StartCoding.py

import cv2
import numpy as np
from scipy import misc
import matplotlib.pyplot as plt


i = misc.ascent()


i_transformed = np.copy(i)
size_x = i_transformed.shape[0]
size_y = i_transformed.shape[1]

#******************original img show

# plt.grid(False)
# plt.gray()
# plt.axis('off')
# plt.imshow(i)
# plt.show()



# This filter detects edges nicely
# It creates a filter that only passes through sharp edges and straight lines. 
# Experiment with different values for fun effects.
#filter = [ [0, 1, 0], [1, -4, 1], [0, 1, 0]] 
# A couple more filters to try for fun!
filter = [ [-1, -2, -1], [0, 0, 0], [1, 2, 1]]
#filter = [ [-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]
 # If all the digits in the filter don't add up to 0 or 1, you 
# should probably do a weight to get it to do so
# so, for example, if your weights are 1,1,1 1,2,1 1,1,1
# They add up to 10, so you would set a weight of .1 if you want to normalize them
weight  = 1



for x in range(1,size_x-1):
  for y in range(1,size_y-1):
      output_pixel = 0.0
      output_pixel = output_pixel + (i[x - 1, y-1] * filter[0][0])
      output_pixel = output_pixel + (i[x, y-1] * filter[0][1])
      output_pixel = output_pixel + (i[x + 1, y-1] * filter[0][2])
      output_pixel = output_pixel + (i[x-1, y] * filter[1][0])
      output_pixel = output_pixel + (i[x, y] * filter[1][1])
      output_pixel = output_pixel + (i[x+1, y] * filter[1][2])
      output_pixel = output_pixel + (i[x-1, y+1] * filter[2][0])
      output_pixel = output_pixel + (i[x, y+1] * filter[2][1])
      output_pixel = output_pixel + (i[x+1, y+1] * filter[2][2])
      output_pixel = output_pixel * weight
      if(output_pixel<0):
        output_pixel=0
      if(output_pixel>255):
        output_pixel=255
      i_transformed[x, y] = output_pixel


      # Plot the image. Note the size of the axes -- they are 512 by 512

#******************filter img show
plt.gray()
plt.grid(False)
plt.imshow(i_transformed)
plt.axis('off')
plt.show()