I am trying to measure land plot area Using OpenCV in Python

问题

SO far I have been able to perform medianBlur and Edge Detection, Now I want to o further remove noise from the image, Matlabs region **property functions ** was used to remove all white regions that had a total pixel area of less than the mean pixel area value. How can I implement this on 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()

Here is a Matlab code that works for the same task.

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

来源：https://stackoverflow.com/questions/61190681/i-am-trying-to-measure-land-plot-area-using-opencv-in-python