minor updates

1.Image_Rotation/image_rotation_bound.py

@@ -63,7 +63,7 @@ def rot(image,angle):
 
 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) :- "))
+rotation_angle=int(input("Enter the angle :- "))
 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_nobound.py

@@ -56,7 +56,7 @@ def rot(image,angle):
 
 file_name=input("Enter the name of the file:- ")
 im = np.array(Image.open(file_name))
-rotation_angle=float(input("Enter the no. of turns in clockwise direction(by 90 degree) :- "))
+rotation_angle=float(input("Enter the angle :- "))
 im_copy=rot(im,rotation_angle)
 pil_img=Image.fromarray((im_copy).astype(np.uint8))
 pil_img.save("rotated_without_bound.png")

1.Image_Rotation/image_shearing_bound.py

@@ -0,0 +1,76 @@
+from PIL import Image
+import numpy as np
+import math
+
+def floating_point(number):
+    temp=int(number)
+    if number>temp:
+        return temp+1
+    else :
+        return number
+
+def mat_Multi(angle,x,y,k):
+    tangent=math.tan(angle/2)
+    if k==2 or k==0:
+        X=x-y*tangent
+        Y=y
+    if  k==1:
+        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]))
+    image2=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
+
+    for k in range(3):
+        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,Y=mat_Multi(angle,x,y,k)
+                if k==0:
+                    X,Y=mat_Multi(angle,x,y,0)
+                elif k==1:
+                    X,Y=mat_Multi(angle,x,y,0)
+                    X,Y=mat_Multi(angle,X,Y,1)
+                elif k==2:
+                    X,Y=mat_Multi(angle,x,y,0)
+                    X,Y=mat_Multi(angle,X,Y,1)
+                    X,Y=mat_Multi(angle,X,Y,2)
+                X=centre_height-X
+                Y=centre_width-Y
+                if X<image1.shape[1] and Y<image1.shape[0] and X>=0 and Y>=0:
+                    if k==2:
+                        image2[Y,X,:]=image[i,j,:]
+                    else:
+                        image1[Y,X,:]=image[i,j,:]
+
+    return image2
+
+
+
+
+
+file_name="rotate.png"#input("Enter the name of the file:- ")
+im = np.array(Image.open(file_name))
+rotation_angle=-int(input("Enter the angle :- "))
+im_copy=rot(im,rotation_angle)
+pil_img=Image.fromarray((im_copy).astype(np.uint8))
+pil_img.save("rotated_sheared_bound.png")

1.Image_Rotation/image_shearing_nobound.py

@@ -0,0 +1,67 @@
+from PIL import Image
+import numpy as np
+import math
+
+def floating_point(number):
+    temp=int(number)
+    if number>temp:
+        return temp+1
+    else :
+        return number
+
+def mat_Multi(angle,x,y,k):
+    tangent=math.tan(angle/2)
+    if k==2 or k==0:
+        X=x-y*tangent
+        Y=y
+    if  k==1:
+        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)
+    image1=np.zeros_like(image)
+    image2=np.zeros_like(image)
+
+    # 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)
+    for k in range(3):
+        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,Y=mat_Multi(angle,x,y,k)
+                if k==0:
+                    X,Y=mat_Multi(angle,x,y,0)
+                elif k==1:
+                    X,Y=mat_Multi(angle,x,y,0)
+                    X,Y=mat_Multi(angle,X,Y,1)
+                elif k==2:
+                    X,Y=mat_Multi(angle,x,y,0)
+                    X,Y=mat_Multi(angle,X,Y,1)
+                    X,Y=mat_Multi(angle,X,Y,2)
+                X=centre_column-X
+                Y=centre_row-Y
+                if X<image1.shape[1] and Y<image1.shape[0] and X>=0 and Y>=0:
+                    if k==2:
+                        image2[Y,X,:]=image[i,j,:]
+                    else:
+                        image1[Y,X,:]=image[i,j,:]
+    return image2
+
+
+
+
+
+file_name="rotate.png"#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_sheared_without_bound.png")

5.Masking/masking.py

@@ -0,0 +1,44 @@
+import cv2
+import numpy as np
+from PIL import Image
+
+def masking_image(image,lower,upper):
+    image = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_BGR2HSV)
+    image_copy=image
+    for x in range(image.shape[0]):
+        for y in range(image.shape[1]):
+            for z in range(image.shape[2]):
+                if image[x,y,z]>=lower[z] and image[x,y,z]<=upper[z]:
+                    image[x,y,z]=360
+                else:
+                    image[x,y,z]=0
+    image=~(image_copy ^ image)
+    image= cv2.cvtColor(image, cv2.COLOR_HSV2RGB)
+    return image
+
+
+def convolve3d(image, kernel):
+    output = np.zeros_like(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]):
+                output[y,x,z]=(kernel * image_padded[y: y+kernel.shape[0], x: x+kernel.shape[1],z]).sum()
+    return output
+
+gaussian_blurr=np.array([[1,  4,  6,  4, 1],
+                         [4, 16, 24, 16, 4],
+                         [6, 24, 36, 24, 6],
+                         [4, 16, 24, 16, 4],
+                         [1,  4,  6,  4, 1]])/256
+
+file_name="mask.jpeg"
+im = np.array(Image.open(file_name))
+lower_blue = np.array([94,130,38])
+upper_blue = np.array([179,255,255])
+im=masking_image(convolve3d(im,gaussian_blurr),lower_blue,upper_blue)
+pil_img=Image.fromarray(im.astype(np.uint8))
+pil_img.save('masked.jpeg')

OpenCV/blur.py

@@ -0,0 +1,9 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+from PIL import Image
+
+img = cv2.imread('test_kernel.png')
+blur =cv2.GaussianBlur(img,(5,5),1)
+pil_img=Image.fromarray(blur)
+pil_img.save('gaussian_blur.png')

OpenCV/canny_edge_detector.py

@@ -0,0 +1,19 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+from PIL import Image
+
+img = cv2.imread("edge-detection1.png")
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+canny = cv2.Canny(img, 602, 452)
+
+titles = ['image', 'canny']
+images = [img, canny]
+pil_img=Image.fromarray(canny).convert('RGB')
+pil_img.save('result_canny.png')
+for i in range(2):
+    plt.subplot(1, 2, i+1), plt.imshow(images[i], 'gray')
+    plt.title(titles[i])
+    plt.xticks([]),plt.yticks([])
+
+plt.show()

OpenCV/sobel_edge_detector.py

@@ -0,0 +1,12 @@
+import cv2
+import numpy as np
+from PIL import Image
+
+img = cv2.imread('edge-detection1.png', cv2.IMREAD_GRAYSCALE)
+rows, cols = img.shape
+
+sobel_horizontal = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3)
+sobel_vertical = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=3)
+sobel=np.hypot(sobel_horizontal,sobel_vertical)
+pil_img=Image.fromarray(sobel).convert('RGB')
+pil_img.save('result_sobel.png')