diff --git a/.gitignore b/.gitignore index e0171fa38d5a..b180d2c1c277 100644 --- a/.gitignore +++ b/.gitignore @@ -27,6 +27,7 @@ _deps # Python virtual environment: **/venv* +**/.venv* .python-version # Python build artifacts: diff --git a/examples/python/README.md b/examples/python/README.md index f6aed2e657cb..835d79ff42d8 100644 --- a/examples/python/README.md +++ b/examples/python/README.md @@ -1,4 +1,173 @@ # Rerun Python examples + +--- + +## Example dependency management + +We need the following for a smooth example experience: +- Per-example dependency specification: different example must be able to have conflicting dependencies. +- Per-example Python version requirement: although the Rerun SDK has a well-defined Python compatibility range, specific example should be able to restrict it when required by their specific dependencies. +- Per-example isolated environment: it follows from what precedes that each example should be run in their own venv. +- Minimal overhead and as "standard" as possible. +- (Nice to have) Lockfile. + +In addition, we should support both dev environment (install `rerun-sdk` from the working directory) and end-user environment (install `rerun-sdk` from PyPI). + +--- + +## Using `uv` + +Example: `human_pose_tracking` + +### Tooling Installation + +```shell +pipx install uv + +# or + +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +### Environment Setup + +Must be manually created but `uv` has facilities for that: + +```shell +cd example/python/human_pose_tracking +uv venv --python 3.11 +source .venv/bin/activate +uv pip install -r requirements.txt + +# to use local rerun-sdk (this triggers a maturin compilation) +uv pip install -e ../../../rerun_py +``` + +⚠️There don't seem to be a way to specify Python version requirement for `uv` yet, so the user must pick a compatible python version. + +Note: since this is basically based on a (locked) requirement file, "standard" Python tooling will also work: + +```shell +cd example/python/human_pose_tracking +python3.11 -m venv .venv +source .venv/bin/activate +pip install -r requirements.txt +pip install -e ../../../rerun_py +``` + +In that sense, `uv` is only required for example authors (to update the `requirements.txt` file). + +### Run the example + +```shell +python main.py +``` + +### Create/update the lockfile + +The direct dependencies are specified in `requirements.in`. From that, `uv` is able to generate a `requirements.txt` which essentially is a lockfile: + +```shell +uv pip compile requirements.in -o requirements.txt +``` + +--- + +## Using `poetry` with `package-mode = false` + +Example: `human_pose_tracking_poetry` + +Note: `package-mode = false` is very nice for lone scripts that don't need to be "pip installed" to run, just like our examples. + +### Tooling Installation + +```shell +pipx install poetry + +# or + +curl -sSL https://install.python-poetry.org | python3 - +``` + +### Environment Setup + +```shell +poetry install + +# to use the local rerun-sdk (this triggers maturin compilation) +poetry run pip install -e ../../../rerun_py +``` + +This will do the following: +- Unless a venv is already active, it will create one using some python interpreter that it can find and is compatible with constraints listed in `pyproject.toml`. +- Install all dependencies. +- Create the `main` executable script as specified in the `pyproject.toml`. + +Additional facilities provided by Poetry: +```shell +poetry run ${ARGS} # run stuff inside the default environment +poetry env use 3.8 # create another environment with the specified python version +poetry env list # list all available environments +poetry shell # activate the default environment +``` + +### Run the example + +```shell +poetry run python main.py + +# or + +poetry shell # activate the venv +python main.py +``` + +### Create/update the lockfile + +```shell +poetry update +``` + +--- + +## Using `hatch` + +Example: `human_pose_tracking_hatch` + +`hatch` is similar to `poetry`, but with wider scope and a richer plug-in ecosystem to support it. + +### Tooling Installation + +```shell +pipx install hatch +``` + +### Environment Setup + +Basically nothing, `hatch` creates a venv on the fly. + +To use the dev rerun-sdk: +```shell +hatch run pip install ../../../rerun_py +``` + +### Run the example + +```shell +hatch run python main.py + +# or + +hatch shell +python main.py +``` + + + + +--- +_original content follows_ + The simplest example is [`minimal`](minimal/main.py). You may want to start there! Read more about our examples at . diff --git a/examples/python/human_pose_tracking/.gitignore b/examples/python/human_pose_tracking/.gitignore index 93db21baf618..ccbb86112b63 100644 --- a/examples/python/human_pose_tracking/.gitignore +++ b/examples/python/human_pose_tracking/.gitignore @@ -1 +1,2 @@ +/.venv dataset/ diff --git a/examples/python/human_pose_tracking/requirements.in b/examples/python/human_pose_tracking/requirements.in new file mode 100644 index 000000000000..5bec67ecf1ad --- /dev/null +++ b/examples/python/human_pose_tracking/requirements.in @@ -0,0 +1,8 @@ +# no 3.12 version yet (https://pypi.org/project/mediapipe/) +# 0.10.10 no longer supports the legacy Pose model: https://github.com/rerun-io/rerun/issues/5859 +mediapipe==0.10.9 ; python_version <= '3.11' + +numpy +opencv-python>4.6 # Avoid opencv-4.6 since it rotates images incorrectly (https://github.com/opencv/opencv/issues/22088) +requests>=2.31,<3 +rerun-sdk diff --git a/examples/python/human_pose_tracking/requirements.txt b/examples/python/human_pose_tracking/requirements.txt index 5bec67ecf1ad..d58ddffcc3a6 100644 --- a/examples/python/human_pose_tracking/requirements.txt +++ b/examples/python/human_pose_tracking/requirements.txt @@ -1,8 +1,67 @@ -# no 3.12 version yet (https://pypi.org/project/mediapipe/) -# 0.10.10 no longer supports the legacy Pose model: https://github.com/rerun-io/rerun/issues/5859 -mediapipe==0.10.9 ; python_version <= '3.11' - -numpy -opencv-python>4.6 # Avoid opencv-4.6 since it rotates images incorrectly (https://github.com/opencv/opencv/issues/22088) -requests>=2.31,<3 -rerun-sdk +# This file was autogenerated by uv via the following command: +# uv pip compile requirements.in -o requirements.txt +absl-py==2.1.0 + # via mediapipe +attrs==23.2.0 + # via + # mediapipe + # rerun-sdk +certifi==2024.2.2 + # via requests +cffi==1.16.0 + # via sounddevice +charset-normalizer==3.3.2 + # via requests +contourpy==1.2.1 + # via matplotlib +cycler==0.12.1 + # via matplotlib +flatbuffers==24.3.25 + # via mediapipe +fonttools==4.51.0 + # via matplotlib +idna==3.6 + # via requests +kiwisolver==1.4.5 + # via matplotlib +matplotlib==3.8.4 + # via mediapipe +mediapipe==0.10.9 +numpy==1.26.4 + # via + # contourpy + # matplotlib + # mediapipe + # opencv-contrib-python + # opencv-python + # pyarrow + # rerun-sdk +opencv-contrib-python==4.9.0.80 + # via mediapipe +opencv-python==4.9.0.80 +packaging==24.0 + # via matplotlib +pillow==10.3.0 + # via + # matplotlib + # rerun-sdk +protobuf==3.20.3 + # via mediapipe +pyarrow==15.0.2 + # via rerun-sdk +pycparser==2.22 + # via cffi +pyparsing==3.1.2 + # via matplotlib +python-dateutil==2.9.0.post0 + # via matplotlib +requests==2.31.0 +rerun-sdk==0.15.0 +six==1.16.0 + # via python-dateutil +sounddevice==0.4.6 + # via mediapipe +typing-extensions==4.11.0 + # via rerun-sdk +urllib3==2.2.1 + # via requests diff --git a/examples/python/human_pose_tracking_hatch/.gitignore b/examples/python/human_pose_tracking_hatch/.gitignore new file mode 100644 index 000000000000..93db21baf618 --- /dev/null +++ b/examples/python/human_pose_tracking_hatch/.gitignore @@ -0,0 +1 @@ +dataset/ diff --git a/examples/python/human_pose_tracking_hatch/README.md b/examples/python/human_pose_tracking_hatch/README.md new file mode 100644 index 000000000000..e439d7fb3cda --- /dev/null +++ b/examples/python/human_pose_tracking_hatch/README.md @@ -0,0 +1,157 @@ + + +Use the [MediaPipe Pose Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker) solution to detect and track a human pose in video. + + + + + + + + + +## Used Rerun types +[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image), [`Points2D`](https://www.rerun.io/docs/reference/types/archetypes/points2d), [`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d), [`ClassDescription`](https://www.rerun.io/docs/reference/types/datatypes/class_description), [`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context), [`SegmentationImage`](https://www.rerun.io/docs/reference/types/archetypes/segmentation_image) + +## Background +Human pose tracking is a task in computer vision that focuses on identifying key body locations, analyzing posture, and categorizing movements. +At the heart of this technology is a pre-trained machine-learning model to assess the visual input and recognize landmarks on the body in both image coordinates and 3D world coordinates. +The use cases and applications of this technology include but are not limited to Human-Computer Interaction, Sports Analysis, Gaming, Virtual Reality, Augmented Reality, Health, etc. + +In this example, the [MediaPipe Pose Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker) solution was utilized to detect and track human pose landmarks and produces segmentation masks for humans. +Rerun was employed to visualize the output of the Mediapipe solution over time to make it easy to analyze the behavior. + + +## 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 two [`timelines`](https://www.rerun.io/docs/concepts/timelines) `time` and `frame_idx`. + +```python +rr.set_time_seconds("time", bgr_frame.time) +rr.set_time_sequence("frame_idx", bgr_frame.idx) +``` + +### Video +The input video is logged as a sequence of +[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image) objects to the 'Video' entity. +```python +rr.log( + "video/rgb", + rr.Image(rgb).compress(jpeg_quality=75) +) +``` + +### Segmentation mask + +The segmentation result 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. The color is determined by the +[`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context) which is +logged with `static=True` as it should apply to the whole sequence. + +#### Label mapping + +```python +rr.log( + "video/mask", + rr.AnnotationContext( + [ + rr.AnnotationInfo(id=0, label="Background"), + rr.AnnotationInfo(id=1, label="Person", color=(0, 0, 0)), + ] + ), + static=True, +) +``` + +#### Segmentation image + +```python +rr.log( + "video/mask", + rr.SegmentationImage(segmentation_mask.astype(np.uint8)) +) +``` + +### Body pose points +Logging the body pose landmarks involves specifying connections between the points, extracting pose landmark points and logging them to the Rerun SDK. +The 2D points are visualized over the image/video for a better understanding and visualization of the body pose. The 3D points allows the creation of a 3D model of the body posture for a more comprehensive representation of the human pose. + + + +The 2D and 3D points are logged through a combination of two archetypes. First, a timeless +[`ClassDescription`](https://www.rerun.io/docs/reference/types/datatypes/class_description) is logged, that contains the information which maps keypoint ids to labels and how to connect +the keypoints. +Defining these connections automatically renders lines between them. Mediapipe provides the `POSE_CONNECTIONS` variable which contains the list of `(from, to)` landmark indices that define the connections. Second, the actual keypoint positions are logged in 2D +and 3D as [`Points2D`](https://www.rerun.io/docs/reference/types/archetypes/points2d) and +[`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d) archetypes, respectively. + +#### Label mapping and keypoint connections + +```python +rr.log( + "/", + rr.AnnotationContext( + rr.ClassDescription( + info=rr.AnnotationInfo(id=1, label="Person"), + keypoint_annotations=[rr.AnnotationInfo(id=lm.value, label=lm.name) for lm in mp_pose.PoseLandmark], + keypoint_connections=mp_pose.POSE_CONNECTIONS, + ) + ), + static=True, +) +``` + +#### 2D points + +```python +rr.log( + "video/pose/points", + rr.Points2D(landmark_positions_2d, class_ids=1, keypoint_ids=mp_pose.PoseLandmark) +) +``` + +#### 3D points + +```python +rr.log( + "person/pose/points", + rr.Points3D(landmark_positions_3d, class_ids=1, keypoint_ids=mp_pose.PoseLandmark), +) +``` + +## 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/human_pose_tracking/requirements.txt +``` +To experiment with the provided example, simply execute the main Python script: +```bash +python examples/python/human_pose_tracking/main.py # run the example +``` + +If you wish to customize it for various videos, adjust the maximum frames, or explore additional features, use the CLI with the `--help` option for guidance: + +```bash +python examples/python/human_pose_tracking/main.py --help +``` diff --git a/examples/python/human_pose_tracking_hatch/main.py b/examples/python/human_pose_tracking_hatch/main.py new file mode 100755 index 000000000000..a7e619824a2e --- /dev/null +++ b/examples/python/human_pose_tracking_hatch/main.