minor changes

1.Image_Rotation/image_rotation_bound.py

@@ -0,0 +1,69 @@
+from PIL import Image
+import numpy as np
+import math
+
+def floating_point(number):
+    temp=int(number)
+    if number>temp:
+        return round(temp+1)
+    else :
+        return round(temp)
+
+def mat_Multi(angle,x,y,k):
+    tangent=math.tan(angle/2)
+    if k==0 or k==2:
+        X=x-y*tangent
+        Y=y
+    else:
+        X=x
+        Y=x*math.sin(angle)+y
+    return round(X),round(Y)
+
+def rot(image,angle):
+    # Define the most occuring variables
+    angle=math.radians(angle)
+    cosine=math.cos(angle)
+    sine=math.sin(angle)
+
+    # Define the height and width of the new image that is to be formed
+    new_width = floating_point(abs(image.shape[0]*cosine)+abs(image.shape[1]*sine))
+    new_height  = floating_point(abs(image.shape[1]*cosine)+abs(image.shape[0]*sine))
+
+    image1=np.zeros((floating_point(new_width),floating_point(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)    #with respect to the original image
+    centre_column= round(((image.shape[1]+1)/2)-1)    #with respect to the original image
+
+    centre_height= round(((new_height+1)/2)-1)        #with respect to the new image
+    centre_width= round(((new_width+1)/2)-1)          #with respect to the new image
+
+    # Now let's traverse through the image matrix
+    for i in range(image.shape[0]):
+            for j in range(image.shape[1]):
+                y=image.shape[0]-1-i-centre_row
+                x=image.shape[1]-1-j-centre_column
+                X=round(x*cosine+y*sine)
+                Y=round(-x*sine+y*cosine)
+                X=centre_height-X
+                Y=centre_width-Y
+
+                if X<image1.shape[1] and Y<image1.shape[0] and X>=0 and Y>=0:
+                    image1[Y,X,:]=image[i,j,:]
+    for i in range(image1.shape[0]):
+        prev = [image1[i][0][0], image1[i][0][1], image1[i][0][2], image1[i][0][3]]
+        for j in range(image1.shape[1]-1):
+            if (not any(image1[i][j][:])):
+                if (any(image1[i][j+1][:])):
+                    image1[i][j][:] = prev
+            else:
+                prev = image1[i][j][:]
+    return image1
+
+
+file_name=input("Enter the name of the file:- ")
+im = np.array(Image.open(file_name))
+rotation_angle=int(input("Enter the no. of turns in clockwise direction(by 90 degree) :- "))
+im_copy=rot(im,rotation_angle)
+pil_img=Image.fromarray((im_copy).astype(np.uint8))
+pil_img.save("rotated_with_bound.png")

1.Image_Rotation/image_rotation.py → 1.Image_Rotation/image_rotation_nobound.py

@@ -2,18 +2,6 @@
 import numpy as np
 import math
 
-
-# def rot_perfect_angles(image1,turns):
-#     if turns==0:
-#         return image1
-#     else :
-#         image=np.zeros((image1.shape[1],image1.shape[0],image1.shape[2]))
-#         for j in range(image1.shape[0]): 
-#             for i in range(image1.shape[1]): 
-#                 image[i,j,:] = image1[j,i,:]  
-#         return rot(image[::-1],turns-1)
-
-
 def floating_point(number):
     temp=int(number)
     if number>temp:
@@ -32,27 +20,16 @@ def mat_Multi(angle,x,y,k):
     return round(X),round(Y)
 
 def rot(image,angle):
+
     # Define the most occuring variables
     angle=math.radians(angle)
     cosine=math.cos(angle)
     sine=math.sin(angle)
-
     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))
-
-    # 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)
     centre_column= round(((image.shape[1]+1)/2)-1)
-
-    # centre_height= round(((new_height+1)/2)-1)
-    # centre_width= round(((new_width+1)/2)-1)
-
-    # Now let's traverse through the image matrix
     for i in range(image.shape[0]):
             for j in range(image.shape[1]):
                 y=image.shape[0]-1-i-centre_row
@@ -63,16 +40,23 @@ def rot(image,angle):
                 Y=centre_row-Y
                 if X<image1.shape[1] and Y<image1.shape[0] and X>=0 and Y>=0:
                     image1[Y,X,:]=image[i,j,:]
+    for i in range(image1.shape[0]):
+        prev = [image1[i][0][0], image1[i][0][1], image1[i][0][2], image1[i][0][3]]
+        for j in range(image1.shape[1]-1):
+            if (not any(image1[i][j][:])):
+                if (any(image1[i][j+1][:])):
+                    image1[i][j][:] = prev
+            else:
+                prev = image1[i][j][:]
     return image1
 
 
 
 
 
-file_name="rotate.png"
+file_name=input("Enter the name of the file:- ")
 im = np.array(Image.open(file_name))
-#rotation_angle=int(input("Enter the no. of turns in clockwise direction(by 90 degree) :- "))
-im_copy=rot(im,80)
+rotation_angle=float(input("Enter the no. of turns in clockwise direction(by 90 degree) :- "))
+im_copy=rot(im,rotation_angle)
 pil_img=Image.fromarray((im_copy).astype(np.uint8))
-pil_img.save("Rotated.png")
-
+pil_img.save("rotated_without_bound.png")

2.Applying_Kernels/blurring.py

@@ -21,10 +21,10 @@ def convolve3d(image, kernel):
 
 box_blur=np.array([[1,1,1],
                    [1,1,1],
-                   [1,1,1]])
-file_name="test_blur.jpg"
+                   [1,1,1]])/9
+file_name="test_sharpen.png"
 im = np.array(Image.open(file_name))
-pil_img=Image.fromarray(convolve3d(im, gaussian_blurr))
-pil_img.save('result_blur.jpg')
+pil_img=Image.fromarray(convolve3d(im, box_blur))
+pil_img.save('result_box_blur.png')
 
 

2.Applying_Kernels/sharpen.py

@@ -14,13 +14,14 @@ def convolve3d(image, kernel):
     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,-1],[-1,-1,-1,-1,-1],[-1,-1,25,-1,-1],[-1,-1,-1,-1,-1],[-1,-1,-1,-1,-1]])/-25
+
+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))
 im=convolve3d(im,Sharpen)

3.Edge_Detection/canny.py

@@ -58,24 +58,17 @@ def non_max_suppression(img,D):
     return Z
 
 def threshold(img, weak,strong,lowThresholdRatio=0.05, highThresholdRatio=0.09):
-
     highThreshold = img.max() * highThresholdRatio
     lowThreshold = highThreshold * lowThresholdRatio
-
     M, N = img.shape
     res = np.zeros((M,N), dtype=np.int32)
-
     weak = np.int32(weak)
     strong = np.int32(strong)
-
     strong_i, strong_j = np.where(img >= highThreshold)
     zeros_i, zeros_j = np.where(img < lowThreshold)
-
     weak_i, weak_j = np.where((img <= highThreshold) & (img >= lowThreshold))
-
     res[strong_i, strong_j] = strong
     res[weak_i, weak_j] = weak
-
     return res
 
 def hysteresis(img, weak, strong):