'분류 전체보기' 카테고리의 글 목록 (4 Page)

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Janne Heikkila & Olli Silven "A Four-step Camera Calibration Procedure with Implicit Image Correction" (0)	2010.09.24
Learning OpenCV: Chapter 11 Camera Models and Calibration (0)	2010.09.24
Ramon E. Moore, R. Baker Kearfott, and Michael J. Cloud <Introduction to Interval Analysis> (0)	2010.09.18
OpenCV: chessboard corners detection Test (1)	2010.09.07
Dirk Walther et al. "Attentional Selection for Object Recognition – a Gentle Way" (0)	2010.08.23

Ramon E. Moore, R. Baker Kearfott, and Michael J. Cloud <Introduction to Interval Analysis>

2010. 9. 18. 16:09 Computer Vision

Introduction to Interval Analysis
Ramon E. Moore, R. Baker Kearfott, and Michael J. Cloud

http://www.siam.org/books/ot110/

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Dirk Walther et al. "Attentional Selection for Object Recognition – a Gentle Way" (0)	2010.08.23
GIMP on mac (0)	2010.08.06

posted by maetel

ISMAR 2010: B O R D E R L E S S (0)	2010.10.16
David M. L. Williams 서강대 영상대학원 특강 (0)	2010.10.07
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Bayard and Eco: How to Talk About Books You Haven't Read

2010. 9. 18. 06:05 Cases

What You Didn't Learn In School
Bayard and Eco: How to Talk About Books You Haven't Read

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posted by maetel

2010ETRIcg: report 0915

2010. 9. 16. 00:40

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2010ETRIcg: report 0916

2010. 9. 15. 21:33

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2010ETRIcg: report 0914

2010. 9. 14. 22:32

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OpenCV: chessboard corners detection Test

2010. 9. 7. 17:29 Computer Vision

OpenCV 함수 cvFindChessboardCorners()와 cvDrawChessboardCorners() 사용

bool findChessboardCorners(const Mat& image, Size patternSize, vector<Point2f>& corners, int flags=CV_CALIB_CB_ADAPTIVE_THRESH+ CV_CALIB_CB_NORMALIZE_IMAGE)¶

Finds the positions of the internal corners of the chessboard.

Parameters:

Parameters:	image – Source chessboard view; it must be an 8-bit grayscale or color image patternSize – The number of inner corners per chessboard row and column ( patternSize = cvSize(points _ per _ row,points _ per _ colum) = cvSize(columns,rows) ) corners – The output array of corners detected flags – Various operation flags, can be 0 or a combination of the following values: CV_CALIB_CB_ADAPTIVE_THRESH use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness). CV_CALIB_CB_NORMALIZE_IMAGE normalize the image gamma with EqualizeHist before applying fixed or adaptive thresholding. CV_CALIB_CB_FILTER_QUADS use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads that are extracted at the contour retrieval stage.

image – Source chessboard view; it must be an 8-bit grayscale or color image
patternSize – The number of inner corners per chessboard row and column ( patternSize = cvSize(points _ per _ row,points _ per _ colum) = cvSize(columns,rows) )
corners – The output array of corners detected
flags –
Various operation flags, can be 0 or a combination of the following values:
- CV_CALIB_CB_ADAPTIVE_THRESH use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness).
- CV_CALIB_CB_NORMALIZE_IMAGE normalize the image gamma with EqualizeHist before applying fixed or adaptive thresholding.
- CV_CALIB_CB_FILTER_QUADS use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads that are extracted at the contour retrieval stage.

file:///Users/lym/opencv/src/cv/cvcalibinit.cpp

cvcalibinit.cpp

https://code.ros.org/trac/opencv/browser/tags/2.1/opencv/src/cv/cvcalibinit.cpp

void drawChessboardCorners(Mat& image, Size patternSize, const Mat& corners, bool patternWasFound)¶

Renders the detected chessboard corners.

Parameters:

Parameters:	image – The destination image; it must be an 8-bit color image patternSize – The number of inner corners per chessboard row and column. (patternSize = cvSize(points _ per _ row,points _ per _ colum) = cvSize(columns,rows) ) corners – The array of corners detected patternWasFound – Indicates whether the complete board was found or not . One may just pass the return value FindChessboardCorners her

image – The destination image; it must be an 8-bit color image
patternSize – The number of inner corners per chessboard row and column. (patternSize = cvSize(points _ per _ row,points _ per _ colum) = cvSize(columns,rows) )
corners – The array of corners detected
patternWasFound – Indicates whether the complete board was found or not . One may just pass the return value FindChessboardCorners her

Learning OpenCV: Chapter 11. Camera Models and Calibration: Chessboards
381p

640x480

console:

finding chessboard corners...
what = 1
chessboard corners: 215.5, 179
#0=(215.5, 179) #1=(237.5, 178.5) #2=(260.5, 178) #3=(283.5, 177.5) #4=(307, 177) #5=(331.5, 175.5) #6=(355.5, 174.5) #7=(380.5, 174) #8=(405.5, 173.5) #9=(430.5, 172.5) #10=(212.5, 201.5) #11=(235.5, 201.5) #12=(258, 200.5) #13=(280.5, 200.5) #14=(305.5, 199.5) #15=(330, 198.5) #16=(354.5, 198) #17=(379.5, 197.5) #18=(405.5, 196.5) #19=(430.5, 196) #20=(210, 224.5) #21=(232.5, 224.5) #22=(256, 223.5) #23=(280, 224) #24=(304, 223) #25=(328.5, 222.5) #26=(353.5, 222) #27=(378.5, 221.5) #28=(404.5, 221.5) #29=(430.5, 220.5) #30=(207, 247.5) #31=(230.5, 247.5) #32=(253.5, 247.5) #33=(277.5, 247) #34=(303, 247) #35=(327, 246.5) #36=(352, 246.5) #37=(377.5, 246) #38=(403.5, 245.5) #39=(430, 245.5) #40=(204.5, 271.5) #41=(227.5, 271.5) #42=(251.5, 271.5) #43=(275.5, 271.5) #44=(300, 272) #45=(325.5, 271.5) #46=(351, 271) #47=(376.5, 271.5) #48=(403, 271.5) #49=(429.5, 271) #50=(201.5, 295.5) #51=(225.5, 295.5) #52=(249.5, 296) #53=(273.5, 296.5) #54=(299, 297) #55=(324, 296) #56=(349.5, 296.5) #57=(375.5, 296.5) #58=(402.5, 296.5) #59=(429, 297)

