Python 中的连通分量标记算法

Question

我的工作需要在图像上应用本地二元运算符。为此，我已经将图像转换为灰色，然后还对图像实施了连通分量分析。

代码如下：

添加库

 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from skimage.io import imread, imshow
 from skimage.color import rgb2gray
 from skimage.morphology import (erosion, dilation, closing, opening,area_closing, area_opening)
 from skimage.measure import label, regionprops, regionprops_table

渲染图像

 plt.figure(figsize=(6,6))
 painting = imread("E:/Project/for_annotation/Gupi Gain0032.jpg")
 plt.imshow(painting);

 plt.figure(figsize=(6,6))

二值化图像

 gray_painting = rgb2gray(painting)
 binarized = gray_painting<0.55
 plt.imshow(binarized);

4.Declaring 内核

    square = np.array([[1,1,1],
                      [1,1,1],
                      [1,1,1]])

膨胀函数

 def multi_dil(im, num, element=square):
 for i in range(num):
     im = dilation(im, element)
     return im

侵蚀函数

def multi_ero(im, num, element=square):
for i in range(num):
    im = erosion(im, element)
    return im

应用的函数

plt.figure(figsize=(6,6))
multi_dilated = multi_dil(binarized, 7)
area_closed = area_closing(multi_dilated, 50000)
multi_eroded = multi_ero(area_closed, 7)
opened = opening(multi_eroded)
plt.imshow(opened);

标签函数

plt.figure(figsize=(6,6))
label_im = label(opened)
regions = regionprops(label_im)
plt.imshow(label_im);

提取特征

properties = ['area','convex_area','bbox_area', 'extent', 'mean_intensity','solidity', 'eccentricity', 'orientation']
pd.DataFrame(regionprops_table(label_im, gray_painting, 
properties=properties))

过滤区域

masks = []
bbox = []
list_of_index = []
for num, x in enumerate(regions):
    area = x.area
    convex_area = x.convex_area
    if (num!=0 and (area>100) and (convex_area/area <1.05)
    and (convex_area/area >0.95)):
    masks.append(regions[num].convex_image)
    bbox.append(regions[num].bbox)   
    list_of_index.append(num)
 count = len(masks)

提取图像

 fig, ax = plt.subplots(2, int(count/2), figsize=(15,8))
 for axis, box, mask in zip(ax.flatten(), bbox, masks):
     red  =  painting[:,:,0][box[0]:box[2], box[1]:box[3]] * mask
     green = painting[:,:,1][box[0]:box[2], box[1]:box[3]] * mask
     blue  = painting[:,:,2][box[0]:box[2], box[1]:box[3]] * mask
     image = np.dstack([red,green,blue])
     axis.imshow(image)
 plt.tight_layout()

 plt.figure(figsize=(6,6))

 rgb_mask = np.zeros_like(label_im)
 for x in list_of_index:
     rgb_mask += (label_im==x+1).astype(int)
     red  =  painting[:,:,0] * rgb_mask
     green = painting[:,:,1] * rgb_mask
     blue  = painting[:,:,2] * rgb_mask
     image = np.dstack([red,green,blue])
 plt.imshow(image);

我收到一个错误。

ValueError: Number of columns must be a positive integer, not 0

Answer 1

有一种可能的方法与您尝试的方法相差不大。假设背景像素被分配标签 0，对象像素被分配值 1。

逐行扫描图像；
当遇到像素1时，设置一个新的label并进行flood fill操作，将1替换为新的label。

洪水填充可以很简单地实现：

将起始像素设置为新标签；
递归填充八个邻居，如果它们有一个 1。

https://en.wikipedia.org/wiki/Flood_fill

这个版本的代码非常简单。但是您会注意到它很容易溢出堆栈，因为待处理的填充数可能与图像大小一样大。

def FloodFill(X, Y, Label):
    I[X,Y]= Label
    for all 8-way neighbors (X'=X±1, Y'=Y±1, inside image):
        if I[X',Y'] == 1:
            FloodFill(X', Y', Label)

def CCL(Image I):
    Label= 1
    for Y in range(I.Height):
        for X in range(I.Width):
            if I[X, Y] == 1:
                Label+= 1
                FloodFill(X, Y, Label)

所以我会推荐扫描线版本，它涉及更多。

https://en.wikipedia.org/wiki/Flood_fill#Scanline_fill

Python 中的连通分量标记算法

Connected Component Labeling Algorithm in Python

python

algorithm

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connected-components