Я пытаюсь измерить площадь земельного участка с помощью OpenCV в Python

Question

До сих пор я был в состоянии выполнить medianBlur и Edge Detection, теперь я хочу o дополнительно удалить шум из изображения, функция свойств области Matlabs ** использовалась ** для удаления всех белых областей, которые имели общую площадь пикселей меньше, чем среднее значение площади пикселя. Как я могу реализовать это на python

import matplotlib.image as mpimg
import numpy as np
import cv2
import os
import math
from collections import defaultdict
from  matplotlib  import pyplot as plt
import imutils
#import generalized_hough

gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
print(gray.shape)
blur = cv2.bilateralFilter(gray,9,75,75) 
median = cv2.medianBlur(gray,5) 
# display input and output image    
titles = ["bilateral Smoothing","median bulr"]
images = [ blur, median]
plt.figure(figsize=(20, 20))
for i in range(2):
    plt.subplot(1,2,i+1)
    plt.imshow(images[i])
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])

plt.show()

sobelX = cv2.Sobel(median,cv2.cv2.CV_64F, 1, 0)
sobelY = cv2.Sobel(median,cv2.cv2.CV_64F, 0,1)
sobelX = np.uint8(np.absolute(sobelX))
sobelY = np.uint8(np.absolute(sobelY))
SobelCombined = cv2.bitwise_or(sobelX,sobelY)
cv2.imshow('img', SobelCombined)
cv2.waitKey(0)
cv2.destroyAllWindows()

Вот код Matlab, который работает для той же задачи.

close all
%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