Image_Rotation_updated

2.Applying_Kernels/blurring.py

@@ -2,14 +2,17 @@
 import numpy as np
 
 def convolve3d(image, kernel):
-    kernel = np.flipud(np.fliplr(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
+    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]):
-                output[y, x,z]=(kernel * image_padded[y: y+5, x: x+5,z]).sum()
+                output[y,x,z]=(kernel * image_padded[y: y+5, x: x+5,z]).sum()
     return output
 
 gaussian_blurr=np.array([[1,  4,  6,  4, 1],
@@ -21,17 +24,16 @@ def convolve3d(image, kernel):
 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]])
+# 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="blur.jpg"
+file_name="test_blur.jpg"
 im = np.array(Image.open(file_name))
 pil_img=Image.fromarray(convolve3d(im, gaussian_blurr/256))
-pil_img.save('gaussian-blurr.jpg')
-#print(im)
+pil_img.save('result_blur.jpg')
 
 

2.Applying_Kernels/sharpen.py

@@ -1,30 +1,36 @@
 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[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]):
-                output[y, x,z]=(kernel * image_padded[y: y+3, x: x+3,z]).sum()
+                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
+    output[:,:,3]=image[:,:,3]
     return output
 
 
-Sharpen=np.array([[0 ,-1, 0],
-                  [-1, 5,-1],
-                  [0 ,-1, 0]])
-Edge=np.array([[-1 ,-1, -1],
-                  [-1, 8,-1],
+Sharpen=np.array([[-1,-1, -1],
+                  [-1, 9,-1],
                   [-1 ,-1, -1]])
-#file_name=input("Enter the name of the image to blurr:- ")
-file_name="sharpening.png"
+# Edge=np.array([   [0 ,-1, 0],
+#                   [-1, 4,-1],
+#                   [0 ,-1, 0]])
+
+file_name="test_sharpen.png"
 im = np.array(Image.open(file_name))
-pil_img=Image.fromarray(convolve3d(im, Sharpen))
-pil_img.save('sharpened.png')
-print(im.shape)
-#print(im)
+pil_img=Image.fromarray(convolve3d(im,Sharpen*0.9))
+pil_img.save('result_sharpen.png')
+