょう。
Hough Transform
画像の中から円を検出してみましょう。
Image segmentation based on the Watershed algorithm
Let's divide the image area using the Watershed algorithm.
Interactive foreground extraction using GrabCut algorithm
Let's extract the foreground with the GrabCut algorithm.
###** Feature detection and feature description **
Understand features
What are the main features of the image? How useful are these features found?
Harris Corner Detection
ええ、コーナーはよい特徴？ でもどうやって見つけますか？
[Shi-Tomasi Corner Detector and Good Features to Track](http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.html#shi- tomasi)
Let's take a look at the details of Shi-Tomasi corner detection.
Introduction to SIFT (Scale-Invariant Feature Transform) Features
Harris corner detectors are not good enough when the scale of the image changes. Lowe has developed a breakthrough method to find features that do not affect scale. It is called SIFT features.
Introduction to SURF (Speeded-Up Robust Features) Features
SIFT features are certainly good features. But it's not fast enough. Therefore, a high-speed version called SURF features was created.
FAST algorithm for corner detection
上に示した特徴検出器は全てよいものです。しかし、SLAM(訳注：SimultaneousLocalizationandMapping、自己位置推定と環境地図作成を同時に行うこと)のようなリアルタイムの用途に使えるほど十分に速いとは言えません。そこでFASTアルゴリズムの登場です。これは本当に"FAST(速い)"です。
BRIEF Independent Elementary Features
SIFT特徴量は、128個の浮動小数点からなる特徴記述子を用いています。そのような特徴量を数千個あつかうことを考えてごらんなさい。そのときたくさんのメモリーとマッチングのためにたくさんの時間を使
is. You can compress the features to make them faster, but you still have to calculate the features first. That's where BRIEF comes in, offering a shortcut to finding binary descriptors with less memory, faster matching, and higher recognition.
ORB (Oriented FAST and Rotated BRIEF) features
SIFT特徴量とSURF特徴量はとてもよく動くのだけれども、あなたの用途の中で使うには毎年数ドル払わなければならないとしたらどうしますか？ それらは特許が成立しているのです。その問題を解決するには、OpenCVの開発者はSIFT特徴量とSURF特徴量への新しい"FREE"な代替品、ORBを思いつきました。
Feature Matching
特徴検出器と記述子についてたくさん理解しました。異なる記述子を対応付ける方法を学ぶときです。OpenCVはそのために２つの手法、Brute-Forceマッチング手法とFLANNに基づくマッチング手法です。
Feature matching and homography for finding objects
いま特徴量マッチングについて知っているので、複雑な画像中の物体を見つけるためにcalib3dモジュールとともに混ぜ合わせてみましょう。
Mahotas contains Haralic features that can distinguish between smooth and patterned images.
SURF features are also included in mahotas.
###** Video analysis **
Meanshift and Camshift Tracking (http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_video/py_meanshift/py_meanshift.html#meanshift)
We have already seen an example of color-based tracking. It's simple. Let's take a look at how the better algorithm, mean shift and its improved version, CamShift, find and track objects.
[Optical flow]
(http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_video/py_lucas_kanade/py_lucas_kanade.html#lucas-kanade)
Let's learn about the important concept, optical flow. It is related to video and has many uses.
Background Removal
In some applications, it is necessary to extract the foreground like object tracking. Background removal is a well-known technique that helps in those cases.
###** Camera calibration and 3D reconstruction **
Camera Calibration
利用しているカメラがどれだけ良いものか試してみましょう。それで撮影した画像に歪みが見られるでしょうか？もしあれば、どう補正しましょうか？
Posture estimation
A short session to help you create a little cool 3D effect with the calib module.
Epipolar Geometry
エピポーラ幾何とエピポーラ制約を理解しましょう。
Depth distance information from stereo image
2D画像群から奥行き情報を得ます。
###** Machine learning **
K-nearest neighbor method
K最近傍法の使い方を学ぶとともに、K最近傍法を用いて手書きの数字認識について学びます。
Support Vector Machine (SVM)
SVMの考え方を理解します
K-means clustering
A group of data using K-means clustering Learn to classify into clusters. Then you will learn to perform color quantization using the K-means method.
Handwritten digit recognition with OpenCV
($OPENCV_DIR)\sources\samples\python2\digits.py
You can see the results by SVM and KNearest.
###Computational Photography
Here you will learn about the various features of OpenCV related to Computational Photography, such as image denoising.
Image Noise Removal
Non-local Meansノイズ除去と呼ばれる画像からノイズを除去する良好な手法を見ていただきます。
Image Repair
たくさんの黒点とひっかきを生じた古い劣化した写真を持っていませんか？それを持ってきて、画像修復と呼ばれる方法でそれらを復元してみましょう。
###** Object detection **
Face detection using Haar cascade detector
Haar カスケード検出器を用いた顔検出
###** OpenCV-Python binding **
Now let's learn how OpenCV-Python bindings are made.
How does the OpenCV-Python binding work?
OpenCV-Pythonバインディングがどのように作られているのか学びましょう。
Postscript:
Machine learning starting with Python-feature engineering and machine learning basics learned with scikit-learn
Is published in May 2017.
"Practical computer vision" related
I tried all the exercises in "Practical Computer Vision"