Gromit's Cabin

# Subammunition Detection using OpticalFlow # Grass-Fire labeling, Optical flow, BetaCam VCR, sgiOctane IRIX, MipsPro # C/C++, irisVision, OpenInventor, VRML 3D View DPICM

# THOB detection using StereoVision # Stereo Camera Calibration, Reference Poles, Multi-Projection Linear Algebra, Pseudo-Inverse Matrix # Matrox Capture Board control, GenRock, IR Sensor Triggering # Windows2000, Win32, Delphi, VCL, Scanline Image Processing, IDL SDK

# Skin Pattern classification using NeuralNetwork # MultiLayer Perceptron, Shape Features(RANSAC, HT, LBP, moment), Feature selection by Kohonen network(SOM unsupervised map) # WindowsXP, win32 dll, delphi, VCL, VRML, ActiveX

# Background Gradient Removal using Homomorphic Filter # Homomorphic Filter, 2D FFT, 2D iFFT, Inverse Gaussian Kernel, Log Scaling # Frequency HPF, Shadow Removal, C#.Net, RSImage API Build. Image Preprocessing.

# Phase Only Correlation, 2D ideal Nortch Filter design, Median Filtering, Gaussian Peak Finding # Hamming Window, Tukey Window function, Hadamard Product, Complex Conjugate, 2D FFT, 2D iFFT  # Windows7 x64, VisualStudio2012, C#.Net, RSImage API build, LogicCreator+Python.

# Pedestrian Detection for Schoolbus # TI DSP, HOG, LBP, SVM, Overall Movement, Python CaptureProgram for Training Data. # Ubuntu, Python, OpenCV, cv2.py, Processing Sketch # CV 10-commendment, outdoor vision, embeded, non-rigid body, changing background.

1) 이미지 프로세싱 관련 - 운영환경 및 개발 Language 기록 1. 운영체계: Windows7 x64 2. 개발환경: C#.Net 4.5, Visual Studio 2012 C++/ C#  3. 머신비전 라이브러리: OpenCV 2.x/3.x, Octave(Proto-Typing) etc. Matlab의 오픈소스 Clone인 Octave를 사용하여 빠른 영상처리를 테스트한후 검증된 알고리즘을 OpenCV라이브러리를 이용하여 구현하거나 API화 한다. - 사용된 이미지 처리 기법/알고리즘 제시 1. 영상전처리 - 잡음제거: Weiter Filtering, Bilateral Filtering(or Median Filtering) 윤곽선을 잘 보존하면서 영상에 포함된 잡음을 제거해 기하학정보를 정확히 추출하도록 영상을 개선한다. - 영상 매칭: Histogram Matching 기준이 되는 영상의 밝기값분포Histogram과 테스트하려는 영상의 Histogram을 일치시켜 두영상간의 밝기값을 동기화해 조명환경변화에 따른  기하학정보 측정의 오차를 줄임으로써 정보 측정의 정밀도를 향상시켜준다. - 영상균일도 향상: Homomorphic Filtering 단일 영상내의 그림자나 불균일 조도분포와 같이 영상내 불필요하고 지역적인 조명의 영향을 제거하고 측정하려는 물체의 정보만을 보존해 기하학적 측정의 정밀도를 향상시킨다.      - 윤곽선 강조: Unsharp Masking(Laplacian Convolution) 기하학적인 정보가 집중되어있는 윤곽선을 배경영상에 대해 상대적으로 강조해 측정을 용이하게 영상을 전처리 한다. - 영상 이진화/ 세그멘테이션: Bi-Modal Thresholding, Otsu Thresholding, GLCM, Local Entropy Segmentation 배경패턴과 측정하려는 대상물체를 이진화 하거나 ...

# Heart Rate Detection in iPhone # iOS, XCode, OpenCV, Cepstrum, FFT 1D, # HSV, Color Channel PCA, Objective-C, Heart Rate, Color Amplification, face ROI # iPhone Front & Rear Cam Control, Realtime Overlay Realtime, Vital sign, skin detection.