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python3 +"""Use the MediaPipe Pose solution to detect and track a human pose in video.""" +from __future__ import annotations + +import argparse +import logging +import os +from contextlib import closing +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Final, Iterator + +import cv2 +import mediapipe as mp +import numpy as np +import numpy.typing as npt +import requests +import rerun as rr # pip install rerun-sdk +import rerun.blueprint as rrb + +DESCRIPTION = """ +# Human pose tracking +This example uses Rerun to visualize the output of [MediaPipe](https://developers.google.com/mediapipe)-based tracking +of a human pose in 2D and 3D. + +The full source code for this example is available +[on GitHub](https://github.com/rerun-io/rerun/blob/latest/examples/python/human_pose_tracking). +""".strip() + +EXAMPLE_DIR: Final = Path(os.path.dirname(__file__)) +DATASET_DIR: Final = EXAMPLE_DIR / "dataset" / "pose_movement" +DATASET_URL_BASE: Final = "https://storage.googleapis.com/rerun-example-datasets/pose_movement" + + +def track_pose(video_path: str, *, segment: bool, max_frame_count: int | None) -> None: + mp_pose = mp.solutions.pose + + rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), static=True) + + rr.log( + "/", + rr.AnnotationContext( + rr.ClassDescription( + info=rr.AnnotationInfo(id=1, label="Person"), + keypoint_annotations=[rr.AnnotationInfo(id=lm.value, label=lm.name) for lm in mp_pose.PoseLandmark], + keypoint_connections=mp_pose.POSE_CONNECTIONS, + ) + ), + static=True, + ) + # Use a separate annotation context for the segmentation mask. + rr.log( + "video/mask", + rr.AnnotationContext( + [ + rr.AnnotationInfo(id=0, label="Background"), + rr.AnnotationInfo(id=1, label="Person", color=(0, 0, 0)), + ] + ), + static=True, + ) + rr.log("person", rr.ViewCoordinates.RIGHT_HAND_Y_DOWN, static=True) + + with closing(VideoSource(video_path)) as video_source, mp_pose.Pose(enable_segmentation=segment) as pose: + for idx, bgr_frame in enumerate(video_source.stream_bgr()): + if max_frame_count is not None and idx >= max_frame_count: + break + + 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("video/rgb", rr.Image(rgb).compress(jpeg_quality=75)) + + results = pose.process(rgb) + h, w, _ = rgb.shape + landmark_positions_2d = read_landmark_positions_2d(results, w, h) + if landmark_positions_2d is not None: + rr.log( + "video/pose/points", + rr.Points2D(landmark_positions_2d, class_ids=1, keypoint_ids=mp_pose.PoseLandmark), + ) + + landmark_positions_3d = read_landmark_positions_3d(results) + if landmark_positions_3d is not None: + rr.log( + "person/pose/points", + rr.Points3D(landmark_positions_3d, class_ids=1, keypoint_ids=mp_pose.PoseLandmark), + ) + + segmentation_mask = results.segmentation_mask + if segmentation_mask is not None: + rr.log("video/mask", rr.SegmentationImage(segmentation_mask.astype(np.uint8))) + + +def read_landmark_positions_2d( + results: Any, + image_width: int, + image_height: int, +) -> npt.NDArray[np.float32] | None: + if results.pose_landmarks is None: + return None + else: + normalized_landmarks = [results.pose_landmarks.landmark[lm] for lm in mp.solutions.pose.PoseLandmark] + return np.array([(image_width * lm.x, image_height * lm.y) for lm in normalized_landmarks]) + + +def read_landmark_positions_3d( + results: Any, +) -> npt.NDArray[np.float32] | None: + if results.pose_landmarks is None: + return None + else: + landmarks = [results.pose_world_landmarks.landmark[lm] for lm in mp.solutions.pose.PoseLandmark] + return np.array([(lm.x, lm.y, lm.z) for lm in landmarks]) + + +@dataclass +class VideoFrame: + data: npt.NDArray[np.uint8] + time: float + idx: int + + +class VideoSource: + def __init__(self, path: str): + self.capture = cv2.VideoCapture(path) + + if not self.capture.isOpened(): + logging.error("Couldn't open video at %s", path) + + def close(self) -> None: + self.capture.release() + + def stream_bgr(self) -> Iterator[VideoFrame]: + while self.capture.isOpened(): + idx = int(self.capture.get(cv2.CAP_PROP_POS_FRAMES)) + is_open, bgr = self.capture.read() + time_ms = self.capture.get(cv2.CAP_PROP_POS_MSEC) + + if not is_open: + break + + yield VideoFrame(data=bgr, time=time_ms * 1e-3, idx=idx) + + +def get_downloaded_path(dataset_dir: Path, video_name: str) -> str: + video_file_name = f"{video_name}.mp4" + destination_path = dataset_dir / video_file_name + if destination_path.exists(): + logging.info("%s already exists. No need to download", destination_path) + return str(destination_path) + + source_path = f"{DATASET_URL_BASE}/{video_file_name}" + + logging.info("Downloading video from %s to %s", source_path, destination_path) + os.makedirs(dataset_dir.absolute(), exist_ok=True) + with requests.get(source_path, stream=True) as req: + req.raise_for_status() + with open(destination_path, "wb") as f: + for chunk in req.iter_content(chunk_size=8192): + f.write(chunk) + return str(destination_path) + + +def main() -> None: + # Ensure the logging gets written to stderr: + logging.getLogger().addHandler(logging.StreamHandler()) + logging.getLogger().setLevel("INFO") + + parser = argparse.ArgumentParser(description="Uses the MediaPipe Pose solution to track a human pose in video.") + parser.add_argument( + "--video", + type=str, + default="backflip", + choices=["backflip", "soccer"], + help="The example video to run on.", + ) + parser.add_argument("--dataset-dir", type=Path, default=DATASET_DIR, help="Directory to save example videos to.") + parser.add_argument("--video-path", type=str, default="", help="Full path to video to run on. Overrides `--video`.") + parser.add_argument("--no-segment", action="store_true", help="Don't run person segmentation.") + parser.add_argument( + "--max-frame", + type=int, + help="Stop after processing this many frames. If not specified, will run until interrupted.", + ) + rr.script_add_args(parser) + + args = parser.parse_args() + rr.script_setup( + args, + "rerun_example_human_pose_tracking", + default_blueprint=rrb.Horizontal( + rrb.Vertical( + rrb.Spatial2DView(origin="video", name="Result"), + rrb.Spatial3DView(origin="person", name="3D pose"), + ), + rrb.Vertical( + rrb.Spatial2DView(origin="video/rgb", name="Raw video"), + rrb.TextDocumentView(origin="description", name="Description"), + row_shares=[2, 3], + ), + column_shares=[3, 2], + ), + ) + + video_path = args.video_path # type: str + if not video_path: + video_path = get_downloaded_path(args.dataset_dir, args.video) + + track_pose(video_path, segment=not args.no_segment, max_frame_count=args.max_frame) + + rr.script_teardown(args) + + +if __name__ == "__main__": + main() diff --git a/examples/python/human_pose_tracking_hatch/pyproject.toml b/examples/python/human_pose_tracking_hatch/pyproject.toml new file mode 100644 index 000000000000..00ccf9536b70 --- /dev/null +++ b/examples/python/human_pose_tracking_hatch/pyproject.toml @@ -0,0 +1,22 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "human-pose-tracking" +version = "0.1.0" +requires-python = ">=3.8,<3.12" +keywords = [] + +dependencies = [ + "mediapipe==0.10.9", + "numpy", + "opencv-python>4.6", + "requests>=2.31,<3", + "rerun-sdk", +] + + +[[tool.hatch.envs.all.matrix]] +python = ["3.8", "3.9", "3.10", "3.11"] + diff --git a/examples/python/human_pose_tracking_poetry/.gitignore b/examples/python/human_pose_tracking_poetry/.gitignore new file mode 100644 index 000000000000..93db21baf618 --- /dev/null +++ b/examples/python/human_pose_tracking_poetry/.gitignore @@ -0,0 +1 @@ +dataset/ diff --git a/examples/python/human_pose_tracking_poetry/README.md b/examples/python/human_pose_tracking_poetry/README.md new file mode 100644 index 000000000000..e439d7fb3cda --- /dev/null +++ b/examples/python/human_pose_tracking_poetry/README.md @@ -0,0 +1,157 @@ + + +Use the [MediaPipe Pose Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker) solution to detect and track a human pose in video. + + + + + + + + + +## Used Rerun types +[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image), [`Points2D`](https://www.rerun.io/docs/reference/types/archetypes/points2d), [`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d), [`ClassDescription`](https://www.rerun.io/docs/reference/types/datatypes/class_description), [`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context), [`SegmentationImage`](https://www.rerun.io/docs/reference/types/archetypes/segmentation_image) + +## Background +Human pose tracking is a task in computer vision that focuses on identifying key body locations, analyzing