OpenCV：如何解释inRange的结果？

Question

I am processing video images and I would like to detect if the video contains any pixels of a certain range of red. Is this possible?

Here is the code I am adapting from a tutorial:

#ifdef __cplusplus
- (void)processImage:(Mat&)image;
{
    cv::Mat orig_image = image.clone();
    cv::medianBlur(image, image, 3);
    cv::Mat hsv_image;
    cv::cvtColor(image, hsv_image, cv::COLOR_BGR2HSV);
    cv::Mat lower_red_hue_range;
    cv::Mat upper_red_hue_range;
    cv::inRange(hsv_image, cv::Scalar(0, 100, 100), cv::Scalar(10, 255, 255), lower_red_hue_range);
    cv::inRange(hsv_image, cv::Scalar(160, 100, 100), cv::Scalar(179, 255, 255), upper_red_hue_range);
    // Interpret values here
}

Interpreting values

I would like to detect if the results from the inRange operations are nil or not. In other words I want to understand if there are any matching pixels in the original image with a colour inRange from the given lower and upper red scale. How can I interpret the results?

Answer 1

First you need to OR the lower and upper mask:

Mat mask = lower_red_hue_range | upper_red_hue_range;

Then you can countNonZero to see if there are non zero pixels (ie you found something).

int number_of_non_zero_pixels = countNonZero(mask);

It could be better to first apply morphological erosion or opening to remove small (probably noisy) blobs:

Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
morphologyEx(mask, mask, MORPH_OPEN, kernel); // or MORPH_ERODE

or find connected components ( findContours , connectedComponentsWithStats ) and prune / search for according to some criteria:

vector<vector<Point>> contours
findContours(mask.clone(), contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

double threshold_on_area = 100.0;
for(int i=0; i<contours.size(); ++i)
{
    double area = countourArea(contours[i]);
    if(area < threshold_on_area)
    {
        // don't consider this contour
        continue;
    } 
    else
    {
        // do something (e.g. drawing a bounding box around the contour)
        Rect box = boundingRect(contours[i]);
        rectangle(hsv_image, box, Scalar(0, 255, 255));
    }
}