bzr branch
http://suren.me/webbzr/normxcorr/trunk
1
by Suren A. Chilingaryan
Initial import |
1 |
% written by Chris
|
2 |
||
3 |
function [validx, validy]=pickpeak; |
|
4 |
||
5 |
clear all |
|
6 |
||
7 |
[Image,PathImage] = uigetfile('*.tif','Open Image'); |
|
8 |
cd(PathImage); |
|
9 |
load('filenamelist'); |
|
10 |
filenumber=size(filenamelist); |
|
11 |
filenumber=filenumber(1); |
|
12 |
||
13 |
% filelist_generator
|
|
14 |
||
15 |
I=imread(Image); % read Image |
|
16 |
Itemp=mean(double(I),3); |
|
17 |
I=Itemp; |
|
18 |
[Isizey, Isizex]=size(I); |
|
19 |
||
20 |
prompt = {'How many peaks do you want to follow?'}; |
|
21 |
dlg_title = 'Manual peak picking'; |
|
22 |
num_lines= 1; |
|
23 |
def = {'2'}; |
|
24 |
answer = inputdlg(prompt,dlg_title,num_lines,def); |
|
25 |
numberofpeaks= str2num(cell2mat(answer(1,1))); |
|
26 |
||
27 |
figure, imshow(uint8(I)); %show Image |
|
28 |
axis('equal'); |
|
29 |
drawnow
|
|
30 |
title(sprintf('Mark the region of interest: Click on the on the lower left corner and and then on the upper right corner')); |
|
31 |
hold on |
|
32 |
cropxy(1,1:7)=0; |
|
33 |
||
34 |
for i=1:numberofpeaks; |
|
35 |
||
36 |
[xprof, yprof]=ginput(2); % Get the Area of Interest |
|
37 |
cropxy(i,1) = i; |
|
38 |
cropxy(i,2) = (round(xprof(2,1)-xprof(1,1))/2)+xprof(1,1); |
|
39 |
cropxy(i,3) = (round(yprof(1,1)-yprof(2,1))/2)+yprof(2,1); |
|
40 |
cropxy(i,4) = xprof(1,1); |
|
41 |
cropxy(i,5) = yprof(2,1); |
|
42 |
cropxy(i,6) = round((xprof(2,1)-xprof(1,1))/2); |
|
43 |
cropxy(i,7) = round((yprof(1,1)-yprof(2,1))/2); |
|
44 |
% xmin = xprof(1,1);
|
|
45 |
% xmax = xprof(2,1);
|
|
46 |
% ymin = yprof(2,1);
|
|
47 |
% ymax= yprof(1,1);
|
|
48 |
||
49 |
plot(cropxy(i,2),cropxy(i,3),'o'); |
|
50 |
drawnow; |
|
51 |
||
52 |
end
|
|
53 |
||
54 |
tic; % start timer for time estimation |
|
55 |
% I2 = imsubtract (I, imopen(I,strel('disk',15))); % subtract background
|
|
56 |
I2=I; |
|
57 |
% [Isizey, Isizex]=size(I2);
|
|
58 |
% image(I2); %show with subtracted background5
|
|
59 |
% axis('equal');
|
|
60 |
t(1,1)=toc; |
|
61 |
tic; |
|
62 |
||
63 |
close all |
|
64 |
||
65 |
||
66 |
% Start fitting process of the peaks, labeled by bwlabel
|
|
67 |
||
68 |
tic; % start timer |
|
69 |
counter=0; |
|
70 |
g = waitbar(0,'Processing image'); % nucleating the progress bar |
|
71 |
fitcountertemp=size(cropxy); % number off peaks to cycle through |
|
72 |
fitcounter=fitcountertemp(1,1); % number off peaks to cycle through |
|
73 |
for c=1:fitcounter %start the loop to process all points |
|
74 |
waitbar(c/(fitcounter-1)); % growth of the progress bar |
|
75 |
cropI=imcrop(I2,[round(cropxy(c,4)) round(cropxy(c,5)) round(cropxy(c,6))*2 round(cropxy(c,7))*2]); % crop the region around the detected peak (bwlabel) |
|
76 |
||
77 |
% get line profile in x direction for the fitting routine
|
|
78 |
||
79 |
xdata = [(round(cropxy(c,2))-round(cropxy(c,6))):1:(round(cropxy(c,2))+round(cropxy(c,6)))]; % x-coordinate for the fitting which is equivalent to the x coordinate in the image |
|
80 |
ydata=sum(cropI)/(2*cropxy(c,7)); % y-coordinate for the fitting which is equivalent to the greyvalues in the image - integrated in y direction of the image |
|
81 |
||
82 |
% fitting in x-direction
|
|
83 |
% guess some parameters for the fitting routine --> bad guesses lead to
|
|
84 |
% an error message which stops the fitting
|
|
85 |
||
86 |
back_guess=(ydata(1)+ydata(round(cropxy(c,6))*2))/2; % guess for the background level - average of the first and last greyvalue |
|
87 |
sig1_guess=(cropxy(c,6)*2)/5; % guess for the peak width - take a fith of the cropping width |
|
88 |
amp_guess1=ydata(round((length(ydata))/2))-back_guess; % guess for the amplitude - take the greyvalue at the peak position |
|
89 |
mu1_guess=cropxy(c,2); % guess for the position of the peak - take the position from bwlabel |
|
90 |
||
91 |
% start fitting routine
|
|
92 |
[x,resnormx,residual,exitflagx,output] = lsqcurvefit(@gauss_onepk, [amp_guess1 mu1_guess sig1_guess back_guess], xdata, ydata); %give the initial guesses and data to the gauss fitting routine |
|
93 |
||
94 |
% show the fitting results
|
|
95 |
xtest = [(round(cropxy(c,2))-round(cropxy(c,6))):0.1:(round(cropxy(c,2))+round(cropxy(c,6)))]; % x values for the plot of the fitting result |
|
96 |
ytest = (x(1)*exp((-(xtest-x(2)).^2)./(2.*x(3).^2))) + x(4); % y values of the fitting result |
|
97 |
yguess=(amp_guess1*exp((-(xtest-mu1_guess).^2)./(2.*sig1_guess.^2))) + back_guess; %y values for the guess plot |
|
98 |
plot(xdata,ydata,'o') % plot the experimental data |
|
99 |
hold on |
|
100 |
plot(xtest,ytest,'r') % plot the fitted function |
|
101 |
plot(xtest,yguess,'b') % plot the guessed function |
|
102 |
drawnow
|
|
103 |
hold off |
|
104 |
% pause
|
|
105 |
% fitting in y-direction
|
|
106 |
% guess parameters for the fitting routine --> bad guesses lead to
|
|
107 |
% an error message which stops the fitting
|
|
108 |
||
109 |
% get line profile in x direction for the fitting routine
|
|
110 |
||
111 |
xdata = [(round(cropxy(c,3))-round(cropxy(c,7))):1:(round(cropxy(c,3))+round(cropxy(c,7)))]; % x data in y direction of the image |
|
112 |
ydata=sum(cropI')/(2*cropxy(c,6)); % integrate greyvalues in x direction and normalize it to the number of integrated lines |
|
113 |
||
114 |
% fitting in y-direction
|
|
115 |
% guess parameters for the fitting routine --> bad guesses lead to
|
|
116 |
% an error message which stops the fitting
|
|
117 |
||
118 |
back_guess=(ydata(1)+ydata(round(cropxy(c,7))*2))/2; % guess for the background level - average of the first and last greyvalue |
|
119 |
sig1_guess=(cropxy(c,6)*2)/5; % guess for the peak width - take a fith of the cropping width |
|
120 |
amp_guess1=ydata(round((length(ydata))/2))-back_guess; % guess for the amplitude - take the greyvalue at the peak position |
|
121 |
mu1_guess=cropxy(c,3); % guess for the position of the peak - take the position from bwlabel |
|
122 |
||
123 |
% start fitting routine
|
|
124 |
[y,resnormy,residual,exitflagy,output] = lsqcurvefit(@gauss_onepk, [amp_guess1 mu1_guess sig1_guess back_guess], xdata, ydata); %give the initial guesses and data to the gauss fitting routine |
|
125 |
||
126 |
% show the fitting results
|
|
127 |
xtest = [(round(cropxy(c,3))-round(cropxy(c,7))):0.1:(round(cropxy(c,3))+round(cropxy(c,7)))]; % x values for the plot of the fitting result |
|
128 |
ytest = (y(1)*exp((-(xtest-y(2)).^2)./(2.*y(3).^2))) + y(4); % y values of the fitting result |
