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基于边缘检测和HSV的图像识别算法

DryDetect.h

#pragma once
#include <iostream>
#include <io.h>
#include <fstream>
#include <algorithm>
#include "opencv2/opencv.hpp"
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"

using namespace std;
using namespace cv;

struct DryParam
{
	//cv::Rect DryCard = cv::Rect(375, 95, 295, 505); //1, x,y,w,h (375，95), 670,600)
	//cv::Rect DryCard = cv::Rect(880, 385, 420, 745); //2, (880,385), (1300,1100)
	cv::Rect DryCard = cv::Rect(1070, 300, 390, 710); //8, (1070,300), (1460,1010)
	int drydetect = 1; //if 0, 不用算法定位
	float resizeRatio = 0;
	//边缘检测法
	double thresh1 = 40;   //边缘检测阈值
	double thresh2 = 255;
	int minWidth = 80;  //限制格子的最大最小宽高
	int minHeight = 80;
	int maxWidth = 130;
	int maxHeight = 130;
	float alpha = 2; //图像对比度
	float beta = 5;   //图像亮度

	cv::Vec3b lower_black = cv::Vec3b(26, 14, 24);
	cv::Vec3b upper_black = cv::Vec3b(115, 158, 125);     // 黑色
	cv::Vec3b lower_black1 = cv::Vec3b(14, 0, 0);
	cv::Vec3b upper_black1 = cv::Vec3b(70, 148, 82);     // 黑色1
	cv::Vec3b lower_black2 = cv::Vec3b(14, 0, 0);
	cv::Vec3b upper_black2 = cv::Vec3b(110, 255, 82);     // 黑色2
	/*cv::Vec3b lower = cv::Vec3b(55, 24, 134); //白色
	cv::Vec3b upper = cv::Vec3b(95, 50, 200);*/
	cv::Vec3b lower = cv::Vec3b(43, 18, 134); //白色
	cv::Vec3b upper = cv::Vec3b(95, 80, 200);
	cv::Vec3b lower_white = cv::Vec3b(0, 0, 130); // 0, 0, 95); //亮白色
	cv::Vec3b upper_white = cv::Vec3b(150, 50, 250); // 150, 42, 250);
	cv::Vec3b lower_blue = cv::Vec3b(90, 133, 42);  //深蓝
	cv::Vec3b upper_blue = cv::Vec3b(107, 255, 155);
	cv::Vec3b lower_blue2 = cv::Vec3b(65, 113, 30);  //暗蓝
	cv::Vec3b upper_blue2 = cv::Vec3b(95, 255, 124);
	cv::Vec3b lower_green = cv::Vec3b(28, 151, 55);       // 绿色
	cv::Vec3b upper_green = cv::Vec3b(40, 255, 145);
	cv::Vec3b lower_green2 = cv::Vec3b(54, 74, 75);       // 浅绿
	cv::Vec3b upper_green2 = cv::Vec3b(71, 145, 140);
	//HSV颜色空间法
	//L(26, 21, 42), H(118, 156, 130)
	//亮白色 0,95,0,18,252,255
	//蓝色：: 86,95,148,225,71,124
	//浅白色： 55，95，24，50，134，200
	// 绿色：28，40，216，250，74，148
	//浅绿： 59,73,77,135,80,126
};

void PrintCostTime(const char* str, double& t1, double& t2);
void getFiles(string path, vector<string>& files);
bool findSquares(const Mat& image, std::vector<cv::Rect>& resultBoxes, DryParam& DP);
void BrightnessContrast(Mat& src, DryParam& DP);

void get_mask_image(Mat &HSV, Mat &mask_img, DryParam& DP);
void get_morphology_image(Mat &mask_img);
bool HSVDet(Mat& input, std::vector<cv::Rect>& detectBoxes, DryParam& DP);
bool CannyDet(Mat& _input, std::vector<cv::Rect>& detectBoxes, DryParam& DP);

void sort_boxes(std::vector<cv::Rect>& resultBoxes);
/*
DryAlg       干化学检测卡定位接口函数
src:         裁剪后的干化学图片
resultBoxes: 识别定位的结果
DP:          定位算法可调参数
*/
int DryAlg(Mat& src, std::vector<cv::Rect>& resultBoxes, DryParam& DP);

