threshold algorithm: The simplest image segmentation method.

All thresholding algorithms take a source image (src) and a threshold value (thresh) as input and produce an output image (dst) by comparing the pixel value at source pixel( x , y ) to the threshold. If src ( x , y ) > thresh , then dst ( x , y ) is assigned a some value. Otherwise dst ( x , y ) is assigned some other value.

Otsu binarization: in simple words, it automatically calculates a threshold value from image histogram for a bimodal image. (For images which are not bimodal,binarization won’t be accurate.). working with bimodal images, Otsu’s algorithmtries to find a threshold value (t) which minimizes the weighted within-class variance. It actually finds a value of t which lies in between two peaks such that variances to both classes are minimum.

Otsu's thresholding method involves iterating through all the possible threshold values and calculating a measure of spread for the pixel levels each side of the threshold, i.e. the pixels that either fall in foreground or background.The aim is to find the threshold value where the sum of foreground and background spreads is at its minimum.

Triangle algorithm: A line is constructed between the maximum of the histogram at brightness bmax and the lowest value bmin in the image. The distance d between the line and the histogram h[b] is computed for all values of b from b = bmin to b = bmax. The brightness value bo where the distance between h[bo] and the line is maximal is the threshold value, that is, threshold = bo. This technique is particularly effective when the object pixels produce a weak peak in the histogram.

图像二值化就是将图像上的像素点的灰度值设置为两个值,一般为0,255或者指定的某个值。

Otsu:

目前fbc_cv库中支持uchar和float两种数据类型,经测试,与OpenCV3.1结果完全一致。

实现代码threshold.hpp:

// fbc_cv is free software and uses the same licence as OpenCV
// Email: fengbingchun@163.com

#ifndef FBC_CV_THRESHOLD_HPP_
#define FBC_CV_THRESHOLD_HPP_

/* reference: include/opencv2/imgproc.hpp
              modules/imgproc/src/thresh.cpp
*/

#include <typeinfo>
#include "core/mat.hpp"
#include "imgproc.hpp"

namespace fbc {

template<typename _Tp, int chs> static double getThreshVal_Otsu_8u(const Mat_<_Tp, chs>& src);
template<typename _Tp, int chs> static double getThreshVal_Triangle_8u(const Mat_<_Tp, chs>& src);
template<typename _Tp, int chs> static void thresh_8u(const Mat_<_Tp, chs>& _src, Mat_<_Tp, chs>& _dst, uchar thresh, uchar maxval, int type);
template<typename _Tp, int chs> static void thresh_32f(const Mat_<_Tp, chs>& _src, Mat_<_Tp, chs>& _dst, float thresh, float maxval, int type);

// applies fixed-level thresholding to a single-channel array
// the Otsu's and Triangle methods are implemented only for 8-bit images
// support type: uchar/float, single-channel
template<typename _Tp, int chs>
double threshold(const Mat_<_Tp, chs>& src, Mat_<_Tp, chs>& dst, double thresh, double maxval, int type)
{
	FBC_Assert(typeid(uchar).name() == typeid(_Tp).name() || typeid(float).name() == typeid(_Tp).name()); // uchar || float
	if (dst.empty()) {
		dst = Mat_<_Tp, chs>(src.rows, src.cols);
	} else {
		FBC_Assert(src.rows == dst.rows && src.cols == dst.cols);
	}

	int automatic_thresh = (type & ~THRESH_MASK);
	type &= THRESH_MASK;

	FBC_Assert(automatic_thresh != (THRESH_OTSU | THRESH_TRIANGLE));
	if (automatic_thresh == THRESH_OTSU) {
		FBC_Assert(sizeof(_Tp) == 1);
		thresh = getThreshVal_Otsu_8u(src);
	} else if (automatic_thresh == THRESH_TRIANGLE) {
		FBC_Assert(sizeof(_Tp) == 1);
		thresh = getThreshVal_Triangle_8u(src);
	}

	if (sizeof(_Tp) == 1) {
		int ithresh = fbcFloor(thresh);
		thresh = ithresh;
		int imaxval = fbcRound(maxval);
		if (type == THRESH_TRUNC)
			imaxval = ithresh;
		imaxval = saturate_cast<uchar>(imaxval);

		if (ithresh < 0 || ithresh >= 255) {
			if (type == THRESH_BINARY || type == THRESH_BINARY_INV ||
				((type == THRESH_TRUNC || type == THRESH_TOZERO_INV) && ithresh < 0) ||
				(type == THRESH_TOZERO && ithresh >= 255)) {
				int v = type == THRESH_BINARY ? (ithresh >= 255 ? 0 : imaxval) :
					type == THRESH_BINARY_INV ? (ithresh >= 255 ? imaxval : 0) :
					/*type == THRESH_TRUNC ? imaxval :*/ 0;
				dst.setTo(v);
			}
			else
				src.copyTo(dst);
			return thresh;
		}
		thresh = ithresh;
		maxval = imaxval;
	} else if (sizeof(_Tp) == 4) {
	} else {
		FBC_Error("UnsupportedFormat");
	}

	if (sizeof(_Tp) == 1) {
		thresh_8u(src, dst, (uchar)thresh, (uchar)maxval, type);
	} else {
		thresh_32f(src, dst, (float)thresh, (float)maxval, type);
	}

	return 0;
}

template<typename _Tp, int chs>
static double getThreshVal_Otsu_8u(const Mat_<_Tp, chs>& _src)
{
	Size size = _src.size();
	const int N = 256;
	int i, j, h[N] = { 0 };

	for (i = 0; i < size.height; i++) {
		const uchar* src = _src.ptr(i);
		j = 0;
		for (; j <= size.width - 4; j += 4) {
			int v0 = src[j], v1 = src[j + 1];
			h[v0]++; h[v1]++;
			v0 = src[j + 2]; v1 = src[j + 3];
			h[v0]++; h[v1]++;
		}
		for (; j < size.width; j++)
			h[src[j]]++;
	}

	double mu = 0, scale = 1. / (size.width*size.height);
	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;
}

template<typename _Tp, int chs>
static double getThreshVal_Triangle_8u(const Mat_<_Tp, chs>& _src)
{
	Size size = _src.size();
	const int N = 256;
	int i, j, h[N] = { 0 };

	for (i = 0; i < size.height; i++) {
		const uchar* src = _src.ptr(i);
		j = 0;
		for (; j <= size.width - 4; j += 4) {
			int v0 = src[j], v1 = src[j + 1];
			h[v0]++; h[v1]++;
			v0 = src[j + 2]; v1 = src[j + 3];
			h[v0]++; h[v1]++;
		}

		for (; j < size.width; j++)
			h[src[j]]++;
	}

	int left_bound = 0, right_bound = 0, max_ind = 0, max = 0;
	int temp;
	bool isflipped = false;

	for (i = 0; i < N; i++) {
		if (h[i] > 0) {
			left_bound = i;
			break;
		}
	}
	if (left_bound > 0)
		left_bound--;

	for (i = N - 1; i > 0; i--) {
		if (h[i] > 0) {
			right_bound = i;
			break;
		}
	}
	if (right_bound < N - 1)
		right_bound++;

	for (i = 0; i < N; i++) {
		if (h[i] > max) {
			max = h[i];
			max_ind = i;
		}
	}

	if (max_ind - left_bound < right_bound - max_ind) {
		isflipped = true;
		i = 0, j = N - 1;
		while (i < j) {
			temp = h[i]; h[i] = h[j]; h[j] = temp;
			i++; j--;
		}
		left_bound = N - 1 - right_bound;
		max_ind = N - 1 - max_ind;
	}

	double thresh = left_bound;
	double a, b, dist = 0, tempdist;

