|
| | point_cloud_generator.box = Generator.generateUniformlySampledBox(1.0, 2.0, 3.0, number_of_points, translation, rotation) |
| |
| | point_cloud_generator.circle = Generator.generateUniformlySampledCircle(1.0, number_of_points, translation, rotation) |
| |
| | point_cloud_generator.cylinder = Generator.generateUniformlySampledCylinder(1.0, 2.0, number_of_points, translation, rotation) |
| |
| | point_cloud_generator.data = DP(DP.load('../data/car_cloud401.csv')) |
| |
| | point_cloud_generator.DP = PM.DataPoints |
| |
| | point_cloud_generator.Generator = pm.PointCloudGenerator |
| |
| bool | point_cloud_generator.is_3D = True |
| |
| int | point_cloud_generator.number_of_points = 10000 |
| |
| string | point_cloud_generator.output_base_directory = "tests/generator/" |
| |
| | point_cloud_generator.plane = Generator.generateUniformlySampledPlane(np.array([1.0, 2.0, 3.0]), number_of_points, translation, rotation) |
| |
| | point_cloud_generator.PM = pm.PointMatcher |
| |
| | point_cloud_generator.ref = DP(DP.load('../data/car_cloud400.csv')) |
| |
| | point_cloud_generator.rotation = np.array([1, 0, 0, 0], dtype=np.float32) |
| |
| | point_cloud_generator.sphere = Generator.generateUniformlySampledSphere(1.0, number_of_points, translation, rotation) |
| |
| string | point_cloud_generator.test_base = "3D" |
| |
| | point_cloud_generator.translation = np.array([[0], [0], [0]]) |
| |