Search Results

Search found 44 results on 2 pages for 'imshow'.

Page 2/2 | < Previous Page | 1 2 

  • Matlab Image watermarking question , using both SVD and DWT

    - by Georgek
    Hello all . here is a code that i got over the net ,and it is supposed to embed a watermark of size(50*20) called _copyright.bmp in the Code below . the size of the cover object is (512*512), it is called _lena_std_bw.bmp.What we did here is we did DWT2 2 times for the image , when we reached our second dwt2 cA2 size is 128*128. You should notice that the blocksize and it equals 4, it is used to determine the max msg size based on cA2 according to the following code:max_message=RcA2*CcA2/(blocksize^2). in our current case max_message would equal 128*128/(4^2)=1024. i want to embed a bigger watermark in the 2nd dwt2 and lets say the size of that watermark is 400*10(i can change the dimension using MS PAINT), what i have to do is change the size of the blocksize to 2. so max_message=4096.Matlab gives me 3 errors and they are : ??? Error using == plus Matrix dimensions must agree. Error in == idwt2 at 93 x = upsconv2(a,{Lo_R,Lo_R},sx,dwtEXTM,shift)+ ... % Approximation. Error in == two_dwt_svd_low_low at 88 CAA1 = idwt2(cA22,cH2,cV2,cD2,'haar',[RcA1,CcA1]); The origional Code is (the origional code where blocksize =4): %This algorithm makes DWT for the whole image and after that make DWT for %cH1 and make SVD for cH2 and embed the watermark in every level after SVD %(1) -------------- Embed Watermark ------------------------------------ %Add the watermar W to original image I and give the watermarked image in J %-------------------------------------------------------------------------- % set the gain factor for embeding and threshold for evaluation clc; clear all; close all; % save start time start_time=cputime; % set the value of threshold and alpha thresh=.5; alpha =0.01; % read in the cover object file_name='_lena_std_bw.bmp'; cover_object=double(imread(file_name)); % determine size of watermarked image Mc=size(cover_object,1); %Height Nc=size(cover_object,2); %Width % read in the message image and reshape it into a vector file_name='_copyright.bmp'; message=double(imread(file_name)); T=message; Mm=size(message,1); %Height Nm=size(message,2); %Width % perform 1-level DWT for the whole cover image [cA1,cH1,cV1,cD1] = dwt2(cover_object,'haar'); % determine the size of cA1 [RcA1 CcA1]=size(cA1) % perform 2-level DWT for cA1 [cA2,cH2,cV2,cD2] = dwt2(cA1,'haar'); % determine the size of cA2 [RcA2 CcA2]=size(cA2) % set the value of blocksize blocksize=4 % reshape the watermark to a vector message_vector=round(reshape(message,Mm*Nm,1)./256); W=message_vector; % determine maximum message size based on cA2, and blocksize max_message=RcA2*CcA2/(blocksize^2) % check that the message isn't too large for cover if (length(message) max_message) error('Message too large to fit in Cover Object') end %----------------------- process the image in blocks ---------------------- x=1; y=1; for (kk = 1:length(message_vector)) [cA2u cA2s cA2v]=svd(cA2(y:y+blocksize-1,x:x+blocksize-1)); % if message bit contains zero, modify S of the original image if (message_vector(kk) == 0) cA2s = cA2s*(1 + alpha); % otherwise mask is filled with zeros else cA2s=cA2s; end cA22(y:y+blocksize-1,x:x+blocksize-1)=cA2u*cA2s*cA2v; % move to next block of mask along x; If at end of row, move to next row if (x+blocksize) >= CcA2 x=1; y=y+blocksize; else x=x+blocksize; end end % perform IDWT CAA1 = idwt2(cA22,cH2,cV2,cD2,'haar',[RcA1,CcA1]); watermarked_image= idwt2(CAA1,cH1,cV1,cD1,'haar',[Mc,Nc]); % convert back to uint8 watermarked_image_uint8=uint8(watermarked_image); % write watermarked Image to file imwrite(watermarked_image_uint8,'dwt_watermarked.bmp','bmp'); % display watermarked image figure(1) imshow(watermarked_image_uint8,[]) title('Watermarked Image') %(2) ---------------------------------------------------------------------- %---------- Extract Watermark from attacked watermarked image ------------- %-------------------------------------------------------------------------- % read in the watermarked object file_name='dwt_watermarked.bmp'; watermarked_image=double(imread(file_name)); % determine size of watermarked image Mw=size(watermarked_image,1); %Height Nw=size(watermarked_image,2); %Width % perform 1-level DWT for the whole watermarked image [ca1,ch1,cv1,cd1] = dwt2(watermarked_image,'haar'); % determine the size of ca1 [Rca1 Cca1]=size(ca1); % perform 2-level DWT for ca1 [ca2,ch2,cv2,cd2] = dwt2(ca1,'haar'); % determine the size of ca2 [Rca2 Cca2]=size(ca2); % process the image in blocks % for each block get a bit for message x=1; y=1; for (kk = 1:length(message_vector)) % sets correlation to 1 when patterns are identical to avoid /0 errors % otherwise calcluate difference between the cover image and the % watermarked image [cA2u cA2s cA2v]=svd(cA2(y:y+blocksize-1,x:x+blocksize-1)); [ca2u1 ca2s1 ca2v1]=svd(ca2(y:y+blocksize-1,x:x+blocksize-1)); correlation(kk)=diag(ca2s1-cA2s)'*diag(ca2s1-cA2s)/(alpha*alpha)/(diag(cA2s)*diag(cA2s)); % move on to next block. At and of row move to next row if (x+blocksize) >= Cca2 x=1; y=y+blocksize; else x=x+blocksize; end end % if correlation exceeds average correlation correlation(kk)=correlation(kk)+mean(correlation(1:Mm*Nm)); for kk = 1:length(correlation) if (correlation(kk) > thresh*alpha);%thresh*mean(correlation(1:Mo*No))) message_vector(kk)=0; end end % reshape the message vector and display recovered watermark. figure(2) message=reshape(message_vector(1:Mm*Nm),Mm,Nm); imshow(message,[]) title('Recovered Watermark') % display processing time elapsed_time=cputime-start_time, please do help,its my graduation project and i have been trying this code for along time but failed miserable. Thanks in advance

