I want to calculate my camera's position in world coordinates. This should be fairly easy, but I don't get the results I expect. I believe I've read everything on this topic, but my code isn't working. Here's what I do:
I have a camera looking at an area.
1) I drew a map of the area.
2) I calculated the homography by matching 4 image points to 4 points on my map using cv2.getPerspectiveTransform
3) The H homography transforms every world coordinate to camera coordinate; this is working properly
4) To calculate the camera matrix I followed this:
translation = np.zeros((3,1))
translation[:,0] = homography[:,2]
rotation = np.zeros((3,3))
rotation[:,0] = homography[:,0]
rotation[:,1] = homography[:,1]
rotation[:,2] = np.cross(homography[0:3,0],homography[0:3,1])
cameraMatrix = np.zeros((3,4))
cameraMatrix[:,0:3] = rotation
cameraMatrix[:,3] = homography[:,2]
cameraMatrix = cameraMatrix/cameraMatrix[2][3] #normalize the matrix
5) According to this, the camera's position should be calculated like this:
x,y,z = np.dot(-np.transpose(rotation),translation)
The coordinates I'm getting are totally wrong. The problem should be somewhere in step 4 or 5 I guess. What's wrong with my method?
Camera Calibration in Python with OpenCV 1 Intrinsic parameters. These intrinsic parameters define the properties of the camera produced by it in the real world. ... 2 Zhang’s method for Camera Calibration. ... 3 Reprojection Error. ...
Face Detection using Opencv python, here is the function which simply detects the face and returns face width, in pixels Face data function take only one argument which is an image. Measured_distance is the distance from the camera to object while capturing the Reference image, Known_distance = 72.2 centimetres
Inputs : A collection of images with points whose 2D image coordinates and 3D world coordinates are known. Outputs: The 3×3 camera intrinsic matrix, the rotation and translation of each image. Note : In OpenCV the camera intrinsic matrix does not have the skew parameter.
Camera Calibration can be done in a step-by-step approach: Step 1: First define real world coordinates of 3D points using known size of checkerboard pattern. Step 2: Different viewpoints of check-board image is captured. Step 3: findChessboardCorners () is a method in OpenCV and used to find pixel coordinates (u, v) for each 3D point in ...
I think I've got it now. The problem was with the method described in step 4. The camera position cannot be calculated from the homography matrix alone. The camera intrinsics matrix is also necessary. So, the correct procedure is the following:
1) draw a map of the area
2) calibrate the camera using the chessboard image with cv2.findChessboardCorners
this yields the camera matrix and the distortion coefficients
3) solvePnP with the world coordinates (3D) and image coordinates (2D). The solvePnP returns the object's origo in the camera's coordinate system given the 4 corresponding points and the camera matrix.
4) Now I need to calculate the camera's position in world coordinates. The rotation matrix is: rotM = cv2.Rodrigues(rvec)[0]
5) The x,y,z position of the camera is: cameraPosition = -np.matrix(rotM).T * np.matrix(tvec)
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With