Update README.md

README.md

@@ -987,7 +987,105 @@ program 12:
 
 
 **
+ 27) Implement a program to perform various edge detection techniques 
+                a) Canny Edge detection
+                import cv2
+                import numpy as np 
+                import matplotlib.pyplot as plt 
+                plt.style.use('seaborn')
+                loaded_image = cv2.imread("shape1.jpeg") 
+                loaded_image = cv2.cvtColor(loaded_image,cv2.COLOR_BGR2RGB)
+                gray_image = cv2.cvtColor(loaded_image,cv2.COLOR_BGR2GRAY)
+                edged_image = cv2.Canny(gray_image, threshold1=30, threshold2=100)
+                plt.figure(figsize=(20,20))
+                plt.subplot(1,3,1)
+                plt.imshow(loaded_image, cmap="gray") 
+                plt.title("original Image")
+                plt.axis("off")
+                plt.subplot(1,3,2)
+                plt.imshow(gray_image,cmap="gray")
+                plt.axis("off")
+                plt.title("GrayScale Image")
+                plt.subplot(1,3,3)
+                plt.imshow(edged_image, cmap="gray")
+                plt.axis("off")
+                plt.title("Canny Edge Detected Image")
+                plt.show()
+
+![image](https://user-images.githubusercontent.com/19484531/187897964-f84a4108-f8ea-47c3-b22a-f3eea6deaec7.png)
 
 
+b) Edge detection schemes - the gradient (Sobel - first order derivatives)  based edge detector and the Laplacian (2nd order derivative, so it is  extremely sensitive to noise) based edge detector.
+
+                import cv2
+                import numpy as np 
+                import matplotlib.pyplot as plt
+                img0=cv2.imread("shape1.jpeg")
+                gray=cv2.cvtColor(img0,cv2.COLOR_BGR2GRAY)
+                img=cv2.GaussianBlur(gray,(3,3),0)
+                laplacian=cv2.Laplacian(img,cv2.CV_64F)
+                sobelx=cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
+                sobely=cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
 
+                plt.subplot(2,2,1),plt.imshow(img,cmap='gray')
+                plt.title('Original'),plt.xticks([]),plt.yticks([])
+
+                plt.subplot(2,2,2),plt.imshow(laplacian,cmap='gray')
+                plt.title('Laplacian'),plt.xticks([]),plt.yticks([])
+
+                plt.subplot(2,2,3),plt.imshow(sobelx,cmap='gray')
+                plt.title('Sobel X'),plt.xticks([]),plt.yticks([])
+
+                plt.subplot(2,2,4),plt.imshow(sobely,cmap='gray')
+                plt.title('Sobel Y'),plt.xticks([]),plt.yticks([])
+                plt.show()
+                
+![image](https://user-images.githubusercontent.com/19484531/187898068-746c7880-ce68-4029-89b3-0c08867d454b.png)
+
+c) Edge detection using Prewitt Operator
+                import cv2
+                import numpy as np
+                from matplotlib import pyplot as plt
+                imge=cv2.imread("shape.jpg")
+                gray=cv2.cvtColor(imge,cv2.COLOR_BGR2GRAY)
+                img_gaussian=cv2.GaussianBlur(gray,(3,3),0)
+
+                kernelx= np.array([[1,1,1], [0,0,0],[-1,-1,-1]])
+                kernely=np.array([[-1,0,1], [-1,0,1],[-1,0,1]])
+
+                img_prewittx = cv2.filter2D(img_gaussian, -1, kernelx)
+                img_prewitty = cv2.filter2D(img_gaussian, -1, kernely)
+
+                cv2.imshow("Original Image", img)
+                cv2.imshow("Prewitt x", img_prewittx) 
+                cv2.imshow("Prewitt y", img_prewitty)
+                cv2.imshow("Prewitt", img_prewittx + img_prewitty)
+                cv2.waitKey()
+                
+  ![image](https://user-images.githubusercontent.com/19484531/187898258-a4f406cf-3e13-4721-b126-4d7de34ca3b3.png)
+
+
+d) Roberts Edge Detection- Roberts cross operator
+
+
+            import cv2
+            import numpy as np
+            from scipy import ndimage
+            from matplotlib import pyplot as plt
+            roberts_cross_v=np.array([[1,0],[0,-1]])
+            roberts_cross_h=np.array([[0,1],[-1,0]])
+            img=cv2.imread("shape.jpg",0).astype('float64')
+            img/=255.0
+            vertical=ndimage.convolve(img,roberts_cross_v)
+            horizontal=ndimage.convolve(img,roberts_cross_h)
+
+            edged_img=np.sqrt(np.square(horizontal)+np.square(vertical))
+            edged_img*=255
+            cv2.imwrite("output.jpg",edged_img)
+            cv2.imshow("OutputImage",edged_img)
+            cv2.waitKey()
+            cv2.destroyAllWindows()
+            
+            
+ ![image](https://user-images.githubusercontent.com/19484531/187898372-3192011c-e666-4666-a7b8-42ff18a6e85d.png)