    Read the article

  • Confusion Matrix with number of classified/misclassified instances on it (Python/Matplotlib)

    - by Pinkie
    I am plotting a confusion matrix with matplotlib with the following code: from numpy import * import matplotlib.pyplot as plt from pylab import * conf_arr = [[33,2,0,0,0,0,0,0,0,1,3], [3,31,0,0,0,0,0,0,0,0,0], [0,4,41,0,0,0,0,0,0,0,1], [0,1,0,30,0,6,0,0,0,0,1], [0,0,0,0,38,10,0,0,0,0,0], [0,0,0,3,1,39,0,0,0,0,4], [0,2,2,0,4,1,31,0,0,0,2], [0,1,0,0,0,0,0,36,0,2,0], [0,0,0,0,0,0,1,5,37,5,1], [3,0,0,0,0,0,0,0,0,39,0], [0,0,0,0,0,0,0,0,0,0,38] ] norm_conf = [] for i in conf_arr: a = 0 tmp_arr = [] a = sum(i,0) for j in i: tmp_arr.append(float(j)/float(a)) norm_conf.append(tmp_arr) plt.clf() fig = plt.figure() ax = fig.add_subplot(111) res = ax.imshow(array(norm_conf), cmap=cm.jet, interpolation='nearest') cb = fig.colorbar(res) savefig("confmat.png", format="png") But I want to the confusion matrix to show the numbers on it like this graphic (the right one): http://i48.tinypic.com/2e30kup.jpg How can I plot the conf_arr on the graphic?

    Read the article

  • opencv image conversion from rgb to hsv

    - by kaushalyjain
    When I run this following code on a sample image ie an rgb image; and then execute it to display the converted hsv image, both appear to be different... can anyone explain why? or can you suggest a solution for this not to happen... coz its the same image afterall Mat img_hsv,img_rgb,red_blob,blue_blob; img_rgb = imread("pic.png",1); cvtColor(img_rgb,img_hsv,CV_RGB2HSV); namedWindow("win1", CV_WINDOW_AUTOSIZE); imshow("win1", img_hsv);

    Read the article

  • pyplot: really slow creating heatmaps

    - by cvondrick
    I have a loop that executes the body about 200 times. In each loop iteration, it does a sophisticated calculation, and then as debugging, I wish to produce a heatmap of a NxM matrix. But, generating this heatmap is unbearably slow and significantly slow downs an already slow algorithm. My code is along the lines: import numpy import matplotlib.pyplot as plt for i in range(200): matrix = complex_calculation() plt.set_cmap("gray") plt.imshow(matrix) plt.savefig("frame{0}.png".format(i)) The matrix, from numpy, is not huge --- 300 x 600 of doubles. Even if I do not save the figure and instead update an on-screen plot, it's even slower. Surely I must be abusing pyplot. (Matlab can do this, no problem.) How do I speed this up?

    Read the article

  • Averaging corrupted images to eliminate the noise

    - by Mertie Pertie
    Hi all As you can get it from the title, I want to average some .jpg images which are corrupted by zero-mean Gaussian additive. After searching over internet, I figured out to add image matrices and divide the sum by the # of matrices. However the resultant image is totally black. Normally when the number of image increases then the resultant image gets better. But When I use more images it gets darker. I am using 800x600 black and white images with .jpg ext Here is the script I used; image1 = imread ('PIC1.jpg'); image2 = imread ('PIC2.jpg'); image3 = imread ('PIC3.jpg'); image4 = imread ('PIC4.jpg'); sum = image1 + image2 + image3 + image4; av = sum / 4; imshow(av); Thanks in advance

    Read the article

  • Problem with averaging corrupted images to eliminate the noise in MATLAB

    - by Mertie Pertie
    I want to average some .jpg images which are corrupted by zero-mean Gaussian additive noise. After searching around, I figured out to add the image matrices and divide the sum by the number of matrices. However, the resultant image is totally black. Normally when the number of image increases then the resultant image gets better. But when I use more images it gets darker. I am using 800x600 black and white .jpg images. Here is the script I used: image1 = imread ('PIC1.jpg'); image2 = imread ('PIC2.jpg'); image3 = imread ('PIC3.jpg'); image4 = imread ('PIC4.jpg'); sum = image1 + image2 + image3 + image4; av = sum / 4; imshow(av);

    Read the article

  • Averaging corrupted images to eliminate the noise in Matlab

    - by Mertie Pertie
    Hi all As you can get it from the title, I want to average some .jpg images which are corrupted by zero-mean Gaussian additive. After searching over internet, I figured out to add image matrices and divide the sum by the # of matrices. However the resultant image is totally black. Normally when the number of image increases then the resultant image gets better. But When I use more images it gets darker. I am using 800x600 black and white images with .jpg ext Here is the script I used; image1 = imread ('PIC1.jpg'); image2 = imread ('PIC2.jpg'); image3 = imread ('PIC3.jpg'); image4 = imread ('PIC4.jpg'); sum = image1 + image2 + image3 + image4; av = sum / 4; imshow(av); Thanks in advance

    Read the article

  • How i can do image CROP in OpenCV

    - by Nolik
    How i can do image crop such in PIL in OpenCV. Working example on PIL im = Image.open('0.png').convert('L') im = im.crop((1, 1, 98, 33)) im.save('_0.png') But how i can do it on OpenCV? I wanted to do so im = cv.imread('0.png', cv.CV_LOAD_IMAGE_GRAYSCALE) (thresh, im_bw) = cv.threshold(im, 128, 255, cv.THRESH_OTSU) im = cv.getRectSubPix(im_bw, (98, 33), (1, 1)) cv.imshow('Img', im) cv.waitKey(0) But it doesnt work. I think, i wrong use getRectSubPix. If it true, please explain how i can correctly use this function. Thanks.

