• Installation
• Building from source
• Examples

Our official wrapper is pyrealsense2 We also provide a beta package for early access to new features called pyrealsense2-beta

To install the package, run:

pip install pyrealsense2

Or

pip install pyrealsense2-beta

Users who wish to create a python package (wheel) can create it from this folder by running

pip install -U setuptools build hatchling

python -m build .

Windows users can install the RealSense SDK 2.0 from the release tab to get pre-compiled binaries of the wrapper, for both x86 and x64 architectures. (Python versions 3.10, 3.11, 3.12, 3.13 and 3.14 are supported).

Note: EOL Python 3.9 distributables can be found for pyrealsense2 versions <= 2.57.4 EOL Python 3.8 distributables can be found for pyrealsense2 versions <= 2.55.2 EOL Python 3.7 distributables can be found for pyrealsense2 versions <= 2.55.1

1. Ensure apt-get is up to date

• sudo apt-get update && sudo apt-get upgrade Note: Use sudo apt-get dist-upgrade, instead of sudo apt-get upgrade, in case you have an older Ubuntu 14.04 version

2. Install Python and its development files via apt-get

• sudo apt-get install python3 python3-dev

3. Clone the librealsense repository and navigate into the directory:
   • git clone https://github.com/realsenseai/librealsense.git
   • cd librealsense
4. Configure and make:

• mkdir build
• cd build
• cmake ../ -DBUILD_PYTHON_BINDINGS:bool=true -DPYTHON_EXECUTABLE=$(which python3)
• make -j4
• sudo make install

Note: For building a self-contained (statically compiled) pyrealsense2 library add the CMake flag:

-DBUILD_SHARED_LIBS=false

Note: To force compilation with a specific version on a system with a few Python versions installed, add the following flag to CMake command:

-DPYTHON_EXECUTABLE=[full path to the exact python executable]

5. update your PYTHONPATH environment variable to add the path to the pyrealsense library

• export PYTHONPATH=$PYTHONPATH:/usr/local/lib

Note: If this doesn't work, try using the following path instead: export PYTHONPATH=$PYTHONPATH:/usr/local/lib/[python version]/pyrealsense2

6. Alternatively, copy the build output (librealsense2.so and pyrealsense2.so) next to your script. Note: Python 3 module filenames may contain additional information, e.g. pyrealsense2.cpython-35m-arm-linux-gnueabihf.so)

1. Install Python 3 for windows. You can find the downloads on the official Python website here

2. When running cmake-gui, select the BUILD_PYTHON_BINDINGS option

   Note: For building a self-contained (statically compiled) pyrealsense2 library add the CMake flag:

   -DBUILD_SHARED_LIBS=false

3. If you have multiple python installations on your machine you can use: -DPYTHON_EXECUTABLE=<path to python executable> For example: -DPYTHON_EXECUTABLE=C:/Python310/python.exe

The precompiled binaries shipped with the installer assume Python 3.11. The error ImportError: DLL load failed: The specified module could not be found might indicate versions mismatch or architecture (x86 vs x64) mismatch.

4. Open realsense2.sln that was created in the previous step, and build the pyrealsense2 project
5. Open the output folder of the project (e.g build\x64-Release\Release\) and copy pyrealsense2.pyd into your python's site-packages (e.g C:\Python310\Lib\site-packages)
6. Alternatively, copy the build output (realsense2.dll and pyrealsense2.pyd) next to your script.

For a list of full code examples see the examples folder

# First import the library
import pyrealsense2 as rs

# Create a context object. This object owns the handles to all connected realsense devices
pipeline = rs.pipeline()
pipeline.start()

try:
    while True:
        # Create a pipeline object. This object configures the streaming camera and owns it's handle
        frames = pipeline.wait_for_frames()
        depth = frames.get_depth_frame()
        if not depth: continue

        # Print a simple text-based representation of the image, by breaking it into 10x20 pixel regions and approximating the coverage of pixels within one meter
        coverage = [0]*64
        for y in range(480):
            for x in range(640):
                dist = depth.get_distance(x, y)
                if 0 < dist and dist < 1:
                    coverage[x//10] += 1

            if y%20 == 19:
                line = ""
                for c in coverage:
                    line += " .:nhBXWW"[c//25]
                coverage = [0]*64
                print(line)

finally:
    pipeline.stop()

Librealsense frames support the buffer protocol. A numpy array can be constructed using this protocol with no data marshalling overhead:

import numpy as np
depth = frames.get_depth_frame()
depth_data = depth.as_frame().get_data()
np_image = np.asanyarray(depth_data)