# Gradient And Edge Detection With OpenCV #### Edge Detection With Canny

1- RGB to Gray

2- Noise Removal (Gaussian Filter)

3- Edge Detection (Sobel Operator)

4-Edge Thinning ( Non Maximal Supression )

5- Connect Week Edges (Hysteresis Thresholding) (Double Threshold)

``````Mat src, src_gray;
Mat dst, detected_edges;
int lowThreshold = 0;
const int max_lowThreshold = 100;
const int ratio = 3;
const int kernel_size = 3;
const char* window_name = "Edge Map";
static void CannyThreshold(int, void*)
{
blur( src_gray, detected_edges, Size(3,3) );
Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size );
dst = Scalar::all(0);
src.copyTo( dst, detected_edges);
imshow( window_name, dst );
}
``````

#### Gradient With Sobel , Scharr and Laplacian

OpenCV provides three types of gradient filters or High-pass filters Or Sharpening Filter Sobel, Scharr and Laplacian. We will see each one of them.

1. Sobel and Scharr Derivatives
Sobel operators is a joint Gausssian smoothing plus differentiation operation, so it is more resistant to noise. You can specify the direction of derivatives to be taken, vertical or horizontal (by the arguments, yorder and xorder respectively). You can also specify the size of kernel by the argument ksize. If ksize = -1, a 3×3 Scharr filter is used which gives better results than 3×3 Sobel filter. Please see the docs for kernels used

2- Laplacian
It calculates the Laplacian of the image given by the relation

``````img = cv.imread('dave.jpg',0)
laplacian = cv.Laplacian(img,cv.CV_64F)
sobelx = cv.Sobel(img,cv.CV_64F,1,0,ksize=5)
sobely = cv.Sobel(img,cv.CV_64F,0,1,ksize=5)``````

If you want to detect both edges, better option is to keep the output datatype to some higher forms, like cv.CV_16S, cv.CV_64F etc, take its absolute value and then convert back to cv.CV_8U. Below code demonstrates this procedure for a horizontal Sobel filter and difference in results.

``````
# Output dtype = cv.CV_8U
sobelx8u = cv.Sobel(img,cv.CV_8U,1,0,ksize=5)
# Output dtype = cv.CV_64F. Then take its absolute and convert to cv.CV_8U
sobelx64f = cv.Sobel(img,cv.CV_64F,1,0,ksize=5)
abs_sobel64f = np.absolute(sobelx64f)
sobel_8u = np.uint8(abs_sobel64f)``````