Can someone please convert the python code to matlab code??

import cv2
import numpy as np
w=cv2.imread('weed2',1)
cv2.imshow('image',w)
b,g,r = cv2.split(w)
height = np.size(w, 0)
width = np.size(w, 1)
bw = np.zeros((height,width))
  
for i in range(1,height):
    for j in range(1,width):
        if(b[i,j]<g[i,j] and r[i,j]<g[i,j] and g[i,j]>125):
            bw[i,j]=1

cv2.imshow('Black and White image',bw)
kernel = np.ones((5,5),np.uint8)
bw= cv2.erode(bw,kernel,iterations = 4)
cv2.imshow('Eroded image',bw)
bw = cv2.dilate(bw,kernel,iterations = 3)
cv2.imshow('Final Image',bw)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

[Walter Roberson]

w = imread('weed2');
imshow(w);
title('image');
r = w(:,:,1);
g = w(:,:,2);
b = w(:,:,3);
height = size(w,1);
width = size(w,2);
bw = b < g & r < g & g > 125;
figure()
imshow(bw);
title('Black and White image');
kernel = strel('square', 5);
for iteration = 1 : 4
  bw = imerode(bw, kernel);
end
figure()
imshow(bw);
title('Eroded image');
for iteration = 1  : 3
  bw = imdilate(bw, kernel);
end
figure()
imshow(bw);
title('Final Image);
pause;
close all

[krishna kripa]

Thank you very much.

Answer 2

[Okolie Chukwuka]

Can someone please convert the Matlab code to Python code??

%upload image of farm
figure,
farm = imread('small_farms.JPG');%change this to the file path of image
imshow(farm);%this shows the original image
%convert the image to grayscale for 2D manipulation
gfarm = rgb2gray(farm);
figure,
imshow(gfarm);%show grayscaled image

%median filters take a m*n area around a coordinate and
%find the median pixel value and set that coordinate to that
%pixel value. It's a method of removing noise or details in an
%image. may want to tune dimensions of filter. 
A = medfilt2(gfarm,[4 4]);
figure,
imshow(A);
%perform a logarithmic edge detection filter,
%this picks out the edges of the image, log setting
%was found to wrok best, although 'Sobel' can also be tried
B = edge(A,'log');
%show results of the edge filter
figure,
imshow(B,[]);

%find the areas of the lines made
areas = regionprops(B,'Area');
%find the mean and one standard deviation
men = mean([areas.Area])+0*std([areas.Area]);
%find max pixel area
big = max([areas.Area]);
%remove regions that are too small
C = bwpropfilt(B,'Area',[men big]);
%perform a dilation on the remaining pixels, this 
%helps fill in gaps. The size and shape of the dilation 
%can be tuned below.
SE = strel('square',4);
C = imdilate(C,SE);
areas2 = regionprops(C,'Area');


%place white border around image to find areas of farms
%that go off the picture
[h,w] = size(C);
C(1,:) = 1;
C(:,1) = 1;
C(h,:) = 1;
C(:,w) = 1;
C = C<1;
%fill in holes
C = imfill(C,'holes');
%show final processed image
figure,imshow(C);


%the section below is for display purpose
%it creates the boundaries of the image and displays them
%in a rainbow fashion
figure,
[B,L,n,A] = bwboundaries(C,'noholes');
imshow(label2rgb(L, @jet, [.5 .5 .5]))
hold on
for k = 1:length(B)
   boundary = B{k};
   plot(boundary(:,2), boundary(:,1), 'w', 'LineWidth', 2)
end


%The section below prints out the areas of each found 
%region by pixel values. These values need to be scaled 
%by the real measurements of the images to get relevant 
%metrics
centers = regionprops(C,'Centroid','Area');
for k=1:length(centers)
    if(centers(k).Area > mean([centers.Area])-std([areas.Area]))
    text(centers(k).Centroid(1),centers(k).Centroid(2),string(centers(k).Area));
    end
end

Answer 3

[Okolie Chukwuka]

How can I Implement this matlab function in python

BW2 = bwpropfilt(BW,attrib,range)

%For instance I have an image B, I want to extract the region that fall within
% a range of value.

C = bwpropfilt(B,'Area',[men big]);