import numpy as np import xmltodict import cv2.cv2 as cv2 def load_camera_parameters(path='calibration_result.xml'): parameters = {'proj': {}, 'cam': {}} with open(path) as f: cam_mat = xmltodict.parse(f.read()) # get projector and camera intrinsics for key in parameters: K_shape = int(cam_mat['opencv_storage'][f'{key}_int']['rows']), int(cam_mat['opencv_storage'][f'{key}_int']['cols']) parameters[key]['K'] = np.array(cam_mat['opencv_storage'][f'{key}_int']['data'].split(), dtype=float).reshape(K_shape).T dist_shape = int(cam_mat['opencv_storage'][f'{key}_dist']['rows']), int(cam_mat['opencv_storage'][f'{key}_dist']['cols']) parameters[key]['dist'] = np.array(cam_mat['opencv_storage'][f'{key}_dist']['data'].split(), dtype=float).reshape(dist_shape).T # get image size # weird casting cause the values are str(float) (eg. '123.'), but we want int imsize_shape = int(cam_mat['opencv_storage'][f'img_shape']['rows']), int(cam_mat['opencv_storage'][f'img_shape']['cols']) parameters['imsize'] = np.array([float(x) for x in cam_mat['opencv_storage']['img_shape']['data'].split()], dtype='uint16').reshape(imsize_shape).T # get extrinsics parameters['ext'] = {} rot_shape = int(cam_mat['opencv_storage']['rotation']['rows']), int(cam_mat['opencv_storage']['rotation']['cols']) parameters['ext']['R'] = np.array(cam_mat['opencv_storage'][f'rotation']['data'].split(), dtype=float).reshape(rot_shape).T # switched cols and rows for mult compat with R trans_shape = int(cam_mat['opencv_storage']['translation']['cols']), int(cam_mat['opencv_storage']['translation']['rows']) parameters['ext']['T'] = np.array(cam_mat['opencv_storage'][f'translation']['data'].split(), dtype=float).reshape(trans_shape).T return parameters params = load_camera_parameters() # print(params) print( params['cam']['K'].shape, params['cam']['dist'].shape, params['proj']['K'].shape, params['proj']['dist'].shape, params['imsize'].shape, params['ext']['R'].shape, params['ext']['T'].shape, ) # print(params['imsize'].reshape((2, 1))) params['imsize'] = params['imsize'].reshape((2, 1)) # params['imsize'] = np.array([488, 688]) # print(params['imsize'].reshape((2, 1))) # print(np.transpose(params['ext']['T'], params['ext']['T'])) R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify( params['cam']['K'], params['cam']['dist'], params['proj']['K'], params['proj']['dist'], # params['imsize'], (688, 488), params['ext']['R'], params['ext']['T'], ) ################# SCRATCH ############################## import math def isclose(x, y, rtol=1.e-5, atol=1.e-8): return abs(x-y) <= atol + rtol * abs(y) def euler_angles_from_rotation_matrix(R): ''' From a paper by Gregory G. Slabaugh (undated), "Computing Euler angles from a rotation matrix ''' phi = 0.0 if isclose(R[2,0],-1.0): theta = math.pi/2.0 psi = math.atan2(R[0,1],R[0,2]) elif isclose(R[2,0],1.0): theta = -math.pi/2.0 psi = math.atan2(-R[0,1],-R[0,2]) else: theta = -math.asin(R[2,0]) cos_theta = math.cos(theta) psi = math.atan2(R[2,1]/cos_theta, R[2,2]/cos_theta) phi = math.atan2(R[1,0]/cos_theta, R[0,0]/cos_theta) return psi, theta, phi #################################################### print('R1:\n', R1) print(euler_angles_from_rotation_matrix(R1)) print('R2:\n', R2) print(euler_angles_from_rotation_matrix(R2)) print('P1:\n', P1) print('P2:\n', P2) print('Q :\n', Q) pattern = cv2.imread('kinect_pattern.png') sampled_pattern = cv2.imread('sampled_kinect_pattern.png') proj_rect_map1, proj_rect_map2 = cv2.initInverseRectificationMap( params['proj']['K'], params['proj']['dist'], R1, # None, P1, # (688, 488), (1280, 1024), cv2.CV_16SC2, ) rect_pat = cv2.remap(pattern, proj_rect_map1, proj_rect_map2, cv2.INTER_LINEAR) # FIXME rect_pat is always zero cv2.imshow('get rect', rect_pat) cv2.waitKey() # cv2.imshow(rect_pat2) cv2.waitKey()