问题描述
我正在尝试使用 OpenCV 的 cv::threshold 函数(更具体的 THRESH_OTSU)，只是我想用掩码(任何形状)，以便在计算时忽略外部(背景).
I'm trying to use OpenCV's cv::threshold function (more specific THRESH_OTSU), only that I'd like to do it with a mask (any shape), so that the outside (background) is ignored during calculation.
图像是单通道的(必须是)，下面的红色仅用于在图像上标记示例多边形.
Image is single channel (as it must be), red color bellow is only to mark an example polygon on an image.
我尝试使用 adaptiveThreshold，但有几个问题使它不适合我的情况.
I tried using adaptiveThreshold, but there are a couple of problems that make it inappropriate in my case.
推荐答案
一般来说，你可以简单地使用 cv::threshold 计算阈值，然后复制 src> 图像在 dst 上使用倒置的 mask.
In general, you can simply compute the threshold using cv::threshold, and then copy the src image on dst using the inverted mask.
// Apply cv::threshold on all image
thresh = cv::threshold(src, dst, thresh, maxval, type);
// Copy original image on inverted mask
src.copyTo(dst, ~mask);
使用THRESH_OTSU，但是，您还需要仅在蒙版图像上计算阈值.以下代码是thresh.cpp中static double getThreshVal_Otsu_8u(const Mat& _src)的修改版:
With THRESH_OTSU, however, you also need to compute the threshold value only on the masked image. The following code is a modified version of static double getThreshVal_Otsu_8u(const Mat& _src) in thresh.cpp:
double otsu_8u_with_mask(const Mat1b src, const Mat1b& mask)
{
const int N = 256;
int M = 0;
int i, j, h[N] = { 0 };
for (i = 0; i < src.rows; i++)
{
const uchar* psrc = src.ptr(i);
const uchar* pmask = mask.ptr(i);
for (j = 0; j < src.cols; j++)
{
if (pmask[j])
{
h[psrc[j]]++;
++M;
}
}
}
double mu = 0, scale = 1. / (M);
for (i = 0; i < N; i++)
mu += i*(double)h[i];
mu *= scale;
double mu1 = 0, q1 = 0;
double max_sigma = 0, max_val = 0;
for (i = 0; i < N; i++)
{
double p_i, q2, mu2, sigma;
p_i = h[i] * scale;
mu1 *= q1;
q1 += p_i;
q2 = 1. - q1;
if (std::min(q1, q2) < FLT_EPSILON || std::max(q1, q2) > 1. - FLT_EPSILON)
continue;
mu1 = (mu1 + i*p_i) / q1;
mu2 = (mu - q1*mu1) / q2;
sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2);
if (sigma > max_sigma)
{
max_sigma = sigma;
max_val = i;
}
}
return max_val;
}
然后您可以将所有这些都包装在一个函数中，这里称为 threshold_with_mask，它为您包装了所有不同的情况.如果没有遮罩，或者遮罩是全白的，则使用cv::threshold.否则，请使用上述方法之一.请注意，此包装器仅适用于 CV_8UC1 图像(为简单起见，您可以轻松地将其扩展为使用其他类型，如果需要)，并接受所有 THRESH_XXX 组合作为原始cv::threshold.
You then can wrap all in a function, here called threshold_with_mask, that wraps all different cases for you. If there is no mask, or the mask is all-white, then use cv::threshold. Otherwise, use one of the above mentioned approaches. Note that this wrapper works only for CV_8UC1 images (for simplicity sake, you can easily expand it to work with other types, if needed), and accepts all THRESH_XXX combinations as original cv::threshold.
double threshold_with_mask(Mat1b& src, Mat1b& dst, double thresh, double maxval, int type, const Mat1b& mask = Mat1b())
{
if (mask.empty() || (mask.rows == src.rows && mask.cols == src.cols && countNonZero(mask) == src.rows * src.cols))
{
// If empty mask, or all-white mask, use cv::threshold
thresh = cv::threshold(src, dst, thresh, maxval, type);
}
else
{
// Use mask
bool use_otsu = (type & THRESH_OTSU) != 0;
if (use_otsu)
{
// If OTSU, get thresh value on mask only
thresh = otsu_8u_with_mask(src, mask);
// Remove THRESH_OTSU from type
type &= THRESH_MASK;
}
// Apply cv::threshold on all image
thresh = cv::threshold(src, dst, thresh, maxval, type);
// Copy original image on inverted mask
src.copyTo(dst, ~mask);
}
return thresh;
}
这里是完整的代码供参考:
Here is the full code for reference:
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
// Modified from thresh.cpp
// static double getThreshVal_Otsu_8u(const Mat& _src)
double otsu_8u_with_mask(const Mat1b src, const Mat1b& mask)
{
const int N = 256;
int M = 0;
int i, j, h[N] = { 0 };
for (i = 0; i < src.rows; i++)
{
const uchar* psrc = src.ptr(i);
const uchar* pmask = mask.ptr(i);
for (j = 0; j < src.cols; j++)
{
if (pmask[j])
{
h[psrc[j]]++;
++M;
}
}
}
double mu = 0, scale = 1. / (M);
for (i = 0; i < N; i++)
mu += i*(double)h[i];
mu *= scale;
double mu1 = 0, q1 = 0;
double max_sigma = 0, max_val = 0;
for (i = 0; i < N; i++)
{
double p_i, q2, mu2, sigma;
p_i = h[i] * scale;
mu1 *= q1;
q1 += p_i;
q2 = 1. - q1;
if (std::min(q1, q2) < FLT_EPSILON || std::max(q1, q2) > 1. - FLT_EPSILON)
continue;
mu1 = (mu1 + i*p_i) / q1;
mu2 = (mu - q1*mu1) / q2;
sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2);
if (sigma > max_sigma)
{
max_sigma = sigma;
max_val = i;
}
}
return max_val;
}
double threshold_with_mask(Mat1b& src, Mat1b& dst, double thresh, double maxval, int type, const Mat1b& mask = Mat1b())
{
if (mask.empty() || (mask.rows == src.rows && mask.cols == src.cols && countNonZero(mask) == src.rows * src.cols))
{
// If empty mask, or all-white mask, use cv::threshold
thresh = cv::threshold(src, dst, thresh, maxval, type);
}
else
{
// Use mask
bool use_otsu = (type & THRESH_OTSU) != 0;
if (use_otsu)
{
// If OTSU, get thresh value on mask only
thresh = otsu_8u_with_mask(src, mask);
// Remove THRESH_OTSU from type
type &= THRESH_MASK;
}
// Apply cv::threshold on all image
thresh = cv::threshold(src, dst, thresh, maxval, type);
// Copy original image on inverted mask
src.copyTo(dst, ~mask);
}
return thresh;
}
int main()
{
// Load an image
Mat1b img = imread("D:\SO\img\nice.jpg", IMREAD_GRAYSCALE);
// Apply OpenCV version
Mat1b cvth;
double cvth_value = threshold(img, cvth, 100, 255, THRESH_OTSU);
// Create a binary mask
Mat1b mask(img.rows, img.cols, uchar(0));
rectangle(mask, Rect(100, 100, 200, 200), Scalar(255), CV_FILLED);
// Apply threshold with a mask
Mat1b th;
double th_value = threshold_with_mask(img, th, 100, 255, THRESH_OTSU, mask);
// Show results
imshow("cv::threshod", cvth);
imshow("threshold_with_balue", th);
waitKey();
return 0;
}
这篇关于带掩码的 OpenCV 阈值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！