Updated READMEs for the examples - Batch 1

docs/cspell.json

@@ -46,6 +46,8 @@
     "binsearching",
     "binstall",
     "binutils",
+    "blendshape",
+    "blendshapes",
     "Birger",
     "Birkl",
     "booktitle",
@@ -116,6 +118,8 @@
     "ewebsock",
     "extrinsics",
     "farbfeld",
+    "FACEMESH",
+    "facemesh",
     "Farooq",
     "Feichtenhofer",
     "fieldname",
@@ -166,6 +170,7 @@
     "keypointid",
     "keypoints",
     "Kirillov",
+    "klass",
     "kpreid",
     "Landmarker",
     "Larsson",
@@ -314,6 +319,7 @@
     "scipy",
     "scrollwheel",
     "segs",
+    "Segmentations",
     "serde",
     "Shaohui",
     "Shap",
@@ -383,6 +389,7 @@
     "Viktor",
     "virtualenv",
     "visualizability",
+    "voxels",
     "vstack",
     "vsuryamurthy",
     "vulkan",

examples/python/detect_and_track_objects/README.md

@@ -8,6 +8,7 @@ channel = "release"
 -->
 
 
+
 <picture data-inline-viewer="examples/detect_and_track_objects">
   <source media="(max-width: 480px)" srcset="https://static.rerun.io/detect_and_track_objects/59f5b97a8724f9037353409ab3d0b7cb47d1544b/480w.png">
   <source media="(max-width: 768px)" srcset="https://static.rerun.io/detect_and_track_objects/59f5b97a8724f9037353409ab3d0b7cb47d1544b/768w.png">
@@ -16,11 +17,158 @@ channel = "release"
   <img src="https://static.rerun.io/detect_and_track_objects/59f5b97a8724f9037353409ab3d0b7cb47d1544b/full.png" alt="">
 </picture>
 
-Another more elaborate example applying simple object detection and segmentation on a video using the Huggingface `transformers` library. Tracking across frames is performed using [CSRT](https://arxiv.org/pdf/1611.08461.pdf) from OpenCV.
+Visualize object detection and segmentation using the [Huggingface's Transformers](https://huggingface.co/docs/transformers/index) and [CSRT](https://arxiv.org/pdf/1611.08461.pdf) from OpenCV.
+
+# Used Rerun Types
+[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image), [`SegmentationImage`](https://www.rerun.io/docs/reference/types/archetypes/segmentation_image), [`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context), [`Boxes2D`](https://www.rerun.io/docs/reference/types/archetypes/boxes2d), [`TextLog`](https://www.rerun.io/docs/reference/types/archetypes/text_log)
+
+
+# Logging and Visualizing with Rerun
+The visualizations in this example were created with the following Rerun code.
+
+
+## Timelines
+
+For each processed video frame, all data sent to Rerun is associated with the [`timelines`](https://www.rerun.io/docs/concepts/timelines) `frame_idx`.
+
+```python
+rr.set_time_sequence("frame", frame_idx)
+```
+
+## Video
+The input video is logged as a sequence of [`Image`](https://www.rerun.io/docs/reference/types/archetypes/image) to the `image` entity.
+
+```python
+rr.log(
+    "image", 
+    rr.Image(rgb).compress(jpeg_quality=85)
+)
+```
+
+Since the detection and segmentation model operates on smaller images the resized images are logged to the separate `segmentation/rgb_scaled` entity. 
+This allows us to subsequently visualize the segmentation mask on top of the video.
+
+```python
+rr.log(
+    "segmentation/rgb_scaled", 
+    rr.Image(rgb_scaled).compress(jpeg_quality=85)
+)
+```
+
+## Segmentations
+The segmentation results is logged through a combination of two archetypes.
+The segmentation image itself is logged as an
+[`SegmentationImage`](https://www.rerun.io/docs/reference/types/archetypes/segmentation_image) and
+contains the id for each pixel. It is logged to the `segmentation` entity.
+
+
+```python
+rr.log(
+    "segmentation", 
+    rr.SegmentationImage(mask)
+)
+```
+
+The color and label for each class is determined by the
+[`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context) which is
+logged to the root entity using `rr.log("/", …, timeless=True)` as it should apply to the whole sequence and all
+entities that have a class id.
+
+```python
+class_descriptions = [ rr.AnnotationInfo(id=cat["id"], color=cat["color"], label=cat["name"]) for cat in coco_categories ]
+rr.log(
+     "/", 
+     rr.AnnotationContext(class_descriptions), 
+     timeless=True
+)
+```
+
+## Detections
+The detections and tracked bounding boxes are visualized by logging the [`Boxes2D`](https://www.rerun.io/docs/reference/types/archetypes/boxes2d) to Rerun.
+
+### Detections
+```python
+rr.log(
+    "segmentation/detections/things",
+    rr.Boxes2D(
+        array=thing_boxes,
+        array_format=rr.Box2DFormat.XYXY,
+        class_ids=thing_class_ids,
+    ),
+)
+```
 
