在 RGB 图像上绘制多类语义分割透明叠加

我有语义分割掩码的结果（值在 0-1 之间，需要大津阈值来确定什么是积极的），我想直接在 RGB 图像上绘制，在 RGB 图像上每个预测类具有不同的随机颜色。

我使用以下内容绘制了具有单一颜色的单个蒙版。是否有一个包或简单的策略可以为多类别做到这一点？

fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(5, 5))
  ax.imshow(image, cmap='gray')
  ax.axis('off')
  mask = (fused_mosaic[..., channel]*255).astype('uint8')
  ret3,th3 = cv2.threshold(mask,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
  fig, ax = image_show(full_im)
  ax.imshow(mask>ret3, alpha=0.3)

I’m looking for something like this, just simples without the boxes and labels. I tried using detectron2 (the package that generates this annotation in the example, but they require some odd metadata object which I can’t figure out).

Thanks

Scikit 图像有一个内置的label2rgb()根据标签通道着色的函数：

#!/usr/bin/env python3

from skimage import io
from skimage import color
from skimage import segmentation
import matplotlib.pyplot as plt

# URL for tiger image from Berkeley Segmentation Data Set BSDS
url=('http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300/html/images/plain/normal/color/108073.jpg')

# Load tiger image from URL
tiger = io.imread(url)

# Segment image with SLIC - Simple Linear Iterative Clustering
seg = segmentation.slic(tiger, n_segments=30, compactness=40.0, enforce_connectivity=True, sigma=3)

# Generate automatic colouring from classification labels
io.imshow(color.label2rgb(seg,tiger))
plt.show()