将连接的组件分离到多个图像

Question

我有 this 种黑白图像，我想将每个白色形状保存为适合形状大小的图像。

我正在使用 connectedComponentsWithStats() 来标记连接的区域，然后我使用一个矩形包围该区域以将其提取并分开保存。

img = imread('shapes.png', IMREAD_GRAYSCALE)
_ , img = threshold(img,120,255,THRESH_BINARY)
n_labals, labels, stats, centroids = connectedComponentsWithStats(img)
for label in range(1,n_labals):
    width = stats[label, CC_STAT_WIDTH]
    height = stats[label, CC_STAT_HEIGHT]
    x = stats[label, CC_STAT_LEFT]
    y = stats[label, CC_STAT_TOP]
    roi = img[y-5:y + height+5, x-5:x + width+5]
    pyplot.imshow(roi,cmap='gray')
    pyplot.show()

但是，这样我在形状之间有一些交集，如图所示here

我想将每个连接的区域保存到一个单独的图像中，没有任何交集，如图所示here

更新

我拿了一个矩形覆盖感兴趣的区域然后我 ommoted 其他标签

img = imread('shapes.png', IMREAD_GRAYSCALE)
_ , img = threshold(img,120,255,THRESH_BINARY)
n_labals, labels, stats, centroids = connectedComponentsWithStats(img)
for label in range(1,n_labals):
    width = stats[label, CC_STAT_WIDTH]
    height = stats[label, CC_STAT_HEIGHT]
    x = stats[label, CC_STAT_LEFT]
    y = stats[label, CC_STAT_TOP]
    roi = labels[y-1:y + height+1, x-1:x + width+1].copy() # create a copy of the interest region from the labeled image
    roi[ roi != label] = 0  # set the other labels to 0 to eliminate untersections with other labels
    roi[ roi == label] = 255 # set the interest region to white
    pyplot.imshow(roi,cmap='gray')
    pyplot.show()

Answer 1

来自 this post 的已接受答案，详细介绍了函数 connectedComponentsWithStats：

Labels is a matrix the size of the input image where each element has a value equal to its label.

因此，这意味着对象 1 的所有像素都具有值 1，对象 2 的所有像素都具有值 2，依此类推。

我对处理您的问题的建议是 regionprops 这在 skimage 中实现（这对于 python 中的图像处理非常有用）

可以使用pip或者conda安装，详见here

因此，在整数数组上调用 regionprops 将 return 生成器列表，这些生成器几乎可以计算您可能希望得到的所有基本对象属性。具体来说，可以通过 'filled_image':

访问您要创建的图像

import numpy as np
from skimage.measure import regionprops

# generate dummy image:
labels = np.zeros((100,100), dtype=np.int) # this does not work on floats
# adding two rectangles, similar to output of your label function
labels[10:20, 10:20] = 1
labels[40:50, 40:60] = 2

props = regionprops(labels)
print(type(props))

现在，我们可以浏览列表中的每一项：

for prop in props:
   print(prop['label']) # individual properties can be accessed via square brackets
   cropped_shape = prop['filled_image'] # this gives you the content of the bounding box as an array of bool.
   cropped_shape = 1 * cropped_shape # convert to integer
   # save image with your favourite imsave. Data conversion might be neccessary if you use cv2

将连接的组件分离到多个图像

separate connected components to multiple images

python

opencv

connected-components