    Read the article

  • How to compute power spectrum from 2D FFT

    - by user1452954
    I've encounter a problem when doing my lab assignment, not sure how to implement this: Use fft2 on a gray image and to do Fourier transform and then compute the power spectrum. This is my code so far: >> Pc = imread('pckint.jpg'); >> whos Pc; Name Size Bytes Class Attributes Pc 256x256 65536 uint8 >> imshow(Pc); >> result = fft2(Pc); My question is from the result. How to computer power spectrum?

    Read the article

  • 3x3 Average filter in matlab

    - by turingcomplete
    I've written code to smooth an image using a 3x3 averaging filter, however the output is strange, it is almost all black. Here's my code. function [filtered_img] = average_filter(noisy_img) [m,n] = size(noisy_img); filtered_img = zeros(m,n); for i = 1:m-2 for j = 1:n-2 sum = 0; for k = i:i+2 for l = j:j+2 sum = sum+noisy_img(k,l); end end filtered_img(i+1,j+1) = sum/9.0; end end end I call the function as follows: img=imread('img.bmp'); filtered = average_filter(img); imshow(uint8(filtered)); I can't see anything wrong in the code logic so far, I'd appreciate it if someone can spot the problem.

    Read the article

  • Getting snapshot from webcam in Matlab

    - by Harsh
    I have created a simple GUI to preview webcam stream and to get snapshot from it. For this I have created on axes to show video, one push button(pushbutton1) to start preview, one push button(pushbutton2) to get snapshot. Following is the code for these two push buttons. function pushbutton1_Callback(hObject, eventdata, handles) % hObject handle to pushbutton1 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) axes(handles.axes1); vidObj = videoinput('winvideo',1); videoRes = get(vidObj, 'VideoResolution'); numberOfBands = get(vidObj, 'NumberOfBands'); handleToImage = image( zeros([videoRes(2), videoRes(1), numberOfBands], 'uint8') ); preview(vidObj, handleToImage); % --- Executes on button press in pushbutton2. function pushbutton2_Callback(hObject, eventdata, handles) % hObject handle to pushbutton2 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) a=getsnapshot(get(axes,'Children')); imshow(a); In pushbutton2_Callback I am trying to get child of axes ie. vidObj. But this gives me error ??? Undefined function or method 'getsnapshot' for input arguments of type 'double'.. Why is it returing double type instead of child object vidObj? How can I fix it and get snapshot? Is there any other better way? (I just started learning GUI.) Thanks.

    Read the article

  • OpenCV: Shift/Align face image relative to reference Image (Image Registration)

    - by Abhischek
    I am new to OpenCV2 and working on a project in emotion recognition and would like to align a facial image in relation to a reference facial image. I would like to get the image translation working before moving to rotation. Current idea is to run a search within a limited range on both x and y coordinates and use the sum of squared differences as error metric to select the optimal x/y parameters to align the image. I'm using the OpenCV face_cascade function to detect the face images, all images are resized to a fixed (128x128). Question: Which parameters of the Mat image do I need to modify to shift the image in a positive/negative direction on both x and y axis? I believe setImageROI is no longer supported by Mat datatypes? I have the ROIs for both faces available however I am unsure how to use them. void alignImage(vector<Rect> faceROIstore, vector<Mat> faceIMGstore) { Mat refimg = faceIMGstore[1]; //reference image Mat dispimg = faceIMGstore[52]; // "displaced" version of reference image //Rect refROI = faceROIstore[1]; //Bounding box for face in reference image //Rect dispROI = faceROIstore[52]; //Bounding box for face in displaced image Mat aligned; matchTemplate(dispimg, refimg, aligned, CV_TM_SQDIFF_NORMED); imshow("Aligned image", aligned); } The idea for this approach is based on Image Alignment Tutorial by Richard Szeliski Working on Windows with OpenCV 2.4. Any suggestions are much appreciated.

    Read the article

  • MATLAB: impoint getPosition strange behaviour

    - by tguclu
    I have a question about the values returned by getPosition. Below is my code. It lets the user set 10 points on a given image: figure ,imshow(im); colorArray=['y','m','c','r','g','b','w','k','y','m','c']; pointArray = cell(1,10); % Construct boundary constraint function fcn = makeConstrainToRectFcn('impoint',get(gca,'XLim'),get(gca,'YLim')); for i = 1:10 p = impoint(gca); % Enforce boundary constraint function using setPositionConstraintFcn setPositionConstraintFcn(p,fcn); setColor(p,colorArray(1,i)); pointArray{i}=p; getPosition(p) end When I start to set points on the image I get results like [675.000 538.000], which means that the x part of the coordinate is 675 and the y part is 538, right? This is what the MATLAB documentation says, but since the image is 576*120 (as displayed in the window) this is not logical. It seemed to me like, somehow, getPosition returns the y coordinate first. I need some clarification on this. Thanks for help