	// We do not need to compute precise distance here. Distance is maximized, so some constants can
	// be omitted. This speeds up a computation a bit.
	a = max; b = left_bound - max_ind;
	for (i = left_bound + 1; i <= max_ind; i++) {
		tempdist = a*i + b*h[i];
		if (tempdist > dist) {
			dist = tempdist;
			thresh = i;
		}
	}
	thresh--;

	if (isflipped)
		thresh = N - 1 - thresh;

	return thresh;
}

template<typename _Tp, int chs>
static void thresh_8u(const Mat_<_Tp, chs>& _src, Mat_<_Tp, chs>& _dst, uchar thresh, uchar maxval, int type)
{
	int i, j, j_scalar = 0;
	uchar tab[256];
	Size roi = _src.size();
	roi.width *= _src.channels;

	switch (type) {
	case THRESH_BINARY:
		for (i = 0; i <= thresh; i++)
			tab[i] = 0;
		for (; i < 256; i++)
			tab[i] = maxval;
		break;
	case THRESH_BINARY_INV:
		for (i = 0; i <= thresh; i++)
			tab[i] = maxval;
		for (; i < 256; i++)
			tab[i] = 0;
		break;
	case THRESH_TRUNC:
		for (i = 0; i <= thresh; i++)
			tab[i] = (uchar)i;
		for (; i < 256; i++)
			tab[i] = thresh;
		break;
	case THRESH_TOZERO:
		for (i = 0; i <= thresh; i++)
			tab[i] = 0;
		for (; i < 256; i++)
			tab[i] = (uchar)i;
		break;
	case THRESH_TOZERO_INV:
		for (i = 0; i <= thresh; i++)
			tab[i] = (uchar)i;
		for (; i < 256; i++)
			tab[i] = 0;
		break;
	default:
		FBC_Error("Unknown threshold type");
	}

	if (j_scalar < roi.width) {
		for (i = 0; i < roi.height; i++) {
			const uchar* src = _src.ptr(i);
			uchar* dst = _dst.ptr(i);
			j = j_scalar;

			for (; j <= roi.width - 4; j += 4) {
				uchar t0 = tab[src[j]];
				uchar t1 = tab[src[j + 1]];

				dst[j] = t0;
				dst[j + 1] = t1;

				t0 = tab[src[j + 2]];
				t1 = tab[src[j + 3]];

				dst[j + 2] = t0;
				dst[j + 3] = t1;
			}

			for (; j < roi.width; j++)
				dst[j] = tab[src[j]];
		}
	}
}

template<typename _Tp, int chs>
static void thresh_32f(const Mat_<_Tp, chs>& _src, Mat_<_Tp, chs>& _dst, float thresh, float maxval, int type)
{
	int i, j;
	Size roi = _src.size();
	roi.width *= _src.channels;
	const float* src = (const float*)_src.ptr();
	float* dst = (float*)_dst.ptr();
	size_t src_step = _src.step / sizeof(src[0]);
	size_t dst_step = _dst.step / sizeof(dst[0]);

	switch (type) {
	case THRESH_BINARY:
		for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step) {
			for (j = 0; j < roi.width; j++)
				dst[j] = src[j] > thresh ? maxval : 0;
		}
		break;

	case THRESH_BINARY_INV:
		for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step) {
			for (j = 0; j < roi.width; j++)
				dst[j] = src[j] <= thresh ? maxval : 0;
		}
		break;

	case THRESH_TRUNC:
		for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step) {
			for (j = 0; j < roi.width; j++)
				dst[j] = std::min(src[j], thresh);
		}
		break;

	case THRESH_TOZERO:
		for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step) {
			for (j = 0; j < roi.width; j++) {
				float v = src[j];
				dst[j] = v > thresh ? v : 0;
			}
		}
		break;

	case THRESH_TOZERO_INV:
		for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step) {
			for (j = 0; j < roi.width; j++) {
				float v = src[j];
				dst[j] = v <= thresh ? v : 0;
			}
		}
		break;
	default:
		FBC_Error("BadArg");
	}
}

} // namespace fbc

#endif // FBC_CV_THRESHOLD_HPP_

测试代码test_threshold.cpp:

#include "test_threshold.hpp"
#include <assert.h>

#include <threshold.hpp>
#include <opencv2/opencv.hpp>

int test_threshold_uchar()
{
	cv::Mat matSrc = cv::imread("E:/GitCode/OpenCV_Test/test_images/lena.png", 1);
	if (!matSrc.data) {
		std::cout << "read image fail" << std::endl;
		return -1;
	}
	cv::cvtColor(matSrc, matSrc, CV_BGR2GRAY);

	int width = matSrc.cols;
	int height = matSrc.rows;
	int types[8] = {0, 1, 2, 3, 4, 7, 8, 16};

	for (int i = 0; i < 8; i++) {
		if (types[i] == 7) continue;
		double thresh = 135.0;
		double maxval = 255.0;

		fbc::Mat_<uchar, 1> mat1(height, width, matSrc.data);
		fbc::Mat_<uchar, 1> mat2(height, width);
		fbc::threshold(mat1, mat2, thresh, maxval, types[i]);

		cv::Mat mat1_(height, width, CV_8UC1, matSrc.data);
		cv::Mat mat2_;
		cv::threshold(mat1_, mat2_, thresh, maxval, types[i]);

		assert(mat2.rows == mat2_.rows && mat2.cols == mat2_.cols && mat2.step == mat2_.step);
		for (int y = 0; y < mat2.rows; y++) {
			const fbc::uchar* p1 = mat2.ptr(y);
			const uchar* p2 = mat2_.ptr(y);

			for (int x = 0; x < mat2.step; x++) {
				assert(p1[x] == p2[x]);
			}
		}
	}

	return 0;
}

int test_threshold_float()
{
	cv::Mat matSrc = cv::imread("E:/GitCode/OpenCV_Test/test_images/lena.png", 1);
	if (!matSrc.data) {
		std::cout << "read image fail" << std::endl;
		return -1;
	}
	cv::cvtColor(matSrc, matSrc, CV_BGR2GRAY);
	matSrc.convertTo(matSrc, CV_32FC1);

	int width = matSrc.cols;
	int height = matSrc.rows;
	int types[6] = { 0, 1, 2, 3, 4, 7 };

	for (int i = 0; i < 6; i++) {
		if (types[i] == 7) continue;
		double thresh = 135.0;
		double maxval = 255.0;

		fbc::Mat_<float, 1> mat1(height, width, matSrc.data);
		fbc::Mat_<float, 1> mat2(height, width);
		fbc::threshold(mat1, mat2, thresh, maxval, types[i]);

		cv::Mat mat1_(height, width, CV_32FC1, matSrc.data);
		cv::Mat mat2_;
		cv::threshold(mat1_, mat2_, thresh, maxval, types[i]);

		assert(mat2.rows == mat2_.rows && mat2.cols == mat2_.cols && mat2.step == mat2_.step);
		for (int y = 0; y < mat2.rows; y++) {
			const fbc::uchar* p1 = mat2.ptr(y);
			const uchar* p2 = mat2_.ptr(y);

			for (int x = 0; x < mat2.step; x++) {
				assert(p1[x] == p2[x]);
			}
		}
	}

	return 0;
}

GitHubhttps://github.com/fengbingchun/OpenCV_Test

OpenCV代码提取: threshold函数的实现的更多相关文章

  1. OpenCV代码提取:transpose函数的实现

    OpenCV中的transpose函数实现图像转置,公式为: 目前fbc_cv库中也实现了transpose函数,支持多通道,uchar和float两种数据类型,经测试,与OpenCV3.1结果完全一 ...

  2. OpenCV代码提取:flip函数的实现

    OpenCV中实现图像翻转的函数flip,公式为: 目前fbc_cv库中也实现了flip函数,支持多通道,uchar和float两种数据类型,经测试,与OpenCV3.1结果完全一致. 实现代码fli ...

  3. OpenCV代码提取:dft函数的实现

    The Fourier Transform will decompose an image into its sinus and cosines components. In other words, ...

