首先抱歉,如果提出问题。我正在开发一个可以检测文档角落的应用程序。我现在正在使用openCV来检测边缘。我已经使用 openCV 实现了这一点,但我没有得到完美的结果。
我也尝试过BradLarson GPUImage,但我能够从这个开始。
我的代码检测文档的角落但不是完美的结果。
void find_squares(Mat& image, cv::vector<cv::vector<cv::Point>>&squares)
{
// blur will enhance edge detection
Mat blurred(image);
//cv::resize(image, image, cvSize(0.25, 0.25));
Mat gray0(blurred.size(), CV_8U), gray;
//medianBlur(image, blurred, 9); //default 9;
GaussianBlur(image, blurred, cvSize(9, 9), 2.0,2.0);
vector<vector<cv::Point> > contours;
// find squares in every color plane of the image
for (int c = 0; c < 3; c++)
{
int ch[] = {c, 0};
mixChannels(&blurred, 1, &gray0, 1, ch, 1);
// try several threshold levels
const int threshold_level = 4;
for (int l = 0; l < threshold_level; l++)
{
// Use Canny instead of zero threshold level!
// Canny helps to catch squares with gradient shading
if (l == 0)
{
Canny(gray0, gray, 10, 20, 3); //
// Dilate helps to remove potential holes between edge segments
dilate(gray, gray, Mat(), cv::Point(-1,-1));
}
else
{
gray = gray0 >= (l+1) * 255 / threshold_level;
}
// Find contours and store them in a list
findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
// Test contours
vector<cv::Point> approx;
for (size_t i = 0; i < contours.size(); i++)
{
// approximate contour with accuracy proportional
// to the contour perimeter
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if (approx.size() == 4 &&
fabs(contourArea(Mat(approx))) > 1000 &&
isContourConvex(Mat(approx)))
{
double maxCosine = 0;
for (int j = 2; j < 5; j++)
{
double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
maxCosine = MAX(maxCosine, cosine);
}
if (maxCosine < 0.3)
squares.push_back(approx);
}
}
}
}
}
所以我的问题是:
1)有没有其他图书馆可以做到这一点。
2)上面的代码有什么问题吗?我应该在检测之前添加一些图像处理吗?
3) BradLarson GPUImage可以做到这一点吗?如果可以,那么是否有用于边缘检测的示例代码来源?