DryDetect.cpp

#include "DryDetect.h"

void PrintCostTime(const char* str, double& t1, double& t2) {
	double t = (t2 - t1) * 1000 / cv::getTickFrequency();
	printf("%s ===> %.2f ms\n", str, t);
}

void getFiles(string path, vector<string>& files)
{
	//文件句柄
	intptr_t hFile = 0;
	//文件信息
	struct _finddata_t fileinfo;
	string p;
	char* files_format[2] = { "\\*.jpg" ,"\\*.png" };
	for (int i = 0; i < sizeof(files_format) / sizeof(char*); i++) {
		p.assign(path).append(files_format[i]);
		hFile = _findfirst(p.c_str(), &fileinfo);
		if (hFile != -1)
		{
			do
			{
				//如果是目录,迭代之,如果不是,加入列表
				if ((fileinfo.attrib &  _A_SUBDIR))
				{
					if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)
						getFiles(p.assign(path).append("\\").append(fileinfo.name), files);
				}
				else
				{
					files.push_back(p.assign(path).append("\\").append(fileinfo.name));
				}
			} while (_findnext(hFile, &fileinfo) == 0);
			_findclose(hFile);
		}
	}
}

bool cmp(cv::Rect a, cv::Rect b)
{
	bool big = a.width * a.height > b.width * b.height;
	return big;
}

int sort_indexes(std::vector<cv::Rect>& b)
{
	std::sort(b.begin(), b.end(), cmp);
	return 0;
}

static inline float intersection_area(const cv::Rect& a, const cv::Rect& b)
{
	const float eps = 1e-5;
	//cv::Rect_<float> inter = a & b;
	float x1max = max(a.x, b.x);  // 求两个窗口左上角x坐标最大值
	float x2min = min(a.width + a.x, b.width + b.x);  // 求两个窗口右下角x坐标最小值
	float y1max = max(a.y, b.y);  // 求两个窗口左上角y坐标最大值
	float y2min = min(a.height + a.y, b.height + b.y);  // 求两个窗口右下角y坐标最小值
	float overlapWidth = x2min - x1max;   // 计算两矩形重叠的宽度
	float overlapHeight = y2min - y1max;  // 计算两矩形重叠的高度
	if (overlapHeight > 0 && overlapHeight > 0) {
		float inter1 = overlapWidth * overlapHeight;
		return inter1;
	}
	else {
		return -1;
	}
	//float inter2 = inter.area();
	
}

std::vector<cv::Rect> nms_sorted_bboxes(std::vector<cv::Rect>& boxes, float nms_threshold)
{
	sort_indexes(boxes);
	std::vector<cv::Rect> finalResults;
	std::vector<int> keep;
	finalResults.clear();
	keep.clear();
	const int n = boxes.size();

	std::vector<float> areas(n);
	for (int i = 0; i < n; i++)
	{
		areas[i] = boxes[i].width * boxes[i].height;
		keep.push_back(1);
	}
	for (int i = 0; i < n; i++)
	{
		const cv::Rect& a = boxes[i];
		if (keep[i]) {
			for (int j = i + 1; j < n; j++)
			{
				const cv::Rect& b = boxes[j];
				// intersection over union
				float inter_area = intersection_area(a, b);
				if (inter_area > 0) {
					float union_area = areas[i] + areas[j] - inter_area;
					// float IoU = inter_area / union_area
					if (inter_area / union_area > nms_threshold)
						keep[i] = 0;
				}
			}
		}
	}
	for (int i = 0; i < n; i++)
	{
		if (keep[i])
		{
			finalResults.push_back(boxes[i]);
		}
	}
	return finalResults;
}

bool CannyDet(Mat& _input, std::vector<cv::Rect>& detectBoxes, DryParam& DP)
{
	Mat input = _input.clone();
	Mat gray, canny, gray2;
	BrightnessContrast(input, DP);
	cv::cvtColor(input, gray, cv::COLOR_BGR2GRAY);
	//cv::threshold(gray, canny, 20, 255, cv::THRESH_OTSU);
	//cv::adaptiveThreshold(gray, gray2, 255, cv::THRESH_BINARY_INV, cv::ADAPTIVE_THRESH_GAUSSIAN_C, 5,4);
	Canny(gray, canny, DP.thresh1, DP.thresh2);