|
129 |
yguess=(amp_guess1*exp((-(xtest-mu1_guess).^2)./(2.*sig1_guess.^2))) + back_guess; %y values for the guess plot |
|
130 |
plot(xdata,ydata,'o') % plot the experimental data |
|
131 |
hold on |
|
132 |
plot(xtest,ytest,'g') % plot the fitted function |
|
133 |
plot(xtest,yguess,'b') % plot the guessed function |
|
134 |
drawnow
|
|
135 |
hold off |
|
136 |
% pause
|
|
137 |
% sort out the bad points and save the good ones in fitxy
|
|
138 |
% this matrix contains the to be used points from the first image
|
|
139 |
||
140 |
if exitflagx>0 % if the fitting routine didn't find end before the 4000th iteration (check that in lsqcurvefit.m) then exitflag will be equal or smaller then 0 |
|
141 |
cropxy(c,8)=1
|
|
142 |
if exitflagy>0 % the same for the y direction fitting
|
|
143 |
cropxy(c,8)=2
|
|
144 |
if x(3)>0.05 % the width of the peak should be wider than 1 pixel - this is negotiable: different powder particle or cameras can give back results with very narrow peaks
|
|
145 |
cropxy(c,8)=3
|
|
146 |
if y(3)>0.05 % the same for y direction fitting
|
|
147 |
cropxy(c,8)=4
|
|
148 |
if resnormx<5000 % A measure of the "goodness" of fit is the residual, the difference between the observed and predicted data. (in the help file: Mathematics: Data Analysis and Statistics: Analyzing Residuals)
|
|
149 |
cropxy(c,8)=5
|
|
150 |
if resnormy<5000 % the same for the y- direction - - - a good value is as far as I know until now between 30 and 50. The good fits stay well beyond that (between 0 and 10)
|
|
151 |
cropxy(c,8)=6
|
|
152 |
if (round(x(2))-round(cropxy(c,6)))>0
|
|
153 |
cropxy(c,8)=7
|
|
154 |
if (round(x(2))+round(cropxy(c,6)))<Isizex
|
|
155 |
cropxy(c,8)=8
|
|
156 |
if (round(y(2))-round(cropxy(c,7)))>0
|
|
157 |
cropxy(c,8)=9
|
|
158 |
if (round(y(2))+round(cropxy(c,7)))<Isizey
|
|
159 |
cropxy(c,8)=10
|
|
160 |
counter=counter+1;
|
|
161 |
fitxy(counter,1)=c; % points get their final number
|
|
162 |
fitxy(counter,2)=x(1); % fitted amplitude x-direction
|
|
163 |
fitxy(counter,3)=abs(x(2)); % fitted position of the peak x-direction
|
|
164 |
fitxy(counter,4)=abs(x(3)); % fitted peak width in x-direction
|
|
165 |
fitxy(counter,5)=(x(4)); % fitted background in x-direction
|
|
166 |
fitxy(counter,6)=y(1); % fitted amplitude y-direction
|
|
167 |
fitxy(counter,7)=abs(y(2)); % fitted position of the peak y-direction
|
|
168 |
fitxy(counter,8)=abs(y(3)); % fitted peak width in y-direction
|
|
169 |
fitxy(counter,9)=abs(y(4)); % fitted background in y-direction
|
|
170 |
fitxy(counter,10)=cropxy(c,6); % cropping width in x-direction
|
|
171 |
fitxy(counter,11)=cropxy(c,7); % cropping width in ydirection
|
|
172 |
end
|
|
173 |
end
|
|
174 |
end
|
|
175 |
end
|
|
176 |
end
|
|
177 |
end
|
|
178 |
end
|
|
179 |
end
|
|
180 |
end
|
|
181 |
end
|
|
182 |
|
|
183 |
end
|
|
184 |
||
185 |
||
186 |
close(g) % close progress bar window
|
|
187 |
t(1,3)=toc; % stop timer
|
|
188 |
image_time_s=t(1,3); % take time per image
|
|
189 |
estimated_totaltime_h=image_time_s*filenumber/3600; % calculate estimated time
|
|
190 |
sum(t);
|
|
191 |
total_time_h=sum(t);
|
|
192 |
close all
|
|
193 |
||
194 |
% plot image with peaks labeled by bwlabel (crosses) and the chosen points
