我使用U2NET成功地计算了图像的掩膜，如下所示：
然而，可以看到，掩膜内部并不是完全白色，同时掩膜外部也存在一些不需要的伪影，即左侧部分。
我试图使用膨胀和腐蚀操作来解决上述问题，但我认为这是错误的方法，因为它无法产生预期的结果。
我尝试了以下类似的操作，但并没有解决问题：
from PIL import ImageFilter
dilation_img = image.filter(ImageFilter.MaxFilter(15))
erotion_img = dilation_img.filter(ImageFilter.MinFilter(15))
您可以使用Otsu算法动态阈值化图像。之后，您可以通过在OpenCV轮廓上绘制来填充任意空洞。我不确定U2Net在掩膜时可以返回什么值范围，但您也可以手动设置一个小的阈值值~50。
import cv2
import numpy as np
# load image
img = cv2.imread("mask.jpg", cv2.IMREAD_GRAYSCALE);
# otsu thresholding
_, mask = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU);
# show
cv2.imshow("Mask", mask);
cv2.waitKey(0);
# close everything inside
contour, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE);
# get the biggest contour # returns _, contours, _ if using OpenCV 3
biggest_area = -1;
biggest = None;
for con in contour:
area = cv2.contourArea(con);
if biggest_area < area:
biggest_area = area;
biggest = con;
# fill in the contour
cv2.drawContours(mask, [biggest], -1, 255, -1);
# show
cv2.imshow("Filled Mask", mask);
cv2.waitKey(0);
#include <stdio.h>
#include <opencv2/opencv.hpp>
#include <Windows.h>
#include <string>
using namespace cv;
int main(int argc, char** argv)
{
//C:/Local Software/voyDICOM/resources/images/oXsnC.jpg
std::string fileName = "C:/Local Software/voyDICOM/resources/images/oXsnC.jpg";
Mat tempImage = imread(fileName, cv::IMREAD_GRAYSCALE);
Mat bwImg;
//binary thresh (both of these work, otsu just gets a "smarter" threshold value rather than a hardcoded one)
cv::threshold(tempImage, bwImg, 150, 255, cv::THRESH_BINARY);
//cv::threshold(tempImage, bwImg, 0, 255, cv::THRESH_OTSU);
Mat openedImage;
//opening
cv::erode(bwImg, openedImage, cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(3, 3)), cv::Point(-1, -1), 2);
cv::dilate(openedImage, openedImage, cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(3, 3)), cv::Point(-1, -1), 2);
Mat closedImg;
//closing
cv::dilate(openedImage, closedImg, cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(3, 3)),cv::Point(-1,-1),5);
cv::erode(closedImg, closedImg, cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(3, 3)), cv::Point(-1, -1), 5);
namedWindow("Original", WINDOW_AUTOSIZE);
imshow("Original", tempImage);
namedWindow("Thresh", WINDOW_AUTOSIZE);
imshow("Thresh", bwImg);
namedWindow("Opened", WINDOW_AUTOSIZE);
imshow("Opened", openedImage);
namedWindow("Closed", WINDOW_AUTOSIZE);
imshow("Closed", closedImg);
waitKey(0);
system("pause");
return 0;
}
使用最小值滤波器（例如MinFilter(8)）= 结果1
然后对结果1应用相同值的最大值滤波器（例如MaxFilter(8)）= 结果2
result 2作为原始掩码的掩码，方法如下：
...
//assumptions: white=255, black=0
threshold = 128;// you can change it to give more weight for white or black
for(... loop all the the pixels)
for(...)
{
if((OrginalMask[x,y] > threshold) && (result2[x,y] < threshold))
finalResult[x,y] = result2[x,y];//to remove unwanted artifacts outside the mask
else if ((OrginalMask[x,y] < threshold) && (result2[x,y] > threshold))
finalResult[x,y] = result2[x,y];//to make the mask completely white inside
else
finalResult[x,y] = OrginalMask[x,y];
}
一种解决方案是使用二值阈值处理，使不是白色但接近白色的部分变为白色。
另一种解决方案是使用形态学运算符（morphological operators）来填充或删除任何间隙（请注意，形态学运算符如腐蚀/膨胀可能需要对图像进行多次处理才能获得所需的结果）。
两者都可以使用。
为了自动/自适应计算所需的阈值，我想到的过程类似于使用适当的大型结构元素执行形态学操作。也就是说，扫描图像的区域并使用该区域像素的多数表决来着色该区域的所有像素。