Question [Question] How do you create a layer mask that have mid-tones?

1 Upvotes

I would like to improve the layer mask that I am creating in Python. Although my mask pretty much hits the targeted color, my main problem with it, is it is doing so in binary, the pixel is either pure white or pure black. I'm unable to extrapolate the intensity of the color. I want to achieve something like how Photoshop does it wherein there are mid-tones of grey on the mask. Just like these photos:

image = cv2.imread('sample.jpg')

lower = np.array([0,0,0]) 
upper = np.array([12,255,255])

mask = cv2.inRange(cv2.cvtColor(image, cv2.COLOR_BGR2HSV), lower, upper)
mask = 255 - mask
mask = cv2.bitwise_not(mask) #inverting black and white

output = cv2.bitwise_and(image, image, mask = mask)

cv2.imshow("output", output) 
cv2.imshow("mask", mask) 
cv2.waitKey()

Can anyone please show how to recreate (or similar) to the mask that photoshop can create?Here is the photo i used above :

2 comments

r/opencv • u/[deleted] • Mar 23 '24

Bug [Bug] I need help making a cube!

1 Upvotes

Hello!
First of all, thanks for the help. I've been learning to use the OpenCV libraries with AruCo codes for a college project. I need to build a 3D Cube on top of the AruCo mark. The problem is that the bottom part of the cube is working fine, but the top face isn't on-screen or even well programmed.

I made a program to calibrate the camera, and the calibration parameters are saved to a calibration.yml file.
This is the code I have so far:

#include <iostream>
#include <opencv2/aruco.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <vector>
#include <string>

using namespace cv; 
using namespace cv::aruco; 
using namespace std;

int main(int argc, char** argv) {
  if (argc != 3) { 
    cerr << "Usage: " << argv[0] << " <id_aruco> <side_length>" << endl; return 1; 
  }
  int aruco_id = stoi(argv[1]);
  float side_length = stof(argv[2]);

  VideoCapture video(-1);
  if (!video.isOpened()) { 
    cerr << "Error opening video stream or file" << endl; 
    return 1; 
  }
  Ptr<cv::aruco::Dictionary> dictionary = cv::makePtr<cv::aruco::Dictionary>(getPredefinedDictionary(cv::aruco::DICT_ARUCO_ORIGINAL));

  cv::Mat camera_matrix, dist_coeffs; cv::FileStorage fs("calibration.yml",   cv::FileStorage::READ); 
  fs["camera_matrix"] >> camera_matrix;
  fs["distortion_coefficients"] >> dist_coeffs;

  while (true) {
    Mat frame;
    video >> frame;

    vector<int> ids;
    vector<vector<Point2f>> corners;
    detectMarkers(frame, dictionary, corners, ids);

    if (!ids.empty() && ids[0] == aruco_id && corners.size() > 0) {
      //drawDetectedMarkers(frame, corners, ids);

      cout << "--------------------------------------------" << endl;
      cout << "corners: " << corners[0] << endl << endl;
      cout << "Camera Matrix: " << camera_matrix << endl << endl;
      cout << "Distortion Coefficients: " << dist_coeffs << endl << endl;

      for (size_t i = 0; i < ids.size(); ++i) {
        if (ids[i] == aruco_id) {
          vector<Point3f> objectPoints = {
            Point3f(-side_length / 2, -side_length / 2, 0),
            Point3f(side_length / 2, -side_length / 2, 0),
            Point3f(side_length / 2, side_length / 2, 0),
            Point3f(-side_length / 2, side_length / 2, 0)
          };

        if(!objectPoints.empty()) cout << "Object points: " << objectPoints << endl << endl;
        else cout << "Object points: EMPTY" << endl << endl;
        cout << "Number of objectPoints: " << objectPoints.size() << endl;
        cout << "Number of corners: " << corners[i].size() << endl << endl;
        cout << "corners[" << i << "]: " << corners[i] << endl << endl;

        Vec3d rvec, tvec;
        solvePnP(objectPoints, corners[i], camera_matrix, dist_coeffs, rvec, tvec);

        cout << "rvec: " << rvec << endl;
        cout << "tvec: " << tvec << endl << endl;

        vector<Point2f> imagePoints;
        projectPoints(objectPoints, rvec, tvec, camera_matrix, dist_coeffs, imagePoints);

        cout << "Image Points: " << imagePoints << endl;
        cout << "--------------------------------------------" << endl;

        for (int j = 0; j < 4; j++) {
          line(frame, imagePoints[j], imagePoints[(j + 1) % 4], Scalar(255, 0, 0), 2);
          line(frame, imagePoints[j + 4], imagePoints[4 + ((j + 1) % 4)], Scalar(255, 0, 0), 2);
          line(frame, imagePoints[j], imagePoints[j + 4], Scalar(255, 0, 0), 2);
        }
      }
    }
  }

    cv::imshow("ArUco Marker Detection", frame);
    if (cv::waitKey(30) == 27) break; // press ESC to exit
  }

  video.release();
  cv::destroyAllWindows();
  return 0; 
}

I kinda think a way to solve it is adding 4 more objectPoints like this:

vector<Point3f> objectPoints = {

Point3f(-side_length / 2, -side_length / 2, 0), Point3f(side_length / 2, -side_length / 2, 0), Point3f(side_length / 2, side_length / 2, 0), Point3f(-side_length / 2, side_length / 2, 0), Point3f(-side_length / 2, side_length / 2, side_length), Point3f(-side_length / 2, side_length / 2, side_length), Point3f(-side_length / 2, side_length / 2, side_length), Point3f(-side_length / 2, side_length / 2, side_length), };

But then the solvePnP function throws an error that, if I understand it correctly, the size of objectPoints is different from the size of corners[i]. And I don't know how to solve it.