gellston / FastROI

The FAST ROI library is useful for quickly extracting the coordinates of a rotating rectangular ROI

Fast ROI v1.3

The FAST ROI library is useful for quickly extracting the coordinates of a rotating rectangular ROI and donut ROI also it support blob detection;

Development Environment

• Visual Studio 2022 C++17 (Latest Window10 SDK)
• Need eigen3 library to compile
• Need vcpkg tool to install eigen3 library (https://github.com/microsoft/vcpkg)

// Eigen3 library installation via vcpkg
// Please paste the command below
vpckg install eigen3
vcpkg integrate install

Performance

fastRect baseline = searching gap : 1

Name	fps
fast::fastRect (500x500 roi size)	2100 fps
fast::fastRect (1000x1000 roi size)	550 fps
fast::fastRect (2000x2000 roi size)	35 fps

fastDonut baseline = start_ratio : 1.3, end_ratio : 0.7, angle_step=1

Name	fps
fast::fastDonut (800 radius)	3560 fps
fast::fastDonut (1500 radius)	666 fps

fastBlob

Name	fps
fast::fastBlob (video 1920x1080)	15~20ms elapse time
fast::fastBlob(blobFill) (video 1920x1080)	Unknown

How to use?

fastRect

#include <fastRect.h>

int centerX = 2000;     // rect center y
int centerY = 2000;     // rect center y
double angle = 40;      // rotation angle
int range = 1000;       // same as width
int distance = 1000;    // same as height 
bool search_direction = false //search direction (false : foward direction, true : backward direction)
int searching gap = 10;   //gap pixels

std::vector<std::vector<fast::calPoint>> result = fast::fastRect(centerX, centerY, angle, range, distance, false, skip_pixels);

//iteration of vertical line
for (auto vertical_line : vertical_lines) {

    //iteration of point in vertical line
    for (auto calPoint : vertical_line) {
    
        //Check current position is in image
        if (calPoint.x < 0 || calPoint.x >= 4000 || calPoint.y < 0 || calPoint.y >= 4000)
            continue;
    
        //Do whatever you want here
        
    }
}
		

DEMO

fastDonut

#include <fastDonut.h>

int centerX = 2000;        // rect center y
int centerY = 2000;        // rect center y
int radius = 1500;         // base radius
double start_ratio = 1.5;  // start radius ratio , start radius = start_ratio * base radius
double end_ratio = 0.5;    // end radius ratio , end radius = end_ratio * base radius
double step_angle = 1;     // step angle (angle will increase per step angle CCW)

std::vector<std::vector<fast::calPoint>> result = fast::fastDonut(centerX, centerY, radius, start_ratio, end_ratio, step_angle);

//iteration of vertical line
for (auto vertical_line : vertical_lines) {

    //iteration of point in vertical line
    for (auto calPoint : vertical_line) {
    
        //Check current position is in image
        if (calPoint.x < 0 || calPoint.x >= 4000 || calPoint.y < 0 || calPoint.y >= 4000)
            continue;
    
        //Do whatever you want here
        
    }
}

		

DEMO

fastBlob

#include <iostream>
#include <stack>
#include <chrono>
#include <opencv2/opencv.hpp>
#include <fastBlob.h>

int main()
{
    cv::namedWindow("binary", cv::WINDOW_NORMAL);
	cv::namedWindow("result_image", cv::WINDOW_NORMAL);
    cv::resizeWindow("binary", cv::Size(600, 1200));
    cv::resizeWindow("result_image", cv::Size(600, 1200));
    cv::VideoCapture cap("d://test_video.mp4");
    system("pause");
    while (cap.isOpened()){
        cv::Mat current_frame;
        if(cap.read(current_frame) == false)
            return 0;
        cv::Mat gray_image;
        cv::cvtColor(current_frame, gray_image, cv::COLOR_BGR2GRAY);
        cv::Mat binary;
        cv::threshold(gray_image, binary, 250, 255, cv::THRESH_BINARY + cv::THRESH_OTSU);
        std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now();
        //Blob Detection 
        //Blob Detection 
        fast::fastBlob blobDetector;
        blobDetector.compute(binary.data, binary.cols, binary.rows);
        auto blobs = blobDetector.blobInfo();
        //Blob Detection 
        //Blob Detection 

        for (auto& info : blobs) {
            cv::rectangle(current_frame, cv::Rect(info.rectX(), info.rectY(), info.rectWidth(), info.rectHeight()), cv::Scalar(0, 255, 0), 5) ;
        }
        std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
        std::cout << "Time difference = " << std::chrono::duration_cast<std::chrono::milliseconds>(end - begin).count() << "[ms]" << std::endl;

        cv::imshow("binary", binary);
        cv::imshow("result_image", current_frame);
        cv::waitKey(11);
    }
}	

DEMO

Blob box detection example

Blob filter example (fast::fastBlob::blobFill)