def setUpClass(cls):
# excess epsilon to add margin
cls.eps = np.nextafter(0, 1)
# set up input data dir, parameters
interior_corners = (8,6)
side_length = 30 # in mm
# use results from matlab script to test
calib_folder = rel_to_file(os.path.join("test-data", "swept-plane", "calib"), __file__)
calib_images = load_from_directory(calib_folder)
cls.calib_results = scipy.io.loadmat(os.path.join(calib_folder, "Calib_Results.mat"), squeeze_me=True)
# run the calibration
(cls.focal_length, cls.principal_point, cls.alpha, cls.distortion) = intrinsic_calibration_with_checkerboard(
calib_images, interior_corners, side_length)
def setUpClass(cls):
# get the relevant data
structured_light_dir = rel_to_file(os.path.join("test-data", "structured-light"),
__file__)
cls.calib = scipy.io.loadmat(os.path.join(structured_light_dir, "calib",
"calib_results", "calib_cam_proj.mat"),
squeeze_me=True)
# Initialize the camera
cam_extrinsic_matrix = Camera.extrinsic_matrix(cls.calib['Rc_1_cam'],
cls.calib['Tc_1_cam'])
cam_intrinsic_matrix = Camera.intrinsic_matrix(cls.calib['fc_cam'],
cls.calib['cc_cam'],
cls.calib['alpha_c_cam'])
cam_distortion = cls.calib['kc_cam']
cam_resolution = (cls.calib['nx_cam'], cls.calib['ny_cam'])
cls.cam = Camera(None, cam_extrinsic_matrix, cam_intrinsic_matrix, cam_distortion, cam_resolution)
# Initialize the projector
proj_extrinsic_matrix = DLPProjector.extrinsic_matrix(cls.calib['Rc_1_proj'],
cls.calib['Tc_1_proj'])
proj_intrinsic_matrix = DLPProjector.intrinsic_matrix(cls.calib['fc_proj'],
cls.calib['cc_proj'],
cls.calib['alpha_c_proj'])
proj_distortion = cls.calib['kc_proj']
proj_resolution = (cls.calib['nx_proj'], cls.calib['ny_proj'])
cls.proj = DLPProjector(proj_extrinsic_matrix, proj_intrinsic_matrix, proj_distortion, proj_resolution)
# Get the Images
img_folder = os.path.join(structured_light_dir, "data", "Gray", "man", "v1")
loaded_images = load_from_directory(img_folder)
# Get the Gray code patterns that were projected
pattern_shape = (cls.calib['nx_proj'], cls.calib['ny_proj'])
patterns = [DLPPattern(np.ones(pattern_shape, dtype=np.uint8) * 255),
DLPPattern(np.zeros(pattern_shape, dtype=np.uint8))]
patterns.extend(gray_code_patterns(pattern_shape))
patterns.extend(gray_code_patterns(pattern_shape, vertical_stripes=False))
cls.gen_patterns = []
for p in patterns:
cls.gen_patterns.append([GeneratedPattern([(p, cls.proj)])])
# make sure patterns and images are the same length
if len(cls.gen_patterns) != len(loaded_images):
raise Exception("Pattern and Image list should be the same length. Please figure out why they aren't")
# reconstruct the images
images = []
for i in range(len(loaded_images)):
images.append(Image(loaded_images[i].data, cls.cam, cls.gen_patterns[i]))
n = (len(images) - 2) / 2
# finally at the point we would have been at if our machine scanned the
# object :)
cls.point_cloud = extract_point_cloud(images[0:2], images[2:2+n], images[2+n:])
viewer = pcl.PCLVisualizer("View Gray Scan")
viewer.add_point_cloud(cls.point_cloud)
viewer.add_coordinate_system(translation=cls.proj.T, scale=10)
viewer.add_text_3d("projector", cls.proj.T, text_id="projector")
viewer.add_coordinate_system(translation=cls.cam.T, scale=10)
viewer.add_text_3d("camera", cls.cam.T, text_id="camera")
viewer.init_camera_parameters()
viewer.spin()
viewer.close()
