Find closed shapes in image

Question

I need to find all closed shapes in an image and get coordinates of it. I need this in Python but a explanation on how to do this is also enough. Feel free to answer with Python code if you want though. I already searched a lot on Google and found these two things:

The answer in the first link paints all areas instead of giving me coordinates of closed areas. I don’t understand the first answer in the second link and some comments say it doesn’t work. The second answer in the second link doesn’t work for images like this:

some image

I tried to make my own code too, but it took longer than a second to calculate and it has to be much faster (not really, really fast, but at least faster than 1/10 second).

How can I find these areas?

PS: There are some lines in the images that aren’t part of a closed shape.

Asked By: user2746752

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Source

Answer 1

Here’s a function find_groups that groups each pixel in the image into one of three categories: free, closed and border, along with a function print_groups to test it in a readable way.

from collections import namedtuple
from copy import deepcopy

def find_groups(inpixels):
    """
    Group the pixels in the image into three categories: free, closed, and
    border.
        free: A white pixel with a path to outside the image.
        closed: A white pixels with no path to outside the image.
        border: A black pixel.

    Params:
        pixels: A collection of columns of rows of pixels. 0 is black 1 is
                white.

    Return:
        PixelGroups with attributes free, closed and border.
        Each is a list of tuples (y, x).
    """

    # Pad the entire image with white pixels.
    width = len(inpixels[0]) + 2
    height = len(inpixels) + 2
    pixels = deepcopy(inpixels)
    for y in pixels:
        y.insert(0, 1)
        y.append(1)
    pixels.insert(0, [1 for x in range(width)])
    pixels.append([1 for x in range(width)])

    # The free pixels are found through a breadth first traversal.
    queue = [(0,0)]
    visited = [(0,0)]
    while queue:
        y, x = queue.pop(0)

        adjacent = ((y+1, x), (y-1, x), (y, x+1), (y, x-1))
        for n in adjacent:
            if (-1 < n[0] < height and -1 < n[1] < width and
                                        not n in visited and 
                                    pixels[n[0]][n[1]] == 1):
                queue.append(n)
                visited.append(n)

    # Remove the padding and make the categories.
    freecoords = [(y-1, x-1) for (y, x) in visited if
                 (0 < y < height-1 and 0 < x < width-1)]
    allcoords = [(y, x) for y in range(height-2) for x in range(width-2)]
    complement = [i for i in allcoords if not i in freecoords]
    bordercoords = [(y, x) for (y, x) in complement if inpixels[y][x] == 0]
    closedcoords = [(y, x) for (y, x) in complement if inpixels[y][x] == 1]

    PixelGroups = namedtuple('PixelGroups', ['free', 'closed', 'border'])
    return PixelGroups(freecoords, closedcoords, bordercoords)

def print_groups(ysize, xsize, pixelgroups):
    ys= []
    for y in range(ysize):
        xs = []
        for x in range(xsize):
            if (y, x) in pixelgroups.free:
                xs.append('.')
            elif (y, x) in pixelgroups.closed:
                xs.append('X')
            elif (y, x) in pixelgroups.border:
                xs.append('#')
        ys.append(xs)
    print('n'.join([' '.join(k) for k in ys]))

Now to use it:

pixels = [[0, 1, 0, 0, 1, 1],
          [1, 0, 1, 1, 0, 1], 
          [1, 0, 1, 1, 0, 1],
          [1, 0 ,1 ,1 ,0, 1],
          [1, 0, 1 ,0 ,1, 1],
          [1, 0, 0, 1, 1, 1],
          [1, 1, 1, 1, 1, 1]]
pixelgroups = find_groups(pixels)
print_groups(7, 6, pixelgroups)
print("closed: " + str(pixelgroups.closed))

Outputs:

# . # # . .
. # X X # .
. # X X # .
. # X X # .
. # X # . .
. # # . . .
. . . . . .

closed: [(1, 2), (1, 3), (2, 2), (2, 3), (3, 2), (3, 3), (4, 2)]

You’ll notice random dots and streaks are classified as borders. But you can always distinguish between real borders and streaks as follows.

# pseudo code
realborders = [i for i in pixelgroups.border if i has an adjacent closed pixel]
streaks = [otherwise]

Answered By: Trevor Merrifield

Answer 2

You can try to use mahotas

import mahotas
import numpy as np

import matplotlib.pyplot as plt
 
# loading nuclear image
f = mahotas.demos.load('nuclear')
 
# setting filter to the image
f = f[:, :, 0]
 
# setting gaussian filter
f = mahotas.gaussian_filter(f, 4)
 
# setting threshold value
f = (f> f.mean())
 
# creating a labelled image
labelled, n_nucleus = mahotas.label(f)
 
#plotting
axs = plt.subplots(1,2,figsize=(10,3))[1]
plt.sca(axs[0])
plt.imshow(f)
plt.colorbar()
plt.title('orginal image')

plt.sca(axs[1])
plt.imshow(labelled)
plt.colorbar()
plt.title('labelled_image')
plt.tight_layout()
plt.show()

Answered By: Oren

Find closed shapes in image

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