	//auto kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(3, 3));
	Mat kernel = Mat::ones(cv::Size(5, 5), CV_8UC1);
	morphologyEx(canny, canny, cv::MORPH_CLOSE, kernel);
	//morphologyEx(canny, canny, cv::MORPH_OPEN, kernel);
	std::vector<std::vector<cv::Point>> Contours;
	cv::findContours(canny, Contours, cv::RETR_TREE, cv::CHAIN_APPROX_NONE);
	if (int(Contours.size()) == 0) {
		return false;
	}
	//int boxNumber = 0;
	for (int i = 0; i < int(Contours.size()); i++)
	{
		vector<Point> p = Contours[i];
		//auto Area = contourArea(p);
		cv::Rect rect = boundingRect(p);
		if (rect.width < DP.minWidth || rect.height < DP.minHeight)
		{
			continue;
		}
		if (rect.width > DP.maxWidth || rect.height > DP.maxHeight)
		{
			continue;
		}
		//boxNumber += 1;
		detectBoxes.push_back(rect);
		//cout << "area: " << "   w,h: " << rect.width << "x" << rect.height << endl;
		//rectangle(image, rect, Scalar(0, 0, 255), 1, 8); //画矩形
	}
	return true;
}
bool HSVDet(Mat& input, std::vector<cv::Rect>& detectBoxes, DryParam& DP)
{
	Mat blur, mask_img;
	Mat HSV = Mat(input.size(), CV_8UC3);
	//GaussianBlur(input, blur, Size(5, 5), 0);
	//medianBlur(input, blur, 5);
	cvtColor(input, HSV, COLOR_BGR2HSV);
	get_mask_image(HSV, mask_img, DP); //获取二值化图像
	get_morphology_image(mask_img);

	std::vector<std::vector<cv::Point>> Contours;
	cv::findContours(mask_img, Contours, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE);
	if (int(Contours.size()) == 0) 
	{
		return false;
	}
	//int boxNumber = 0;
	for (int i = 0; i < int(Contours.size()); i++)
	{
		vector<Point> p = Contours[i];
		cv::Rect rect = boundingRect(p);
		if (rect.width < DP.minWidth || rect.height < DP.minHeight)
		{
			continue;
		}
		if (rect.width > DP.maxWidth || rect.height > DP.maxHeight)
		{
			continue;
		}
		//boxNumber += 1;
		detectBoxes.push_back(rect);
	}
	return true;
}
bool findSquares(const Mat& _src, std::vector<cv::Rect>& resultBoxes, DryParam& DP)
{
	std::vector<cv::Rect>detectBoxes;
	resultBoxes.clear();
	detectBoxes.clear();
	Mat image = _src.clone();
	Mat blur, gray, dst, canny, hsv, mask;
	//medianBlur(image, blur, 5);
	//GaussianBlur(image, blur, Size(5, 5), 0);
	HSVDet(image, detectBoxes, DP);  // HSVDet

	CannyDet(image, detectBoxes, DP); // CannyDet

	if (int(detectBoxes.size()) == 0) {
		return false;
	}
	resultBoxes = nms_sorted_bboxes(detectBoxes, 0.35);
	//resultBoxes = detectBoxes;
	//for (int i = 0; i < int(resultBoxes.size()); i++)
	//{
	//	rectangle(image, resultBoxes[i], Scalar(0, 0, 255), 1, 8); //画矩形
	//}
	//cout << "boxNumber: " << resultBoxes.size() << endl;

	//cv::imshow("result", image);
	//cv::waitKey(0);
	if (int(resultBoxes.size()) == 8) {
		return true;
	}
	return false;
}

void BrightnessContrast(Mat& src, DryParam& DP)
{
	int height = src.rows;
	int width = src.cols;
	//float alpha = 1.3;
	//float beta = 30;
	//dst = Mat::zeros(src.size(), src.type());
	for (int row = 0; row < height; row++) {
		uchar *pixel = src.ptr<uchar>(row);
		for (int col = 0; col < width; col++) {
			if (src.channels() == 3) {
				pixel[0] = saturate_cast<uchar>(pixel[0] * DP.alpha + DP.beta);
				pixel[1] = saturate_cast<uchar>(pixel[1] * DP.alpha + DP.beta);
				pixel[2] = saturate_cast<uchar>(pixel[2] * DP.alpha + DP.beta);
				pixel += 3;
			}
			else if (src.channels() == 1) {
				pixel[col] = pixel[col] * DP.alpha + DP.beta;
			}
		}
	}
}

void get_mask_image(Mat &HSV, Mat &mask_img, DryParam& DP)
{
	Mat mask1, mask2, mask3, mask4, mask5, mask6;
	cv::inRange(HSV, DP.lower_black2, DP.upper_black2, mask_img);
	bitwise_not(mask_img, mask_img);

	/*cv::inRange(HSV, DP.lower, DP.upper, mask6);
	cv::inRange(HSV, DP.lower_white, DP.upper_white, mask1);
	
	cv::inRange(HSV, DP.lower_blue, DP.upper_blue, mask2);
	cv::inRange(HSV, DP.lower_blue2, DP.upper_blue2, mask5);
	cv::inRange(HSV, DP.lower_green, DP.upper_green, mask3);
	cv::inRange(HSV, DP.lower_green2, DP.upper_green2, mask4);
	mask_img = mask6 +mask2 + mask3 + mask4 + mask5 + mask1;*/
	//bitwise_not(mask_img, mask_img);

