text: # This will be used to test the effect of changing the value of sigma within the canny filter
  
  import math
  import skimage
  from skimage import io, viewer, color, data, filters, feature
  from pathlib import Path
  import matplotlib.pyplot as plt 
  
  sample_image ="tester_bird.jpg"
  
  scan = io.imread(fname=sample_image)
  scan_gs = io.imread(fname=sample_image, as_gray=True) #filename, then load it as greyscale
  
  scan_gs_canny1 = feature.canny(scan_gs,sigma= 1)
  scan_gs_canny2 = feature.canny(scan_gs,sigma= 2)
  scan_gs_canny3 = feature.canny(scan_gs,sigma= 3)
  
  fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3), sharex=True, sharey=True)
  fig.suptitle('Changing Sigma', fontsize=25)
  
  ax1.imshow(scan_gs_canny1, cmap=plt.cm.hot)
  ax1.axis('off')
  ax1.set_title(r'Canny filter, $\sigma=1$', fontsize=20)
  
  ax2.imshow(scan_gs_canny2, cmap=plt.cm.hot)
  ax2.axis('off')
  ax2.set_title(r'Canny filter, $\sigma=2$', fontsize=20)
  
  ax3.imshow(scan_gs_canny3, cmap=plt.cm.hot)
  ax3.axis('off')
  ax3.set_title(r'Canny filter, $\sigma=3$', fontsize=20)
  
  fig.tight_layout()
  
  plt.show()
  
  
  scan_gs_canny4 = feature.canny(scan_gs,sigma= 3,low_threshold=0.1, high_threshold= 0.2)
  scan_gs_canny5 = feature.canny(scan_gs,sigma= 3,low_threshold= 0.2, high_threshold= 0.3)
  scan_gs_canny6 = feature.canny(scan_gs,sigma= 3, low_threshold=0.3, high_threshold= 0.4)
  
  fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3), sharex=True, sharey=True)
  fig.suptitle('Changing Thresholds', fontsize=25)
  
  ax1.imshow(scan_gs_canny4, cmap=plt.cm.hot)
  ax1.axis('off')
  ax1.set_title('low 0.1 high 0.2', fontsize=20)
  
  ax2.imshow(scan_gs_canny5, cmap=plt.cm.hot)
  ax2.axis('off')
  ax2.set_title('low 0.2 high 0.3', fontsize=20)
  
  ax3.imshow(scan_gs_canny6, cmap=plt.cm.hot)
  ax3.axis('off')
  ax3.set_title('low 0.3 high 0.4', fontsize=20)
  
  fig.tight_layout()
  
  plt.show()