|
|
195 |
% which are easy to fit with a gaussian distribution (circles)
|
|
196 |
||
197 |
figure, image(I2); %show Image
|
|
198 |
title(['Number of selected Images: ', num2str(filenumber), '; Estimated time [h] ', num2str((round(estimated_totaltime_h*10)/10)), ' Crosses are determined peaks, circles are chosen for the analysis. If you want to run the analysis hit ENTER']) |
|
199 |
axis('equal');
|
|
200 |
hold on;
|
|
201 |
plot(cropxy(:,2),cropxy(:,3),'+','Color','white') % peaks from bwlabel |
|
202 |
plot(fitxy(:,3),fitxy(:,7),'o','Color','white'); % "good" points |
|
203 |
drawnow
|
|
204 |
||
205 |
total_progress=1/filenumber; |
|
206 |
||
207 |
pause
|
|
208 |
||
209 |
close all |
|
210 |
fitlength=size(fitxy); |
|
211 |
fitcounter=fitlength(1,1) |
|
212 |
% again for all images
|
|
213 |
for m=1:(filenumber-1) % loop through all images |
|
214 |
tic; %start timer |
|
215 |
counter=0; |
|
216 |
f = waitbar(0,'Working on Image'); |
|
217 |
I = imread(filenamelist(m,:)); %read image |
|
218 |
Itemp=mean(double(I),3); |
|
219 |
I=Itemp; |
|
220 |
||
221 |
||
222 |
% loop number
|
|
223 |
for c=1:fitcounter %loop trough all points |
|
224 |
waitbar(c/(fitcounter-1)); %progress bar |
|
225 |
||
226 |
% load variables
|
|
227 |
pointnumber=fitxy(c,(m-1)*12+1); |
|
228 |
amp_guess_x=fitxy(c,(m-1)*12+2); |
|
229 |
mu_guess_x=fitxy(c,(m-1)*12+3); |
|
230 |
sig_guess_x=fitxy(c,(m-1)*12+4); |
|
231 |
back_guess_x=fitxy(c,(m-1)*12+5); |
|
232 |
amp_guess_y=fitxy(c,(m-1)*12+6); |
|
233 |
mu_guess_y=fitxy(c,(m-1)*12+7); |
|
234 |
sig_guess_y=fitxy(c,(m-1)*12+8); |
|
235 |
back_guess_y=fitxy(c,(m-1)*12+9); |
|
236 |
crop_x=fitxy(c,(m-1)*12+10); |
|
237 |
crop_y=fitxy(c,(m-1)*12+11); |
|
238 |
||
239 |
% crop the area around the point to fit
|
|
240 |
||
241 |
cropedI=imcrop(I,[(round(mu_guess_x)-round(crop_x)) (round(mu_guess_y)-round(crop_y)) 2*round(crop_x) 2*round(crop_y)]); |
|
242 |
% cropI=imsubtract (cropedI, imopen(cropedI,strel('disk',15))); % subtract background
|
|
243 |
cropI=cropedI;% imshow(cropI) |
|
244 |
% get line profile in x direction
|
|
245 |
xdatax = [(round(mu_guess_x)-round(crop_x)):1:(round(mu_guess_x)+round(crop_x))]; |
|
246 |
ydatax=sum(cropI)/(2*(crop_y)); |
|
247 |
xguessx = [(round(mu_guess_x)-round(crop_x)):0.1:(round(mu_guess_x)+round(crop_x))]; |
|
248 |
yguessx = (amp_guess_x*exp((-(xguessx-mu_guess_x).^2)./(2.*sig_guess_x.^2))) + back_guess_x; |
|
249 |
[x,resnormx,residualx,exitflagx,output] = lsqcurvefit(@gauss_onepk, [amp_guess_x mu_guess_x sig_guess_x back_guess_x], xdatax, ydatax); |
|
250 |
xtestx = [(round(mu_guess_x)-round(crop_x)):0.1:(round(mu_guess_x)+round(crop_x))]; |
|
251 |
ytestx = (x(1)*exp((-(xtestx-x(2)).^2)./(2.*x(3).^2))) + x(4); |
|
252 |
% plot(xdatax,ydatax,'o')
|
|
253 |
% hold on
|
|
254 |
% plot(xtestx,ytestx,'r')
|
|
255 |
% plot(xguessx,yguessx,'b')
|
|
256 |
% title(['Filename: ',filenamelist(m,:), '; Progress [%]: ',num2str((round(total_progress*10))/10), '; Tot. t [h] ', num2str((round(total_time_h*10)/10)), '; Est. t [h] ', num2str((round(estimated_totaltime_h*10)/10))])
|
|
257 |
% drawnow
|
|
258 |
% hold off
|
|
259 |
% pause
|
|
260 |
xdatay = [(round(mu_guess_y)-round(crop_y)):1:(round(mu_guess_y)+round(crop_y))]; |
