fix: config error in grip_downsample

pyproject.toml

@@ -21,7 +21,7 @@ tqdm = "^4.66.4"
 scipy = "^1.13.0"
 wandb = "^0.17.0"
 # torchimize = "^0.0.16"
-small-gicp = "^0.0.6"
+#small-gicp = "^0.0.6"
 
 
 

src/component/eval.py

@@ -14,6 +14,7 @@ class WandbLogger:
     def __init__(self, run_name: str | None = None, config: dict = None):
         """
         Initialize the Weights & Biases logging.
+        use wandb login with api key https://wandb.ai/authorize
         """
         if run_name is None:
             run_name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
@@ -198,6 +199,7 @@ def __init__(
             extra_config = {}
         wandb_config = registration_config._asdict()
         wandb_config.update({"name": name, **extra_config})
+        self.grid_downsample = registration_config.grid_downsample_resolution
         self.logger = WandbLogger(run_name=name, config=wandb_config)
 
     def run(self, max_images: int = 2000):
@@ -215,7 +217,7 @@ def run(self, max_images: int = 2000):
             pre_pose = rgbd_image.pose
 
             # NOTE: down sample
-            new_pcd = rgbd_image.pointclouds(1)
+            new_pcd = rgbd_image.pointclouds(self.grid_downsample)
 
             # NOTE: align interface
             if i == 0:

src/grip_downsample.py

@@ -21,13 +21,18 @@
     grid_downsample_resolutions = [1, 2, 4, 8, 10, 12, 16, 20]
     grid_downsample_resolutions.reverse()
 
-    rooms = ["room" + str(i) for i in range(3)] + ["office" + str(i) for i in range(5)][
-        :1
-    ]
+    rooms = ["room" + str(i) for i in range(3)] + ["office" + str(i) for i in range(5)]
+    finished = []
     for room in rooms:
         for method in methods:
             for voxel_downsampling_resolution in voxel_downsampling_resolutions:
                 for grid_downsample_resolution in grid_downsample_resolutions:
+                    if (
+                        room,
+                        grid_downsample_resolution,
+                        voxel_downsampling_resolution,
+                    ) in finished:
+                        continue
                     registration_config = RegistrationConfig(
                         registration_type=method,
                         voxel_downsampling_resolutions=voxel_downsampling_resolution,

src/pcd_vis.py

@@ -1,7 +1,6 @@
 from collections import deque
 
 import cv2
-import numba
 import numpy as np
 from matplotlib import pyplot as plt
 
@@ -10,8 +9,6 @@
 from src.slam_data.dataset import Replica
 from src.slam_data.Image import RGBDImage
 
-numba.config.THREADING_LAYER_PRIORITY = ["omp", "tbb", "workqueue"]
-
 
 class PointCloudProcessor(Scan2ScanICP):
     """
@@ -70,13 +67,13 @@ def process_pcd(self):
 
             if i % 30 == 0:
                 self.vis.update_render(new_pcd, estimate_pose)
-                fps = 1 / np.mean(self.stamps)
-                self.vis.vis_trajectory(
-                    gt_poses=self.gt_poses,
-                    estimated_poses=self.estimated_poses,
-                    downsampling_resolution=self.downsampling_resolution,
-                    fps=fps,
-                )
+                # fps = 1 / np.mean(self.stamps)
+                # self.vis.vis_trajectory(
+                #     gt_poses=self.gt_poses,
+                #     estimated_poses=self.estimated_poses,
+                #     downsampling_resolution=self.voxel_downsampling_resolutions,
+                #     fps=fps,
+                # )
         self.vis.close()
 
     #     if save: