Kernel operation added

1.Image_Rotation/image_rotation.py

@@ -37,13 +37,13 @@ def rot(image,angle):
     cosine=math.cos(angle)
     sine=math.sin(angle)
 
-    image1=np.zeros_like(image)
+    #image1=np.zeros_like(image)
 
     # Define the height and width of the new image that is to be formed
-    # new_height = floating_point(abs(image.shape[0]*cosine)+abs(image.shape[1]*sine))
-    # new_width  = floating_point(abs(image.shape[1]*cosine)+abs(image.shape[0]*sine))
+    new_height = floating_point(abs(image.shape[0]*cosine)+abs(image.shape[1]*sine))
+    new_width  = floating_point(abs(image.shape[1]*cosine)+abs(image.shape[0]*sine))
 
-    # image1=np.zeros((new_width,new_height,image.shape[2]))
+    image1=np.zeros((new_width,new_height,image.shape[2]))
 
     # Find the centre of the image about which we have to rotate the image
     centre_row   = round(((image.shape[0]+1)/2)-1)

2.Applying_Kernels/blurring.py

@@ -3,7 +3,6 @@
 
 def convolve3d(image, kernel):
     threshold=200
-    #kernel = np.flipud(np.fliplr(kernel))
     output = np.zeros_like(image)
     image_padded = np.zeros((image.shape[0]+4,image.shape[1] + 4,image.shape[2]))
     image_padded[3:-1:,3:-1:,:] = image
@@ -19,21 +18,14 @@ def convolve3d(image, kernel):
                          [4, 16, 24, 16, 4],
                          [6, 24, 36, 24, 6],
                          [4, 16, 24, 16, 4],
-                         [1,  4,  6,  4, 1]])
+                         [1,  4,  6,  4, 1]])/256
 
 box_blur=np.array([[1,1,1],
                    [1,1,1],
                    [1,1,1]])
-# Sharpen=np.array([[0 ,-1, 0],
-#                   [-1, 5,-1],
-#                   [0 ,-1, 0]])
-# Edge=np.array([[-1 ,-1, -1],
-#                   [-1, 8,-1],
-#                   [-1 ,-1, -1]])
-#file_name=input("Enter the name of the image to blurr:- ")
 file_name="test_blur.jpg"
 im = np.array(Image.open(file_name))
-pil_img=Image.fromarray(convolve3d(im, gaussian_blurr/256))
+pil_img=Image.fromarray(convolve3d(im, gaussian_blurr))
 pil_img.save('result_blur.jpg')
 
 

2.Applying_Kernels/sharpen.py

@@ -1,35 +1,30 @@
 from PIL import Image
 import numpy as np
 def convolve3d(image, kernel):
-    threshold=100
-    kernel = np.flipud(np.fliplr(kernel))
     output = np.zeros_like(image)
-    image_padded = np.zeros((image.shape[0]+2,image.shape[1] + 2,image.shape[2]))
-    image_padded[1:-1:,1:-1:,:] = image
+    image_padded = np.zeros((image.shape[0]+kernel.shape[0]-1,image.shape[1] + kernel.shape[1]-1,image.shape[2]))
+    image_padded[kernel.shape[0]-2:-1:,kernel.shape[1]-2:-1:,:] = image
     image_padded[0,0,:]=image[0,0,:]
     image_padded[-1,-1,:]=image[-1,-1,:]
     for x in range(image.shape[1]):
         for y in range(image.shape[0]):
             for z in range(image.shape[2]):
-                summation=(kernel * image_padded[y: y+3, x: x+3,z]).sum()
-                if summation<threshold :
-                    output[y, x,z]=50
-                else:
-                    output[y, x,z]=summation
+                summation=(kernel * image_padded[y: y+kernel.shape[0], x: x+kernel.shape[1],z]).sum()
+                output[y, x,z]=summation
     output[:,:,3]=image[:,:,3]
     return output
 
+Sharpen1=np.array([[-1, -1, -1, -1, -1],
+                   [-1,  2,  2,  2, -1],
+                   [-1,  2,  8,  2, -1],
+                   [-1,  2,  2,  2, -1],
+                   [-1, -1, -1, -1, -1,]])
 
-Sharpen=np.array([[-1,-1, -1],
-                  [-1, 9,-1],
-                  [-1 ,-1, -1]])
-# Edge=np.array([   [0 ,-1, 0],
-#                   [-1, 4,-1],
-#                   [0 ,-1, 0]])
-
+Sharpen=np.array([[-1,-1,-1,-1,-1],[-1,-1,-1,-1,-1],[-1,-1,25,-1,-1],[-1,-1,-1,-1,-1],[-1,-1,-1,-1,-1]])/-25
 file_name="test_sharpen.png"
 im = np.array(Image.open(file_name))
-pil_img=Image.fromarray(convolve3d(im,Sharpen*0.9))
+im=convolve3d(im,Sharpen)
+pil_img=Image.fromarray(im.astype(np.uint8))
 pil_img.save('result_sharpen.png')