finished

finding chessboard corners...
what = 0
chessboard corners: 0, 0
#0=(0, 0) #1=(0, 0) #2=(0, 0) #3=(0, 0) #4=(0, 0) #5=(0, 0) #6=(0, 0) #7=(0, 0) #8=(0, 0) #9=(0, 0) #10=(0, 0) #11=(0, 0) #12=(0, 0) #13=(0, 0) #14=(0, 0) #15=(0, 0) #16=(0, 0) #17=(0, 0) #18=(0, 0) #19=(0, 0) #20=(0, 0) #21=(0, 0) #22=(0, 0) #23=(0, 0) #24=(0, -2.22837e-29) #25=(-2.22809e-29, -1.99967) #26=(4.2039e-45, -2.22837e-29) #27=(-2.22809e-29, -1.99968) #28=(4.2039e-45, 1.17709e-43) #29=(6.72623e-44, 1.80347e-42) #30=(0, 0) #31=(4.2039e-45, 1.45034e-42) #32=(-2.2373e-29, -1.99967) #33=(4.2039e-45, 2.52094e-42) #34=(-2.2373e-29, -1.99969) #35=(-2.22634e-29, -1.99968) #36=(4.2039e-45, 1.17709e-43) #37=(6.72623e-44, 1.80347e-42) #38=(0, 0) #39=(0, 1.80347e-42) #40=(3.36312e-44, 5.46787e-42) #41=(6.45718e-42, 5.04467e-44) #42=(0, 1.80347e-42) #43=(6.48101e-42, 5.48188e-42) #44=(0, 1.4013e-45) #45=(4.2039e-45, 0) #46=(1.12104e-44, -2.22837e-29) #47=(-2.22809e-29, -1.99969) #48=(4.2039e-45, 6.72623e-44) #49=(6.16571e-44, 1.80347e-42) #50=(0, 0) #51=(1.4013e-45, -2.27113e-29) #52=(4.56823e-42, -1.99969) #53=(4.2039e-45, -2.20899e-29) #54=(-2.2373e-29, -1.9997) #55=(-2.22619e-29, -1.99969) #56=(4.2039e-45, 6.72623e-44) #57=(-1.9997, 1.80347e-42) #58=(0, -2.22957e-29) #59=(-2.23655e-29, -2.20881e-29)

finished

finding chessboard corners...
what = 0
chessboard corners: 0, 0
#0=(0, 0) #1=(0, 0) #2=(0, 0) #3=(0, 0) #4=(0, 0) #5=(0, 0) #6=(0, 0) #7=(0, 0) #8=(0, 0) #9=(0, 0) #10=(0, 0) #11=(0, 0) #12=(0, 0) #13=(0, 0) #14=(0, 0) #15=(0, 0) #16=(0, 0) #17=(0, 0) #18=(0, 0) #19=(0, 0) #20=(0, 0) #21=(0, 0) #22=(0, 0) #23=(0, 0) #24=(0, -2.22837e-29) #25=(-2.22809e-29, -1.99967) #26=(4.2039e-45, -2.22837e-29) #27=(-2.22809e-29, -1.99968) #28=(4.2039e-45, 1.17709e-43) #29=(6.72623e-44, 1.80347e-42) #30=(0, 0) #31=(4.2039e-45, 1.45034e-42) #32=(-2.2373e-29, -1.99967) #33=(4.2039e-45, 2.52094e-42) #34=(-2.2373e-29, -1.99969) #35=(-2.22634e-29, -1.99968) #36=(4.2039e-45, 1.17709e-43) #37=(6.72623e-44, 1.80347e-42) #38=(0, 0) #39=(0, 1.80347e-42) #40=(3.36312e-44, 5.46787e-42) #41=(6.45718e-42, 5.04467e-44) #42=(0, 1.80347e-42) #43=(6.48101e-42, 5.48188e-42) #44=(0, 1.4013e-45) #45=(4.2039e-45, 0) #46=(1.12104e-44, -2.22837e-29) #47=(-2.22809e-29, -1.99969) #48=(4.2039e-45, 6.72623e-44) #49=(6.16571e-44, 1.80347e-42) #50=(0, 0) #51=(1.4013e-45, -2.27113e-29) #52=(4.56823e-42, -1.99969) #53=(4.2039e-45, -2.20899e-29) #54=(-2.2373e-29, -1.9997) #55=(-2.22619e-29, -1.99969) #56=(4.2039e-45, 6.72623e-44) #57=(-1.9997, 1.80347e-42) #58=(0, -2.22957e-29) #59=(-2.23655e-29, -2.20881e-29)

finished

source code:

// Test: chessboard detection

#include <OpenCV/OpenCV.h> // frameworks on mac
//#include <cv.h>
//#include <highgui.h>

#include <iostream>
using namespace std;

int main()
{

    IplImage* image = cvLoadImage( "DSCN3310.jpg", 1 );

/*    IplImage* image = 0;
    // initialize capture from a camera
    CvCapture* capture = cvCaptureFromCAM(0); // capture from video device #0
    cvNamedWindow("camera");

    while(1) {
        if ( !cvGrabFrame(capture) ){
            printf("Could not grab a frame\n\7");
            exit(0);
        }
        else {
            cvGrabFrame( capture ); // capture a frame
            image = cvRetrieveFrame(capture); // retrieve the captured frame
*/
//            cvShowImage( "camera", image );
            cvNamedWindow( "camera" ); cvShowImage( "camera", image );

            cout << endl << "finding chessboard corners..." << endl;
            CvPoint2D32f corners[60];
            int numCorners[60];
            //cvFindChessboardCorners(<#const void * image#>, <#CvSize pattern_size#>, <#CvPoint2D32f * corners#>, <#int * corner_count#>, <#int flags#>)
            int what = cvFindChessboardCorners( image, cvSize(10,6), corners, numCorners, CV_CALIB_CB_ADAPTIVE_THRESH );
            cout << "what = " << what << endl;
            cout << "chessboard corners: " << corners[0].x << ", " << corners[0].y << endl;

    for( int n = 0; n < 60; n++ )
    {
        cout << "#" << n << "=(" << corners[n].x << ", " << corners[n].y << ")\t";
    }
    cout << endl;

            // cvDrawChessboardCorners(<#CvArr * image#>, <#CvSize pattern_size#>, <#CvPoint2D32f * corners#>, <#int count#>, <#int pattern_was_found#>)
    cvDrawChessboardCorners( image, cvSize(10,6), corners, 60, what );


    cvNamedWindow( "chessboard" ); cvMoveWindow( "chessboard", 200, 200 ); cvShowImage( "chessboard", image );
    cvSaveImage( "chessboard.bmp", image );
            cvWaitKey(0);
//        }
//    }

    cout << endl << "finished" << endl;
//   cvReleaseCapture( &capture ); // release the capture source
    cvDestroyAllWindows();

    return 0;
}

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posted by maetel

2010ETRIcg: report 0906

2010. 9. 6. 23:28

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2010ETRIcg: report 0904

2010. 9. 4. 05:36

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Masakazu Suzuki "Evapotranspiration Estimates of Forested Watersheds in Japan Using the Short-time Period Water-budget Methods"

2010. 9. 3. 16:29 Computer Vision/2010ETRI-MultiAutoCalib

Suzuki, M. (1985) Evapotranspiration Estimates of Forested Watersheds in Japan Using the Short-time Period Water-budget Methods. Journal of Japanese Forest Society, 67: 115-125. (in Japanese with English summary)

masakazuSuzuki_1985jjfs.pdf

短期水収支法による森林流域からの蒸発散量推定
Evapotranspiration estimates of forested watersheds in Japan using the short-time period water-budget method [in Japanese]
鈴木雅一 (SUZUKI Masakazu)
京都大学農学部 (Fac.of Agr., Kyoto Univ.)

長期間にわたって水収支観測がなされている日本各地の森林流域の記録をもとに, 短期水収支法を用いて流域の蒸発散量とその季節変化を求めた。検討には桐生, 川向, 竜の口山, 釜淵, 去川の5試験地, 9流域のそれぞれ10年から40年間の日雨量, 日流出量記録が用いられた。短期水収支法では, 渇水による蒸発散低下は水収支期間内の最小流量に対応して生じ, 蒸発散低下をもたらす限界流量が流域ごとに定められた。渇水による蒸発低下の例を除外して求めた蒸発散量季節変化は, 植生が著しく変わらないとき集計期間が異なってもほぼ同様の結果となった。森林の伐採や山火事によって蒸発散量が減少する傾向は各流域とも同様であるが, その変化が通年にわたり生じた流域とおもに夏期に生じた流域があった。求められた蒸発散量とその季節変化は各流域の気象, 植生を反映する値として, 森林流域の蒸発散量推定式作成の基礎資料になるといえる。

The annual and monthly evapotranspiration were estimated using the method of the short-time period water-budget on 9 water-sheds located in 5 experimental areas in Japan. Daily precipitation and discharge records and a period of 10 to 40 years for each watershed were used in this estimation. The appearance of an evapotranspiration decline because of drought has a relationship with the minimum discharge-rate in a water-budget period. The critical discharge-rate for an evapotranspiration decline can be determined for each watershed. On a watershed without a remarkable change of vegetation, seasonal variations of different averaging periods of years for evapotranspiration under no drought conditions change in the same way. Evapotranspiration decreases after clear-cuttings and forest fires on every watershed where such events have occurred, but the tendency for changes in seasonal variations vary with each watershed.

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OpenCV: cvFindContours( )

2010. 9. 3. 16:23 Computer Vision/2010ETRI-MultiAutoCalib

OpenCV: cvFindContours( )

cvFindContours()

int cvFindContours(CvArr* image, CvMemStorage* storage, CvSeq** first_contour, int header_size=sizeof(CvContour), int mode=CV_RETR_LIST, int method=CV_CHAIN_APPROX_SIMPLE, CvPoint offset=cvPoint(0, 0))¶

Finds the contours in a binary image.

Parameters:

Parameters:	image – The source, an 8-bit single channel image. Non-zero pixels are treated as 1’s, zero pixels remain 0’s - the image is treated as `binary` . To get such a binary image from grayscale, one may use Threshold , AdaptiveThreshold or Canny . The function modifies the source image’s content storage – Container of the retrieved contours first_contour – Output parameter, will contain the pointer to the first outer contour header_size – Size of the sequence header, $\ge \texttt{sizeof(CvChain)}$ if $\texttt{method} =\texttt{CV\_CHAIN\_CODE}$ , and $\ge \texttt{sizeof(CvContour)}$ otherwise mode – Retrieval mode CV_RETR_EXTERNAL retrives only the extreme outer contours CV_RETR_LIST retrieves all of the contours and puts them in the list CV_RETR_CCOMP retrieves all of the contours and organizes them into a two-level hierarchy: on the top level are the external boundaries of the components, on the second level are the boundaries of the holes CV_RETR_TREE retrieves all of the contours and reconstructs the full hierarchy of nested contours method – Approximation method (for all the modes, except `CV_LINK_RUNS` , which uses built-in approximation) CV_CHAIN_CODE outputs contours in the Freeman chain code. All other methods output polygons (sequences of vertices) CV_CHAIN_APPROX_NONE translates all of the points from the chain code into points CV_CHAIN_APPROX_SIMPLE compresses horizontal, vertical, and diagonal segments and leaves only their end points CV_CHAIN_APPROX_TC89_L1,CV_CHAIN_APPROX_TC89_KCOS applies one of the flavors of the Teh-Chin chain approximation algorithm. CV_LINK_RUNS uses a completely different contour retrieval algorithm by linking horizontal segments of 1’s. Only the `CV_RETR_LIST` retrieval mode can be used with this method. offset – Offset, by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context

image – The source, an 8-bit single channel image. Non-zero pixels are treated as 1’s, zero pixels remain 0’s - the image is treated as binary . To get such a binary image from grayscale, one may use Threshold , AdaptiveThreshold or Canny . The function modifies the source image’s content
storage – Container of the retrieved contours
first_contour – Output parameter, will contain the pointer to the first outer contour
header_size – Size of the sequence header, $\ge \texttt{sizeof(CvChain)}$ if $\texttt{method} =\texttt{CV\_CHAIN\_CODE}$ , and $\ge \texttt{sizeof(CvContour)}$ otherwise
mode –
Retrieval mode
- CV_RETR_EXTERNAL retrives only the extreme outer contours
- CV_RETR_LIST retrieves all of the contours and puts them in the list
- CV_RETR_CCOMP retrieves all of the contours and organizes them into a two-level hierarchy: on the top level are the external boundaries of the components, on the second level are the boundaries of the holes
- CV_RETR_TREE retrieves all of the contours and reconstructs the full hierarchy of nested contours
method –
Approximation method (for all the modes, except CV_LINK_RUNS , which uses built-in approximation)
- CV_CHAIN_CODE outputs contours in the Freeman chain code. All other methods output polygons (sequences of vertices)
- CV_CHAIN_APPROX_NONE translates all of the points from the chain code into points
- CV_CHAIN_APPROX_SIMPLE compresses horizontal, vertical, and diagonal segments and leaves only their end points
- CV_CHAIN_APPROX_TC89_L1,CV_CHAIN_APPROX_TC89_KCOS applies one of the flavors of the Teh-Chin chain approximation algorithm.
- CV_LINK_RUNS uses a completely different contour retrieval algorithm by linking horizontal segments of 1’s. Only the CV_RETR_LIST retrieval mode can be used with this method.
offset – Offset, by which every contour point is shifted. This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context

Learning OpenCV: Chater 8. Contours: Contour Finding
: 234p
"the concept of a contour tree"

Suzuki, M. (1985) Evapotranspiration Estimates of Forested Watersheds in Japan Using the Short-time Period Water-budget Methods. Journal of Japanese Forest Society, 67: 115-125. (in Japanese with English summary)

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posted by maetel

2010 공개 SW 개발자대회 2차 기술세미나 - 모바일 오픈소스 플랫폼 '안드로이드'

2010. 8. 24. 21:13 Footmarks

Session #1. 안드로이드의 현황과 전망
Lecturer: 박성호 (정보통신산업진흥원(NIPA) 공개/지역SW팀) shpark2@nipa.kr

http://source.android.com/

http://developer.android.com/

http://www.android.com/market/

OESF (Open Embedded System Foundation)
http://www.oesf.jp/en/

People of Lava
http://www.peopleoflava.com/

Session #2. 안드로이드의 다양한 Screen Device를 위한 UI 처리
Lecturer: 박성서 (안드로이드펍 운영자)

http://www.androidpub.com/

http://graynote.tistory.com/

Session #3. 인터넷 서비스 연동 애플리케이션 개발
Lecturer: 강순권 (네오위즈 인터넷 팀장)

Session #4. 안드로이드 센서/카메라/위치정보의 활용
Lecturer: 백유태 (주식회사 라람인터랙티브)

http://rharham.com/

http://rharham.tistory.com/

http://nyatla.jp/nyartoolkit/wiki/index.php?FrontPage.en

http://code.google.com/p/andar/

http://www.mixare.org/

Session #5. Android AR (Augmented Reality)
Lecturer: 고종욱 (주식회사 포비커 대표) ceo@fobikr.com

http://fobikr.com/

저작자표시 비영리 동일조건

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posted by maetel

Dirk Walther et al. "Attentional Selection for Object Recognition – a Gentle Way"

2010. 8. 23. 20:13 Computer Vision

Attentional Selection for Object Recognition – a Gentle Way
Dirk Walther, Laurent Itti, Maximilian Riesenhuber, Tomaso Poggio, and Christof Koch

bmcv-walther.pdf

Center for Biological & Computational Learning (CBCL) at MIT
http://cbcl.mit.edu/

저작자표시 비영리 동일조건

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Seitz et al. "A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms" (0)	2010.07.29
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posted by maetel

