python演示验证图像叠加过程_python中如何将分割后的图像叠加在主图像上

我尝试了四种不同的方法：OpenCV

枕头/枕头

使用ImageMagick命令行

撇渣形态

方法1-OpenCV开放分割图像为灰度

打开主图像作为灰度，并使颜色允许注释

使用cv2.findContours()查找轮廓

迭代等高线并使用cv2.drawContours()根据分割图像中的标签以颜色将每个轮廓绘制到主图像上。在

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所以，从这幅图开始：

这张分割图像：

当对比度被拉伸，三明治被标记为grey(1)，鼻部被标记为grey(2)，看起来是这样的：

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/枕头和Numpy打开分段图像，找到独特的颜色

打开主图像并去饱和度

在列表中的每个唯一颜色上迭代

。。。将所有像素设为白色，其他像素为黑色

。。。找到边缘并使用边缘作为遮罩在主图像上绘制颜色

代码如下：

^{pr2}$

您将得到以下结果：

方法3-ImageMagick

您也可以从命令行执行相同的操作，而无需编写任何Python，只需使用ImageMagick，它安装在大多数Linux发行版上，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-撇渣形态学

这里我使用形态学来查找1像素附近的黑色像素和靠近2像素的黑色像素。在#!/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有损-不要将分割后的图像保存为JPEG，请使用PNG或GIF！在

关键词：Python、PIL、pill、OpenCV、分割、分割、标记、图像、图像处理、边缘、轮廓、skimage、ImageMagick、scikit图像、形态学、排名、排序过滤器、像素邻接。在