python 如何在主图像之上叠加分割图像
How to overlay segmented image on top of main image in python
我有一个 RGB 图像和另一个分段图像，其中像素有 3 个值（分段图像）。我想将分割图像叠加在主图像之上，因为分割区域在主图像上形成轮廓，如下图所示。这里分割后的图像像素值为0、1、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。
图片来自论文"Dilated-Inception Net: Multi-Scale FeatureAggregation for Cardiac Right VentricleSegmentation"
这是一个分割图像的例子。
segmented image
背景图片可以是任何图片。我只需要这些矩形计数器作为类似于上面的红线和黄线的两个轮廓出现在背景图像上。因此，输出将类似于下图。
output image
抱歉，我手画的矩形不准确。我只想让您深入了解输出。
我尝试了四种不同的方法：
- OpenCV
- PIL/Pillow 和 Numpy
- 命令行与 ImageMagick
- 来自 skimage 的形态
方法 1 - OpenCV
- 以灰度打开分割图像
- 将主图打开为灰度并制作颜色以允许注释
- 使用
cv2.findContours() 查找轮廓
- 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颜色绘制到主图像上。
文档是 here。
因此，从这张图片开始：
和这个分割图像：
对比拉伸时看起来像这样，三明治标记为 grey(1)，鼻子标记为 grey(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)
结果：
方法 2 - PIL/Pillow 和 Numpy
- 打开分割图像并找到独特的颜色
- 打开主图并去色
- 遍历列表中的每个唯一颜色
- ...使所有像素为白色，所有其他像素为黑色
- ...寻找边缘并使用边缘作为蒙版在主图像上绘制颜色
代码如下：
#!/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')
你会得到这样的结果：
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 上的 ImageMagick发行版并可用于 macOS 和 Windows:
#!/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
方法 4 - 来自 skimage 的形态学
我在这里使用形态学来查找 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)
注意：JPEG 是有损的 - 不要将分割后的图像保存为 JPEG，请使用 PNG 或 GIF！
关键词: Python, PIL, Pillow, OpenCV, segmentation, segmented, labeled, image, image processing, edges, contours, skimage, ImageMagick, scikit-图像、形态、等级、等级过滤器、像素邻接。
如果 semi-transparent 分割蒙版要显示在图像顶部，skimage 有一个 built-in label2rgb() 函数，通过标签通道着色：
输入图片
from skimage import io, color
import matplotlib.pyplot as plt
import numpy as np
seg = np.zeros((256,256)) # create a matrix of zeroes of same size as image
seg[gt > 0.95] = 1 # Change zeroes to label "1" as per your condition(s)
seg[zz == 255] = 2
io.imshow(color.label2rgb(seg,img,colors=[(255,0,0),(0,0,255)],alpha=0.01, bg_label=0, bg_color=None))
plt.show()
这些是快速的单行代码，可以自动为 category/class 整数值选择颜色并在原始图像上执行叠加。
给整个分割区域上色：
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')
我有一个 RGB 图像和另一个分段图像，其中像素有 3 个值（分段图像）。我想将分割图像叠加在主图像之上，因为分割区域在主图像上形成轮廓，如下图所示。这里分割后的图像像素值为0、1、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。
图片来自论文"Dilated-Inception Net: Multi-Scale FeatureAggregation for Cardiac Right VentricleSegmentation"
这是一个分割图像的例子。
segmented image
背景图片可以是任何图片。我只需要这些矩形计数器作为类似于上面的红线和黄线的两个轮廓出现在背景图像上。因此，输出将类似于下图。
output image
抱歉，我手画的矩形不准确。我只想让您深入了解输出。
我尝试了四种不同的方法：
- OpenCV
- PIL/Pillow 和 Numpy
- 命令行与 ImageMagick
- 来自 skimage 的形态
方法 1 - OpenCV
- 以灰度打开分割图像
- 将主图打开为灰度并制作颜色以允许注释
- 使用
cv2.findContours() 查找轮廓
- 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颜色绘制到主图像上。
文档是 here。
因此，从这张图片开始：
和这个分割图像：
对比拉伸时看起来像这样，三明治标记为 grey(1)，鼻子标记为 grey(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)
结果：
方法 2 - PIL/Pillow 和 Numpy
- 打开分割图像并找到独特的颜色
- 打开主图并去色
- 遍历列表中的每个唯一颜色
- ...使所有像素为白色，所有其他像素为黑色
- ...寻找边缘并使用边缘作为蒙版在主图像上绘制颜色
代码如下：
#!/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')
你会得到这样的结果：
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 上的 ImageMagick发行版并可用于 macOS 和 Windows:
#!/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
方法 4 - 来自 skimage 的形态学
我在这里使用形态学来查找 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)
注意：JPEG 是有损的 - 不要将分割后的图像保存为 JPEG，请使用 PNG 或 GIF！
关键词: Python, PIL, Pillow, OpenCV, segmentation, segmented, labeled, image, image processing, edges, contours, skimage, ImageMagick, scikit-图像、形态、等级、等级过滤器、像素邻接。
如果 semi-transparent 分割蒙版要显示在图像顶部，skimage 有一个 built-in label2rgb() 函数，通过标签通道着色：
输入图片
from skimage import io, color
import matplotlib.pyplot as plt
import numpy as np
seg = np.zeros((256,256)) # create a matrix of zeroes of same size as image
seg[gt > 0.95] = 1 # Change zeroes to label "1" as per your condition(s)
seg[zz == 255] = 2
io.imshow(color.label2rgb(seg,img,colors=[(255,0,0),(0,0,255)],alpha=0.01, bg_label=0, bg_color=None))
plt.show()
这些是快速的单行代码，可以自动为 category/class 整数值选择颜色并在原始图像上执行叠加。
给整个分割区域上色：
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')