|
261 |
ydatay=sum(cropI')/(2*(crop_y)); |
|
262 |
xguessy = [(round(mu_guess_y)-round(crop_y)):0.1:(round(mu_guess_y)+round(crop_y))]; |
|
263 |
yguessy = (amp_guess_y*exp((-(xguessy-mu_guess_y).^2)./(2.*sig_guess_y.^2))) + back_guess_y; |
|
264 |
[y,resnormy,residualy,exitflagy,output] = lsqcurvefit(@gauss_onepk, [amp_guess_y mu_guess_y sig_guess_y back_guess_y], xdatay, ydatay); |
|
265 |
xtesty = [(round(mu_guess_y)-round(crop_y)):0.1:(round(mu_guess_y)+round(crop_y))]; |
|
266 |
ytesty= (y(1)*exp((-(xtesty-y(2)).^2)./(2.*y(3).^2))) + y(4); |
|
267 |
% plot(xdatay,ydatay,'o')
|
|
268 |
% hold on
|
|
269 |
% plot(xtesty,ytesty,'g')
|
|
270 |
% plot(xguessy,yguessy,'b')
|
|
271 |
% title(['Filename: ',filenamelist(m,:), '; Progress [%]: ',num2str((round(total_progress*10))/10), '; Tot. t [h] ', num2str((round(total_time_h*10)/10)), '; Est. t [h] ', num2str((round(estimated_totaltime_h*10)/10))])
|
|
272 |
% drawnow
|
|
273 |
% hold off
|
|
274 |
% pause
|
|
275 |
||
276 |
if exitflagx>0 |
|
277 |
if exitflagy>0 |
|
278 |
counter=counter+1; |
|
279 |
fitxy(counter,m*12+1)=pointnumber; |
|
280 |
fitxy(counter,m*12+2)=abs(x(1)); |
|
281 |
fitxy(counter,m*12+3)=abs(x(2)); |
|
282 |
fitxy(counter,m*12+4)=abs(x(3)); |
|
283 |
fitxy(counter,m*12+5)=abs(x(4)); |
|
284 |
fitxy(counter,m*12+6)=abs(y(1)); |
|
285 |
fitxy(counter,m*12+7)=abs(y(2)); |
|
286 |
fitxy(counter,m*12+8)=abs(y(3)); |
|
287 |
fitxy(counter,m*12+9)=abs(y(4)); |
|
288 |
fitxy(counter,m*12+10)=crop_x; |
|
289 |
fitxy(counter,m*12+11)=crop_y; |
|
290 |
fitxy(counter,m*12+12)=resnormx; |
|
291 |
||
292 |
end
|
|
293 |
end
|
|
294 |
||
295 |
||
296 |
end
|
|
297 |
imshow(uint8((I))); %show Image |
|
298 |
title(['Filename: ',filenamelist(m,:), '; Progress [%]: ',num2str((round(total_progress*10))/10), '; Tot. t [h] ', num2str((round(total_time_h*10)/10)), '; Est. t [h] ', num2str((round(estimated_totaltime_h*10)/10))]) |
|
299 |
axis('equal'); |
|
300 |
hold on; |
|
301 |
plot(fitxy(:,m*12+3),fitxy(:,m*12+7),'o','Color','white'); % "good" points |
|
302 |
drawnow
|
|
303 |
||
304 |
total_progress=1/filenumber; |
|
305 |
||
306 |
% pause
|
|
307 |
hold off; |
|
308 |
||
309 |
% plot(fitxy(:,m*12+1),fitxy(:,m*12+12),'+');
|
|
310 |
% title(['Filename: ',filenamelist(m,:), '; Progress [%]: ',num2str((round(total_progress*10))/10), '; Tot. t [h] ', num2str((round(total_time_h*10)/10)), '; Est. t [h] ', num2str((round(estimated_totaltime_h*10)/10))])
|
|
311 |
fitcounter=counter; |
|
312 |
close(f); |
|
313 |
time(m)=toc; |
|
314 |
total_time_s=sum(time); |
|
315 |
total_time_h=sum(time)/3600; |
|
316 |
image_time_s=total_time_s/m; |
|
317 |
estimated_totaltime_h=image_time_s*(filenumber)/3600; |
|
318 |
progress_percent=total_time_h/estimated_totaltime_h*100; |
|
319 |
total_progress=(m+1)/(filenumber)*100; |
|
320 |
||
321 |
end
|
|
322 |
||
323 |
% save the stuff |
|
324 |
save fitxy.dat fitxy -ascii -tabs |
|
325 |
||
326 |
[validx,validy]=sortvalidpoints(fitxy); |
|
327 |
title(['Processing Images finished!']) |
|
328 |
||
329 |
save('validx'); |
|
330 |
save('validy'); |
|
331 |
||
332 |
save validx.dat validx -ascii -tabs; |
|
333 |
save validy.dat validy -ascii -tabs; |
|
334 |
||
335 |
close all |