Clean up our examples

crates/re_components/src/tensor.rs

@@ -1192,11 +1192,6 @@ impl DecodedTensor {
             | TensorData::F64(_) => Ok(Self(maybe_encoded_tensor)),
 
             TensorData::JPEG(jpeg_bytes) => {
-                re_log::debug!(
-                    "Decoding JPEG image of shape {:?}",
-                    maybe_encoded_tensor.shape()
-                );
-
                 let [h, w, c] = maybe_encoded_tensor
                     .image_height_width_channels()
                     .ok_or_else(|| {

crates/re_space_view_text/src/scene_part.rs

@@ -101,6 +101,11 @@ impl ScenePart<TextSpaceView> for SceneText {
             }
         }
 
+        {
+            re_tracing::profile_scope!("sort");
+            self.text_entries.sort_by_key(|entry| entry.time);
+        }
+
         Vec::new()
     }
 }

examples/python/arkitscenes/main.py

@@ -57,7 +57,7 @@ def log_annotated_bboxes(annotation: dict[str, Any]) -> tuple[npt.NDArray[np.flo
     # Generate a color per object that can be reused across both 3D obb and their 2D projections
     # TODO(pablovela5620): Once #1581 or #1728 is resolved this can be removed
     color_positions = np.linspace(0, 1, num_objects)
-    colormap = plt.cm.get_cmap("viridis")
+    colormap = plt.colormaps["viridis"]
     colors = [colormap(pos) for pos in color_positions]
 
     for i, label_info in enumerate(annotation["data"]):

examples/python/mp_pose/main.py

@@ -10,7 +10,7 @@
 from pathlib import Path
 from typing import Any, Final, Iterator
 
-import cv2 as cv
+import cv2
 import mediapipe as mp
 import numpy as np
 import numpy.typing as npt
@@ -42,7 +42,7 @@ def track_pose(video_path: str, segment: bool) -> None:
 
     with closing(VideoSource(video_path)) as video_source, mp_pose.Pose(enable_segmentation=segment) as pose:
         for bgr_frame in video_source.stream_bgr():
-            rgb = cv.cvtColor(bgr_frame.data, cv.COLOR_BGR2RGB)
+            rgb = cv2.cvtColor(bgr_frame.data, cv2.COLOR_BGR2RGB)
             rr.set_time_seconds("time", bgr_frame.time)
             rr.set_time_sequence("frame_idx", bgr_frame.idx)
             rr.log_image("video/rgb", rgb)
@@ -91,7 +91,7 @@ class VideoFrame:
 
 class VideoSource:
     def __init__(self, path: str):
-        self.capture = cv.VideoCapture(path)
+        self.capture = cv2.VideoCapture(path)
 
         if not self.capture.isOpened():
             logging.error("Couldn't open video at %s", path)
@@ -101,9 +101,9 @@ def close(self) -> None:
 
     def stream_bgr(self) -> Iterator[VideoFrame]:
         while self.capture.isOpened():
-            idx = int(self.capture.get(cv.CAP_PROP_POS_FRAMES))
+            idx = int(self.capture.get(cv2.CAP_PROP_POS_FRAMES))
             is_open, bgr = self.capture.read()
-            time_ms = self.capture.get(cv.CAP_PROP_POS_MSEC)
+            time_ms = self.capture.get(cv2.CAP_PROP_POS_MSEC)
 
             if not is_open:
                 break

examples/python/plots/main.py

@@ -26,7 +26,7 @@ def log_bar_chart() -> None:
     mean = 0
     std = 1
     variance = np.square(std)
-    x = np.arange(-5, 5, 0.01)
+    x = np.arange(-5, 5, 0.1)
     y = np.exp(-np.square(x - mean) / 2 * variance) / (np.sqrt(2 * np.pi * variance))
     rr.log_tensor("bar_chart", y)
 

examples/python/stable_diffusion/main.py

@@ -52,7 +52,7 @@ def get_downloaded_path(dataset_dir: Path, image_name: str) -> str:
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(description="Logs Objectron data using the Rerun SDK.")
+    parser = argparse.ArgumentParser(description="Stable diffusion.")
     parser.add_argument(
         "--image",
         type=str,

examples/python/tracking_hf_opencv/main.py

@@ -10,7 +10,7 @@
 from pathlib import Path
 from typing import Any, Final, Sequence
 