    Read the article

  • Finding edge and corner values of an image in matlab

    - by James
    Hi, this problem links to two other questions i've asked on here. I am tracing the outline of an image and plotting this to a dxf file. I would like to use the bwboundaries function to find the coordinates of the edges of the image, find the corner coordinates using the cornermetric function and then remove any edge coordinates that are not a corner. The important thing I need to be able to do is keep the order of the corner elements obtained from bwboundaries, so that the section traces properly. The dxf function I have that draws from the coordinates draws lines between coordinates that are next to each other, so the line has to be drawn "around" the section rather than straight between the corner points. The reason I am doing this is because there are less coordinates obtained this way, so it is easier to amend the dxf file (as there are less points to manipulate). The code I have so far is: %# Shape to be traced bw = zeros(200); bw(20:40,20:180) = 1; bw(20:180,90:110) = 1; bw(140:180,20:185) = 1; %# Boundary Finding Section [Boundary] = bwboundaries(bw); %Traces the boundary of each section figure, imshow(bw); hold on; colors=['b' 'g' 'r' 'c' 'm' 'y']; for k=1:length(Boundary) perim = Boundary{k}; %Obtains perimeter coordinates (as a 2D matrix) from the cell array cidx = mod(k,length(colors))+1;% Obtains colours for the plot plot(perim(:,2), perim(:,1),... colors(cidx),'LineWidth',2); end Coordmat = cell2mat(Boundary) %Converts the traced regions to a matrix X = Coordmat(:,1) Y = Coordmat(:,2) % This gives the edge coordinates in matrix form %% Corner Finding Section (from Jonas' answer to a previous question %# get corners cornerProbability = cornermetric(bw); cornerIdx = find(cornerProbability==max(cornerProbability(:))); %# Label the image. bwlabel puts 1 for the first feature, 2 for the second, etc. %# Since concave corners are placed just outside the feature, grow the features %# a little before labeling bw2 = imdilate(bw,ones(3)); labeledImage = bwlabel(bw2); %# read the feature number associated with the corner cornerLabels = labeledImage(cornerIdx); %# find all corners that are associated with feature 1 corners_1 = cornerIdx(cornerLabels==1) [Xcorners, Ycorners] = ind2sub(200,corners_1) % Convert subscripts The code I have is, to give a matrix Xfin for the final x coordinates (which are on the edge AND at a corner. Xfin = zeros(length(X),1) for i = Xcorners XFin(i) = Xcorners if i~= Xcorners XFin(i) = [] end end However, this does not work correctly, because the values in the solution are sorted into order, and only one of each value remains. As I said, I would like the corner elements to be in the same order as obtained from bwboundaries, to allow the image to trace properly. Thanks

    Read the article

  • What is the wrong of this converted code?

    - by Gum Slashy
    I'm developing shape identification project using javacv and I have found some opencv code to identify U shapes in particular image and I have try to convert it in to javacv but it doesn't provide same out put. Can you please help me to convert this opencv code into javacv? This is Opencv code import cv2 import numpy as np img = cv2.imread('sofud.jpg') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) ret,thresh = cv2.threshold(gray,127,255,1) contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: x,y,w,h = cv2.boundingRect(cnt) if 10 < w/float(h) or w/float(h) < 0.1: cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2) cv2.imshow('res',img) cv2.waitKey(0) cv2.destroyAllWindows() This is the expected output This is the code that I have converted import com.googlecode.javacpp.Loader; import com.googlecode.javacv.CanvasFrame; import static com.googlecode.javacpp.Loader.*; import static com.googlecode.javacv.cpp.opencv_core.*; import static com.googlecode.javacv.cpp.opencv_imgproc.*; import static com.googlecode.javacv.cpp.opencv_highgui.*; import java.io.File; import javax.swing.JFileChooser; public class TestBeam { public static void main(String[] args) { CvMemStorage storage=CvMemStorage.create(); CvSeq squares = new CvContour(); squares = cvCreateSeq(0, sizeof(CvContour.class), sizeof(CvSeq.class), storage); JFileChooser f=new JFileChooser(); int result=f.showOpenDialog(f);//show dialog box to choose files File myfile=null; String path=""; if(result==0){ myfile=f.getSelectedFile();//selected file taken to myfile path=myfile.getAbsolutePath();//get the path of the file } IplImage src = cvLoadImage(path);//hear path is actual path to image IplImage grayImage = IplImage.create(src.width(), src.height(), IPL_DEPTH_8U, 1); cvCvtColor(src, grayImage, CV_RGB2GRAY); cvThreshold(grayImage, grayImage, 127, 255, CV_THRESH_BINARY); CvSeq cvSeq=new CvSeq(); CvMemStorage memory=CvMemStorage.create(); cvFindContours(grayImage, memory, cvSeq, Loader.sizeof(CvContour.class), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE); System.out.println(cvSeq.total()); for (int i = 0; i < cvSeq.total(); i++) { CvRect rect=cvBoundingRect(cvSeq, i); int x=rect.x(),y=rect.y(),h=rect.height(),w=rect.width(); if (10 < (w/h) || (w/h) < 0.1){ cvRectangle(src, cvPoint(x, y), cvPoint(x+w, y+h), CvScalar.RED, 1, CV_AA, 0); //cvSeqPush(squares, rect); } } CanvasFrame cnvs=new CanvasFrame("Beam"); cnvs.setDefaultCloseOperation(javax.swing.JFrame.EXIT_ON_CLOSE); cnvs.showImage(src); //cvShowImage("Final ", src); } } This is the out put that I got please can some one help me to solve this problem ?

    Read the article

  • image processing algorithm in MATLAB

    - by user261002
    I am trying to reconstruct an algorithm belong to this paper: Decomposition of biospeckle images in temporary spectral bands Here is an explanation of the algorithm: We recorded a sequence of N successive speckle images with a sampling frequency fs. In this way it was possible to observe how a pixel evolves through the N images. That evolution can be treated as a time series and can be processed in the following way: Each signal corresponding to the evolution of every pixel was used as input to a bank of filters. The intensity values were previously divided by their temporal mean value to minimize local differences in reflectivity or illumination of the object. The maximum frequency that can be adequately analyzed is determined by the sampling theorem and s half of sampling frequency fs. The latter is set by the CCD camera, the size of the image, and the frame grabber. The bank of filters is outlined in Fig. 1. In our case, ten 5° order Butterworth11 filters were used, but this number can be varied according to the required discrimination. The bank was implemented in a computer using MATLAB software. We chose the Butter-worth filter because, in addition to its simplicity, it is maximally flat. Other filters, an infinite impulse response, or a finite impulse response could be used. By means of this bank of filters, ten corresponding signals of each filter of each temporary pixel evolution were obtained as output. Average energy Eb in each signal was then calculated: where pb(n) is the intensity of the filtered pixel in the nth image for filter b divided by its mean value and N is the total number of images. In this way, en values of energy for each pixel were obtained, each of hem belonging to one of the frequency bands in Fig. 1. With these values it is possible to build ten images of the active object, each one of which shows how much energy of time-varying speckle there is in a certain frequency band. False color assignment to the gray levels in the results would help in discrimination. and here is my MATLAB code base on that : clear all for i=0:39 str = num2str(i); str1 = strcat(str,'.mat'); load(str1); D{i+1}=A; end new_max = max(max(A)); new_min = min(min(A)); for i=20:180 for j=20:140 ts = []; for k=1:40 ts = [ts D{k}(i,j)]; %%% kth image pixel i,j --- ts is time series end ts = double(ts); temp = mean(ts); ts = ts-temp; ts = ts/temp; N = 5; % filter order W = [0.00001 0.05;0.05 0.1;0.1 0.15;0.15 0.20;0.20 0.25;0.25 0.30;0.30 0.35;0.35 0.40;0.40 0.45;0.45 0.50]; N1 = 5; for ind = 1:10 Wn = W(ind,:); [B,A] = butter(N1,Wn); ts_f(ind,:) = filter(B,A,ts); end for ind=1:10 imag_test1{ind}(i,j) =sum((ts_f(ind,:)./mean(ts_f(ind,:))).^2); end end end for i=1:10 temp_imag = imag_test1{i}(:,:); x=isnan(temp_imag); temp_imag(x)=0; temp_imag=medfilt2(temp_imag); t_max = max(max(temp_imag)); t_min = min(min(temp_imag)); temp_imag = (temp_imag-t_min).*(double(new_max-new_min)/double(t_max-t_min))+double(new_min); imag_test2{i}(:,:) = temp_imag; end for i=1:10 A=imag_test2{i}(:,:); B=A/max(max(A)); B=histeq(B); figure,imshow(B) colorbar end but I am not getting the same result as paper. has anybody has aby idea why? or where I have gone wrong? Refrence Link to the paper