posture, and categorizing movements. +At the heart of this technology is a pre-trained machine-learning model to assess the visual input and recognize landmarks on the body in both image coordinates and 3D world coordinates. +The use cases and applications of this technology include but are not limited to Human-Computer Interaction, Sports Analysis, Gaming, Virtual Reality, Augmented Reality, Health, etc. + +In this example, the [MediaPipe Pose Landmark Detection](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker) solution was utilized to detect and track human pose landmarks and produces segmentation masks for humans. +Rerun was employed to visualize the output of the Mediapipe solution over time to make it easy to analyze the behavior. + + +## 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 two [`timelines`](https://www.rerun.io/docs/concepts/timelines) `time` and `frame_idx`. + +```python +rr.set_time_seconds("time", bgr_frame.time) +rr.set_time_sequence("frame_idx", bgr_frame.idx) +``` + +### Video +The input video is logged as a sequence of +[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image) objects to the 'Video' entity. +```python +rr.log( + "video/rgb", + rr.Image(rgb).compress(jpeg_quality=75) +) +``` + +### Segmentation mask + +The segmentation result 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. The color is determined by the +[`AnnotationContext`](https://www.rerun.io/docs/reference/types/archetypes/annotation_context) which is +logged with `static=True` as it should apply to the whole sequence. + +#### Label mapping + +```python +rr.log( + "video/mask", + rr.AnnotationContext( + [ + rr.AnnotationInfo(id=0, label="Background"), + rr.AnnotationInfo(id=1, label="Person", color=(0, 0, 0)), + ] + ), + static=True, +) +``` + +#### Segmentation image + +```python +rr.log( + "video/mask", + rr.SegmentationImage(segmentation_mask.astype(np.uint8)) +) +``` + +### Body pose points +Logging the body pose landmarks involves specifying connections between the points, extracting pose landmark points and logging them to the Rerun SDK. +The 2D points are visualized over the image/video for a better understanding and visualization of the body pose. The 3D points allows the creation of a 3D model of the body posture for a more comprehensive representation of the human pose. + + + +The 2D and 3D points are logged through a combination of two archetypes. First, a timeless +[`ClassDescription`](https://www.rerun.io/docs/reference/types/datatypes/class_description) is logged, that contains the information which maps keypoint ids to labels and how to connect +the keypoints. +Defining these connections automatically renders lines between them. Mediapipe provides the `POSE_CONNECTIONS` variable which contains the list of `(from, to)` landmark indices that define the connections. Second, the actual keypoint positions are logged in 2D +and 3D as [`Points2D`](https://www.rerun.io/docs/reference/types/archetypes/points2d) and +[`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d) archetypes, respectively. + +#### Label mapping and keypoint connections + +```python +rr.log( + "/", + rr.AnnotationContext( + rr.ClassDescription( + info=rr.AnnotationInfo(id=1, label="Person"), + keypoint_annotations=[rr.AnnotationInfo(id=lm.value, label=lm.name) for lm in mp_pose.PoseLandmark], + keypoint_connections=mp_pose.POSE_CONNECTIONS, + ) + ), + static=True, +) +``` + +#### 2D points + +```python +rr.log( + "video/pose/points", + rr.Points2D(landmark_positions_2d, class_ids=1, keypoint_ids=mp_pose.PoseLandmark) +) +``` + +#### 3D points + +```python +rr.log( + "person/pose/points", + rr.Points3D(landmark_positions_3d, class_ids=1, keypoint_ids=mp_pose.PoseLandmark), +) +``` + +## 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/human_pose_tracking/requirements.txt +``` +To experiment with the provided example, simply execute the main Python script: +```bash +python examples/python/human_pose_tracking/main.py # run the example +``` + +If you wish to customize it for various videos, adjust the maximum frames, or explore additional features, use the CLI with the `--help` option for guidance: + +```bash +python examples/python/human_pose_tracking/main.py --help +``` diff --git a/examples/python/human_pose_tracking_poetry/main.py b/examples/python/human_pose_tracking_poetry/main.py new file mode 100755 index 000000000000..a7e619824a2e --- /dev/null +++ b/examples/python/human_pose_tracking_poetry/main.