Intrinsics & Extrinsics of projection cameras

[qiaodongxing]

Is your feature request related to a problem? Please describe.
Thanks for the great work. I am using tiger's AX method to get a projection of a CT image, but what are the intrinsic and extrinsic matrices for this projection?
How should I calculate these?
Describe the solution you'd like
···
class ConeGeometry(object):
"""
Cone beam CT geometry. Note that we convert to meter from millimeter.
"""
def init(self, data):

    # VARIABLE                                          DESCRIPTION                    UNITS
    # -------------------------------------------------------------------------------------
    self.DSD = data["DSD"]/1000 # Distance Source Detector      (m) 2
    self.DSO = data["DSO"]/1000  # Distance Source Origin        (m) 1
    # Detector parameters
    self.nDetector = np.array(data["nDetector"])  # number of pixels      256        (px)
    self.dDetector = np.array(data["dDetector"])/1000  # size of each pixel   0.002         (m)
    self.sDetector = self.nDetector * self.dDetector  # total size of the detector  0.512   (m)
    # Image parameters
    self.nVoxel = np.array(data["nVoxel"])  # number of voxels       256       (vx)   
    self.dVoxel = np.array(data["dVoxel"])/1000  # size of each voxel     0.001       (m)
    self.sVoxel = self.nVoxel * self.dVoxel  # total size of the image    0.256   (m)

    # Offsets
    self.offOrigin = np.array(data["offOrigin"])/1000  # Offset of image from origin   (m)
    self.offDetector = np.array(data["offDetector"])/1000  # Offset of Detector            (m)

    # Auxiliary
    self.accuracy = data["accuracy"]  # Accuracy of FWD proj          (vx/sample)  # noqa: E501
    # Mode
    self.mode = data["mode"]  # parallel, cone                ...
    self.filter = data["filter"]

def compute_intrinsics(self):
    # Focal lengths in terms of pixel size
    f_x = self.DSD /self.dDetector[0]
    f_y = self.DSD /self.dDetector[1]
    
    # Principal point 
    c_x = self.nDetector[0] / 2 + self.offDetector[0] / self.dDetector[0]
    c_y = self.nDetector[1] / 2 + self.offDetector[1] / self.dDetector[1]
    
    # Intrinsic camera matrix
    K = np.array([
        [f_x, 0, c_x],
        [0, f_y, c_y],
        [0, 0, 1]
    ])   
    return K
def compute_extrinsics(self,angle):
    # Extrinsic camera matrix
    
    phi1 = -np.pi / 2
    R1 = np.array([[1.0, 0.0, 0.0],
                [0.0, np.cos(phi1), -np.sin(phi1)],
                [0.0, np.sin(phi1), np.cos(phi1)]])
    phi2 = np.pi / 2
    R2 = np.array([[np.cos(phi2), -np.sin(phi2), 0.0],
                [np.sin(phi2), np.cos(phi2), 0.0],
                [0.0, 0.0, 1.0]])
    R3 = np.array([[np.cos(angle), -np.sin(angle), 0.0],
                [np.sin(angle), np.cos(angle), 0.0],
                [0.0, 0.0, 1.0]])
    rot = np.dot(np.dot(R3, R2), R1)
    trans = np.array([self.DSO * np.cos(angle), self.DSO * np.sin(angle), 0])
    T = np.eye(4)
    T[:-1, :-1] = rot
    T[:-1, -1] = trans
    return T
···

Is this calculation correct?

[AnderBiguri]

I don't know.... You don't need intrinsic for TIGRE, so I can't really answer...

Is there any reason why you think it's not correct ? Is there any doubt of your steps that you think I can help with?

[AnderBiguri]

I'll close this due to no answer, but let me know if you need more help.