獲取python中光柵化邊界線內柵格文件的單元格索引

Question

我有一個由柵格單元格組成的封閉線，其中我知道索引（存儲在列表中的每個單元格的col和raw）。名單就像是 -

我想獲得這個封閉線內的細胞的指標，並將其存儲在一個單獨的列表。我想在python中做到這一點。這裏是一個圖像更清晰：光柵分界線接近，這是實現自己的（幼稚）算法，這是我的第一個想法

Answer 1

的一種方式。另一方面，爲什麼重新發明輪子：

人們可以很容易地看到，問題可以被解釋爲黑色和白色（光柵/像素）圖像。然後外部和內部區域形成背景（黑色），而邊界是閉合（白色）循環。 （很顯然，顏色也可以切換，但是現在我會在黑色上使用白色。）碰巧有一些相當複雜的python圖像處理庫，即skimage,ndimage和mahotas。

我不是專家，但我認爲skimage.draw.polygon,skimage.draw.polygon_perimiter是解決您的問題最簡單的方法。

我的實驗得出了以下：

import matplotlib.pyplot as plt 
import numpy as np 

from skimage.draw import polygon, polygon_perimeter 
from skimage.measure import label, regionprops 

# some test data 
# I used the format that your input data is in 
# These are 4+99*4 points describing the border of a 99*99 square 
border_points = (
    [[100,100]] + 
    [[100,100+i] for i in range(1,100)] + 
    [[100,200]] + 
    [[100+i,200] for i in range(1,100)] + 
    [[200,200]] + 
    [[200,200-i] for i in range(1,100)] + 
    [[200,100]] + 
    [[200-i,100] for i in range(1,100)] 
    ) 

# convert to numpy arrays which hold the x/y coords for all points 
# repeat first point at the end to close polygon. 
border_points_x = np.array([p[0] for p in border_points] + [border_points[0][0]]) 
border_points_y = np.array([p[1] for p in border_points] + [border_points[0][1]]) 

# empty (=black) 300x300 black-and-white image 
image = np.zeros((300, 300)) 

# polygon() calculates the indices of a filled polygon 
# one would expect this to be inner+border but apparently it is inner+border/2 
# probably some kind of "include only the left/top half" 
filled_rr, filled_cc = polygon(border_points_y, border_points_x) 
# set the image to white at these points 
image[filled_rr, filled_cc] = 1 

# polygon_perimeter() calculates the indices of a polygon perimiter (i.e. border) 
border_rr, border_cc = polygon_perimeter(border_points_y, border_points_x) 
# exclude border, by setting it to black 
image[border_rr, border_cc] = 0 

# label() detects connected patches of the same color and enumerates them 
# the resulting image has each of those regions filled with its index 
label_img, num_regions = label(image, background=0, return_num=True) 

# regionprops() takes a labeled image and computes some measures for each region 
regions = regionprops(label_img) 
inner_region = regions[0] 

print("area", inner_region.area) 
# expecting 9801 = 99*99 for inner 

# this is what you want, the coords of all inner points 
inner_region.coords 

# print it 
fig, ax = plt.subplots() 
ax.imshow(image, cmap=plt.cm.gray)