测量区域属性

此示例演示如何测量标记图像区域的属性。我们首先分析一个包含两个椭圆的图像。下面我们将展示如何交互式地探索标记对象的属性。

import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from skimage.draw import ellipse
from skimage.measure import label, regionprops, regionprops_table
from skimage.transform import rotate


image = np.zeros((600, 600))

rr, cc = ellipse(300, 350, 100, 220)
image[rr, cc] = 1

image = rotate(image, angle=15, order=0)

rr, cc = ellipse(100, 100, 60, 50)
image[rr, cc] = 1

label_img = label(image)
regions = regionprops(label_img)

我们使用 skimage.measure.regionprops() 结果在每个区域上绘制某些属性。例如，我们以红色绘制每个椭圆的主轴和副轴。

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

for props in regions:
    y0, x0 = props.centroid
    orientation = props.orientation
    x1 = x0 + math.cos(orientation) * 0.5 * props.axis_minor_length
    y1 = y0 - math.sin(orientation) * 0.5 * props.axis_minor_length
    x2 = x0 - math.sin(orientation) * 0.5 * props.axis_major_length
    y2 = y0 - math.cos(orientation) * 0.5 * props.axis_major_length

    ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)
    ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)
    ax.plot(x0, y0, '.g', markersize=15)

    minr, minc, maxr, maxc = props.bbox
    bx = (minc, maxc, maxc, minc, minc)
    by = (minr, minr, maxr, maxr, minr)
    ax.plot(bx, by, '-b', linewidth=2.5)

ax.axis((0, 600, 600, 0))
plt.show()

我们使用 skimage.measure.regionprops_table() 函数计算每个区域的（选定）属性。请注意，skimage.measure.regionprops_table 实际上计算了属性，而 skimage.measure.regionprops 在使用时才计算它们（延迟计算）。

props = regionprops_table(
    label_img,
    properties=('centroid', 'orientation', 'axis_major_length', 'axis_minor_length'),
)

我们现在显示一个包含这些选定属性的表格（每个区域一行），skimage.measure.regionprops_table 的结果是一个与 pandas 兼容的字典。

pd.DataFrame(props)

	质心-0	质心-1	方向	主轴长度	副轴长度
0	100.000000	100.000000	0.000000	119.807049	99.823995
1	286.914167	348.412995	-1.308966	440.015503	199.918850

还可以通过在标签的悬停信息中可视化来交互式地探索标记对象的属性。此示例使用 plotly 来显示悬停在物体上的属性。

import plotly
import plotly.express as px
import plotly.graph_objects as go
from skimage import data, filters, measure, morphology

img = data.coins()
# Binary image, post-process the binary mask and compute labels
threshold = filters.threshold_otsu(img)
mask = img > threshold
mask = morphology.remove_small_objects(mask, 50)
mask = morphology.remove_small_holes(mask, 50)
labels = measure.label(mask)

fig = px.imshow(img, binary_string=True)
fig.update_traces(hoverinfo='skip')  # hover is only for label info

props = measure.regionprops(labels, img)
properties = ['area', 'eccentricity', 'perimeter', 'intensity_mean']

# For each label, add a filled scatter trace for its contour,
# and display the properties of the label in the hover of this trace.
for index in range(1, labels.max()):
    label_i = props[index].label
    contour = measure.find_contours(labels == label_i, 0.5)[0]
    y, x = contour.T
    hoverinfo = ''
    for prop_name in properties:
        hoverinfo += f'<b>{prop_name}: {getattr(props[index], prop_name):.2f}</b><br>'
    fig.add_trace(
        go.Scatter(
            x=x,
            y=y,
            name=label_i,
            mode='lines',
            fill='toself',
            showlegend=False,
            hovertemplate=hoverinfo,
            hoveron='points+fills',
        )
    )

plotly.io.show(fig)