GIMP on mac

2010. 8. 6. 23:44 Computer Vision

설치

$ sudo port install gimp
---> Computing dependencies for gimp
---> Fetching aalib
---> Attempting to fetch aalib-1.4rc5.tar.gz from ftp://ftp.jp.FreeBSD.org/pub/FreeBSD/ports/distfiles/
---> Verifying checksum(s) for aalib
---> Extracting aalib
---> Applying patches to aalib
---> Configuring aalib
---> Building aalib
---> Staging aalib into destroot
---> Installing aalib @1.4rc5_4
---> Activating aalib @1.4rc5_4
---> Cleaning aalib
---> Fetching curl-ca-bundle
---> Attempting to fetch curl-7.20.0.tar.bz2 from http://distfiles.macports.org/curl
---> Attempting to fetch certdata-1.64.txt from http://distfiles.macports.org/curl
---> Verifying checksum(s) for curl-ca-bundle
---> Extracting curl-ca-bundle
---> Applying patches to curl-ca-bundle
---> Configuring curl-ca-bundle
---> Building curl-ca-bundle
---> Staging curl-ca-bundle into destroot
---> Installing curl-ca-bundle @7.20.0_3
---> Activating curl-ca-bundle @7.20.0_3
---> Cleaning curl-ca-bundle
---> Fetching curl
---> Verifying checksum(s) for curl
---> Extracting curl
---> Configuring curl
---> Building curl
---> Staging curl into destroot
---> Installing curl @7.20.0_0+ssl
---> Activating curl @7.20.0_0+ssl
---> Cleaning curl
---> Fetching dbus
---> Attempting to fetch dbus-1.2.16.tar.gz from http://dbus.freedesktop.org/releases/dbus
---> Verifying checksum(s) for dbus
---> Extracting dbus
---> Applying patches to dbus
---> Configuring dbus
---> Building dbus
---> Staging dbus into destroot
---> Installing dbus @1.2.16_1
---> Activating dbus @1.2.16_1
#################################################################################################
# Startup items have been generated that will aid in
# starting dbus with launchd. They are disabled
# by default. Execute the following command to start them,
# and to cause it to launch at startup:
#
# sudo launchctl load -w /Library/LaunchDaemons/org.freedesktop.dbus-system.plist
# launchctl load -w /Library/LaunchAgents/org.freedesktop.dbus-session.plist
##################################################################################################
---> Cleaning dbus
---> Fetching dbus-glib
---> Attempting to fetch dbus-glib-0.86.tar.gz from http://distfiles.macports.org/dbus-glib
---> Verifying checksum(s) for dbus-glib
---> Extracting dbus-glib
---> Configuring dbus-glib
---> Building dbus-glib
---> Staging dbus-glib into destroot
---> Installing dbus-glib @0.86_0
---> Activating dbus-glib @0.86_0
---> Cleaning dbus-glib
---> Fetching libart_lgpl
---> Attempting to fetch libart_lgpl-2.3.21.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libart_lgpl/2.3/
---> Verifying checksum(s) for libart_lgpl
---> Extracting libart_lgpl
---> Applying patches to libart_lgpl
---> Configuring libart_lgpl
---> Building libart_lgpl
---> Staging libart_lgpl into destroot
---> Installing libart_lgpl @2.3.21_0
---> Activating libart_lgpl @2.3.21_0
---> Cleaning libart_lgpl
---> Fetching libcroco
---> Attempting to fetch libcroco-0.6.2.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libcroco/0.6/
---> Verifying checksum(s) for libcroco
---> Extracting libcroco
---> Configuring libcroco
---> Building libcroco
---> Staging libcroco into destroot
---> Installing libcroco @0.6.2_1
---> Activating libcroco @0.6.2_1
---> Cleaning libcroco
---> Fetching libidl
---> Attempting to fetch libIDL-0.8.14.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libIDL/0.8/
---> Verifying checksum(s) for libidl
---> Extracting libidl
---> Configuring libidl
---> Building libidl
---> Staging libidl into destroot
---> Installing libidl @0.8.14_0
---> Activating libidl @0.8.14_0
---> Cleaning libidl
---> Fetching orbit2
---> Attempting to fetch ORBit2-2.14.18.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/ORBit2/2.14/
---> Verifying checksum(s) for orbit2
---> Extracting orbit2
---> Configuring orbit2
---> Building orbit2
---> Staging orbit2 into destroot
---> Installing orbit2 @2.14.18_0
---> Activating orbit2 @2.14.18_0
---> Cleaning orbit2
---> Fetching policykit
---> Attempting to fetch PolicyKit-0.9.tar.gz from http://hal.freedesktop.org/releases/
---> Verifying checksum(s) for policykit
---> Extracting policykit
---> Applying patches to policykit
---> Configuring policykit
---> Building policykit
---> Staging policykit into destroot
---> Installing policykit @0.9_0
---> Activating policykit @0.9_0
---> Cleaning policykit
---> Fetching gconf
---> Attempting to fetch GConf-2.26.2.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/GConf/2.26/
---> Verifying checksum(s) for gconf
---> Extracting gconf
---> Configuring gconf
---> Building gconf
---> Staging gconf into destroot
---> Installing gconf @2.26.2_0
---> Activating gconf @2.26.2_0
---> Cleaning gconf
---> Fetching popt
---> Attempting to fetch popt-1.15.tar.gz from http://distfiles.macports.org/popt
---> Verifying checksum(s) for popt
---> Extracting popt
---> Configuring popt
---> Building popt
---> Staging popt into destroot
---> Installing popt @1.15_0
---> Activating popt @1.15_0
---> Cleaning popt
---> Fetching desktop-file-utils
---> Attempting to fetch desktop-file-utils-0.15.tar.gz from http://distfiles.macports.org/desktop-file-utils
---> Verifying checksum(s) for desktop-file-utils
---> Extracting desktop-file-utils
---> Configuring desktop-file-utils
---> Building desktop-file-utils
---> Staging desktop-file-utils into destroot
---> Installing desktop-file-utils @0.15_1
---> Activating desktop-file-utils @0.15_1
---> Cleaning desktop-file-utils
---> Fetching gnome-mime-data
---> Attempting to fetch gnome-mime-data-2.18.0.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/gnome-mime-data/2.18/
---> Verifying checksum(s) for gnome-mime-data
---> Extracting gnome-mime-data
---> Configuring gnome-mime-data
---> Building gnome-mime-data
---> Staging gnome-mime-data into destroot
---> Installing gnome-mime-data @2.18.0_3
---> Activating gnome-mime-data @2.18.0_3
---> Cleaning gnome-mime-data
---> Fetching gnome-vfs
---> Attempting to fetch gnome-vfs-2.24.2.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/gnome-vfs/2.24/
---> Verifying checksum(s) for gnome-vfs
---> Extracting gnome-vfs
---> Applying patches to gnome-vfs
---> Configuring gnome-vfs
---> Building gnome-vfs
---> Staging gnome-vfs into destroot
---> Installing gnome-vfs @2.24.2_0
---> Activating gnome-vfs @2.24.2_0
---> Cleaning gnome-vfs
---> Fetching libbonobo
---> Attempting to fetch libbonobo-2.24.3.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libbonobo/2.24/
---> Verifying checksum(s) for libbonobo
---> Extracting libbonobo
---> Configuring libbonobo
---> Building libbonobo
---> Staging libbonobo into destroot
---> Installing libbonobo @2.24.3_0
---> Activating libbonobo @2.24.3_0
---> Cleaning libbonobo
---> Fetching libgsf
---> Attempting to fetch libgsf-1.14.17.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libgsf/1.14/
---> Verifying checksum(s) for libgsf
---> Extracting libgsf
---> Configuring libgsf
---> Building libgsf
---> Staging libgsf into destroot
---> Installing libgsf @1.14.17_0
---> Activating libgsf @1.14.17_0
---> Cleaning libgsf
---> Fetching librsvg
---> Attempting to fetch librsvg-2.26.0.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/librsvg/2.26/
---> Verifying checksum(s) for librsvg
---> Extracting librsvg
---> Configuring librsvg
---> Building librsvg
---> Staging librsvg into destroot
---> Installing librsvg @2.26.0_2
---> Activating librsvg @2.26.0_2
---> Cleaning librsvg
---> Fetching boehmgc
---> Attempting to fetch gc-7.1.tar.gz from http://distfiles.macports.org/boehmgc
---> Verifying checksum(s) for boehmgc
---> Extracting boehmgc
---> Configuring boehmgc
---> Building boehmgc
---> Staging boehmgc into destroot
---> Installing boehmgc @7.1_0+darwin_9
---> Activating boehmgc @7.1_0+darwin_9
---> Cleaning boehmgc
---> Fetching w3m
---> Attempting to fetch w3m-0.5.2.tar.gz from http://nchc.dl.sourceforge.net/w3m
---> Verifying checksum(s) for w3m
---> Extracting w3m
---> Applying patches to w3m
---> Configuring w3m
---> Building w3m
---> Staging w3m into destroot
---> Installing w3m @0.5.2_1
---> Activating w3m @0.5.2_1
---> Cleaning w3m
---> Fetching babl
---> Attempting to fetch babl-0.1.0.tar.bz2 from ftp://ftp.u-aizu.ac.jp/pub/graphics/tools/gimp/babl/0.1
---> Verifying checksum(s) for babl
---> Extracting babl
---> Applying patches to babl
---> Configuring babl
---> Building babl
---> Staging babl into destroot
---> Installing babl @0.1.0_1
---> Activating babl @0.1.0_1
---> Cleaning babl
---> Fetching libopenraw
---> Attempting to fetch libopenraw-0.0.8.tar.gz from http://libopenraw.freedesktop.org/download/
---> Verifying checksum(s) for libopenraw
---> Extracting libopenraw
---> Applying patches to libopenraw
---> Configuring libopenraw