    Read the article

  • CodePlex Daily Summary for Monday, November 14, 2011

    CodePlex Daily Summary for Monday, November 14, 2011Popular ReleasesWeapsy: 0.4.1 Alpha: Edit Text bug fixedDesktop Google Reader: 1.4.2: This release remove the like and the broadcast buttons as Google Reader stopped supporting them (no, we don't like this decission...) Additionally and to have at least a small plus: the login window now automaitcally logs you in if you stored username and passwort (no more extra click needed) Finally added WebKit .NET to the about window and removed AwesomiumZombsquare: Solución inicial: Código fuente de la solución. Versión 7099 Tambien puedes descargar de aquí los snippets de código que utilizamos en la demostración.RDRemote: Remote Desktop remote configurator V 1.0.0: Remote Desktop remote configurator V 1.0.0SQL Monitor - tracking sql server activities: SQLMon 4.1 alpha1: 1. improved version compare, now support comparing two text files. right click on object script text box and choose "Compare" or create new query window and right click and choose "Compare" 2. improved version compare, now automatically sync two text boxes. 3. fixed problem with activities (process/job) when refreshing while current activities have less count than previous one. 4. better start up by automatically shows create connection window when there is no connection defined.Rawr: Rawr 4.2.7: This is the Downloadable WPF version of Rawr!For web-based version see http://elitistjerks.com/rawr.php You can find the version notes at: http://rawr.codeplex.com/wikipage?title=VersionNotes Rawr AddonWe now have a Rawr Official Addon for in-game exporting and importing of character data hosted on Curse. The Addon does not perform calculations like Rawr, it simply shows your exported Rawr data in wow tooltips and lets you export your character to Rawr (including bag and bank items) like Char...VidCoder: 1.2.2: Updated Handbrake core to svn 4344. Fixed the 6-channel discrete mixdown option not appearing for AAC encoders. Added handling for possible exceptions when copying to the clipboard, added retries and message when it fails. Fixed issue with audio bitrate UI not appearing sometimes when switching audio encoders. Added extra checks to protect against reported crashes. Added code to upgrade encoding profiles on old queued items.Dynamic PagedCollection (Silverlight / WPF Pagination): PagedCollection: All classes which facilitate your dynamic pagination in Silverlight or WPF !Media Companion: MC 3.422b Weekly: Ensure .NET 4.0 Full Framework is installed. (Available from http://www.microsoft.com/download/en/details.aspx?id=17718) Ensure the NFO ID fix is applied when transitioning from versions prior to 3.416b. (Details here) TV Show Resolutions... Made the TV Shows folder list sorted. Re-visibled 'Manually Add Path' in Root Folders. Sorted list to process during new tv episode search Rebuild Movies now processes thru folders alphabetically Fix for issue #208 - Display Missing Episodes is not popu...DotSpatial: DotSpatial Release Candidate 1 (1.0.823): Supports loading extensions using System.ComponentModel.Composition. DemoMap compiled as x86 so that GDAL runs on x64 machines. How to: Use an Assembly from the WebBe aware that your browser may add an identifier to downloaded files which results in "blocked" dll files. You can follow the following link to learn how to "Unblock" files. Right click on the zip file before unzipping, choose properties, go to the general tab and click the unblock button. http://msdn.microsoft.com/en-us/library...XPath Visualizer: XPathVisualizer v1.3 Latest: This is v1.3.0.6 of XpathVisualizer. This is an update release for v1.3. These workitems have been fixed since v1.3.0.5: 7429 7432 7427MSBuild Extension Pack: November 2011: Release Blog Post The MSBuild Extension Pack November 2011 release provides a collection of over 415 MSBuild tasks. A high level summary of what the tasks currently cover includes the following: System Items: Active Directory, Certificates, COM+, Console, Date and Time, Drives, Environment Variables, Event Logs, Files and Folders, FTP, GAC, Network, Performance Counters, Registry, Services, Sound Code: Assemblies, AsyncExec, CAB Files, Code Signing, DynamicExecute, File Detokenisation, GU...CODE Framework: 4.0.11110.0: Various minor fixes and tweaks.Extensions for Reactive Extensions (Rxx): Rxx 1.2: What's NewRelated Work Items Please read the latest release notes for details about what's new. Content SummaryRxx provides the following features. See the Documentation for details. Many IObservable<T> extension methods and IEnumerable<T> extension methods. Many useful types such as ViewModel, CommandSubject, ListSubject, DictionarySubject, ObservableDynamicObject, Either<TLeft, TRight>, Maybe<T> and others. Various interactive labs that illustrate the runtime behavior of the extensio...Player Framework by Microsoft: HTML5 Player Framework 1.0: Additional DownloadsHTML5 Player Framework Examples - This is a set of examples showing how to setup and initialize the HTML5 Player Framework. This includes examples of how to use the Player Framework with both the HTML5 video tag and Silverlight player. Note: Be sure to unblock the zip file before using. Note: In order to test Silverlight fallback in the included sample app, you need to run the html and xap files over http (e.g. over localhost). Silverlight Players - Visit the Silverlig...MapWindow 4: MapWindow GIS v4.8.6 - Final release - 64Bit: What’s New in 4.8.6 (Final release)A few minor issues have been fixed What’s New in 4.8.5 (Beta release)Assign projection tool. (Sergei Leschinsky) Projection dialects. (Sergei Leschinsky) Projections database converted to SQLite format. (Sergei Leschinsky) Basic code for database support - will be developed further (ShapefileDataClient class, IDataProvider interface). (Sergei Leschinsky) 'Export shapefile to database' tool. (Sergei Leschinsky) Made the GEOS library static. geos.dl...Facebook C# SDK: v5.3.2: This is a RTW release which adds new features and bug fixes to v5.2.1. Query/QueryAsync methods uses graph api instead of legacy rest api. removed dependency from Code Contracts enabled Task Parallel Support in .NET 4.0+ (experimental) added support for early preview for .NET 4.5 (binaries not distributed in codeplex nor nuget.org, will need to manually build from Facebook-Net45.sln) added additional method overloads for .NET 4.5 to support IProgress<T> for upload progress added ne...Delete Inactive TS Ports: List and delete the Inactive TS Ports: UPDATEAdded support for windows 2003 servers and removed some null reference errors when the registry key was not present List and delete the Inactive TS Ports - The InactiveTSPortList.EXE accepts command line arguments The InactiveTSPortList.Standalone.WithoutPrompt.exe runs as a standalone exe without the need for any command line arguments.ClosedXML - The easy way to OpenXML: ClosedXML 0.60.0: Added almost full support for auto filters (missing custom date filters). See examples Filter Values, Custom Filters Fixed issues 7016, 7391, 7388, 7389, 7198, 7196, 7194, 7186, 7067, 7115, 7144Microsoft Research Boogie: Nightly builds: This download category contains automatically released nightly builds, reflecting the current state of Boogie's development. We try to make sure each nightly build passes the test suite. If you suspect that was not the case, please try the previous nightly build to see if that really is the problem. Also, please see the installation instructions.New ProjectsAwpAdmin: AwpAdmin (name tentative) is a powerful BF3 admin tool. This admin tool is being designed to be easy to setup and maintain while having a great deal of customizability and power. This is written in C# and is being designed to be Mono-compatible.ChainReaction.Net: Extension library that aims to allow method chains to be attached to code statements, allowing them to be read more fluently , allowing extra logic to effectively be bolted on in a fluid wayCodeigniter SQL Azure/SQL Server Unicode supported driver.: Codeigniter?SQL Server 2005/2008 SQLAzure????????????。 NPrefix???????、Unicode???(??????)?????????????。 Active record / ???????????、N?????????????????????????。 ???????????????、N???????????????????、?????????N???????????????????。 ????????????????。 See http://msdn.microsoft.com/ja-jp/library/ms191313.aspx ??、????????????????????。 (Codeplex?Apache????????????、Ellislab license????????。) --- SQL Server 2005 / 2008 / SQL Azure driver class for Codeigniter. this driver N prefix uni...EZ-NFC: EZ-NFC is a .NET library, written in C#, aimed at simplifying the use of NFC in applications.Farigola: A library to organize and manage dynamic data. It's developed in C#/.NET language. Forca_adrikei: Forca implementada para o curso de C# da ufscar sorocaba.GemTD: Gem Tower Defense starcraft 2 map simulator implemented via C# and XNA. Components needed: Microsoft .NET Framework 4 Microsoft XNAGSISWebServiceMVC: This is a sample of an MVC application using the Greek GSIS Web Service at http://www.gsis.gr/wsnp.html (in Greek).Ini4Net: Ini4Net is a simple INI class for parsing INI files in your application. There are many INI solutions available around but non of them met my simplicity so I rolled-my-own. I have been using this since 2008 in several applications that are being used in the enterprise.Jogo da Memória: Projeto de C# - Jogo da memóriaJson DataContract Builder - Create JsonAPI SDK from samples & xmls: Yeah, you can access json with dynamic & Json.Net. But why can't we have the old static way? Is there no one miss the happiness of working with intellisense? There must be a easy way.K-Vizinhos: K-VizinhosLie to Me Windows Phone 7 App: Lie to me - application on WindowsPhone7 platform for testing face expressions, basend on popular serial "Lie To Me".Localization Project: Localization project is C# library to simplify localizing .NET applications and websites. Primary purpose of this project is support instant language switching on the fly.Luminji.wp: luminji's melearning windows phone soft.mergemania - pdf merging .net library based on iTextSharp: Merge PDF documents from different source documents into several destination documents. Set the page ranges to merge from and the page ranges to merge into. Everything is configured via an single XML file. You access all elements through strongly typed classes generated from XSD.Metro Pandora: Metro Pandora aims to ship a Pandora SDK and apps for XAML .net platforms.MiniState: MiniState is an attempt to provide simple abstraction layer to reading and writing state information like HTTP cookies to minimise cookie size and increase the quality of code and security.Mocklet: Mocklet is a suite of PowerShell cmdlets designed to help system administrator generate sample or mock data for testing or building test environments.nethelper: silverlight extend libraryNeverForget: NeverForget is a simple KB projectOpenCV examples: Sample project for interprocess image sharing. Using OpenCV and Boost. Server : Capture image from webcam and write image to shared memory region. Client : Read image from shared memory and imshow the image.Portable Class Libraries Contrib: Portable Class Libraries Contrib provides portable adapters and APIs that help bridge the gap between different platforms when using the new Portable Class Library feature. This makes it easier to convert existing platform-specific libraries over to use portable APIs.sbfa: sbfaShortcut Manager: Shortcut Manager (SM) is solution for everyone who is looking for creating keyboard shortcuts in .NET Winforms applications. SM uses Win32 API to create keyboard hook and fires supplied handler after required shortcut is pressed.SIGEMdispro: Proyecto de un curso de la universidadSimpleMvcCaptcha: Captcha HtmlHelper for ASP.NET MVC 3 with simple ariphmetic expression. No WCF required, neither any other communications. Written in C#.Traveling salesman problem solver using google maps: This application provides a solution for the traveling salesman problem using Google maps, developed in C# and ASP.net.WebPALTT: A Web performance and load test tool for testing web sites / applications. Features include easy to use scenario builder and powerful scripting for high customisability. Developed in .Net C#.WriteMyName: Código para escrever o nome do autor no começo de código fonte.zenSQLcompare: Compare SQL