import SimpleITK as sitk import sys reader = sitk.ImageFileReader() reader.SetFileName ( sys.argv[1] ) image = reader.Execute() pixelID=image.GetPixelIDValue() gaussian=sitk.SmoothingRecursiveGaussianImageFilter() gaussian.SetSigma(float(sys.argv[2])) image=gaussian.Execute(image)  caster=sitk.CastImageFilter() caster.SetOutputPixelType(pixelID) image=caster.Execute(image) writer=sitk.ImageFileWriter() writer.SetFileName(sys.argv[3]) writer.Execute(image);

http://www.itk.org/Wiki/SimpleITK/GettingStarted/Visual_guide_to_building_on_Linux

import SimpleITK as sitk   #read the images fixed_image = sitk. ReadImage ( 'training_001_ct.mha' , sitk. sitkFloat32 ) moving_image = sitk. ReadImage ( 'training_001_mr_T1.mha' , sitk. sitkFloat32 )   #initial alignment of the two volumes transform = sitk. CenteredTransformInitializer ( fixed_image ,   moving_image , sitk. Euler3DTransform ( ) , sitk. CenteredTransformInitializerFilter . GEOMETRY )   #multi-resolution rigid registration using Mutual Information registration_method = sitk. ImageRegistrationMethod ( ) registration_method. SetMetricAsMattesMutualInformation ( numberOfHistogramBins = 50 ) registration_method. SetMetricSamplingStrategy ( registration_method. RANDOM ) registration_method. SetMetricSamplingPercentage ( 0.01 ) registration_method. SetInterpolator ( sitk. sitkLinear ) registration_m...

sudo apt-get install python-matplotlib python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev git    sudo pip install Theano    sudo pip install --upgrade Theano    python -c "import theano; theano.test(verbose=2)" sudo pip install pyyaml pil    git clone git://github.com/lisa-lab/pylearn2.git    sudo python setup.py develop --prefix /usr/local/lib/python2.7/dist-packages/    export PYLEARN2_DATA_PATH=~/data    cd ~/data/cifar10 wget http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz    tar zxvf cifar-10-python.tar.gz cd $PYLEARN2_HOME/pylearn2/scripts/tutorials/grbm_smd/    python make_dataset.py    python ../../train.py cifar_grbm_smd.yaml    export PYLEARN2_VIEWER_COMMAND="eog --new-instance"    python ../../show_weights.py cifar_grbm_smd.pkl    python ../../plot_monitor.py cifar_grbm_smd.pkl /pyle...

http://computers.tutsplus.com/tutorials/how-to-natively-boot-linux-on-your-mac--mac-53415

0. Card Install 1. Nvidia driver update sudo add-apt-repository ppa:xorg-edgers/ppa sudo apt-get update sudo apt-get install nvidia-352 sudo add-apt-repository -r ppa:xorg-edgers/ppa 2. CUDA + PyCUDA http://codeyarns.com/2015/02/24/how-to-install-pycuda/

import os import sys from PyQt4 . QtGui import *      # Create window app = QApplication ( sys . argv ) w = QWidget ( ) w . setWindowTitle ( "PyQT4 Pixmap @ pythonspot.com " )   # Create widget label = QLabel ( w ) pixmap = QPixmap ( os . getcwd ( ) + '/logo.png' ) label . setPixmap ( pixmap ) w . resize ( pixmap . width ( ) , pixmap . height ( ) )   # Draw window w . show ( ) app . exec_ ( )    

https://jonghyunkim816.wordpress.com/2015/06/03/linux%EC%97%90%EC%84%9C-kakaotalk-%EC%82%AC%EC%9A%A9%ED%95%98%EA%B8%B0-ubuntu-14-04-14-10/

#! /usr/bin/env python # -*- coding: utf-8 -*- # import sys from PyQt4 . QtGui import * # Create an PyQT4 application object. a = QApplication ( sys . argv )        # The QWidget widget is the base class of all user interface objects in PyQt4. w = QMainWindow ( )   # Set window size. w . resize ( 320 , 100 )   # Set window title   w . setWindowTitle ( "PyQT Python Widget!" )   # Create combobox combo = QComboBox ( w ) combo . addItem ( "Python" ) combo . addItem ( "Perl" ) combo . addItem ( "Java" ) combo . addItem ( "C++" ) combo . move ( 20 , 20 )   # Show window w . show ( ) sys . exit ( a . exec_ ( ) )    

#! /usr/bin/env python # -*- coding: utf-8 -*- # import sys from PyQt4 . QtGui import * # Create an PyQT4 application object. a = QApplication ( sys . argv )        # The QWidget widget is the base class of all user interface objects in PyQt4. w = QMainWindow ( )   # Set window size. w . resize ( 320 , 240 )   # Set window title   w . setWindowTitle ( "Hello World!" )   # Create main menu mainMenu = w . menuBar ( ) mainMenu . setNativeMenuBar ( False ) fileMenu = mainMenu . addMenu ( '&File' )   # Add exit button exitButton = QAction ( QIcon ( 'exit24.png' ) , 'Exit' , w ) exitButton . setShortcut ( 'Ctrl+Q' ) exitButton . setStatusTip ( 'Exit application' ) exitButton . triggered . connect ( w . close ) fileMenu . addAction ( exitButton )     # Show window w . show ( ) sys . exit ( a . exec_ ( ) )    