---> Building libopenraw
---> Staging libopenraw into destroot
---> Installing libopenraw @0.0.8_2
---> Activating libopenraw @0.0.8_2
---> Cleaning libopenraw
---> Fetching libspiro
---> Attempting to fetch libspiro_src-20071029.tar.bz2 from http://nchc.dl.sourceforge.net/libspiro
---> Verifying checksum(s) for libspiro
---> Extracting libspiro
---> Configuring libspiro
---> Building libspiro
---> Staging libspiro into destroot
---> Installing libspiro @20071029_0
---> Activating libspiro @20071029_0
---> Cleaning libspiro
---> Fetching lua
---> Attempting to fetch lua-5.1.4.tar.gz from http://distfiles.macports.org/lua
---> Verifying checksum(s) for lua
---> Extracting lua
---> Configuring lua
---> Building lua
---> Staging lua into destroot
---> Installing lua @5.1.4_0
---> Activating lua @5.1.4_0
---> Cleaning lua
---> Fetching gsed
---> Attempting to fetch sed-4.2.1.tar.gz from http://distfiles.macports.org/gsed
---> Verifying checksum(s) for gsed
---> Extracting gsed
---> Configuring gsed
---> Building gsed
---> Staging gsed into destroot
---> Installing gsed @4.2.1_0
---> Activating gsed @4.2.1_0
---> Cleaning gsed
---> Fetching ilmbase
---> Attempting to fetch ilmbase-1.0.1.tar.gz from http://distfiles.macports.org/ilmbase
---> Verifying checksum(s) for ilmbase
---> Extracting ilmbase
---> Applying patches to ilmbase
---> Configuring ilmbase
---> Building ilmbase
---> Staging ilmbase into destroot
---> Installing ilmbase @1.0.1_2
---> Activating ilmbase @1.0.1_2
---> Cleaning ilmbase
---> Fetching openexr
---> Attempting to fetch openexr-1.6.1.tar.gz from http://distfiles.macports.org/openexr
---> Verifying checksum(s) for openexr
---> Extracting openexr
---> Applying patches to openexr
---> Configuring openexr
---> Building openexr
---> Staging openexr into destroot
---> Installing openexr @1.6.1_1
---> Activating openexr @1.6.1_1
---> Cleaning openexr
---> Fetching gegl
---> Attempting to fetch gegl-0.1.0.tar.bz2 from ftp://ftp.u-aizu.ac.jp/pub/graphics/tools/gimp/gegl/0.1
---> Verifying checksum(s) for gegl
---> Extracting gegl
---> Configuring gegl
---> Building gegl
---> Staging gegl into destroot
---> Installing gegl @0.1.0_4
---> Activating gegl @0.1.0_4
---> Cleaning gegl
---> Fetching lcms
---> Attempting to fetch lcms-1.19.tar.gz from http://nchc.dl.sourceforge.net/lcms
---> Verifying checksum(s) for lcms
---> Extracting lcms
---> Configuring lcms
---> Building lcms
---> Staging lcms into destroot
---> Installing lcms @1.19_2
---> Activating lcms @1.19_2
---> Cleaning lcms
---> Fetching libexif
---> Attempting to fetch libexif-0.6.17.tar.bz2 from http://nchc.dl.sourceforge.net/libexif
---> Verifying checksum(s) for libexif
---> Extracting libexif
---> Configuring libexif
---> Building libexif
---> Staging libexif into destroot
---> Installing libexif @0.6.17_0
---> Activating libexif @0.6.17_0
---> Cleaning libexif
---> Fetching hicolor-icon-theme
---> Attempting to fetch hicolor-icon-theme-0.12.tar.gz from http://icon-theme.freedesktop.org/releases/
---> Verifying checksum(s) for hicolor-icon-theme
---> Extracting hicolor-icon-theme
---> Configuring hicolor-icon-theme
---> Building hicolor-icon-theme
---> Staging hicolor-icon-theme into destroot
---> Installing hicolor-icon-theme @0.12_0
---> Activating hicolor-icon-theme @0.12_0
---> Cleaning hicolor-icon-theme
---> Fetching p5-xml-namespacesupport
---> Attempting to fetch XML-NamespaceSupport-1.11.tar.gz from http://distfiles.macports.org/perl5
---> Verifying checksum(s) for p5-xml-namespacesupport
---> Extracting p5-xml-namespacesupport
---> Configuring p5-xml-namespacesupport
---> Building p5-xml-namespacesupport
---> Staging p5-xml-namespacesupport into destroot
---> Installing p5-xml-namespacesupport @1.11_0
---> Activating p5-xml-namespacesupport @1.11_0
---> Cleaning p5-xml-namespacesupport
---> Fetching p5-xml-sax
---> Attempting to fetch XML-SAX-0.96.tar.gz from http://distfiles.macports.org/perl5
---> Verifying checksum(s) for p5-xml-sax
---> Extracting p5-xml-sax
---> Configuring p5-xml-sax
---> Building p5-xml-sax
---> Staging p5-xml-sax into destroot
---> Installing p5-xml-sax @0.96_1
---> Activating p5-xml-sax @0.96_1
---> Cleaning p5-xml-sax
---> Fetching p5-xml-simple
---> Attempting to fetch XML-Simple-2.18.tar.gz from http://distfiles.macports.org/perl5
---> Verifying checksum(s) for p5-xml-simple
---> Extracting p5-xml-simple
---> Configuring p5-xml-simple
---> Building p5-xml-simple
---> Staging p5-xml-simple into destroot
---> Installing p5-xml-simple @2.18_0
---> Activating p5-xml-simple @2.18_0
---> Cleaning p5-xml-simple
---> Fetching icon-naming-utils
---> Attempting to fetch icon-naming-utils-0.8.90.tar.gz from http://tango.freedesktop.org/releases/
---> Verifying checksum(s) for icon-naming-utils
---> Extracting icon-naming-utils
---> Configuring icon-naming-utils
---> Building icon-naming-utils
---> Staging icon-naming-utils into destroot
---> Installing icon-naming-utils @0.8.90_0
---> Activating icon-naming-utils @0.8.90_0
---> Cleaning icon-naming-utils
---> Fetching gnome-icon-theme
---> Attempting to fetch gnome-icon-theme-2.26.0.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/gnome-icon-theme/2.26/
---> Verifying checksum(s) for gnome-icon-theme
---> Extracting gnome-icon-theme
---> Configuring gnome-icon-theme
---> Building gnome-icon-theme
---> Staging gnome-icon-theme into destroot
---> Installing gnome-icon-theme @2.26.0_0
---> Activating gnome-icon-theme @2.26.0_0
---> Cleaning gnome-icon-theme
---> Fetching libgpg-error
---> Attempting to fetch libgpg-error-1.7.tar.bz2 from http://www.ring.gr.jp/pub/net/gnupg/libgpg-error
---> Verifying checksum(s) for libgpg-error
---> Extracting libgpg-error
---> Applying patches to libgpg-error
---> Configuring libgpg-error
---> Building libgpg-error
---> Staging libgpg-error into destroot
---> Installing libgpg-error @1.7_0
---> Activating libgpg-error @1.7_0
---> Cleaning libgpg-error
---> Fetching libgcrypt
---> Attempting to fetch libgcrypt-1.4.4.tar.bz2 from http://www.ring.gr.jp/pub/net/gnupg/libgcrypt
---> Verifying checksum(s) for libgcrypt
---> Extracting libgcrypt
---> Configuring libgcrypt
---> Building libgcrypt
---> Staging libgcrypt into destroot
---> Installing libgcrypt @1.4.4_0
---> Activating libgcrypt @1.4.4_0
---> Cleaning libgcrypt
---> Fetching libtasn1
---> Attempting to fetch libtasn1-2.2.tar.gz from http://distfiles.macports.org/libtasn1
---> Verifying checksum(s) for libtasn1
---> Extracting libtasn1
---> Configuring libtasn1
---> Building libtasn1
---> Staging libtasn1 into destroot
---> Installing libtasn1 @2.2_0
---> Activating libtasn1 @2.2_0
---> Cleaning libtasn1
---> Fetching gnome-keyring
---> Attempting to fetch gnome-keyring-2.26.3.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/gnome-keyring/2.26/
---> Verifying checksum(s) for gnome-keyring
---> Extracting gnome-keyring
---> Configuring gnome-keyring
---> Building gnome-keyring
---> Staging gnome-keyring into destroot
---> Installing gnome-keyring @2.26.3_0
---> Activating gnome-keyring @2.26.3_0
---> Cleaning gnome-keyring
---> Fetching libglade2
---> Attempting to fetch libglade-2.6.4.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libglade/2.6/
---> Verifying checksum(s) for libglade2
---> Extracting libglade2
---> Configuring libglade2
---> Building libglade2
---> Staging libglade2 into destroot
---> Installing libglade2 @2.6.4_1
---> Activating libglade2 @2.6.4_1
---> Cleaning libglade2
---> Fetching audiofile
---> Attempting to fetch audiofile-0.2.7.tar.gz from http://distfiles.macports.org/audiofile
---> Verifying checksum(s) for audiofile
---> Extracting audiofile
---> Configuring audiofile
---> Building audiofile
---> Staging audiofile into destroot
---> Installing audiofile @0.2.7_0
---> Activating audiofile @0.2.7_0
---> Cleaning audiofile
---> Fetching esound
---> Attempting to fetch esound-0.2.41.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/esound/0.2/
---> Verifying checksum(s) for esound
---> Extracting esound
---> Configuring esound
---> Building esound
---> Staging esound into destroot
---> Installing esound @0.2.41_1
---> Activating esound @0.2.41_1
---> Cleaning esound
---> Fetching libgnome
---> Attempting to fetch libgnome-2.26.0.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libgnome/2.26/
---> Verifying checksum(s) for libgnome
---> Extracting libgnome
---> Configuring libgnome
---> Building libgnome
---> Staging libgnome into destroot
---> Installing libgnome @2.26.0_0
---> Activating libgnome @2.26.0_0
---> Cleaning libgnome
---> Fetching libgnomecanvas
---> Attempting to fetch libgnomecanvas-2.26.0.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libgnomecanvas/2.26/
---> Verifying checksum(s) for libgnomecanvas
---> Extracting libgnomecanvas
---> Configuring libgnomecanvas