}

void get_morphology_image(Mat &mask_img)
{
	auto kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(5, 5));
	morphologyEx(mask_img, mask_img, cv::MORPH_CLOSE, kernel);
	morphologyEx(mask_img, mask_img, cv::MORPH_OPEN, kernel);
}

bool cmp2(cv::Rect a, cv::Rect b)
{
	return a.x < b.x;
}
bool cmp3(cv::Rect a, cv::Rect b)
{
	return a.y < b.y;
}

void sort_boxes(std::vector<cv::Rect>& boxes)
{
	std::vector<cv::Rect> xBoxes, yBoxes;
	xBoxes.clear();
	yBoxes.clear();
	std::sort(boxes.begin(), boxes.end(), cmp2);
	xBoxes.insert(xBoxes.end(), boxes.begin(), boxes.begin() + 4); //左边一列
	yBoxes.insert(yBoxes.end(), boxes.begin() + 4, boxes.end());   //右边一列
	std::sort(xBoxes.begin(), xBoxes.end(), cmp3);
	std::sort(yBoxes.begin(), yBoxes.end(), cmp3);
	boxes.clear();
	boxes.insert(boxes.end(), xBoxes.begin(), xBoxes.end());
	boxes.insert(boxes.end(), yBoxes.begin(), yBoxes.end());
}

int DryAlg(Mat& src, std::vector<cv::Rect>& resultBoxes, DryParam& DP)
{
	if (!src.data) {
		//cout << "load image error..." << endl;
		return -1;
	}
	Mat input;
	//将原图裁剪并缩小 resizeRatio；
	//Mat img = src(DP.DryCard);
	Mat img = src.clone();
	int col = img.cols;
	int row = img.rows;
	if (DP.resizeRatio > 0) {
		cv::resize(img, input, Size(col * DP.resizeRatio, row * DP.resizeRatio));
	}
	else {
		input = img;
	}
	//double t1 = cv::getTickCount();
	if (!findSquares(input, resultBoxes, DP))
	{
		return 0;
	}
	sort_boxes(resultBoxes);
	//double t2 = cv::getTickCount();
	//PrintCostTime("findSquares:", t1, t2);
	for (int i = 0; i < resultBoxes.size(); i++)
	{
		//rectangle(input, resultBoxes[i], Scalar(0, 0, 255), 1);
		//将检测坐标映射回原图
		if (DP.resizeRatio > 0) {
			resultBoxes[i].x /= DP.resizeRatio;
			resultBoxes[i].y /= DP.resizeRatio;
			resultBoxes[i].width /= DP.resizeRatio;
			resultBoxes[i].height /= DP.resizeRatio;
		}
		//rectangle(img, resultBoxes[i], Scalar(0, 0, 255), 2);
		//resultBoxes[i].x += DP.DryCard.x;
		//resultBoxes[i].y += DP.DryCard.y;
		//rectangle(src, resultBoxes[i], Scalar(0, 0, 255), 2);
	}
	//cv::resize(src, src, Size(src.cols * 0.5, src.rows * 0.5));
	cv::imwrite("out.jpg", src);
	//cv::imshow("out", src);
	//cv::imshow("result", img);
	//cv::waitKey(0);
	return 1;
}

main.cpp

#include "DryDetect.h"

int main()
{
	Mat src = cv::imread("H:\\ImageProcess\\Dry\\image\\7.jpg");
	/*
	-1:传入图片错误
	0:没有检测到框
	1:成功检测到了框
	*/
	DryParam DP;
	//以下参数做成配置文件可修改
	DP.minWidth = 80;   // 可调参数，限制格子的最大最小宽高
	DP.minHeight = 80;
	DP.maxWidth = 180;
	DP.maxHeight = 180;
	DP.drydetect = 1; //if 0, 不用算法定位

	std::vector<cv::Rect> resultBoxes; //算法识别结果
	/*
	Dryflag:
	-1: 传入图片错误
	0:  没有检测到框
	1:  成功检测到了框
	*/
	int Dryflag;
	if (DP.drydetect = 0)  //0: 不调用算法定位
	{
		Dryflag = 0;
	}
	else
	{
		Dryflag = DryAlg(src, resultBoxes, DP);
	}

	switch (Dryflag)
	{
	case -1:
	{
		cout << "load image error..." << endl;
		break;
	}
	case 0:
	{
		cout << "detect box fail..." << endl;
		break;
	}
	default:
		break;
	}
	return 0;
}

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