在python中如何在主图像的顶部覆盖分割图像

我有一个RGB图像和另一个分割图像，其中像素有3个值（分割图像）。我想在主图像的顶部覆盖分割图像，因为分割区域在主图像上形成轮廓，如下图。这里，分割图像像素的值为0、1和2。红色轮廓表示值为1的像素轮廓，黄色轮廓表示值为2的像素轮廓，背景像素值为0

该图像来自“扩张起始网络：心脏右心室分割的多尺度特征聚合”一文

下面是一个分割图像的示例

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背景图像可以是任何图像。我只需要这些矩形计数器作为两个轮廓出现在背景图像上，类似于上面的红色和黄色线条。因此，输出将类似于下图

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对不起，我用手画矩形，它们不精确。我只想给你一个关于输出的见解

我尝试了四种不同的方法：

• OpenCV
• PIL/枕头和Numpy
• 带有ImageMagick的命令行
• 撇渣形态学

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方法1-OpenCV

• 打开分割图像作为灰度
• 将主图像打开为灰度，并使用颜色进行标注
• 使用

  cv2.findContours（）

• 迭代轮廓并使用

  cv2.drawContours（）

  根据分割图像中的标签将每个轮廓以彩色绘制到主图像上

文件是

因此，从这张图片开始：

这张分割图像：

当对比度拉伸时，三明治标记为灰色（1），鼻子标记为灰色（2）：

代码如下：

#!/usr/bin/env python3

import numpy as np
import cv2

# Load images as greyscale but make main RGB so we can annotate in colour
seg  = cv2.imread('segmented.png',cv2.IMREAD_GRAYSCALE)
main = cv2.imread('main.png',cv2.IMREAD_GRAYSCALE)
main = cv2.cvtColor(main,cv2.COLOR_GRAY2BGR)

# Dictionary giving RGB colour for label (segment label) - label 1 in red, label 2 in yellow
RGBforLabel = { 1:(0,0,255), 2:(0,255,255) }

# Find external contours
_,contours,_ = cv2.findContours(seg,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)

# Iterate over all contours
for i,c in enumerate(contours):
    # Find mean colour inside this contour by doing a masked mean
    mask = np.zeros(seg.shape, np.uint8)
    cv2.drawContours(mask,[c],-1,255, -1)
    # DEBUG: cv2.imwrite(f"mask-{i}.png",mask)
    mean,_,_,_ = cv2.mean(seg, mask=mask)
    # DEBUG: print(f"i: {i}, mean: {mean}")

    # Get appropriate colour for this label
    label = 2 if mean > 1.0 else 1
    colour = RGBforLabel.get(label)
    # DEBUG: print(f"Colour: {colour}")

    # Outline contour in that colour on main image, line thickness=1
    cv2.drawContours(main,[c],-1,colour,1)

# Save result
cv2.imwrite('result.png',main) 

#!/usr/bin/env python3

from PIL import Image, ImageFilter
import numpy as np

def drawContour(m,s,c,RGB):
    """Draw edges of contour 'c' from segmented image 's' onto 'm' in colour 'RGB'"""
    # Fill contour "c" with white, make all else black
    thisContour = s.point(lambda p:p==c and 255)
    # DEBUG: thisContour.save(f"interim{c}.png")

    # Find edges of this contour and make into Numpy array
    thisEdges   = thisContour.filter(ImageFilter.FIND_EDGES)
    thisEdgesN  = np.array(thisEdges)

    # Paint locations of found edges in color "RGB" onto "main"
    m[np.nonzero(thisEdgesN)] = RGB
    return m

# Load segmented image as greyscale
seg = Image.open('segmented.png').convert('L')

# Load main image - desaturate and revert to RGB so we can draw on it in colour
main = Image.open('main.png').convert('L').convert('RGB')
mainN = np.array(main)

mainN = drawContour(mainN,seg,1,(255,0,0))   # draw contour 1 in red
mainN = drawContour(mainN,seg,2,(255,255,0)) # draw contour 2 in yellow

