Masking added

3.Edge_Detection/horizontal_edges.py

@@ -6,20 +6,34 @@ def rgb2gray(rgb):
     gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
     return gray
 
-def  vertical_edge_detector(image):
-    output=np.zeros_like(image)
-    image_padded=np.zeros((image.shape[0]+2,image.shape[1]))
-    image_padded[1:-1,:]=image
-    image_padded[0,:]=image[0,:]
-    image_padded[-1,:]=image[-1,:]
-    for i in range(image.shape[0]):
-        for j in range(image.shape[1]):
-            resultant=image_padded[i+2,j]-image_padded[i,j]
-            resultant=round((resultant+255)/2)
-            output[i,j]=resultant
+def convolve3d_grayscale(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_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]):
+            output[y,x]=(kernel * image_padded[y: y+kernel.shape[0], x: x+kernel.shape[1]]).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
+
+x_direction_kernel=np.array([[-1,0,1],
+                             [-2,0,2],
+                             [-1,0,1]])
+
+y_direction_kernel=np.array([[-1,-2,-1],
+                             [ 0, 0, 0],
+                             [ 1, 2, 1]])
+
 file_name="edge-detection1.png"
 im = rgb2gray(np.array(Image.open(file_name)))
-pil_img=Image.fromarray(vertical_edge_detector(im)).convert('RGB')
+im=convolve3d_grayscale(im,gaussian_blurr)
+im=convolve3d_grayscale(im,x_direction_kernel)
+pil_img=Image.fromarray(im).convert('RGB')
 pil_img.save('result_horizontal_edge.jpg')

3.Edge_Detection/sobel.py

@@ -31,11 +31,11 @@ def convolve3d_grayscale(image, kernel):
                              [ 0, 0, 0],
                              [ 1, 2, 1]])
 
-file_name="result_dilation.png"
+file_name="edge-detection1.png"
 im = rgb2gray(np.array(Image.open(file_name)))
 im=convolve3d_grayscale(im,gaussian_blurr)
 im_x=convolve3d_grayscale(im,x_direction_kernel)
 im_y=convolve3d_grayscale(im,y_direction_kernel)
 im=np.hypot(im_x,im_y)  
 pil_img=Image.fromarray(im).convert('RGB')
-pil_img.save('result_morphology.jpg')
+pil_img.save('result_sobel.jpg')

3.Edge_Detection/vertical_edges.py

@@ -6,21 +6,35 @@ def rgb2gray(rgb):
     gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
     return gray
 
-def  vertical_edge_detector(image):
-    output=np.zeros_like(image)
-    image_padded=np.zeros((image.shape[0],image.shape[1]+2))
-    image_padded[:,1:-1]=image
-    image_padded[:,0]=image[:,0]
-    image_padded[:,-1]=image[:,-1]
-    for i in range(image.shape[0]):
-        for j in range(image.shape[1]):
-                resultant=image_padded[i,j+2]-image_padded[i,j]
-                resultant=round((resultant+255)/2)
-                output[i,j]=resultant
+def convolve3d_grayscale(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_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]):
+            output[y,x]=(kernel * image_padded[y: y+kernel.shape[0], x: x+kernel.shape[1]]).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
+
+x_direction_kernel=np.array([[-1,0,1],
+                             [-2,0,2],
+                             [-1,0,1]])
+
+y_direction_kernel=np.array([[-1,-2,-1],
+                             [ 0, 0, 0],
+                             [ 1, 2, 1]])
+
 file_name="edge-detection1.png"
 im = np.array(Image.open(file_name))
 im=rgb2gray(im)
-pil_img=Image.fromarray(vertical_edge_detector(im)).convert('RGB')
+im=convolve3d_grayscale(im,gaussian_blurr)
+im=convolve3d_grayscale(im,y_direction_kernel)
+pil_img=Image.fromarray(im).convert('RGB')
 pil_img.save('result_verical_edge.jpg')

4.Morphological_Transformation/morphological_dilation.py

@@ -23,9 +23,6 @@ def dilation(image, kernel):
     return output
 
 
-
-
-
 structuring_element=np.array([[0, 1, 0],
                               [1, 1, 1],
                               [0, 1, 0]])

5.Masking/masking.py

@@ -2,18 +2,28 @@
 import numpy as np
 from PIL import Image
 
+def RGB2BGR(image):
+    image1=np.zeros_like(image)
+    for i in range(image.shape[0]):
+        for j in range(image.shape[1]):
+            image1[i][j][0]=image[i][j][2]
+            image1[i][j][1]=image[i][j][1]
+            image1[i][j][2]=image[i][j][0]
+    return image1
+
 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_BGR2HSV)
+    mask=np.zeros((image.shape[0],image.shape[1],image.shape[2]),dtype=np.uint8)
+    for i in range(image.shape[0]):
+        for j in range(image.shape[1]):
+            if image[i][j][0] >= lower[0] and image[i][j][1] >= lower[1] and image[i][j][2] >= lower[2] and image[i][j][0] <= upper[0] and image[i][j][1] <= upper[1] and image[i][j][2] <= upper[2]:
+                    mask[i][j][0] = image[i][j][0]
+                    mask[i][j][1] = image[i][j][1]
+                    mask[i][j][2] = image[i][j][2]
+    image=np.bitwise_and(mask,image)
     image= cv2.cvtColor(image, cv2.COLOR_HSV2RGB)
+
     return image
 
 
@@ -39,6 +49,7 @@ def convolve3d(image, kernel):
 im = np.array(Image.open(file_name))
 lower_blue = np.array([94,130,38])
 upper_blue = np.array([179,255,255])
+im=RGB2BGR(im)
 im=masking_image(convolve3d(im,gaussian_blurr),lower_blue,upper_blue)
 pil_img=Image.fromarray(im.astype(np.uint8))
 pil_img.save('masked.jpeg')