-import cv2 as cv
+import cv2
 import numpy as np
 import numpy.typing as npt
 import requests
@@ -84,7 +84,7 @@ def detect_objects_to_track(self, rgb: npt.NDArray[np.uint8], frame_idx: int) ->
         inputs = self.feature_extractor(images=pil_im_small, return_tensors="pt")
         _, _, scaled_height, scaled_width = inputs["pixel_values"].shape
         scaled_size = (scaled_width, scaled_height)
-        rgb_scaled = cv.resize(rgb, scaled_size)
+        rgb_scaled = cv2.resize(rgb, scaled_size)
         rr.log_image("image/scaled/rgb", rgb_scaled)
         rr.log_disconnected_space("image/scaled")  # Note: Haven't implemented 2D transforms yet.
 
@@ -161,7 +161,7 @@ def __init__(self, tracking_id: int, detection: Detection, bgr: npt.NDArray[np.u
         self.tracked = detection.scaled_to_fit_image(bgr)
         self.num_recent_undetected_frames = 0
 
-        self.tracker = cv.TrackerCSRT_create()
+        self.tracker = cv2.TrackerCSRT_create()
         bbox_xywh_rounded = [int(val) for val in self.tracked.bbox_xywh]
         self.tracker.init(bgr, bbox_xywh_rounded)
         self.log_tracked()
@@ -201,7 +201,7 @@ def log_tracked(self) -> None:
     def update_with_detection(self, detection: Detection, bgr: npt.NDArray[np.uint8]) -> None:
         self.num_recent_undetected_frames = 0
         self.tracked = detection.scaled_to_fit_image(bgr)
-        self.tracker = cv.TrackerCSRT_create()
+        self.tracker = cv2.TrackerCSRT_create()
         bbox_xywh_rounded = [int(val) for val in self.tracked.bbox_xywh]
         self.tracker.init(bgr, bbox_xywh_rounded)
         self.log_tracked()
@@ -320,7 +320,7 @@ def track_objects(video_path: str) -> None:
     detector = Detector(coco_categories=coco_categories)
 
     logging.info("Loading input video: %s", str(video_path))
-    cap = cv.VideoCapture(video_path)
+    cap = cv2.VideoCapture(video_path)
     frame_idx = 0
 
     label_strs = [cat["name"] or str(cat["id"]) for cat in coco_categories]
@@ -333,7 +333,7 @@ def track_objects(video_path: str) -> None:
             logging.info("End of video")
             break
 
-        rgb = cv.cvtColor(bgr, cv.COLOR_BGR2RGB)
+        rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
         rr.log_image("image/rgb", rgb)
 
         if not trackers or frame_idx % 40 == 0:
@@ -373,18 +373,14 @@ def setup_looging() -> None:
     rerun_handler = rr.log.text.LoggingHandler("logs")
     rerun_handler.setLevel(-1)
     logger.addHandler(rerun_handler)
-    stream_handler = logging.StreamHandler()
-    stream_handler.setLevel(1)
-    logger.addHandler(stream_handler)
-    logger.setLevel(-1)
 
 
 def main() -> None:
     # Ensure the logging gets written to stderr:
     logging.getLogger().addHandler(logging.StreamHandler())
-    logging.getLogger().setLevel("INFO")
+    logging.getLogger().setLevel("DEBUG")
 
-    parser = argparse.ArgumentParser(description="Logs Objectron data using the Rerun SDK.")
+    parser = argparse.ArgumentParser(description="Example applying simple object detection and tracking on a video.")
     parser.add_argument(
         "--video",
         type=str,

scripts/run_python_e2e_test.py

@@ -20,7 +20,7 @@
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(description="Logs Objectron data using the Rerun SDK.")
+    parser = argparse.ArgumentParser(description="Runs end-to-end tests of select python example.")
     parser.add_argument("--no-build", action="store_true", help="Skip building rerun-sdk")
     parser.add_argument("--no-pip-reqs", action="store_true", help="Skip installing pip requirements")