基于边缘检测和HSV的图像识别算法 - ToB企服应用市场:ToB评测及商务社交产业平台

#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);

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#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;
}

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