For my college project I need to identify a species of a plant from plant leaf shape by detecting edges of a leaf. (I use OpenCV 2.4.9 and C++), but the source image has taken in the real environment of the plant and has more than one leaf. See the below example image. So here I need to extract the edge pattern of just one leaf to process further.
Using Canny Edge Detector I can identify edges of the whole image.
But I don't know how to proceed from here to extract edge pattern of just one leaf, may be more clear and complete leaf. I don't know even if this is possible also. Can anyone please tell me if this is possible how to extract edges of one leaf I just want to know the image peocessing steps that I need to apply to the image. I don't want any code samples. I'm new to image processing and OpenCV and learning by doing experiments.
Thanks in advance.
Edit
As Luis said said I have done Morphological close to the image after doing edge detection using Canny edge detection, but it seems still it is difficult me to find the largest contour from the image. Here are the steps I have taken to process the image
Apply Bilateral Filter to reduce noise
bilateralFilter(img_src, img_blur, 31, 31 * 2, 31 / 2);
Adjust contrast by histogram equaliztion
cvtColor(img_blur,img_equalized,CV_BGR2GRAY);
Apply Canny edge detector
Canny(img_equalized, img_edge_detected, 20, 60, 3);
Threshold binary image to remove some background data
threshold(img_edge_detected, img_threshold, 1, 255,THRESH_BINARY_INV);
Morphological close of the image
morphologyEx(img_threshold, img_closed, MORPH_CLOSE, getStructuringElement(MORPH_ELLIPSE, Size(2, 2)));
Following are the resulting images I'm getting.
This result I'm getting for the above original image
Source image and result for second image
Source :
Result :
Is there any way to detect the largest contour and extract it from the image ?
Note that my final target is to create a plant identification system using real environmental image, but here I cannot use template matching or masking kind of things because the user has to take an image and upload it so the system doesn't have any prior idea about the leaf.
Here is the full code
#include <opencv\cv.h>
#include <opencv\highgui.h>
using namespace cv;
int main()
{
Mat img_src, img_blur,img_gray,img_equalized,img_edge_detected,img_threshold,img_closed;
//Load original image
img_src = imread("E:\\IMAG0196.jpg");
//Apply Bilateral Filter to reduce noise
bilateralFilter(img_src, img_blur, 31, 31 * 2, 31 / 2);
//Adjust contrast by histogram equaliztion
cvtColor(img_blur,img_equalized,CV_BGR2GRAY);
//Apply Canny edge detector
Canny(img_equalized, img_edge_detected, 20, 60, 3);
//Threshold binary image to remove some background data
threshold(img_edge_detected, img_threshold, 15, 255,THRESH_BINARY_INV);
//Morphological close of the image
morphologyEx(img_threshold, img_closed, MORPH_CLOSE, getStructuringElement(MORPH_ELLIPSE, Size(2, 2)));
imshow("Result", img_closed);
waitKey(0);
return 0;
}
Thank you.
The most commonly used discontinuity based edge detection techniques are reviewed in this section. Those techniques are Roberts edge detection, Sobel Edge Detection, Prewitt edge detection, Kirsh edge detection, Robinson edge detection, Marr-Hildreth edge detection, LoG edge detection and Canny Edge Detection.
It uses a filter based on the derivative of a Gaussian in order to compute the intensity of the gradients. The Gaussian reduces the effect of noise present in the image. Then, potential edges are thinned down to 1-pixel curves by removing non-maximum pixels of the gradient magnitude.
Some of the commonly known edge detection methods are: Laplacian Operator or Laplacian Based Edge detection (Second order derivative) Canny edge detector (First order derivative) Prewitt operator (First order derivative) Sobel Operator (First order derivative)
Well there is a similar question that was asked here:
It seems that edge information is not a good descriptor for the image, still if you want to try it I'll do the following steps:
I believe that given images are taken in the real world these algorithm will perform poorly but it's a start. Well hope it helps :).
-- POST EDIT 06/07
Well since you have no prior information about the leaf, I think the best we could do is the following:
If you do those steps, you'll end up with images like these:
I won't post the code here, but you can check it out in my messy github. I hope you don't mind it was made in python.
Leaf - Github
Still, I have a couple of things to finish that could improve the result.. Roadmap would be:
Well, again, I hope it helps
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