如何确定感兴趣的区域，然后使用 OpenCV 裁剪图像 [英] How to determine a region of interest and then crop an image using OpenCV

问题描述

我在操作.

在那之后，在 Mat 上进行简单的迭代来寻找角落像素 是微不足道的，我在 回答.

#include #include int main(int argc, char* argv[]){cv::Mat img = cv::imread(argv[1]);std::cout <<原始图像尺寸:" <积分；cv::Mat_::iterator it = gray.begin();cv::Mat_::iterator end = gray.end();for (; it != end; it++){如果它)points.push_back(it.pos());}//从这些点，算出ROI的大小int 左、右、上、下；for (int i = 0; i  右)右 = 点 [i].x;if (points[i].y < top)顶部 = 点 [i].y;如果(点 [i].y > 底部)底部 = 点 [i].y;}std::vectorbox_points;box_points.push_back(cv::Point(left, top));box_points.push_back(cv::Point(left, bottom));box_points.push_back(cv::Point(right, bottom));box_points.push_back(cv::Point(right, top));//计算 ROI 的最小边界框//注意:由于某些未知原因，框的宽度/高度发生了切换.cv::RotatedRect box = cv::minAreaRect(cv::Mat(box_points));std::cout <<框 w:"<<box.size.width <<" h:" <<box.size.height <<std::endl;//在原始图像中绘制边界框(调试目的)//cv::Point2f 顶点[4];//box.points(vertices);//for (int i = 0; i <4; ++i)//{//cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(0, 255, 0), 1, CV_AA);//}//cv::imshow("原始", img);//cv::waitKey(0);//将ROI设置为框定义的区域//注意:因为盒子的宽/高是切换的，//它们是在下面的代码中手动切换的:简历::矩形投资回报率；roi.x = box.center.x - (box.size.height/2);roi.y = box.center.y - (box.size.width/2);roi.width = box.size.height;roi.height = box.size.width;std::cout <<"roi @" <<roi.x<<，"<<roi.y<<" " <<roi.width<

I asked a similar question here but that is focused more on tesseract.

I have a sample image as below. I would like to make the white square my Region of Interest and then crop out that part (square) and create a new image with it. I will be working with different images so the square won't always be at the same location in all images. So I will need to somehow detect the edges of the square.

What are some pre-processing methods I can perform to achieve the result?

解决方案

Using your test image I was able to remove all the noises with a simple erosion operation.

After that, a simple iteration on the Mat to find for the corner pixels is trivial, and I talked about that on this answer. For testing purposes we can draw green lines between those points to display the area we are interested at in the original image:

At the end, I set the ROI in the original image and crop out that part.

The final result is displayed on the image below:

I wrote a sample code that performs this task using the C++ interface of OpenCV. I'm confident in your skills to translate this code to Python. If you can't do it, forget the code and stick with the roadmap I shared on this answer.

#include <cv.h>
#include <highgui.h>

int main(int argc, char* argv[])
{
    cv::Mat img = cv::imread(argv[1]);
    std::cout << "Original image size: " << img.size() << std::endl;

    // Convert RGB Mat to GRAY
    cv::Mat gray;
    cv::cvtColor(img, gray, CV_BGR2GRAY);
    std::cout << "Gray image size: " << gray.size() << std::endl;

    // Erode image to remove unwanted noises
    int erosion_size = 5;
    cv::Mat element = cv::getStructuringElement(cv::MORPH_CROSS,
                                       cv::Size(2 * erosion_size + 1, 2 * erosion_size + 1),
                                       cv::Point(erosion_size, erosion_size) );
    cv::erode(gray, gray, element);

    // Scan the image searching for points and store them in a vector
    std::vector<cv::Point> points;
    cv::Mat_<uchar>::iterator it = gray.begin<uchar>();
    cv::Mat_<uchar>::iterator end = gray.end<uchar>();
    for (; it != end; it++)
    {
        if (*it) 
            points.push_back(it.pos()); 
    }

    // From the points, figure out the size of the ROI
    int left, right, top, bottom;
    for (int i = 0; i < points.size(); i++)
    {
        if (i == 0) // initialize corner values
        {
            left = right = points[i].x;
            top = bottom = points[i].y;
        }

        if (points[i].x < left)
            left = points[i].x;

        if (points[i].x > right)
            right = points[i].x;

        if (points[i].y < top)
            top = points[i].y;

        if (points[i].y > bottom)
            bottom = points[i].y;
    }
    std::vector<cv::Point> box_points;
    box_points.push_back(cv::Point(left, top));
    box_points.push_back(cv::Point(left, bottom));
    box_points.push_back(cv::Point(right, bottom));
    box_points.push_back(cv::Point(right, top));

    // Compute minimal bounding box for the ROI
    // Note: for some unknown reason, width/height of the box are switched.
    cv::RotatedRect box = cv::minAreaRect(cv::Mat(box_points));
    std::cout << "box w:" << box.size.width << " h:" << box.size.height << std::endl;

    // Draw bounding box in the original image (debugging purposes)
    //cv::Point2f vertices[4];
    //box.points(vertices);
    //for (int i = 0; i < 4; ++i)
    //{
    //    cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(0, 255, 0), 1, CV_AA);
    //}
    //cv::imshow("Original", img);
    //cv::waitKey(0);

    // Set the ROI to the area defined by the box
    // Note: because the width/height of the box are switched, 
    // they were switched manually in the code below:
    cv::Rect roi;
    roi.x = box.center.x - (box.size.height / 2);
    roi.y = box.center.y - (box.size.width / 2);
    roi.width = box.size.height;
    roi.height = box.size.width;
    std::cout << "roi @ " << roi.x << "," << roi.y << " " << roi.width << "x" << roi.height << std::endl;

    // Crop the original image to the defined ROI
    cv::Mat crop = img(roi);

    // Display cropped ROI
    cv::imshow("Cropped ROI", crop);
    cv::waitKey(0);

    return 0;
}

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