---> Building libgnomecanvas
---> Staging libgnomecanvas into destroot
---> Installing libgnomecanvas @2.26.0_1
---> Activating libgnomecanvas @2.26.0_1
---> Cleaning libgnomecanvas
---> Fetching libbonoboui
---> Attempting to fetch libbonoboui-2.24.3.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libbonoboui/2.24/
---> Verifying checksum(s) for libbonoboui
---> Extracting libbonoboui
---> Configuring libbonoboui
---> Building libbonoboui
---> Staging libbonoboui into destroot
---> Installing libbonoboui @2.24.3_0
---> Activating libbonoboui @2.24.3_0
---> Cleaning libbonoboui
---> Fetching libgnomeui
---> Attempting to fetch libgnomeui-2.24.2.tar.bz2 from http://ftp.nara.wide.ad.jp/pub/X11/GNOME/sources/libgnomeui/2.24/
---> Verifying checksum(s) for libgnomeui
---> Extracting libgnomeui
---> Configuring libgnomeui
---> Building libgnomeui
---> Staging libgnomeui into destroot
---> Installing libgnomeui @2.24.2_0
---> Activating libgnomeui @2.24.2_0
---> Cleaning libgnomeui
---> Fetching libmng
---> Attempting to fetch libmng-1.0.10.tar.gz from http://nchc.dl.sourceforge.net/libmng
---> Verifying checksum(s) for libmng
---> Extracting libmng
---> Configuring libmng
---> Building libmng
---> Staging libmng into destroot
---> Installing libmng @1.0.10_2
---> Activating libmng @1.0.10_2
---> Cleaning libmng
---> Fetching libwmf
---> Attempting to fetch libwmf-0.2.8.4.tar.gz from http://nchc.dl.sourceforge.net/wvware
---> Verifying checksum(s) for libwmf
---> Extracting libwmf
---> Applying patches to libwmf
---> Configuring libwmf
---> Building libwmf
---> Staging libwmf into destroot
---> Installing libwmf @0.2.8.4_4
---> Activating libwmf @0.2.8.4_4
---> Cleaning libwmf
---> Fetching openjpeg
---> Attempting to fetch openjpeg_v1_3.tar.gz from http://distfiles.macports.org/openjpeg
---> Verifying checksum(s) for openjpeg
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---> Applying patches to openjpeg
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---> Building openjpeg
---> Staging openjpeg into destroot
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---> Activating openjpeg @1.3_3
---> Cleaning openjpeg
---> Fetching poppler-data
---> Attempting to fetch poppler-data-0.4.0.tar.gz from http://distfiles.macports.org/poppler-data
---> Verifying checksum(s) for poppler-data
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---> Attempting to fetch poppler-0.12.4.tar.gz from http://distfiles.macports.org/poppler
---> Verifying checksum(s) for poppler
---> Extracting poppler
---> Configuring poppler
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---> Fetching mpfr
---> Attempting to fetch patch01 from http://distfiles.macports.org/mpfr/2.4.2
---> Attempting to fetch mpfr-2.4.2.tar.bz2 from http://distfiles.macports.org/mpfr/2.4.2
---> Verifying checksum(s) for mpfr
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---> Cleaning mpfr
---> Fetching gcc43
---> Attempting to fetch gcc-core-4.3.4.tar.bz2 from ftp://ftp.dti.ad.jp/pub/GNU//gcc/gcc-4.3.4
---> Attempting to fetch gcc-fortran-4.3.4.tar.bz2 from ftp://ftp.dti.ad.jp/pub/GNU//gcc/gcc-4.3.4
---> Attempting to fetch gcc-g++-4.3.4.tar.bz2 from ftp://ftp.dti.ad.jp/pub/GNU//gcc/gcc-4.3.4
---> Attempting to fetch gcc-java-4.3.4.tar.bz2 from ftp://ftp.dti.ad.jp/pub/GNU//gcc/gcc-4.3.4
---> Attempting to fetch gcc-objc-4.3.4.tar.bz2 from ftp://ftp.dti.ad.jp/pub/GNU//gcc/gcc-4.3.4
---> Verifying checksum(s) for gcc43
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---> Staging gcc43 into destroot
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---> Fetching gzip
---> Attempting to fetch gzip-1.4.tar.gz from ftp://ftp.dti.ad.jp/pub/GNU/gzip
---> Verifying checksum(s) for gzip
---> Extracting gzip
---> Applying patches to gzip
---> Configuring gzip
---> Building gzip
---> Staging gzip into destroot
---> Installing gzip @1.4_0
---> Activating gzip @1.4_0
---> Cleaning gzip
---> Fetching atlas
---> Attempting to fetch atlas3.8.3.tar.bz2 from http://nchc.dl.sourceforge.net/math-atlas/atlas3.8.3.tar.bz2
---> Attempting to fetch atlas3.8.3.tar.bz2 from http://voxel.dl.sourceforge.net/math-atlas/atlas3.8.3.tar.bz2
---> Attempting to fetch atlas3.8.3.tar.bz2 from http://distfiles.macports.org/atlas
---> Attempting to fetch lapack.tgz from http://distfiles.macports.org/atlas
---> Verifying checksum(s) for atlas
---> Extracting atlas
---> Applying patches to atlas
---> Configuring atlas
---> Building atlas
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---> Installing atlas @3.8.3_1
---> Activating atlas @3.8.3_1
---> Cleaning atlas
---> Fetching fftw-3
---> Attempting to fetch fftw-3.2.2.tar.gz from http://distfiles.macports.org/fftw-3
---> Verifying checksum(s) for fftw-3
---> Extracting fftw-3
---> Configuring fftw-3
---> Building fftw-3
---> Staging fftw-3 into destroot
---> Installing fftw-3 @3.2.2_0+i386
---> Activating fftw-3 @3.2.2_0+i386
---> Cleaning fftw-3
---> Fetching py25-distribute
---> Attempting to fetch distribute-0.6.10.tar.gz from http://distfiles.macports.org/python
---> Verifying checksum(s) for py25-distribute
---> Extracting py25-distribute
---> Configuring py25-distribute
---> Building py25-distribute
---> Staging py25-distribute into destroot
---> Installing py25-distribute @0.6.10_0
---> Activating py25-distribute @0.6.10_0
---> Cleaning py25-distribute
---> Fetching py25-nose
---> Attempting to fetch nose-0.11.3.tar.gz from http://distfiles.macports.org/python
---> Verifying checksum(s) for py25-nose
---> Extracting py25-nose
---> Configuring py25-nose
---> Building py25-nose
---> Staging py25-nose into destroot
---> Installing py25-nose @0.11.3_0
---> Activating py25-nose @0.11.3_0
---> Cleaning py25-nose
---> Fetching py25-numpy
---> Attempting to fetch numpy-1.4.0.tar.gz from http://nchc.dl.sourceforge.net/numpy
---> Attempting to fetch numpy-1.4.0.tar.gz from http://voxel.dl.sourceforge.net/numpy
---> Attempting to fetch numpy-1.4.0.tar.gz from http://distfiles.macports.org/python
---> Verifying checksum(s) for py25-numpy
---> Extracting py25-numpy
---> Applying patches to py25-numpy
---> Configuring py25-numpy
---> Building py25-numpy
Error: Target org.macports.build returned: shell command " cd "/opt/local/var/macports/build/_opt_local_var_macports_sources_rsync.macports.org_release_ports_python_py25-numpy/work/numpy-1.4.0" && /opt/local/bin/python2.5 setup.py --no-user-cfg build " returned error 1
Command output:     setup_package()
File "setup.py", line 180, in setup_package
    configuration=configuration )
File "/opt/local/var/macports/build/_opt_local_var_macports_sources_rsync.macports.org_release_ports_python_py25-numpy/work/numpy-1.4.0/numpy/distutils/core.py", line 186, in setup
    return old_setup(**new_attr)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/core.py", line 151, in setup
    dist.run_commands()
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py", line 986, in run_commands
    self.run_command(cmd)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py", line 1006, in run_command
    cmd_obj.run()
File "/opt/local/var/macports/build/_opt_local_var_macports_sources_rsync.macports.org_release_ports_python_py25-numpy/work/numpy-1.4.0/numpy/distutils/command/build.py", line 37, in run
    old_build.run(self)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/command/build.py", line 112, in run
    self.run_command(cmd_name)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/cmd.py", line 333, in run_command
    self.distribution.run_command(command)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/distutils/dist.py", line 1006, in run_command
    cmd_obj.run()
File "/opt/local/var/macports/build/_opt_local_var_macports_sources_rsync.macports.org_release_ports_python_py25-numpy/work/numpy-1.4.0/numpy/distutils/command/build_src.py", line 152, in run
    self.build_sources()
File "/opt/local/var/macports/build/_opt_local_var_macports_sources_rsync.macports.org_release_ports_python_py25-numpy/work/numpy-1.4.0/numpy/distutils/command/build_src.py", line 163, in build_sources
    self.build_library_sources(*libname_info)
File "/opt/local/var/macports/build/_opt_local_var_macports_sources_rsync.macports.org_release_ports_python_py25-numpy/work/numpy-1.4.0/numpy/distutils/command/build_src.py", line 298, in build_library_sources
    sources = self.generate_sources(sources, (lib_name, build_info))
File "/opt/local/var/macports/build/_opt_local_var_macports_sources_rsync.macports.org_release_ports_python_py25-numpy/work/numpy-1.4.0/numpy/distutils/command/build_src.py", line 385, in generate_sources
    source = func(extension, build_dir)
File "numpy/core/setup.py", line 657, in get_mathlib_info
    raise RuntimeError("Broken toolchain: cannot link a simple C program")
RuntimeError: Broken toolchain: cannot link a simple C program