    Read the article

  • Python bindings for C++ code using OpenCV giving segmentation fault

    - by lightalchemist
    I'm trying to write a python wrapper for some C++ code that make use of OpenCV but I'm having difficulties returning the result, which is a OpenCV C++ Mat object, to the python interpreter. I've looked at OpenCV's source and found the file cv2.cpp which has conversions functions to perform conversions to and fro between PyObject* and OpenCV's Mat. I made use of those conversions functions but got a segmentation fault when I tried to use them. I basically need some suggestions/sample code/online references on how to interface python and C++ code that make use of OpenCV, specifically with the ability to return OpenCV's C++ Mat to the python interpreter or perhaps suggestions on how/where to start investigating the cause of the segmentation fault. Currently I'm using Boost Python to wrap the code. Thanks in advance to any replies. The relevant code: // This is the function that is giving the segmentation fault. PyObject* ABC::doSomething(PyObject* image) { Mat m; pyopencv_to(image, m); // This line gives segmentation fault. // Some code to create cppObj from CPP library that uses OpenCV cv::Mat processedImage = cppObj->align(m); return pyopencv_from(processedImage); } The conversion functions taken from OpenCV's source follows. The conversion code gives segmentation fault at the commented line with "if (!PyArray_Check(o)) ...". static int pyopencv_to(const PyObject* o, Mat& m, const char* name = "<unknown>", bool allowND=true) { if(!o || o == Py_None) { if( !m.data ) m.allocator = &g_numpyAllocator; return true; } if( !PyArray_Check(o) ) // Segmentation fault inside PyArray_Check(o) { failmsg("%s is not a numpy array", name); return false; } int typenum = PyArray_TYPE(o); int type = typenum == NPY_UBYTE ? CV_8U : typenum == NPY_BYTE ? CV_8S : typenum == NPY_USHORT ? CV_16U : typenum == NPY_SHORT ? CV_16S : typenum == NPY_INT || typenum == NPY_LONG ? CV_32S : typenum == NPY_FLOAT ? CV_32F : typenum == NPY_DOUBLE ? CV_64F : -1; if( type < 0 ) { failmsg("%s data type = %d is not supported", name, typenum); return false; } int ndims = PyArray_NDIM(o); if(ndims >= CV_MAX_DIM) { failmsg("%s dimensionality (=%d) is too high", name, ndims); return false; } int size[CV_MAX_DIM+1]; size_t step[CV_MAX_DIM+1], elemsize = CV_ELEM_SIZE1(type); const npy_intp* _sizes = PyArray_DIMS(o); const npy_intp* _strides = PyArray_STRIDES(o); bool transposed = false; for(int i = 0; i < ndims; i++) { size[i] = (int)_sizes[i]; step[i] = (size_t)_strides[i]; } if( ndims == 0 || step[ndims-1] > elemsize ) { size[ndims] = 1; step[ndims] = elemsize; ndims++; } if( ndims >= 2 && step[0] < step[1] ) { std::swap(size[0], size[1]); std::swap(step[0], step[1]); transposed = true; } if( ndims == 3 && size[2] <= CV_CN_MAX && step[1] == elemsize*size[2] ) { ndims--; type |= CV_MAKETYPE(0, size[2]); } if( ndims > 2 && !allowND ) { failmsg("%s has more than 2 dimensions", name); return false; } m = Mat(ndims, size, type, PyArray_DATA(o), step); if( m.data ) { m.refcount = refcountFromPyObject(o); m.addref(); // protect the original numpy array from deallocation // (since Mat destructor will decrement the reference counter) }; m.allocator = &g_numpyAllocator; if( transposed ) { Mat tmp; tmp.allocator = &g_numpyAllocator; transpose(m, tmp); m = tmp; } return true; } static PyObject* pyopencv_from(const Mat& m) { if( !m.data ) Py_RETURN_NONE; Mat temp, *p = (Mat*)&m; if(!p->refcount || p->allocator != &g_numpyAllocator) { temp.allocator = &g_numpyAllocator; m.copyTo(temp); p = &temp; } p->addref(); return pyObjectFromRefcount(p->refcount); } My python test program: import pysomemodule # My python wrapped library. import cv2 def main(): myobj = pysomemodule.ABC("faces.train") # Create python object. This works. image = cv2.imread('61.jpg') processedImage = myobj.doSomething(image) cv2.imshow("test", processedImage) cv2.waitKey() if __name__ == "__main__": main()

