- Ensure apt-get is up to date
sudo apt-get update && sudo apt-get upgrade
- Note: Use
sudo apt-get dist-upgrade
, instead ofsudo apt-get upgrade
, in case you have an older Ubuntu 14.04 version
- Install Python and its development files via apt-get (Python 2 and 3 both work)
sudo apt-get install python python-dev
orsudo apt-get install python3 python3-dev
- Note: The project will only use Python2 if it can't use Python3
- run the toplevel cmake command with the following additional flag:
-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]
- Install Python 2 or 3 for windows
- You can find the downloads on the official Python website here
- When running cmake, select the BUILD_PYTHON_BINDINGS option
# 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)
A longer example can be found here
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)