Edge_Detection.py

import numpy as np
import matplotlib.pyplot as plt
import cv2

#define the vertical filter
vertical_filter = [[-1,-2,-1], [0,0,0], [1,2,1]]

#define the horizontal filter
horizontal_filter = [[-1,0,1], [-2,0,2], [-1,0,1]]

#read in the pinwheel image
img = plt.imread('/home/ryan/Documents/Galaxy_Zoo/Jell_Test/Train/Poss_Jellies/880993.jpg')#color jelly
#img = plt.imread('/home/ryan/Documents/Galaxy_Zoo/Jell_Test/Train/Not_Jellies/680527.jpg')#color non jelly

#Apply gaussian filter to smooth image
kernel = np.ones((5,5),np.float32)/50
img = cv2.filter2D(img,-1,kernel)

#get the dimensions of the image
n,m,d = img.shape

#initialize the edges image
edges_img = img.copy()

#loop over all pixels in the image
for row in range(3, n-2):
    for col in range(3, m-2):
        
        #create little local 3x3 box
        local_pixels = img[row-1:row+2, col-1:col+2, 0]
        
        #apply the vertical filter
        vertical_transformed_pixels = vertical_filter*local_pixels
        #remap the vertical score
        vertical_score = vertical_transformed_pixels.sum()/4
        
        #apply the horizontal filter
        horizontal_transformed_pixels = horizontal_filter*local_pixels
        #remap the horizontal score
        horizontal_score = horizontal_transformed_pixels.sum()/4
        
        #combine the horizontal and vertical scores into a total edge score
        edge_score = (vertical_score**2 + horizontal_score**2)**.5
        
        #insert this edge score into the edges image
        edges_img[row, col] = [edge_score]*3

#remap the values in the 0-1 range in case they went out of bounds
edges_img = edges_img/edges_img.max()

plt.imshow(edges_img)
plt.show()