    Read the article

  • creating a color coded time chart using colorbar and colormaps in python

    - by Rusty
    I'm trying to make a time tracking chart based on a daily time tracking file that I used. I wrote code that crawls through my files and generates a few lists. endTimes is a list of times that a particular activity ends in minutes going from 0 at midnight the first day of the month to however many minutes are in a month. labels is a list of labels for the times listed in endTimes. It is one shorter than endtimes since the trackers don't have any data about before 0 minute. Most labels are repeats. categories contains every unique value of labels in order of how well I regard that time. I want to create a colorbar or a stack of colorbars (1 for eachday) that will depict how I spend my time for a month and put a color associated with each label. Each value in categories will have a color associated. More blue for more good. More red for more bad. It is already in order for the jet colormap to be right, but I need to get desecrate color values evenly spaced out for each value in categories. Then I figure the next step would be to convert that to a listed colormap to use for the colorbar based on how the labels associated with the categories. I think this is the right way to do it, but I am not sure. I am not sure how to associate the labels with color values. Here is the last part of my code so far. I found one function to make a discrete colormaps. It does, but it isn't what I am looking for and I am not sure what is happening. Thanks for the help! # now I need to develop the graph import numpy as np from matplotlib import pyplot,mpl import matplotlib from scipy import interpolate from scipy import * def contains(thelist,name): # checks if the current list of categories contains the one just read for val in thelist: if val == name: return True return False def getCategories(lastFile): ''' must determine the colors to use I would like to make a gradient so that the better the task, the closer to blue bad labels will recieve colors closer to blue read the last file given for the information on how I feel the order should be then just keep them in the order of how good they are in the tracker use a color range and develop discrete values for each category by evenly spacing them out any time not found should assume to be sleep sleep should be white ''' tracker = open(lastFile+'.txt') # open the last file # find all the categories categories = [] for line in tracker: pos = line.find(':') # does it have a : or a ? if pos==-1: pos=line.find('?') if pos != -1: # ignore if no : or ? name = line[0:pos].strip() # split at the : or ? if contains(categories,name)==False: # if the category is new categories.append(name) # make a new one return categories # find good values in order of last day newlabels=[] for val in getCategories(lastDay): if contains(labels,val): newlabels.append(val) categories=newlabels # convert discrete colormap to listed colormap python for ii,val in enumerate(labels): if contains(categories,val)==False: labels[ii]='sleep' # create a figure fig = pyplot.figure() axes = [] for x in range(endTimes[-1]%(24*60)): ax = fig.add_axes([0.05, 0.65, 0.9, 0.15]) axes.append(ax) # figure out the colors to use # stole this function to make a discrete colormap # http://www.scipy.org/Cookbook/Matplotlib/ColormapTransformations def cmap_discretize(cmap, N): """Return a discrete colormap from the continuous colormap cmap. cmap: colormap instance, eg. cm.jet. N: Number of colors. Example x = resize(arange(100), (5,100)) djet = cmap_discretize(cm.jet, 5) imshow(x, cmap=djet) """ cdict = cmap._segmentdata.copy() # N colors colors_i = np.linspace(0,1.,N) # N+1 indices indices = np.linspace(0,1.,N+1) for key in ('red','green','blue'): # Find the N colors D = np.array(cdict[key]) I = interpolate.interp1d(D[:,0], D[:,1]) colors = I(colors_i) # Place these colors at the correct indices. A = zeros((N+1,3), float) A[:,0] = indices A[1:,1] = colors A[:-1,2] = colors # Create a tuple for the dictionary. L = [] for l in A: L.append(tuple(l)) cdict[key] = tuple(L) # Return colormap object. return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024) # jet colormap goes from blue to red (good to bad) cmap = cmap_discretize(mpl.cm.jet, len(categories)) cmap.set_over('0.25') cmap.set_under('0.75') #norm = mpl.colors.Normalize(endTimes,cmap.N) print endTimes print labels # make a color list by matching labels to a picture #norm = mpl.colors.ListedColormap(colorList) cb1 = mpl.colorbar.ColorbarBase(axes[0],cmap=cmap ,orientation='horizontal' ,boundaries=endTimes ,ticks=endTimes ,spacing='proportional') pyplot.show()

    Read the article

< Previous Page | 1 2