  4. OpenCV代码提取:遍历指定目录下指定文件的实现

    前言 OpenCV 3.1之前的版本,在contrib目录下有提供遍历文件的函数,用起来比较方便.但是在最新的OpenCV 3.1版本给去除掉了.为了以后使用方便,这里将OpenCV 2.4.9中相关 ...

  5. OpenCV中threshold函数的使用

    转自:https://blog.csdn.net/u012566751/article/details/77046445 一篇很好的介绍threshold文章: 图像的二值化就是将图像上的像素点的灰度 ...

  6. OpenCV 学习笔记03 threshold函数

    opencv-python   4.0.1 简介:该函数是对数组中的每一个元素(each array element)应用固定级别阈值(Applies a fixed-level threshold) ...

  7. opencv二值化的cv2.threshold函数

    (一)简单阈值 简单阈值当然是最简单,选取一个全局阈值,然后就把整幅图像分成了非黑即白的二值图像了.函数为cv2.threshold() 这个函数有四个参数,第一个原图像,第二个进行分类的阈值,第三个 ...

  8. OpenCV中的绘图函数-OpenCV步步精深

    OpenCV 中的绘图函数 画线 首先要为画的线创造出环境,就要生成一个空的黑底图像 img=np.zeros((512,512,3), np.uint8) 这是黑色的底,我们的画布,我把窗口名叫做i ...

  9. 基础学习笔记之opencv(24):imwrite函数的使用

    http://www.cnblogs.com/tornadomeet/archive/2012/12/26/2834336.html 前言 OpenCV中保存图片的函数在c++版本中变成了imwrit ...

随机推荐

  1. POJ 1379 模拟退火

    模拟退火算法,很久之前就写过一篇文章了.双倍经验题(POJ 2420) 题意: 在一个矩形区域内,求一个点的距离到所有点的距离最短的那个,最大. 这个题意,很像二分定义,但是毫无思路,也不能暴力枚举, ...

  2. Python的基本库与第三方库

    一:Python 模块,包,库的概念理解: 1.python模块是: python模块:包含并且有组织的代码片段为模块. 表现形式为:写的代码保存为文件.这个文件就是一个模块.sample.py 其中 ...

  3. Android学习笔记_76_AsyncQueryHandler的应用

    研究AsyncQueryHandler这个类的时候遇到了几个重要的不清楚的知识点 1. Handler与Thread,Looper的关系 2. HandlerThread是干什么用的 3. Threa ...

  4. 我和我的广告前端代码(六):webpack工程合并、也许我不需要gulp

    随着年初开始使用webpack重构公司的广告代码,已经有将近一年的时间了,需求也渐渐的稳定了.我想也是时候将这几个工程整理一下,顺带着处理一些历史问题. 由于当年各个业务线没有整合.需求也没有固定,考 ...

  5. laravel5项目安装debugbar

    链接:https://github.com/barryvdh/laravel-debugbar 1.项目目录运行 composer require barryvdh/laravel-debugbar ...

  6. python 通过 socket 发送文件

    目录结构: client: #!/usr/bin/env python # -*-coding:utf-8 -*- import socket, struct, json download_dir = ...

  7. 推荐几个Mac/Linux下比较好用的工具

    1.Tmux,连接开发机可以让在任务在开发机一直执行,不用nohup &这种了也相对稳定,还有session可以记录当时的状态. 常用命令: tmux new -s name 指定名字开启一个 ...

  8. VS2012 Getting Started with Owin and Katana

    参考地址:http://www.asp.net/aspnet/overview/owin-and-katana/getting-started-with-owin-and-katana 小提示: 该示 ...

  9. PThread 学习笔记

    POSIX 线程,也被称为Pthreads,是一个线程的POSIX标准: pthread.h int pthread_create(pthread_t * thread, pthread_attr_t ...

  10. shell脚本中 [-eq] [-ne] [-gt] [-lt] [ge] [le]

    -eq //等于 -ne //不等于 -gt //大于 (greater ) -lt //小于 (less) -ge //大于等于 -le //小于等于 在linux 中 命令执行状态:0 为真,其他 ...