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ev-mp Fix pybackend compilation
Change-Id: I6689e5cba879a0a46df95c96a6486ce4d3a4bdda
Latest commit 7fe5883 Jun 25, 2019

Python Wrapper

Table of Contents



pyrealsense AKA pyrealsense/2.0 is a community supported Python wrapper for librealsense v1.12.1, This wrapper does not support newer versions and does not work with the RealSense SDK 2.0.

We provide a PyPI distribution which is created from this folder by running python bdist_wheel.

Package is available at

To install the package, run:

pip install pyrealsense2

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. (Note that these binaries are built with Python 2.7, and cannot be import using Python 3).

Building From Source

Ubuntu 14.04/16.04 LTS

  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
  1. Install Python and its development files via apt-get (Python 2 and 3 both work)
  • sudo apt-get install python python-dev or sudo apt-get install python3 python3-dev
  • Note: The project will only use Python 2 if it can't use Python 3
  1. Run the top level CMake command with the following additional flag -DBUILD_PYTHON_BINDINGS=bool:true:
  • mkdir build
  • cd build
  • cmake ../ -DBUILD_PYTHON_BINDINGS=bool:true

Note: To force compilation with a specific version on a system with both Python 2 and Python 3 installed, add the following flag to CMake command: -DPYTHON_EXECUTABLE=[full path to the exact python executable]

  • make -j4
  • sudo make install
  1. update your PYTHONPATH environment variable to add the path to the pyrealsense library
  • export PYTHONPATH=$PYTHONPATH:/usr/local/lib
  1. Alternatively, copy the build output ( and next to your script.
  • Note: Python 3 module filenames may contain additional information, e.g.


  1. Install Python 2 or 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
  3. If you have multiple python installations on your machine you can use: -DPYTHON_EXECUTABLE=<path to python executable> For example: -DPYTHON_EXECUTABLE=C:/Python27/python.exe

The precompiled binaries shipped with the installer assume Python 2.7. The error ImportError: DLL load failed: The specified module could not be found might indicate versions mismatch or architecture (x86 vs x64) mismatch.

  1. Open librealsense2.sln that was created in the previous step, and build the pyrealsense2 project
  2. 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:\Python27\Lib\site-packages)
  3. 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

Streaming using rs.pipeline

# 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()

    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 xrange(480):
            for x in xrange(640):
                dist = depth.get_distance(x, y)
                if 0 < dist and dist < 1:
                    coverage[x/10] += 1

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


NumPy Integration

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)
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