#! /usr/bin/env python # -*- coding: utf-8 -*- # import sys from PyQt4 . QtGui import *   # Create an PyQT4 application object. a = QApplication ( sys . argv )           # The QWidget widget is the base class of all user interface objects in PyQt4. w = QWidget ( )   # Set window size. w . resize ( 320 , 240 )   # Set window title   w . setWindowTitle ( "Hello World!" )   # Show window w . show ( ) sys . exit ( a . exec_ ( ) )

#! /usr/bin/env python # -*- coding: utf-8 -*- # import sys from PyQt4 . QtGui import * # Create an PyQT4 application object. a = QApplication ( sys . argv )        # The QWidget widget is the base class of all user interface objects in PyQt4. w = QWidget ( ) # Show a message box result = QMessageBox . question ( w , 'Message' , "Do you like Python?" , QMessageBox . Yes | QMessageBox . No , QMessageBox . No ) if result == QMessageBox . Yes :      print 'Yes.' else :      print 'No.'          # Show window w . show ( ) sys . exit ( a . exec_ ( ) )

#! /usr/bin/env python # -*- coding: utf-8 -*- # import sys from PyQt4.QtGui import * # Create an PyQT4 application object. a = QApplication(sys.argv)       # The QWidget widget is the base class of all user interface objects in PyQt4. w = QWidget() # Set window size. w.resize(320, 240) # Set window title  w.setWindowTitle("Hello World!") # Add a button btn = QPushButton('Hello World!', w) btn.setToolTip('Click to quit!') btn.clicked.connect(exit) btn.resize(btn.sizeHint()) btn.move(100, 80)       # Show window w.show() sys.exit(a.exec_())

Install via the Package Manager(apt-get): Simply add to your packages: For Sublime-Text-2: sudo add-apt-repository ppa:webupd8team/sublime-text-2 sudo apt-get update sudo apt-get install sublime-text For Sublime-Text-3: sudo add-apt-repository ppa:webupd8team/sublime-text-3 sudo apt-get update sudo apt-get install sublime-text-installer

#! /usr/bin/python import cv2 face_cascade = cv2 . CascadeClassifier ( 'cars.xml' ) vc = cv2 . VideoCapture ( 'video2.avi' ) if vc . isOpened ( ) :      rval , frame = vc . read ( ) else :      rval = False while rval :      rval , frame = vc . read ( )      # car detection.      cars = face_cascade . detectMultiScale ( frame , 1.1 , 2 )      ncars = 0      for ( x , y , w , h ) in cars :          cv2 . rectangle ( frame , ( x , y ) , ( x + w , y + h ) , ( 0 , 0 , 255 ) , 2 )          ncars = ncars + 1         # show result      cv2 . imshow ( "Result" , frame )      cv2 . waitKey ( 1 ) ; vc . release ( ) //-------...

# import the necessary packages import numpy as np import cv2   # load the games image image = cv2 . imread ( "games.jpg" )   # find the red color game in the image upper = np . array ( [ 65 , 65 , 255 ] ) lower = np . array ( [ 0 , 0 , 200 ] ) mask = cv2 . inRange ( image , lower , upper )   # find contours in the masked image and keep the largest one ( _ , cnts , _ ) = cv2 . findContours ( mask . copy ( ) , cv2 . RETR_EXTERNAL , cv2 . CHAIN_APPROX_SIMPLE ) c = max ( cnts , key = cv2 . contourArea )   # approximate the contour peri = cv2 . arcLength ( c , True ) approx = cv2 . approxPolyDP ( c , 0.05 * peri , True )   # draw a green bounding box surrounding the red game cv2 . drawContours ( image , [ approx ] , - 1 , ( 0 , 255 , 0 ) , 4 ) cv2 . imshow ( "Image" , image ) cv2 . waitKey ( 0 )    

# draw histogram in python. import cv2 import numpy as np img = cv2 . imread ( 'image.jpg' ) h = np . zeros ( ( 300 , 256 , 3 ) ) bins = np . arange ( 256 ) . reshape ( 256 , 1 ) color = [ ( 255 , 0 , 0 ) , ( 0 , 255 , 0 ) , ( 0 , 0 , 255 ) ] for ch , col in enumerate ( color ) :      hist_item = cv2 . calcHist ( [ img ] , [ ch ] , None , [ 256 ] , [ 0 , 255 ] )      cv2 . normalize ( hist_item , hist_item , 0 , 255 , cv2 . NORM_MINMAX )      hist = np . int32 ( np . around ( hist_item ) )      pts = np . column_stack ( ( bins , hist ) )      cv2 . polylines ( h , [ pts ] , False , col ) h = np . flipud ( h ) cv2 . imshow ( 'colorhist' , h ) cv2 . waitKey ( 0 )