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python3 +"""Use the MediaPipe Pose solution to detect and track a human pose in video.""" +from __future__ import annotations + +import argparse +import logging +import os +from contextlib import closing +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Final, Iterator + +import cv2 +import mediapipe as mp +import numpy as np +import numpy.typing as npt +import requests +import rerun as rr # pip install rerun-sdk +import rerun.blueprint as rrb + +DESCRIPTION = """ +# Human pose tracking +This example uses Rerun to visualize the output of [MediaPipe](https://developers.google.com/mediapipe)-based tracking +of a human pose in 2D and 3D. + +The full source code for this example is available +[on GitHub](https://github.com/rerun-io/rerun/blob/latest/examples/python/human_pose_tracking). +""".strip() + +EXAMPLE_DIR: Final = Path(os.path.dirname(__file__)) +DATASET_DIR: Final = EXAMPLE_DIR / "dataset" / "pose_movement" +DATASET_URL_BASE: Final = "https://storage.googleapis.com/rerun-example-datasets/pose_movement" + + +def track_pose(video_path: str, *, segment: bool, max_frame_count: int | None) -> None: + mp_pose = mp.solutions.pose + + rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), static=True) + + rr.log( + "/", + rr.AnnotationContext( + rr.ClassDescription( + info=rr.AnnotationInfo(id=1, label="Person"), + keypoint_annotations=[rr.AnnotationInfo(id=lm.value, label=lm.name) for lm in mp_pose.PoseLandmark], + keypoint_connections=mp_pose.POSE_CONNECTIONS, + ) + ), + static=True, + ) + # Use a separate annotation context for the segmentation mask. + rr.log( + "video/mask", + rr.AnnotationContext( + [ + rr.AnnotationInfo(id=0, label="Background"), + rr.AnnotationInfo(id=1, label="Person", color=(0, 0, 0)), + ] + ), + static=True, + ) + rr.log("person", rr.ViewCoordinates.RIGHT_HAND_Y_DOWN, static=True) + + with closing(VideoSource(video_path)) as video_source, mp_pose.Pose(enable_segmentation=segment) as pose: + for idx, bgr_frame in enumerate(video_source.stream_bgr()): + if max_frame_count is not None and idx >= max_frame_count: + break + + 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("video/rgb", rr.Image(rgb).compress(jpeg_quality=75)) + + results = pose.process(rgb) + h, w, _ = rgb.shape + landmark_positions_2d = read_landmark_positions_2d(results, w, h) + if landmark_positions_2d is not None: + rr.log( + "video/pose/points", + rr.Points2D(landmark_positions_2d, class_ids=1, keypoint_ids=mp_pose.PoseLandmark), + ) + + landmark_positions_3d = read_landmark_positions_3d(results) + if landmark_positions_3d is not None: + rr.log( + "person/pose/points", + rr.Points3D(landmark_positions_3d, class_ids=1, keypoint_ids=mp_pose.PoseLandmark), + ) + + segmentation_mask = results.segmentation_mask + if segmentation_mask is not None: + rr.log("video/mask", rr.SegmentationImage(segmentation_mask.astype(np.uint8))) + + +def read_landmark_positions_2d( + results: Any, + image_width: int, + image_height: int, +) -> npt.NDArray[np.float32] | None: + if results.pose_landmarks is None: + return None + else: + normalized_landmarks = [results.pose_landmarks.landmark[lm] for lm in mp.solutions.pose.PoseLandmark] + return np.array([(image_width * lm.x, image_height * lm.y) for lm in normalized_landmarks]) + + +def read_landmark_positions_3d( + results: Any, +) -> npt.NDArray[np.float32] | None: + if results.pose_landmarks is None: + return None + else: + landmarks = [results.pose_world_landmarks.landmark[lm] for lm in mp.solutions.pose.PoseLandmark] + return np.array([(lm.x, lm.y, lm.z) for lm in landmarks]) + + +@dataclass +class VideoFrame: + data: npt.NDArray[np.uint8] + time: float + idx: int + + +class VideoSource: + def __init__(self, path: str): + self.capture = cv2.VideoCapture(path) + + if not self.capture.isOpened(): + logging.error("Couldn't open video at %s", path) + + def close(self) -> None: + self.capture.release() + + def stream_bgr(self) -> Iterator[VideoFrame]: + while self.capture.isOpened(): + idx = int(self.capture.get(cv2.CAP_PROP_POS_FRAMES)) + is_open, bgr = self.capture.read() + time_ms = self.capture.get(cv2.CAP_PROP_POS_MSEC) + + if