Image outline using python/PIL - python-imaging-library

Question

I have a color photo of apple, how can I show only its outline (inside white, background black) with python/PIL?

Answer 1

Something like this should work.
from PIL import Image, ImageFilter
image = Image.open('your_image.png')
image = image.filter(ImageFilter.FIND_EDGES)
image.save('new_name.png')
If that doesn't give you the result you are looking for then you try implementing either Prewitt edge detection, Sobel edge detection or Canny edge detection using PIL and Python and other libraries see related question and the following example .
If you are trying to do particle detection / analysis rather than just edge detection, you can try using py4ij to call the ImageJ method you link to give you expect the same result, or try another Particle Analysis Python library EMAN alternately you can write a Particle detection algorithm using PIL, SciPy and NumPy.

Answer 2

If your object and background have fairly well contrast
from PIL import Image
image = Image.open(your_image_file)
mask=image.convert("L")
th=150 # the value has to be adjusted for an image of interest
mask = mask.point(lambda i: i < th and 255)
mask.save(file_where_to_save_result)
if higher contrast is in one (of 3 colors), you may split the image into bands instead of converting it into grey scale.
if an image or background is fairly complicated, more sophisticated processing will be required

Answer 3

Apple vs Lines
You can do it just using PIL and Python in less than 200 lines of code. Would be easier to use canny-edge-detection from a library.
Here is the steps. Convert to grayscale for lumens. Using kernel image processing detect edges using Sobel. Thin the edge using the magnitude and slope obtain from Sobel.
from PIL import Image
import math
def one_to_two_dimension_array(list_,columns):
#use list slice
return [ list_[i:i+columns] for i in range(0, len(list_),columns) ]
def flatten_matrix(matrix):
return [val for sublist in matrix for val in sublist]
def matrix_convole(matrix, kernel_matrix, multiplier):
return_list=[]
return_matrix=[]
border=(len(kernel_matrix) - 1) / 2;border=int(border)
center_kernel_pos=border
for matrix_row in range( len( matrix )):
for matrix_col in range(len( matrix[matrix_row] ) ):
accumulator = 0
if (matrix_row - border)<0 or \
(matrix_col-border)< 0 or \
(matrix_row+border) > (len( matrix )-border) or \
(matrix_col+border) > (len( matrix[matrix_row] )-border):
return_list.append(matrix[matrix_row][matrix_col])
continue
for kernel_row in range(len (kernel_matrix) ):
for kernel_col in range(len (kernel_matrix[kernel_row]) ):
relative_row= kernel_row - center_kernel_pos
relative_col= kernel_col - center_kernel_pos
kernel = kernel_matrix[kernel_row][kernel_col]
pixel = matrix [matrix_row + relative_row] [matrix_col + relative_col]
accumulator += pixel * kernel
return_list.append(accumulator* multiplier )
return_matrix = one_to_two_dimension_array( return_list, len( matrix[0] ) )
return return_matrix
def canny_round_degree(deg):
#0, 22.5, 45, 67.5, 90, 112.5, 135, 157.5, 180
if deg >= 0 and deg <= 22.5:
return 0
elif deg >= 22.5 and deg <= 67.5:
return 45
elif deg > 67.5 and deg <=112.5:
return 90
elif deg > 112.5 and deg <=157.5:
return 135
elif deg >= 157.5 and deg <= 180:
return 0
if deg <= 0 and deg >= -22.5:
return 0
elif deg <= -22.5 and deg >= -67.5:
return 135
elif deg < -67.5 and deg >= -112.5:
return 90
elif deg < -112.5 and deg >= -157.5:
return 45
elif deg <= -157.5 and deg >= -180:
return 0
image_path='apple.jpg'
gaussian_5x5_kernel=[[2,4,5,4,2],[4,9,12,9,4],[5,12,15,12,5],[4,9,12,9,4],[2,4,5,4,2]] #multiplier 1/159
sobel_kernel_gx=[[-1,0,1],[-2,0,2],[-1,0,1]]
sobel_kernel_gy=[[-1,-2,-1],[0,0,0],[1,2,1]]
im_list=list(Image.open(image_path).convert('L').getdata(0)) #grayscale, get first channel
im_width=Image.open(image_path).width
im_height=Image.open(image_path).height
im_matrix = one_to_two_dimension_array(im_list, im_width)
im_matrix_blur=matrix_convole(im_matrix,gaussian_5x5_kernel, 1/159)
sobel_gx_matrix=matrix_convole(im_matrix_blur,sobel_kernel_gx, 1)
sobel_gy_matrix=matrix_convole(im_matrix_blur,sobel_kernel_gy, 1)
sobel_gy_list=flatten_matrix(sobel_gy_matrix)
sobel_gx_list=flatten_matrix(sobel_gx_matrix)
sobel_g_magnitude_list = [math.hypot(gy,gx) for gx,gy in zip(sobel_gx_list,sobel_gy_list)]
sobel_g_angle_list = [ canny_round_degree(math.degrees(math.atan2(gy,gx))) for gx,gy in zip(sobel_gx_list,sobel_gy_list)]
sobel_g_angle_matrix = one_to_two_dimension_array(sobel_g_angle_list, im_width)
sobel_g_magnitude_matrix = one_to_two_dimension_array(sobel_g_magnitude_list, im_width)
suppression_list = []
for s_row in range( len( sobel_g_angle_matrix)):
for s_col in range(len( sobel_g_angle_matrix[s_row] ) ):
if (s_row - 1)<0 or \
(s_col-1)< 0 or \
(s_row+1) > (len( sobel_g_angle_matrix )-1) or \
(s_col+1) > (len( sobel_g_angle_matrix[s_row] )-1):
suppression_list.append(0)
continue
magnitude_in_question = sobel_g_magnitude_matrix[s_row][s_col]
#thresholding magnitude continue, arbitrary 129
if magnitude_in_question < 36:
suppression_list.append(0)
continue
angle_in_question = sobel_g_angle_matrix[s_row][s_col]
east_magnitude = sobel_g_magnitude_matrix[s_row][s_col-1]
west_magnitude = sobel_g_magnitude_matrix[s_row][s_col+1]
north_magnitude = sobel_g_magnitude_matrix[s_row-1][s_col]
south_magnitude = sobel_g_magnitude_matrix[s_row+1][s_col]
north_east_magnitude = sobel_g_magnitude_matrix[s_row-1][s_col-1]
north_west_magnitude = sobel_g_magnitude_matrix[s_row-1][s_col+1]
south_east_magnitude = sobel_g_magnitude_matrix[s_row+1][s_col-1]
south_west_magnitude = sobel_g_magnitude_matrix[s_row+1][s_col+1]
if angle_in_question == 0 and magnitude_in_question > east_magnitude \
and magnitude_in_question > west_magnitude:
suppression_list.append(1)
elif angle_in_question == 90 and magnitude_in_question > north_magnitude \
and magnitude_in_question > south_magnitude:
suppression_list.append(1)
elif angle_in_question == 135 and magnitude_in_question > north_west_magnitude \
and magnitude_in_question > south_east_magnitude:
suppression_list.append(1)
elif angle_in_question == 45 and magnitude_in_question > north_east_magnitude \
and magnitude_in_question > south_west_magnitude:
suppression_list.append(1)
else:
suppression_list.append(0)
new_img = Image.new('1', (im_width,im_height)) #bw=1;grayscale =L
new_img.putdata( suppression_list )
new_img.save('apple-lines.png', 'PNG')