問題描述
我有以下圖片:
我想使用 cv::inRange 方法和 HSV 顏色空間檢測紅色矩形.
int H_MIN = 0;INT H_MAX = 10;int S_MIN = 70;INT S_MAX = 255;int V_MIN = 50;int V_MAX = 255;cv::cvtColor(輸入，imageHSV，cv::COLOR_BGR2HSV)；cv::inRange(imageHSV, cv::Scalar(H_MIN, S_MIN, V_MIN), cv::Scalar(H_MAX, S_MAX, V_MAX), imgThreshold0);我已經創建了動態軌跡欄以更改 HSV 的值，但我無法獲得所需的結果.
對使用的最佳值(可能還有過濾器)有什么建議嗎?
在 HSV 空間中，紅色環繞大約 180.所以你需要 H 值在 [0,10] 和 [170, 180] 中.
試試這個:
#include 使用命名空間 cv；int main(){Mat3b bgr = imread("path_to_image");Mat3b hsv;cvtColor(bgr, hsv, COLOR_BGR2HSV);Mat1b 掩碼1、掩碼2；inRange(hsv, Scalar(0, 70, 50), Scalar(10, 255, 255), mask1);inRange(hsv, Scalar(170, 70, 50), Scalar(180, 255, 255), mask2);Mat1b 掩碼 = 掩碼 1 |面具2;imshow("面具", 面具);等待鍵()；返回0；} 您之前的結果:
結果添加范圍[170, 180]:
<小時>另一種只需要檢查單個范圍的有趣方法是:
- 反轉 BGR 圖像
- 轉換為 HSV
- 尋找青色顏色
這個想法由
I have the following image:
I would like to detect the red rectangle using cv::inRange method and HSV color space.
int H_MIN = 0;
int H_MAX = 10;
int S_MIN = 70;
int S_MAX = 255;
int V_MIN = 50;
int V_MAX = 255;
cv::cvtColor( input, imageHSV, cv::COLOR_BGR2HSV );
cv::inRange( imageHSV, cv::Scalar( H_MIN, S_MIN, V_MIN ), cv::Scalar( H_MAX, S_MAX, V_MAX ), imgThreshold0 );
I already created dynamic trackbars in order to change the values for HSV, but I can't get the desired result.
Any suggestion for best values (and maybe filters) to use?
In HSV space, the red color wraps around 180. So you need the H values to be both in [0,10] and [170, 180].
Try this:
#include <opencv2opencv.hpp>
using namespace cv;
int main()
{
Mat3b bgr = imread("path_to_image");
Mat3b hsv;
cvtColor(bgr, hsv, COLOR_BGR2HSV);
Mat1b mask1, mask2;
inRange(hsv, Scalar(0, 70, 50), Scalar(10, 255, 255), mask1);
inRange(hsv, Scalar(170, 70, 50), Scalar(180, 255, 255), mask2);
Mat1b mask = mask1 | mask2;
imshow("Mask", mask);
waitKey();
return 0;
}
Your previous result:
Result adding range [170, 180]:
Another interesting approach which needs to check a single range only is:
- invert the BGR image
- convert to HSV
- look for cyan color
This idea has been proposed by fmw42 and kindly pointed out by Mark Setchell. Thank you very much for that.
#include <opencv2opencv.hpp>
using namespace cv;
int main()
{
Mat3b bgr = imread("path_to_image");
Mat3b bgr_inv = ~bgr;
Mat3b hsv_inv;
cvtColor(bgr_inv, hsv_inv, COLOR_BGR2HSV);
Mat1b mask;
inRange(hsv_inv, Scalar(90 - 10, 70, 50), Scalar(90 + 10, 255, 255), mask); // Cyan is 90
imshow("Mask", mask);
waitKey();
return 0;
}
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