From Image data to get estimation depth and diameter using matlab

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شرح پروژه

هزینه را براورد نمایید.

1)pre-processed
2)apply hough transform @ Faster-R-CNN @ Open-Scan Clustering Algorithm
3)hyperbolic fitting(model) @ Parabolic Fitting based Judgement
4)estimation of diameter and depth algorithm @ Restricted Algebraic Distance Based Fitting Algorithm
5)The radius and depth of the object
the requirements
data with me

so far this is what I can do
clc; close all; clear all;
input_dir='Experiment1\MALAGS';
filenames=dir(fullfile(input_dir,'*.rd3'));
num_img=numel(filenames);
for n=1:1 %num_img
filename=fullfile(input_dir,filenames(n).name);

[data,header]=readmala(filename);
imgdata=data;
img=mat2gray(imgdata);figure; imshow(img)
[row,col]=size(img);
img=imadjust(img);

J = histeq(img);
imshowpair(img,J,'montage')


se = strel('disk',5);
marker = imdilate(J,se);
%imshow(marker)

obr = imreconstruct(marker,J);
figure
imshow(obr,[])
imshowpair(marker,obr)

SaltandPepper= imnoise(marker,'salt & pepper',0.05);
%imshowpair(SaltandPepper,marker,'montage')

%DenoisingSaltandPepper= medfilt2(SaltandPepper);
%imshowpair(DenoisingSaltandPepper,SaltandPepper,'montage')

B = ordfilt2(SaltandPepper,9,true(5));
figure
imshow(B)

J = histeq(B);
figure, imshow(J);
BW1 = edge(J,'sobel');
BW2 = edge(J,'canny');
figure, imshow(BW2);


% h_rect=imrect();
% pos_rect=h_rect.getPosition();
% pos_rect=round(pos_rect);

% img_cropped=img(pos_rect(2)+(0:pos_rect(4)),pos_rect(1)+(0:pos_rect(3)));
% img_cropped=imcrop(BW2,[25 54 411 120]);
% h_rect=imrect();
pos_rect=[25 54 411 120];
pos_rect=round(pos_rect);
img_cropped=img(pos_rect(2)+(0:pos_rect(4)),pos_rect(1)+(0:pos_rect(3)));
figure;
imshow(img_cropped);

CN=edge(img_cropped,'canny',[0 0.3]);
imshow(CN)


BW_out = CN;

% Filter image based on image properties.
BW_out = bwpropfilt(BW_out, 'Area', [156, 243]);
BW_out = bwpropfilt(BW_out, 'MajorAxisLength', [0, 246]);
BW_out = bwpropfilt(BW_out, 'Eccentricity', [0, 0.97]);
imshow(BW_out)
% Get properties.
properties = regionprops(BW_out, {'Area', 'Eccentricity', 'EquivDiameter', 'EulerNumber', 'MajorAxisLength', 'MinorAxisLength', 'Orientation', 'Perimeter'});

% Uncomment the following line to return the properties in a table.
% properties = struct2table(properties);

%Hough Transform
% [H,T,R]=hough(BW_out);
% P=houghpeaks(H,5,'threshold',ceil(0.3*max(H(:))));
% x=T(P(:,2));
% y=R(P(:,1));
[H,theta,rho] = hough(BW_out);
% Find lines and plot them
% lines=houghlines(BW_out,T,R,P,'FillGap',5,'MinLength',7);
% figure
% imshow(BW_out), hold on
% max_len=0;
% for k=1:length(lines)
% xy=[lines(k).point1;lines(k).point2];
figure
imshow(imadjust(rescale(H)),[],...
'XData',theta,...
'YData',rho,...
'InitialMagnification','fit');
xlabel('\theta (degrees)')
ylabel('\rho')
axis on
axis normal
hold on
colormap(gca,hot)
% % Plot beginnings and ends of lines
% plot(xy(1,1),xy(1,2),'x','Linewidth',2,'Color','yellow');
% plot(xy(2,1),xy(2,2),'x','Linewidth',2,'Color','red');
P = houghpeaks(H,5,'threshold',ceil(0.3*max(H(:))));
x = theta(P(:,2));
y = rho(P(:,1));
plot(x,y,'s','color','black');
lines = houghlines(BW_out,theta,rho,P,'FillGap',5,'MinLength',7);
figure, imshow(img_cropped), hold on
max_len = 0;
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');

% Plot beginnings and ends of lines
plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');
plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');

% Determine the endpoints of the longest line segment
len = norm(lines(k).point1 + lines(k).point2);
if ( len > max_len)
max_len = len;
xy_long = xy;
end
end
% highlight the longest line segment
plot(xy_long(:,1),xy_long(:,2),'LineWidth',2,'Color','red');


end
I have 1021 data

got problem to detect Hyperbolic

این پروژه شامل 1 فایل مهم است، لطفا قبل از ارسال پیشنهاد حتما نسبت به بررسی این فایل اقدام فرمایید.

مهارت ها و تخصص های مورد نیاز

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