# Save result
Image.fromarray(mainN).save('result.png')

结果：

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方法2-PIL/枕头和Numpy

• 打开分割图像，找到独特的颜色
• 打开主映像并去饱和
• 迭代列表中的每个唯一颜色
• 。。。使所有像素颜色为白色，所有其他像素颜色为黑色
• 。。。找到边并使用边作为遮罩在主图像上绘制颜色

代码如下：

#!/usr/bin/env python3

import numpy as np
import cv2

# Load images as greyscale but make main RGB so we can annotate in colour
seg  = cv2.imread('segmented.png',cv2.IMREAD_GRAYSCALE)
main = cv2.imread('main.png',cv2.IMREAD_GRAYSCALE)
main = cv2.cvtColor(main,cv2.COLOR_GRAY2BGR)

# Dictionary giving RGB colour for label (segment label) - label 1 in red, label 2 in yellow
RGBforLabel = { 1:(0,0,255), 2:(0,255,255) }

# Find external contours
_,contours,_ = cv2.findContours(seg,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)

# Iterate over all contours
for i,c in enumerate(contours):
    # Find mean colour inside this contour by doing a masked mean
    mask = np.zeros(seg.shape, np.uint8)
    cv2.drawContours(mask,[c],-1,255, -1)
    # DEBUG: cv2.imwrite(f"mask-{i}.png",mask)
    mean,_,_,_ = cv2.mean(seg, mask=mask)
    # DEBUG: print(f"i: {i}, mean: {mean}")

    # Get appropriate colour for this label
    label = 2 if mean > 1.0 else 1
    colour = RGBforLabel.get(label)
    # DEBUG: print(f"Colour: {colour}")

    # Outline contour in that colour on main image, line thickness=1
    cv2.drawContours(main,[c],-1,colour,1)

# Save result
cv2.imwrite('result.png',main) 

#!/usr/bin/env python3

from PIL import Image, ImageFilter
import numpy as np

def drawContour(m,s,c,RGB):
    """Draw edges of contour 'c' from segmented image 's' onto 'm' in colour 'RGB'"""
    # Fill contour "c" with white, make all else black
    thisContour = s.point(lambda p:p==c and 255)
    # DEBUG: thisContour.save(f"interim{c}.png")

    # Find edges of this contour and make into Numpy array
    thisEdges   = thisContour.filter(ImageFilter.FIND_EDGES)
    thisEdgesN  = np.array(thisEdges)

    # Paint locations of found edges in color "RGB" onto "main"
    m[np.nonzero(thisEdgesN)] = RGB
    return m

# Load segmented image as greyscale
seg = Image.open('segmented.png').convert('L')

# Load main image - desaturate and revert to RGB so we can draw on it in colour
main = Image.open('main.png').convert('L').convert('RGB')
mainN = np.array(main)

mainN = drawContour(mainN,seg,1,(255,0,0))   # draw contour 1 in red
mainN = drawContour(mainN,seg,2,(255,255,0)) # draw contour 2 in yellow

# Save result
Image.fromarray(mainN).save('result.png')

您将得到以下结果：

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方法3-ImageMagick

您也可以在命令行中执行相同的操作，而无需编写任何Python，只需使用安装在大多数Linux发行版上并可用于macOS和Windows的ImageMagick：

#!/bin/bash

# Make red overlay for "1" labels
convert segmented.png -colorspace gray -fill black +opaque "gray(1)" -fill white -opaque "gray(1)" -edge 1 -transparent black -fill red     -colorize 100% m1.gif
# Make yellow overlay for "2" labels
convert segmented.png -colorspace gray -fill black +opaque "gray(2)" -fill white -opaque "gray(2)" -edge 1 -transparent black -fill yellow  -colorize 100% m2.gif
# Overlay both "m1.gif" and "m2.gif" onto main image
convert main.png -colorspace gray -colorspace rgb m1.gif -composite m2.gif -composite result.png

