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Python Wrapper

Table of Contents

Installation

PyPI Distribution

At the moment we do not provide a package for the Python wrapper using common distribution packages.

Note:

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.

Windows Installation

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 Python2 if it can't use Python3
  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 (librealsense2.so and pyrealsense2.so) next to your script.

Windows

  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 python2.7. 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.

Examples

For a list of full code examples see the examples folder

Streaming using rs.pipeline

# First import the library
import pyrealsense2 as rs

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

    while True:
        # This call waits until a new coherent set of frames is available on a device
        # Calls to get_frame_data(...) and get_frame_timestamp(...) on a device will return stable values until wait_for_frames(...) is called
        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
                print(line)

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)