not is_open: + break + + yield VideoFrame(data=bgr, time=time_ms * 1e-3, idx=idx) + + +def get_downloaded_path(dataset_dir: Path, video_name: str) -> str: + video_file_name = f"{video_name}.mp4" + destination_path = dataset_dir / video_file_name + if destination_path.exists(): + logging.info("%s already exists. No need to download", destination_path) + return str(destination_path) + + source_path = f"{DATASET_URL_BASE}/{video_file_name}" + + logging.info("Downloading video from %s to %s", source_path, destination_path) + os.makedirs(dataset_dir.absolute(), exist_ok=True) + with requests.get(source_path, stream=True) as req: + req.raise_for_status() + with open(destination_path, "wb") as f: + for chunk in req.iter_content(chunk_size=8192): + f.write(chunk) + return str(destination_path) + + +def main() -> None: + # Ensure the logging gets written to stderr: + logging.getLogger().addHandler(logging.StreamHandler()) + logging.getLogger().setLevel("INFO") + + parser = argparse.ArgumentParser(description="Uses the MediaPipe Pose solution to track a human pose in video.") + parser.add_argument( + "--video", + type=str, + default="backflip", + choices=["backflip", "soccer"], + help="The example video to run on.", + ) + parser.add_argument("--dataset-dir", type=Path, default=DATASET_DIR, help="Directory to save example videos to.") + parser.add_argument("--video-path", type=str, default="", help="Full path to video to run on. Overrides `--video`.") + parser.add_argument("--no-segment", action="store_true", help="Don't run person segmentation.") + parser.add_argument( + "--max-frame", + type=int, + help="Stop after processing this many frames. If not specified, will run until interrupted.", + ) + rr.script_add_args(parser) + + args = parser.parse_args() + rr.script_setup( + args, + "rerun_example_human_pose_tracking", + default_blueprint=rrb.Horizontal( + rrb.Vertical( + rrb.Spatial2DView(origin="video", name="Result"), + rrb.Spatial3DView(origin="person", name="3D pose"), + ), + rrb.Vertical( + rrb.Spatial2DView(origin="video/rgb", name="Raw video"), + rrb.TextDocumentView(origin="description", name="Description"), + row_shares=[2, 3], + ), + column_shares=[3, 2], + ), + ) + + video_path = args.video_path # type: str + if not video_path: + video_path = get_downloaded_path(args.dataset_dir, args.video) + + track_pose(video_path, segment=not args.no_segment, max_frame_count=args.max_frame) + + rr.script_teardown(args) + + +if __name__ == "__main__": + main() diff --git a/examples/python/human_pose_tracking_poetry/poetry.lock b/examples/python/human_pose_tracking_poetry/poetry.lock new file mode 100644 index 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pkg:pypi/zipp@3.17.0 + size: 18954 + timestamp: 1695255262261 - kind: conda name: zipp version: 3.17.0 diff --git a/pixi.toml b/pixi.toml index 16a69c46e545..8d833e3fbe34 100644 --- a/pixi.toml +++ b/pixi.toml @@ -23,6 +23,14 @@ version = "0.1.0" # [environments] cpp = ["cpp"] +examples-common = ["examples-common"] +examples-dev = ["examples-common", "rerun-py-dev"] +examples-pypi = ["examples-common", "rerun-py-pypi"] + + +[system-requirements] +macos = "11.0" # needed for some reason otherwise fails to resolve mediapipe package +libc = "2.31" [tasks] # Note: extra CLI argument after `pixi run TASK` are passed to the task cmd. @@ -230,3 +238,31 @@ cxx-compiler = "1.6.0.*" [feature.cpp.target.win-64.dependencies] vs2022_win-64 = "19.37.32822" + + +# EXAMPLES ENVIRONMENT + +[feature.examples-common] +platforms = ["linux-64", "osx-arm64", "osx-64", "win-64"] + +[feature.examples-common.tasks] +py-ex-human-pose-tracking = "python examples/python/human_pose_tracking/main.py" +#TODO: list all other examples here + +[feature.examples-common.dependencies] +python = ">=3.8,<3.12" # some example don't support python 3.12 yet + +# ALL deps of ALL examples should be listed here. In case of conflicts, the conflicting example should be moved to +# another, dedicated environment other than `examples`. +[feature.examples-common.pypi-dependencies] +mediapipe = "==0.10.9" +numpy = ">=1.23,<2" +opencv-python = ">4.6" +requests = ">=2.31,<3" + + +[feature.rerun-py-dev.pypi-dependencies] +rerun-sdk = { path = "rerun_py", editable = true } + +[feature.rerun-py-pypi.pypi-dependencies] +rerun-sdk = "==0.15.0" # How to keep that up to date?