问题描述:

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:

- WPF: Finding all closed areas in an image (C# or even VB)
- determine if a point sits inside an arbitrary shape?

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:

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.

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]
```

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