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This PR adds sipport for Intel RealSense with a README. I know it might be dependant on the camera firmware. This node might need to be updated dependending on pyrealsense required version. ## To get started Make sure to follow the installation at: https://github.com/dora-rs/dora/blob/316a0247a8c914dcb1b00b05e65bdcd3a0fa3936/node-hub/dora-pyrealsense/README.md Then: ```bash cd examples/realsense dora build dataflow.yaml dora run dataflow.yaml ```
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nodes: | ||
- id: camera | ||
build: pip install -e ../../node-hub/dora-pyrealsense | ||
path: dora-pyrealsense | ||
inputs: | ||
tick: dora/timer/millis/20 | ||
outputs: | ||
- image | ||
env: | ||
IMAGE_WIDTH: 640 | ||
IMAGE_HEIGHT: 480 | ||
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- id: plot | ||
build: pip install ../../node-hub/opencv-plot | ||
path: opencv-plot | ||
inputs: | ||
image: | ||
source: camera/image | ||
queue_size: 1 |
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# Quick example of using Pyrealsense | ||
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Make sure to follow the installation at: [`node-hub/dora-pyrealsense/README.md`](node-hub/dora-pyrealsense/README.md) | ||
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Then: | ||
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```bash | ||
dora build dataflow.yaml | ||
dora run dataflow.yaml | ||
``` |
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nodes: | ||
- id: camera | ||
build: pip install -e ../../node-hub/dora-pyrealsense | ||
path: dora-pyrealsense | ||
inputs: | ||
tick: dora/timer/millis/20 | ||
outputs: | ||
- image | ||
- depth | ||
env: | ||
IMAGE_WIDTH: 640 | ||
IMAGE_HEIGHT: 480 | ||
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- id: plot | ||
build: pip install ../../node-hub/opencv-plot | ||
path: opencv-plot | ||
inputs: | ||
image: | ||
source: camera/image | ||
queue_size: 1 |
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nodes: | ||
- id: camera | ||
build: pip install -e ../../node-hub/dora-pyrealsense | ||
path: dora-pyrealsense | ||
inputs: | ||
tick: dora/timer/millis/20 | ||
outputs: | ||
- image | ||
- depth | ||
env: | ||
IMAGE_WIDTH: 640 | ||
IMAGE_HEIGHT: 480 | ||
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- id: plot | ||
build: pip install -e ../../node-hub/dora-rerun | ||
path: dora-rerun | ||
inputs: | ||
image: camera/image | ||
world/camera/depth: camera/depth | ||
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# Dora Node for capturing video with PyRealSense | ||
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This node is used to capture video from a camera using PyRealsense. | ||
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# Installation | ||
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Make sure to use realsense udev config at https://github.com/IntelRealSense/librealsense/blob/master/doc/installation.md | ||
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You can try, the following: | ||
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```bash | ||
wget https://raw.githubusercontent.com/IntelRealSense/librealsense/refs/heads/master/scripts/setup_udev_rules.sh | ||
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mkdir config | ||
cd config | ||
wget https://raw.githubusercontent.com/IntelRealSense/librealsense/refs/heads/master/scripts/config/99-realsense-libusb.rules | ||
cd .. | ||
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chmod +x setup_udev_rules.sh | ||
``` | ||
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# YAML | ||
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```yaml | ||
- id: opencv-video-capture | ||
build: pip install ../../node-hub/opencv-video-capture | ||
path: opencv-video-capture | ||
inputs: | ||
tick: dora/timer/millis/16 # try to capture at 60fps | ||
outputs: | ||
- image: # the captured image | ||
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env: | ||
PATH: 0 # optional, default is 0 | ||
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IMAGE_WIDTH: 640 # optional, default is video capture width | ||
IMAGE_HEIGHT: 480 # optional, default is video capture height | ||
``` | ||
# Inputs | ||
- `tick`: empty Arrow array to trigger the capture | ||
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# Outputs | ||
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- `image`: an arrow array containing the captured image | ||
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```Python | ||
## Image data | ||
image_data: UInt8Array # Example: pa.array(img.ravel()) | ||
metadata = { | ||
"width": 640, | ||
"height": 480, | ||
"encoding": str, # bgr8, rgb8 | ||
} | ||
## Example | ||
node.send_output( | ||
image_data, {"width": 640, "height": 480, "encoding": "bgr8"} | ||
) | ||
## Decoding | ||
storage = event["value"] | ||
metadata = event["metadata"] | ||
encoding = metadata["encoding"] | ||
width = metadata["width"] | ||
height = metadata["height"] | ||
if encoding == "bgr8": | ||
channels = 3 | ||
storage_type = np.uint8 | ||
frame = ( | ||
storage.to_numpy() | ||
.astype(storage_type) | ||
.reshape((height, width, channels)) | ||
) | ||
``` | ||
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## Examples | ||
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Check example at [examples/python-dataflow](examples/python-dataflow) | ||
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## License | ||
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This project is licensed under Apache-2.0. Check out [NOTICE.md](../../NOTICE.md) for more information. |
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import os | ||
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# Define the path to the README file relative to the package directory | ||
readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.md") | ||
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# Read the content of the README file | ||
try: | ||
with open(readme_path, "r", encoding="utf-8") as f: | ||
__doc__ = f.read() | ||
except FileNotFoundError: | ||
__doc__ = "README file not found." |
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import os | ||
import time | ||
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import cv2 | ||
import numpy as np | ||
import pyarrow as pa | ||
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from dora import Node | ||
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RUNNER_CI = True if os.getenv("CI") == "true" else False | ||
import pyrealsense2 as rs | ||
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def main(): | ||
FLIP = os.getenv("FLIP", "") | ||
DEVICE_SERIAL = os.getenv("DEVICE_SERIAL", "") | ||
image_height = int(os.getenv("IMAGE_HEIGHT", "480")) | ||
image_width = int(os.getenv("IMAGE_WIDTH", "640")) | ||
encoding = os.getenv("ENCODING", "rgb8") | ||
ctx = rs.context() | ||
devices = ctx.query_devices() | ||
if devices.size() == 0: | ||
raise ConnectionError("No realsense camera connected.") | ||
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# Serial list | ||
serials = [device.get_info(rs.camera_info.serial_number) for device in devices] | ||
if DEVICE_SERIAL and (DEVICE_SERIAL in serials): | ||
raise ConnectionError( | ||
f"Device with serial {DEVICE_SERIAL} not found within: {serials}." | ||
) | ||
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pipeline = rs.pipeline() | ||
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config = rs.config() | ||
config.enable_device(DEVICE_SERIAL) | ||
config.enable_stream(rs.stream.color, image_width, image_height, rs.format.rgb8, 30) | ||
config.enable_stream(rs.stream.depth, image_width, image_height) | ||
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align_to = rs.stream.color | ||
align = rs.align(align_to) | ||
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profile = pipeline.start(config) | ||
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depth_sensor = profile.get_device().first_depth_sensor() | ||
depth_scale = depth_sensor.get_depth_scale() | ||
# rgb_profile = profile.get_stream(rs.stream.color) | ||
# depth_profile = profile.get_stream(rs.stream.depth) | ||
# rgb_intr = rgb_profile.as_video_stream_profile().get_intrinsics() | ||
# rgb_intr = depth_profile.get_extrinsics_to(rgb_profile) | ||
node = Node() | ||
start_time = time.time() | ||
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pa.array([]) # initialize pyarrow array | ||
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for event in node: | ||
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# Run this example in the CI for 10 seconds only. | ||
if RUNNER_CI and time.time() - start_time > 10: | ||
break | ||
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event_type = event["type"] | ||
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if event_type == "INPUT": | ||
event_id = event["id"] | ||
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if event_id == "tick": | ||
frames = pipeline.wait_for_frames() | ||
aligned_frames = align.process(frames) | ||
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aligned_depth_frame = aligned_frames.get_depth_frame() | ||
color_frame = aligned_frames.get_color_frame() | ||
if not aligned_depth_frame or not color_frame: | ||
continue | ||
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depth_image = np.asanyarray(aligned_depth_frame.get_data()) | ||
scaled_depth_image = depth_image * depth_scale | ||
frame = np.asanyarray(color_frame.get_data()) | ||
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## Change rgb to bgr | ||
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if FLIP == "VERTICAL": | ||
frame = cv2.flip(frame, 0) | ||
elif FLIP == "HORIZONTAL": | ||
frame = cv2.flip(frame, 1) | ||
elif FLIP == "BOTH": | ||
frame = cv2.flip(frame, -1) | ||
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# resize the frame | ||
if ( | ||
image_width is not None | ||
and image_height is not None | ||
and ( | ||
frame.shape[1] != image_width or frame.shape[0] != image_height | ||
) | ||
): | ||
frame = cv2.resize(frame, (image_width, image_height)) | ||
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metadata = event["metadata"] | ||
metadata["encoding"] = encoding | ||
metadata["width"] = int(frame.shape[1]) | ||
metadata["height"] = int(frame.shape[0]) | ||
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# Get the right encoding | ||
if encoding == "bgr8": | ||
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) | ||
elif encoding in ["jpeg", "jpg", "jpe", "bmp", "webp", "png"]: | ||
ret, frame = cv2.imencode("." + encoding, frame) | ||
if not ret: | ||
print("Error encoding image...") | ||
continue | ||
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storage = pa.array(frame.ravel()) | ||
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# metadata["resolution"] = [int(rgb_intr.width), int(rgb_intr.height)] | ||
# metadata["focal_length"] = [int(rgb_intr.fx), int(rgb_intr.fy)] | ||
# metadata["principal_point"] = [int(rgb_intr.ppx), int(rgb_intr.ppy)] | ||
node.send_output("image", storage, metadata) | ||
node.send_output( | ||
"depth", pa.array(scaled_depth_image.ravel()), metadata | ||
) | ||
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elif event_type == "ERROR": | ||
raise RuntimeError(event["error"]) | ||
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if __name__ == "__main__": | ||
main() |
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[tool.poetry] | ||
name = "dora-pyrealsense" | ||
version = "0.0.1" | ||
authors = ["Haixuan Xavier Tao <[email protected]>"] | ||
description = "Dora Node for capturing video with Pyrealsense" | ||
readme = "README.md" | ||
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packages = [{ include = "dora_pyrealsense" }] | ||
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[tool.poetry.dependencies] | ||
dora-rs = "^0.3.6" | ||
numpy = "< 2.0.0" | ||
opencv-python = ">= 4.1.1" | ||
pyrealsense2 = "2.54.1.5216" | ||
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[tool.poetry.scripts] | ||
dora-pyrealsense = "dora_pyrealsense.main:main" | ||
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[build-system] | ||
requires = ["poetry-core>=1.8.0"] | ||
build-backend = "poetry.core.masonry.api" |
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import pytest | ||
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def test_import_main(): | ||
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from dora_pyrealsense.main import main | ||
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# Check that everything is working, and catch dora Runtime Exception as we're not running in a dora dataflow. | ||
with pytest.raises(ConnectionError): | ||
main() |
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