测量区域属性

这个示例展示了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() 的结果在每个区域上绘制某些属性。例如，我们用红色绘制每个椭圆的长轴和短轴。

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() 函数来计算每个区域的（选定）属性。请注意，skimage.measure.regionprops_table 实际上计算了这些属性，而 skimage.measure.regionprops 则在它们被使用时计算（惰性求值）。

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

我们现在展示这些选定属性的表格（每行一个区域），skimage.measure.regionprops_table 的结果是一个与 pandas 兼容的字典。

pd.DataFrame(props)

	centroid-0	centroid-1	orientation	axis_major_length	axis_minor_length
0	100.000000	100.000000	0.000000	119.807049	99.823995
1	286.914167	348.412995	-1.308966	440.015503	199.918850

通过在标签的悬停信息中可视化标签对象的属性，也可以交互式地探索这些属性。此示例使用 plotly 在悬停对象时显示属性。

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)