-For more info see [here](https://huggingface.co/docs/transformers/index)
+```python
+rr.log(
+    f"image/tracked/{self.tracking_id}",
+    rr.Boxes2D(
+        array=self.tracked.bbox_xywh,
+        array_format=rr.Box2DFormat.XYWH,
+        class_ids=self.tracked.class_id,
+    ),
+)
+```
+### Tracked bounding boxes
+```python
+rr.log(
+    "segmentation/detections/background",
+    rr.Boxes2D(
+        array=background_boxes,
+        array_format=rr.Box2DFormat.XYXY,
+        class_ids=background_class_ids,
+    ),
+)
+```
 
+The color and label of the bounding boxes is determined by their class id, relying on the same
+[`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context) as the
+segmentation images. This ensures that a bounding box and a segmentation image with the same class id will also have the
+same color.
+
+Note that it is also possible to log multiple annotation contexts should different colors and / or labels be desired.
+The annotation context is resolved by seeking up the entity hierarchy.
+
+## Text Log
+Rerun integrates with the [Python logging module](https://docs.python.org/3/library/logging.html). 
+Through the [`TextLog`](https://www.rerun.io/docs/reference/types/archetypes/text_log#textlogintegration) text at different importance level can be logged. After an initial setup that is described on the
+[`TextLog`](https://www.rerun.io/docs/reference/types/archetypes/text_log#textlogintegration), statements
+such as `logging.info("...")`, `logging.debug("...")`, etc. will show up in the Rerun viewer. 
+
+```python
+def setup_logging() -> None:
+    logger = logging.getLogger()
+    rerun_handler = rr.LoggingHandler("logs")
+    rerun_handler.setLevel(-1)
+    logger.addHandler(rerun_handler)
+
+def main() -> None:
+    # .... existing code ....
+    setup_logging() # setup logging
+    track_objects(video_path, max_frame_count=args.max_frame) # start tracking
+```
+In the viewer you can adjust the filter level and look at the messages time-synchronized with respect to other logged data.
+
+# Run the Code
+To run this example, make sure you have the Rerun repository checked out and the latest SDK installed:
+```bash
+# Setup
+pip install --upgrade rerun-sdk  # install the latest Rerun SDK
+git clone git@github.com:rerun-io/rerun.git  # Clone the repository
+cd rerun
+git checkout latest  # Check out the commit matching the latest SDK release
+```
+
+Install the necessary libraries specified in the requirements file:
 ```bash
 pip install -r examples/python/detect_and_track_objects/requirements.txt
-python examples/python/detect_and_track_objects/main.py
+```
+To experiment with the provided example, simply execute the main Python script:
+```bash
+python examples/python/detect_and_track_objects/main.py # run the example
+```
+
+If you wish to customize it for various videos, adjust the maximum frames, explore additional features, or save it use the CLI with the `--help` option for guidance:
+
+```bash
+python examples/python/detect_and_track_objects/main.py --help
 ```

examples/python/dicom_mri/README.md

@@ -16,9 +16,47 @@ channel = "main"
   <img src="https://static.rerun.io/dicom_mri/e39f34a1b1ddd101545007f43a61783e1d2e5f8e/full.png" alt="">
 </picture>
 
-Example using a [DICOM](https://en.wikipedia.org/wiki/DICOM) MRI scan. This demonstrates the flexible tensor slicing capabilities of the Rerun viewer.
+Visualize a [DICOM](https://en.wikipedia.org/wiki/DICOM) MRI scan. This demonstrates the flexible tensor slicing capabilities of the Rerun viewer.
 
+## Used Rerun Types
+[`Tensor`](https://www.rerun.io/docs/reference/types/archetypes/tensor), [`TextDocument`](https://www.rerun.io/docs/reference/types/archetypes/text_document)
+
+# Logging and Visualizing with Rerun
+
+The visualizations in this example were created with just the following line.
+```python
+rr.log("tensor", rr.Tensor(voxels_volume_u16, dim_names=["right", "back", "up"]))
+```
+
+A `numpy.array` named `voxels_volume_u16` representing volumetric MRI intensities with a shape of `(512, 512, 512)`.
+To visualize this data effectively in Rerun, we can log the `numpy.array` as [`Tensor`](https://www.rerun.io/docs/reference/types/archetypes/tensor) to the `tensor` entity. 
+
+In the Rerun viewer you can also inspect the data in detail. The `dim_names` provided in the above call to `rr.log` help to
+give semantic meaning to each axis. After selecting the tensor view, you can adjust various settings in the Blueprint
+settings on the right-hand side. For example, you can adjust the color map, the brightness, which dimensions to show as
+an image and which to select from, and more.
+
+# Run the Code
+To run this example, make sure you have the Rerun repository checked out and the latest SDK installed:
+```bash
+# Setup
+pip install --upgrade rerun-sdk  # install the latest Rerun SDK
+git clone git@github.com:rerun-io/rerun.git  # Clone the repository
+cd rerun
+git checkout latest  # Check out the commit matching the latest SDK release
+```
+
+Install the necessary libraries specified in the requirements file:
 ```bash
 pip install -r examples/python/dicom_mri/requirements.txt
-python examples/python/dicom_mri/main.py
+```
+To experiment with the provided example, simply execute the main Python script:
+```bash
+python examples/python/dicom_mri/main.py # run the example
+```
+
+If you wish to customize it, explore additional features, or save it, use the CLI with the `--help` option for guidance:
+
+```bash
+python examples/python/dicom_mri/main.py --help
 ```