Error: The following dependencies failed to build: gimp-app gimp2 py25-gtk py25-cairo py25-numpy py25-gobject gimp-jp2 gimp-lqr-plugin liblqr gtk-nodoka-engine gutenprint icns-gimp macclipboard-gimp macfile-gimp ufraw cfitsio dcraw exiv2 gtkimageview xsane sane-backends libusb-compat libusb
Error: Status 1 encountered during processing.
Before reporting a bug, first run the command again with the -d flag to get complete output.

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Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., and Szeliski, R. 2006. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (June 17 - 22, 2006). CVPR. IEEE Computer Society, Washington, DC, 519-528. DOI= http://dx.doi.org/10.1109/CVPR.2006.19

S. Seitz et al. Multi-view stereo evaluation web page
http://vision.middlebury.edu/mview/

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case study: 리프로젝션 에러 실험 결과 조사

2010. 7. 20. 15:07 Computer Vision/2010ETRI-MultiAutoCalib

1. 실험실 환경에서의 결과
In the case of experiments in a laboratory:

(1) Yasutaka Furukawa & Jean Ponce, "Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment", CVPR 2008
(* 영상의 크기와 리프로젝션 에러 사이의 관계를 수학적으로 설명)

For example, the robot arm (Stanford spherical gantry) used in the multi-view stereo evaluation of [18] has an accuracy of 0.01◦ for a 1m radius sphere observing an object about 15cm in diameter, which yields approximately 1.0[m]×0.01×π/180 = 0.175[mm] errors near an object. Even with the low-resolution 640×480 cameras used in [18], where a pixel covers roughly 0.25mm on the surface of an object, this error corresponds to 0.175/0.25 = 0.7pixels, which is not negligible. If one used a highresolution 4000× 3000 camera, the positioning error would increase to 0.7×4000/640 = 4.4pixels.

The mean reprojection error decreases from 2-3pixels before refinement to about 0.25 to 0.5 pixels for most datasets after six iterations.

In practice, we have found PMVS to be robust to errors in camera parameters as long as the image resolution matches the corresponding reprojection errors—that is, when features to be matched are roughly within two pixels of the corresponding 3D points.

(2) Ivo Ihrke & Lukas Ahrenberg & Marcus Magnor, “External camera calibration for synchronized multi-video systems”, Journal of WSCG(the 12th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision), 2004

(3) Cheng-I Chen & Yong-Sheng Chen "Central Catadioptric Camera Calibration using Planar Objects", ICVS 2007

(4) Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., and Szeliski, R. 2006. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006
: S. Seitz et al. Multi-view stereo evaluation web page at http://vision.middlebury.edu/mview/

Calibration accuracy on these datasets appears to be on the order of a pixel (a pixel spans about 1/4mm on the object). It is difficult to quantify the calibration accuracy because we don't have point correspondences in all views. However, when the images are reprojected onto the laser scanned mesh reconstruction and averaged, 1-2 pixel wide features are clearly visible. Some images and regions of the object are a bit out of focus, due in part to the limited depth of field afforded by the imaging configuration. This is probably a blessing in disguise, as it helps compensate for the lack of sub-pixel registration. While there is certainly room for improvement, we expect that this degree of accuracy will enable good results for most algorithms. Note that the images have been corrected to remove radial distortion.

2. 일반/극한 환경 (실내/실외)에서의 결과
In the case of experiments in an exterior, interior or extreme circumstance:

3. 리프로젝션 에러 최소화 관련 방법과 용어 및 문헌
methods or related terms to be used to minimize reprojection errors

(1) sampson error

(2) LM(Levenberg-marquardt) algorithm
M. Lourakis. levmar: Levenberg-marquardt nonlinear least squares algorithms in C/C++. [web page] http://www.ics.forth.gr/~lourakis/levmar, July 2004.

(3) bundle adjustment

(4) iterative gradient descent (in Bouget's toolbox)

ref.
1) Gregorij Kurillo & Zeyu Li & Ruzena Bajcsy, "Framework for Hierarchical Calibration of Multi-camera Systems for Teleimmersion", ICIT 2009

Figure 1 shows typical error distribution as obtained on the four cameras within the cluster. After the global optimization on all four cameras, the combined error distribution resembles Gaussian distribution with the mean value of 0.153 pixels and standard deviation of 0.091 pixels. Note that the reprojection error on the color camera (#4) is higher than on the grayscale cameras. The maximal error for this set of images was 0.8 though only a small number of points had errors in that range.

Bundle adjustment minimizes the following reprojection error:

Figure 4 shows the reprojection error on all cameras. The cameras whose position and orientation were obtained by indirect transformation path with the reference camera had no significantly different reprojection errors as compared to the cameras calibrated directly with the reference camera. The mean reprojection error between all the cameras was 0.3633 pixels with the standard deviation of 0.0486 pixels.

2) Kai Ide & Steffen Siering & Thomas Sikora, "Automating Multi-Camera Self-Calibration", WACV 2009

In practice the reprojection error is usually larger at image boundaries as radial distortions due to the optical system used in the cameras become more apparent. Somewhat surprisingly this is also true for the optics of the projector even though its high quality object lens should show no signs of radial distortion when projecting rectangular images onto a screen.

The reprojection error for our setup consisting of four cameras and one projector is shown shown in fig. 9. The total mean reprojection error is 0.29 pixels with a standard deviation of 0.20 pixels. It is important to mention that this result, despite being satisfactory, is not necessarily in itself meaningful as one has to compare this value with the distribution of points within the image space that have survived the RANSAC verification. An even spread throughout the image plane is desirable, a property that has been demonstrated in fig. 8. As we decrease radius and spacing to r = s = 1 we get results similar to the unmodified calibration sequence, yielding a relatively large reprojection error of 0.8 pixels in average.

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posted by maetel

Yasutaka Furukawa "Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment"

2010. 7. 18. 17:05 Computer Vision

Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment

yasutakaFurukawa_2008cvpr.pdf

Yasutaka Furukawa & Jean Ponce, CVPR 2008

Yasutaka Furukawa
http://www.cs.washington.edu/homes/furukawa/

Jean Ponce
http://www.di.ens.fr/~ponce/
http://www-cvr.ai.uiuc.edu/ponce_grp/

DxO Labs (렌즈 왜곡 교정 소프트웨어)
http://www.dxo.com/

PMVS (Patch-based Multi-view Stereo Software) version 2
http://grail.cs.washington.edu/software/pmvs/

CMVS (Clustering Views for Multi-view Stereo)
http://grail.cs.washington.edu/software/cmvs/

sba : A Generic Sparse Bundle Adjustment C/C++ Package Based on the Levenberg-Marquardt Algorithm (Manolis Lourakis)
http://www.ics.forth.gr/~lourakis/sba/

image-based modeling

multi-view stereovision (MVS)

> two main approach camera calibration problem
1) chart-based calibration (CBC)
2) structure from motion (SFM) + auto-calibration + bundle adjustment (BA)

* selection of feature correspondences (SFC)
eg. RANSAC

Standard BA algorithms optimize both the scene point and camera parameters by minimizing the sum of squared reprojection errors.

Unlike BA algorithms, multi-view stereo algorithms are aimed at recovering scene information alone given fixed camera parameters.

DxO Optics Pro - Lens Distortion

In practice, we have found PMVS to be robust to errors in camera parameters as long as the image resolution matches the corresponding reprojection errors - that is, when features to be matched are roughly within two pixels of the corresponding 3D points.

> Algorithm

1) initializing feature correspondences
1-1) building image pyramids
1-2) for the given camera parameters, obtaining a conservative estimate of the expected reprojection error
1-3) sub-sampling

2) refining feature correspondences
2-1) determining a patch and the local image texture inside
2-2) optimizing the reference camera with the conjugate gradient method
2-3) removing outliers and updating the corresponding visibility information

3) updating the camera parameters with the SBA bundle adjustment software

4) repeat 1)-3) 4 times with the updated expected reprojection error and the fixed pyramid level

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posted by maetel

J. Weng, P. Cohen, and M. Herniou "Camera calibration with distortion models and accuracy evaluation"

2010. 7. 16. 16:52 Computer Vision/2010ETRI-MultiAutoCalib

J. Weng, P. Cohen, and M. Herniou. "Camera calibration with distortion models and accuracy evaluation." In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 14(10): 965-980, 1992.

juyangWeng_1992pami.pdf

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posted by maetel

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