CODEWITHSUNDEEP
opencvimage-processingcomputer-visionedge-detection
Vedanshu
Vedanshu

Reputation: 2296

Getting edge of image at different level

I want to draw the edges of tomatoes in crate. For this I'm using Canny edge detection algorithm. The problem with it is that in a crate some of the tomatoes are over the top of the other as shown in the figure bellow:

org

As one can see some of the tomatoes have more light over them than the other, due to this the result by Canny is as follows:

canny

As one can see Canny is not able to detect the edges of the tomatoes at the end of the crate where the light is low in the original image.

Does anyone have any solution to this ? (It's not possible to get uniform light on all the tomatoes, but if there is some algorithm that can do that would be really helpful.)

Here is my OpenCV code:

#include <opencv2/opencv.hpp>

using namespace cv;

Mat img, img_gray, img_value, fz, fg, I, pre, cost, vis, G, img_draw, in_que, skip, img_canny;

int main(){
    std::string filepath = "/Users/vedanshu/Desktop/IMG_5207.JPG";
    img = imread(filepath);
    cvtColor(img, img_gray, cv::COLOR_BGRA2GRAY);
    img_gray.copyTo(img_value);
    GaussianBlur(img_value, img_value, Size(3, 3), 0, 0, BORDER_DEFAULT);
    Canny(img_gray, img_canny, 50, 50);
    imwrite( "/Users/vedanshu/Desktop/test_canny.png", img_canny );
}

Upvotes: 0

Views: 812

Answers (1)

Vardan Agarwal
Vardan Agarwal

Reputation: 2181

I am answering in python, it can be easily converted to C++. Histogram equalization and gamma function can be used to increase the contrast of image in grayscale. 

img = cv2.imread('edge_tomatoes.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('original', gray)
denoise = cv2.fastNlMeansDenoising(gray,None,10,7,21)
hist = cv2.equalizeHist(denoise)
gamma = 2
invGamma = 1/gamma
table = np.array([((i / 255.0) ** invGamma) * 255
                  for i in np.arange(0, 256)]).astype("uint8")
gamm = cv2.LUT(hist, table, hist)
blur = cv2.GaussianBlur(gamm,(3,3),0)
cv2.imshow('modified', hist)
cv2.waitKey(0)
cv2.destroyAllWindows()

Contrast enhanced This image shows the difference between the original grayscale image and processed image.

mask = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
canny = cv2.Canny(mask, 40, 200)
cv2.imshow('result', canny)
cv2.waitKey(0)
cv2.destroyAllWindows()

Result

Upvotes: 3

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