To understand edge detection in more detail consider the following one dimensional whose edge needs to be find out :
    [3,10,1,21,23,178,198,205,165,195]

Consider these values to be a part of 2 dimensional image where these values represents the pixel value.

In the above array the value after 4th index or 4th pixel the value changes sharply. Due to big change in pixel value there will be a
huge change in either color shading or brightness or surface orientation of objects in image or material properties or 
scene illumination in image etc. 

Thus, edges gives very useful information of objects, scenes, surface orientation , brightness material properties of an image.

There are number of edge detection algorithms. In this blog we will take in consider canny edge detection algorithm.
This is most popular and commonly used edge detection algorithm. 

Ia a python code first step is to read the image file in to a variable.

import cv2
import numpy as np
from matplotlib import pyplot as plt

cameraman_image = cv2.imread('C:\\My\\Video\\standard_test_images\\standard_test_images\\cameraman.tif',0)

detected_edge_edge = cv2.Canny(cameraman_image,100,200)

plt.subplot(121),plt.imshow(cameraman_image,cmap = 'gray')
plt.title('Cameraman Image : Original'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(detected_edge_edge,cmap = 'gray')
plt.title('Image with Edges Detected'), plt.xticks([]), plt.yticks([])
plt.show()