Kissualizer: a new KISS-ICP visualizer! (#379)

* Visualizer initialized

* Simple buttons added

* Almost done

* Center viewpoint option

* Better static member management

* Better palette and global view

* Global View is not switchable standing

* Arrows on trajectory

* Slight beautification of arrows

* Global points dimensions

* Trajectory not invisible but actually not there in local mode

* Some TODO todo

* Refactor code, palette changed, trajectory swithc in global fixed

* Some separator

* Points size sliders

* Better viewpoint centering

* Screenshot demo added

* Some button key added

* Fixed DEBUG name window

* Parameters visualization and fps added

* Center viewpoint improved

* Trajectory un-registration for better bounding box

* Color palette changed again

* Some cleaning

* Button name with keys and missing keys added

* Embrace Benedikt's colors and fix typo

* Fix Palette and quantity size

* Fix license

* Color approved by Gupta

* Now everything is member of Kissualizer

* Trajectory as points, not anymore a curve

* Screenshot name with timestamp

* Style fix

* Button names constants less ugly

* Now we have a dict to visualize parameters and co

* FPS computation not anymore in visualizer class

* isort ti odio

* Odometry info is now open by default

* Quit button on the right, why not

* Pose picking started

* Pose selection and visualization on the trajectory

---------

Co-authored-by: tizianoGuadagnino <[email protected]>

.gitignore

@@ -4,6 +4,8 @@ results/
 wheelhouse/
 _skbuild/
 .gitlab-ci-local/
+.polyscope.ini
+imgui.ini
 
 # Created by https://www.toptal.com/developers/gitignore/api/python,c++
 # Edit at https://www.toptal.com/developers/gitignore?templates=python,c++

python/kiss_icp/pipeline.py

@@ -35,7 +35,7 @@
 from kiss_icp.metrics import absolute_trajectory_error, sequence_error
 from kiss_icp.tools.pipeline_results import PipelineResults
 from kiss_icp.tools.progress_bar import get_progress_bar
-from kiss_icp.tools.visualizer import RegistrationVisualizer, StubVisualizer
+from kiss_icp.tools.visualizer import Kissualizer, StubVisualizer
 
 
 class OdometryPipeline:
@@ -76,9 +76,13 @@ def __init__(
         )
 
         # Visualizer
-        self.visualizer = RegistrationVisualizer() if visualize else StubVisualizer()
+        self.visualizer = Kissualizer() if visualize else StubVisualizer()
+        self._vis_infos = {
+            "max_range": self.config.data.max_range,
+            "min_range": self.config.data.min_range,
+        }
         if hasattr(self._dataset, "use_global_visualizer"):
-            self.visualizer.global_view = self._dataset.use_global_visualizer
+            self.visualizer._global_view = self._dataset.use_global_visualizer
 
     # Public interface  ------
     def run(self):
@@ -99,8 +103,15 @@ def _run_pipeline(self):
             source, keypoints = self.odometry.register_frame(raw_frame, timestamps)
             self.poses[idx - self._first] = self.odometry.last_pose
             self.times[idx - self._first] = time.perf_counter_ns() - start_time
+
+            # Udate visualizer
+            self._vis_infos["FPS"] = int(np.floor(self._get_fps()))
             self.visualizer.update(
-                source, keypoints, self.odometry.local_map, self.odometry.last_pose
+                source,
+                keypoints,
+                self.odometry.local_map,
+                self.odometry.last_pose,
+                self._vis_infos,
             )
 
     def _next(self, idx):
@@ -169,6 +180,11 @@ def _write_gt_poses(self):
             timestamps=self._get_frames_timestamps(),
         )
 
+    def _get_fps(self):
+        times_nozero = self.times[self.times != 0]
+        total_time_s = np.sum(times_nozero) * 1e-9
+        return float(times_nozero.shape[0] / total_time_s) if total_time_s > 0 else 0
+
     def _run_evaluation(self):
         # Run estimation metrics evaluation, only when GT data was provided
         if self.has_gt:
@@ -180,11 +196,7 @@ def _run_evaluation(self):
             self.results.append(desc="Absolute Rotational Error (ARE)", units="rad", value=ate_rot)
 
         # Run timing metrics evaluation, always
-        def _get_fps():
-            total_time_s = np.sum(self.times) * 1e-9
-            return float(self._n_scans / total_time_s) if total_time_s > 0 else 0
-
-        fps = _get_fps()
+        fps = self._get_fps()
         avg_fps = int(np.floor(fps))
         avg_ms = int(np.ceil(1e3 / fps)) if fps > 0 else 0
         if avg_fps > 0: