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
我问了一个类似的问题 操作。
之后， Mat 查找角落像素是微不足道的，我在回答。
#include< cv.h>
#include< highgui.h>
int main（int argc，char * argv []）
{
cv :: Mat img = cv :: imread（argv [1]）;
std :: cout<< 原始图像大小：<< img.size（）<<的std :: ENDL;
//将RGB Mat转换为GRAY
cv :: Mat grey;
cv :: cvtColor（img，grey，CV_BGR2GRAY）;
std :: cout<< 灰色图像大小：<< gray.size（）<<的std :: ENDL;
//侵蚀图像以消除不必要的噪音
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（灰色，灰色，元素）;
//扫描图像搜索点并将它们存储在向量中
std :: vector< cv :: Point>点;
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（））;
}
//从积分中算出ROI的大小
int left，right，top，bottom;
for（int i = 0; i< points.size（）; i ++）
{
if（i == 0）//初始化角点值
{
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））;
//为ROI计算最小边界框
//注意：由于某些未知原因，框的宽度/高度会被切换。
cv :: RotatedRect box = cv :: minAreaRect（cv :: Mat（box_points））;
std :: cout<< box w：<< box.size.width<< h：<< box.size.height<<的std :: ENDL;
//在原始图像中绘制边界框（调试目的）
// 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）;
//将ROI设置为框$ $ $ b //所定义的区域注意：因为框的宽度/高度被切换，
//它们是手动切换的代码如下：
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<<的std :: ENDL;
//将原始图像裁剪为定义的ROI
cv :: Mat crop = img（roi）;
//显示裁剪的投资回报率
cv :: imshow（裁剪投资回报率，作物）;
cv :: waitKey（0）;
返回0;
}
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?
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;
}
这篇关于如何确定感兴趣的区域，然后使用OpenCV裁剪图像的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