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方法4-撇渣形态学

在这里，我使用形态学查找

1

像素附近的黑色像素和

2

像素附近的黑色像素

#!/usr/bin/env python3

import skimage.filters.rank
import skimage.morphology
import numpy as np
import cv2

# Load images as greyscale but make main RGB so we can annotate in colour
seg  = cv2.imread('segmented.png',cv2.IMREAD_GRAYSCALE)
main = cv2.imread('main.png',cv2.IMREAD_GRAYSCALE)
main = cv2.cvtColor(main,cv2.COLOR_GRAY2BGR)

# Create structuring element that defines the neighbourhood for morphology
selem = skimage.morphology.disk(1)

# Mask for edges of segment 1 and segment 2
# We are basically looking for pixels with value 1 in the segmented image within a radius of 1 pixel of a black pixel...
# ... then the same again but for pixels with a vaue of 2 in the segmented image within a radius of 1 pixel of a black pixel
seg1 = (skimage.filters.rank.minimum(seg,selem) == 0) & (skimage.filters.rank.maximum(seg, selem) == 1)
seg2 = (skimage.filters.rank.minimum(seg,selem) == 0) & (skimage.filters.rank.maximum(seg, selem) == 2)

main[seg1,:] = np.asarray([0, 0,   255]) # Make segment 1 pixels red in main image
main[seg2,:] = np.asarray([0, 255, 255]) # Make segment 2 pixels yellow in main image

# Save result
cv2.imwrite('result.png',main) 

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注意：JPEG是有损的-不要将分割图像保存为JPEG，请使用PNG或GIF

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关键词：Python、PIL、Pill、OpenCV、分割、分割、标记、图像、图像处理、边缘、轮廓、略读、ImageMagick、scikit图像、形态学、等级、排序过滤器、像素邻接。

如果半透明分割遮罩显示在图像顶部，

skimage

有一个内置的

label2rgb（）

函数，通过标签通道着色：

输入图像

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这些是快速的一行程序，可以自动为类别/类整数值选择颜色，并在原始图像上执行覆盖

整个分割区域的颜色：

from skimage import color
result_image = color.label2rgb(segmentation_results, input_image)

分割区域的颜色轮廓：

from skimage import segmentation
result_image = segmentation.mark_boundaries(input_image, segmentation_results, mode='thick')

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你的问题，如果有的话，是相当不清楚的。你说你有一个RGB图像和另一个分割图像。我们两个都要好吗？那个么，你们想对最终图像中的矩形做些什么呢？谢谢。背景图像可以是任何RGB图像，这就是为什么我没有添加任何作为主图像。我只需要第二张图像中矩形的计数器覆盖在背景（主）图像上，第一张图像中的红黄线覆盖在心脏CT图像的背景图像上。我如何知道分割图像的外观？您是否有红线的SVG路径？你有一个透明的背景和一条红线勾勒出一个形状吗？你有没有一张每个组织/物体都有纯色的立体图像？如果你需要帮助，一般来说，让人们更容易地帮助你是个好主意。还有，请问什么是“矩形计数器”？我已经添加了我需要的输出。感谢帮助。矩形轮廓是输出图像中的红色和黄色线条。它们应该画在分割图像中矩形占据的像素上。这很好，但它将输入图像转换为灰度。如果所讨论的图像是RGB，您知道有什么替代方案吗？参数

img

同时接受RGB和灰度图像。所以它应该可以正常工作。文档证实了这一点——它确实接受RGB图像，但在应用分割覆盖之前，它会转换为灰度。我希望看到原始，彩色图像背后的覆盖。编辑：我认为这个

saturation

arg可能是关键，但它只出现在skimage的dev分支上，它似乎没有安装在colab上。