format python examples

examples/python/advanced/colored_pointcloud_registration.py

@@ -17,19 +17,17 @@ def draw_registration_result_original_color(source, target, transformation):
 
     # draw initial alignment
     current_transformation = np.identity(4)
-    draw_registration_result_original_color(source, target,
-                                            current_transformation)
+    draw_registration_result_original_color(source, target, current_transformation)
 
     # point to plane ICP
     current_transformation = np.identity(4)
     print("2. Point-to-plane ICP registration is applied on original point")
     print("   clouds to refine the alignment. Distance threshold 0.02.")
     result_icp = cph.registration.registration_icp(
-        source, target, 0.02, current_transformation,
-        cph.registration.TransformationEstimationPointToPlane())
+        source, target, 0.02, current_transformation, cph.registration.TransformationEstimationPointToPlane()
+    )
     print(result_icp)
-    draw_registration_result_original_color(source, target,
-                                            result_icp.transformation)
+    draw_registration_result_original_color(source, target, result_icp.transformation)
 
     # colored pointcloud registration
     # This is implementation of following paper
@@ -49,18 +47,17 @@ def draw_registration_result_original_color(source, target, transformation):
         target_down = target.voxel_down_sample(radius)
 
         print("3-2. Estimate normal.")
-        source_down.estimate_normals(
-            cph.geometry.KDTreeSearchParamRadius(radius=radius * 2, max_nn=30))
-        target_down.estimate_normals(
-            cph.geometry.KDTreeSearchParamRadius(radius=radius * 2, max_nn=30))
+        source_down.estimate_normals(cph.geometry.KDTreeSearchParamRadius(radius=radius * 2, max_nn=30))
+        target_down.estimate_normals(cph.geometry.KDTreeSearchParamRadius(radius=radius * 2, max_nn=30))
 
         print("3-3. Applying colored point cloud registration")
         result_icp = cph.registration.registration_colored_icp(
-            source_down, target_down, radius, current_transformation,
-            cph.registration.ICPConvergenceCriteria(relative_fitness=1e-6,
-                                                    relative_rmse=1e-6,
-                                                    max_iteration=iter))
+            source_down,
+            target_down,
+            radius,
+            current_transformation,
+            cph.registration.ICPConvergenceCriteria(relative_fitness=1e-6, relative_rmse=1e-6, max_iteration=iter),
+        )
         current_transformation = result_icp.transformation
         print(result_icp)
-    draw_registration_result_original_color(source, target,
-                                            result_icp.transformation)
+    draw_registration_result_original_color(source, target, result_icp.transformation)

examples/python/advanced/fast_global_registration.py

@@ -6,31 +6,28 @@
 import time
 
 
-def execute_fast_global_registration(source_down, target_down, source_fpfh,
-                                     target_fpfh, voxel_size):
+def execute_fast_global_registration(source_down, target_down, source_fpfh, target_fpfh, voxel_size):
     distance_threshold = voxel_size * 0.5
-    print(":: Apply fast global registration with distance threshold %.3f" \
-            % distance_threshold)
+    print(":: Apply fast global registration with distance threshold %.3f" % distance_threshold)
     result = cph.registration.registration_fast_based_on_feature_matching(
-        source_down, target_down, source_fpfh, target_fpfh,
-        cph.registration.FastGlobalRegistrationOption(
-            maximum_correspondence_distance=distance_threshold))
+        source_down,
+        target_down,
+        source_fpfh,
+        target_fpfh,
+        cph.registration.FastGlobalRegistrationOption(maximum_correspondence_distance=distance_threshold),
+    )
     return result
 
 
 if __name__ == "__main__":
     cph.utility.set_verbosity_level(cph.utility.Debug)
 
     voxel_size = 0.05  # means 5cm for the dataset
-    source, target, source_down, target_down, source_fpfh, target_fpfh = \
-            prepare_dataset(voxel_size)
+    source, target, source_down, target_down, source_fpfh, target_fpfh = prepare_dataset(voxel_size)
 
     start = time.time()
-    result_fast = execute_fast_global_registration(source_down, target_down,
-                                                   source_fpfh, target_fpfh,
-                                                   voxel_size)
+    result_fast = execute_fast_global_registration(source_down, target_down, source_fpfh, target_fpfh, voxel_size)
     print("Fast global registration took %.3f sec.\n" % (time.time() - start))
     print(result_fast)
     print(result_fast.transformation)
-    draw_registration_result(source_down, target_down,
-                             result_fast.transformation)
+    draw_registration_result(source_down, target_down, result_fast.transformation)

examples/python/advanced/global_registration.py

@@ -18,23 +18,21 @@ def preprocess_point_cloud(pcd, voxel_size):
 
     radius_normal = voxel_size * 2
     print(":: Estimate normal with search radius %.3f." % radius_normal)
-    pcd_down.estimate_normals(
-        cph.geometry.KDTreeSearchParamRadius(radius=radius_normal, max_nn=30))
+    pcd_down.estimate_normals(cph.geometry.KDTreeSearchParamRadius(radius=radius_normal, max_nn=30))
 
     radius_feature = voxel_size * 5
     print(":: Compute FPFH feature with search radius %.3f." % radius_feature)
     pcd_feature = cph.registration.compute_shot_feature(
-        pcd_down, radius_feature,
-        cph.geometry.KDTreeSearchParamRadius(radius=radius_feature, max_nn=100))
+        pcd_down, radius_feature, cph.geometry.KDTreeSearchParamRadius(radius=radius_feature, max_nn=100)
+    )
     return pcd_down, pcd_feature
 
 
 def prepare_dataset(voxel_size):
     print(":: Load two point clouds and disturb initial pose.")
     source = cph.io.read_point_cloud("../../testdata/icp/cloud_bin_0.pcd")
     target = cph.io.read_point_cloud("../../testdata/icp/cloud_bin_1.pcd")
-    trans_init = np.asarray([[0.0, 0.0, 1.0, 0.0], [1.0, 0.0, 0.0, 0.0],
-                             [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
+    trans_init = np.asarray([[0.0, 0.0, 1.0, 0.0], [1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
     source.transform(trans_init)
     draw_registration_result(source, target, np.identity(4))
 

examples/python/advanced/iss_keypoint_detector.py

@@ -2,8 +2,9 @@
 import sys
 import time
 import cupoch as cph
+
 dir_path = os.path.dirname(os.path.realpath(__file__))
-sys.path.append(os.path.join(dir_path, '../misc'))
+sys.path.append(os.path.join(dir_path, "../misc"))
 import meshes
 
 # Compute ISS Keypoints on Armadillo
@@ -20,4 +21,4 @@
 mesh.compute_vertex_normals()
 mesh.paint_uniform_color([0.5, 0.5, 0.5])
 keypoints.paint_uniform_color([1.0, 0.75, 0.0])
-cph.visualization.draw_geometries([keypoints, mesh])
+cph.visualization.draw_geometries([keypoints, mesh])

examples/python/advanced/kinfu.py

@@ -3,20 +3,20 @@
 
 if __name__ == "__main__":
     camera_intrinsics = cph.camera.PinholeCameraIntrinsic(
-        cph.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault)
+        cph.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault
+    )
     kop = cph.kinfu.KinfuOption()
     kop.distance_threshold = 5.0
     kinfu = cph.kinfu.KinfuPipeline(camera_intrinsics, kop)
 
     markers = []
     for i in range(5):
         print("Integrate {:d}-th image into the volume.".format(i))
-        color = cph.io.read_image(
-            "../../testdata/rgbd/color/{:05d}.jpg".format(i))
-        depth = cph.io.read_image(
-            "../../testdata/rgbd/depth/{:05d}.png".format(i))
+        color = cph.io.read_image("../../testdata/rgbd/color/{:05d}.jpg".format(i))
+        depth = cph.io.read_image("../../testdata/rgbd/depth/{:05d}.png".format(i))
         rgbd = cph.geometry.RGBDImage.create_from_color_and_depth(
-            color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False)
+            color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False
+        )
         res = kinfu.process_frame(rgbd)
         if res:
             markers.append(cph.geometry.LineSet.create_camera_marker(camera_intrinsics, np.linalg.inv(kinfu.cur_pose)))

examples/python/advanced/pointcloud_outlier_removal.py

@@ -26,10 +26,9 @@ def display_inlier_outlier(cloud, ind):
     cph.visualization.draw_geometries([uni_down_pcd])
 
     print("Statistical oulier removal")
-    cl, ind = voxel_down_pcd.remove_statistical_outlier(nb_neighbors=20,
-                                                        std_ratio=2.0)
+    cl, ind = voxel_down_pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
     display_inlier_outlier(voxel_down_pcd, ind)
 
     print("Radius oulier removal")
     cl, ind = voxel_down_pcd.remove_radius_outlier(nb_points=16, radius=0.05)
-    display_inlier_outlier(voxel_down_pcd, ind)
+    display_inlier_outlier(voxel_down_pcd, ind)

examples/python/advanced/rgbd_integration.py

@@ -5,24 +5,22 @@
 if __name__ == "__main__":
     camera_poses = read_trajectory("../../testdata/rgbd/odometry.log")
     volume = cph.integration.ScalableTSDFVolume(
-        voxel_length=8.0 / 512.0,
-        sdf_trunc=0.04,
-        color_type=cph.integration.TSDFVolumeColorType.RGB8)
+        voxel_length=8.0 / 512.0, sdf_trunc=0.04, color_type=cph.integration.TSDFVolumeColorType.RGB8
+    )
 
     for i in range(len(camera_poses)):
         print("Integrate {:d}-th image into the volume.".format(i))
-        color = cph.io.read_image(
-            "../../testdata/rgbd/color/{:05d}.jpg".format(i))
-        depth = cph.io.read_image(
-            "../../testdata/rgbd/depth/{:05d}.png".format(i))
+        color = cph.io.read_image("../../testdata/rgbd/color/{:05d}.jpg".format(i))
+        depth = cph.io.read_image("../../testdata/rgbd/depth/{:05d}.png".format(i))
         rgbd = cph.geometry.RGBDImage.create_from_color_and_depth(
-            color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False)
+            color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False
+        )
         volume.integrate(
             rgbd,
-            cph.camera.PinholeCameraIntrinsic(
-                cph.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault),
-            np.linalg.inv(camera_poses[i].pose))
+            cph.camera.PinholeCameraIntrinsic(cph.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault),
+            np.linalg.inv(camera_poses[i].pose),
+        )
 
     print("Extract a point cloud from the volume and visualize it.")
     pcd = volume.extract_point_cloud()
-    cph.visualization.draw_geometries([pcd])
+    cph.visualization.draw_geometries([pcd])

examples/python/advanced/rgbd_integration_uniform.py

@@ -5,7 +5,8 @@
 if __name__ == "__main__":
     camera_poses = read_trajectory("../../testdata/rgbd/odometry.log")
     camera_intrinsics = cph.camera.PinholeCameraIntrinsic(
-        cph.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault)
+        cph.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault
+    )
     volume = cph.integration.UniformTSDFVolume(
         length=8.0,
         resolution=512,
@@ -16,18 +17,19 @@
     markers = []
     for i in range(len(camera_poses)):
         print("Integrate {:d}-th image into the volume.".format(i))
-        color = cph.io.read_image(
-            "../../testdata/rgbd/color/{:05d}.jpg".format(i))
-        depth = cph.io.read_image(
-            "../../testdata/rgbd/depth/{:05d}.png".format(i))
+        color = cph.io.read_image("../../testdata/rgbd/color/{:05d}.jpg".format(i))
+        depth = cph.io.read_image("../../testdata/rgbd/depth/{:05d}.png".format(i))
         rgbd = cph.geometry.RGBDImage.create_from_color_and_depth(
-            color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False)
+            color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False
+        )
         volume.integrate(
             rgbd,
             camera_intrinsics,
             np.linalg.inv(camera_poses[i].pose),
         )
-        markers.append(cph.geometry.LineSet.create_camera_marker(camera_intrinsics, np.linalg.inv(camera_poses[i].pose)))
+        markers.append(
+            cph.geometry.LineSet.create_camera_marker(camera_intrinsics, np.linalg.inv(camera_poses[i].pose))
+        )
 
     print("Extract triangle mesh")
     mesh = volume.extract_triangle_mesh()

examples/python/advanced/stereo.py

@@ -12,4 +12,4 @@
 sgm = cph.imageproc.SemiGlobalMatching(params)
 disp = sgm.process_frame(limg, rimg)
 disp.linear_transform(255.0 / 127.0)
-cph.visualization.draw_geometries([disp])
+cph.visualization.draw_geometries([disp])

examples/python/advanced/trajectory_io.py

@@ -2,36 +2,33 @@
 
 
 class CameraPose:
-
     def __init__(self, meta, mat):
         self.metadata = meta
         self.pose = mat
 
     def __str__(self):
-        return 'Metadata : ' + ' '.join(map(str, self.metadata)) + '\n' + \
-            "Pose : " + "\n" + np.array_str(self.pose)
+        return "Metadata : " + " ".join(map(str, self.metadata)) + "\n" + "Pose : " + "\n" + np.array_str(self.pose)
 
 
 def read_trajectory(filename):
     traj = []
-    with open(filename, 'r') as f:
+    with open(filename, "r") as f:
         metastr = f.readline()
         while metastr:
             metadata = list(map(int, metastr.split()))
             mat = np.zeros(shape=(4, 4))
             for i in range(4):
                 matstr = f.readline()
-                mat[i, :] = np.fromstring(matstr, dtype=float, sep=' \t')
+                mat[i, :] = np.fromstring(matstr, dtype=float, sep=" \t")
             traj.append(CameraPose(metadata, mat))
             metastr = f.readline()
     return traj
 
 
 def write_trajectory(traj, filename):
-    with open(filename, 'w') as f:
+    with open(filename, "w") as f:
         for x in traj:
             p = x.pose.tolist()
-            f.write(' '.join(map(str, x.metadata)) + '\n')
-            f.write('\n'.join(
-                ' '.join(map('{0:.12f}'.format, p[i])) for i in range(4)))
-            f.write('\n')
+            f.write(" ".join(map(str, x.metadata)) + "\n")
+            f.write("\n".join(" ".join(map("{0:.12f}".format, p[i])) for i in range(4)))
+            f.write("\n")