Find biggest contour for shadow detection - matlab

Question

I have this problem in image processing and I couldn't find an algorithm to perform well under this condition.It's so simple to understand but I don't know how to implement it in ‍‍OpenCV or in Matlab, so any algorithm or function in one of them (MATLAB or opencv) is helpful.
1 . lets suppose that an image and background of scene is like the below
2 . We apply an edge detector to image and my current image will be like the below picture.
now my Problem is that how we can find the biggest contour or area like the below in edge image?
If you want original picture the original picture is
and in matlab you can get edge image by below codes.
clc
clear
img = imread('1.png'); % read Image
gray = rgb2gray(img); % Convert RGB to gray-scale
edgeImage = edge(gray,'canny',0.09); % apply canny to gray-scale image
imshow(edgeImage) % Display result in figure(MATLAB)
In OpenCV you can use below code
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{
Mat img = imread("1.png");
Mat gray;
cvtColor(img, //BGR form image
gray, //Mat for gray(destination)
CV_BGR2GRAY); //type of transform(in here BGR->GRay)
Mat edgeImage;
Canny(gray, //Input Array
edgeImage, //Output Array
40, // Lower threshold
120); //Upper threshold
namedWindow("Edge-Image"); //create a window for display image
imshow("Edge-Image",edgeImage); //Display edgeImage in window that in before line create
waitKey(0); //stick display window and wait for any key
return 0;
}

Answer 1

Here is a solution in Matlab using imdilate to close the contours and regionprops to get the closed objects area:
% Your code to start
img = imread('Image.png'); % read Image
gray = rgb2gray(img); % Convert RGB to gray-scale
edgeImage = edge(gray,'canny',0.09); % apply canny to gray-scale image
% First dilate to close contours
BW = imdilate(edgeImage, strel('disk',4,8));
% Then find the regions
R = regionprops(~BW, {'Area', 'PixelIdxList'});
% Find the second biggest region (the biggest is the background)
[~, I] = sort([R(:).Area], 'descend');
Mask = zeros(size(img));
Mask(R(I(2)).PixelIdxList) = 1;
% Display
clf
imshow(Mask)
An the result is:
Best,

Answer 2

First close the contour with morphological closing since you can't find it now as it is not really a distinct contour, but a part of the larger one.
After closing, just use the findContours() function and use its output to get the area of each contour and eventually find the maximum one by using the contourArea() function.