#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;
}
使用您的测试图像,我可以通过一个简单的erosion操作去除所有噪声。
在那之后,在
Mat
上进行一个简单的迭代来查找角点像素是很简单的,我在this answer上讨论过这个问题。出于测试目的,我们可以在这些点之间绘制绿色线,以显示我们对原始图像感兴趣的区域:最后,我在原始图像中设置ROI并裁剪出该部分。
最终结果显示在下图中:
我编写了一个示例代码,使用OpenCV的<强> C++接口< /强>来执行此任务。我对你将这段代码翻译成Python的技巧很有信心。如果你做不到这一点,就忘了代码,继续使用我在这个答案上共享的roadmap。
鉴于文本是唯一大的blob,而其他所有内容都不超过一个像素,一个简单的形态学打开就足够了
你可以这样做in opencv 或with imagemagic
之后,白色矩形应该是图像中唯一剩下的东西。您可以使用opencvs findcontours、opencv的CvBlobs库或imagemagick-crop函数找到它
这是你的图像,有两个侵蚀步骤,然后是两个扩张步骤: 您可以简单地将此图像插入到opencv findContours函数中,如Squares tutorial example中所示,以获取位置
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