Skip to content

mr-ping/hcsr04sensor

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

53 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

HC-SR04 Ultrasonic Sensor on Raspberry Pi

Calculate distance and depth measurements with an HCSR04 Ultrasonic Sound Sensor and a Raspberry Pi. Instructions assume that you are using Raspbian Linux.

Python2 Install

sudo apt-get install python-pip
sudo pip install hcsr04sensor

Python3 Install

sudo apt-get install python3-pip
sudo pip3 install hcsr04sensor

Description

The module does the following;

  • Returns an error corrected distance by using the median reading of a sorted sample. NOTE - The default sample size is 11 readings.

    You can specify a different sample size by passing sample_size=x to raw_distance (where x is your desired number of readings). This is useful if you need to lower the sample size to take quicker readings. Beware that the probability of getting erroneous readings increases as sample size is reduced. For my purposes a sample of 11 readings gives a consistent value that I can trust and takes approximately 3 seconds to run with a 0.1 second wait time between individual samples.

    It is also possible to speed up the readings by passing a lower value to sample_wait in raw_distance.

    The lower the value the quicker the invidual samples will be taken. A default of 0.1 is a safe wait time but this can be reduced further. CPU usage increases as faster readings are taken as well as the chance for errors.

    This module uses BCM pin values. See the Raspberry Pi pin layout documentation for your model.

  • Rounds the value to a specified decimal place.

  • Adjusts the reading based on temperature by adjusting the speed of sound.

  • Allows measuring distance and depth in metric and imperial units. See;

    pydoc hcsr04sensor.sensor

Accuracy of Readings

If you need highly accurate readings then this module would not be suitable for your project. In that case you should probably use an Arduino instead of a Raspberry Pi.

Linux is not a Real Time OS so you can expect to get a small variance on each reading, usually within a half cm of the actual value. I say "usually" because every once in a while you can get a reading that is way out of range. This is due to the OS executing other tasks before getting your distance reading. It is why I use a sample of readings. I can always trust that the median of my sample of 11 readings is good.

Highly accurate readings are not required for some applications, for example I use this module in an application I wrote for a sump pump monitor. I am not worried about millimeter accuracy for that application. 1 cm variance on a meter deep pit is close enough to alert me to problems.

Usage

See example scripts in https://github.com/alaudet/hcsr04sensor/tree/master/examples.

Access to Raspberry Pi GPIO pins require elevated priviledges if using version 0.5.11 of RPi.GPIO. Run example scripts with sudo.

Recommended

To upgrade to version 0.6.x of RPi.GPIO which does not require elevated priviledges you can simply upgrade as follows;

Note: On Raspbian Wheezy you still seem to have to run RPi.GPIO with sudo in version 6. It works without sudo in Rasbian Jessie which is the latest version of Raspbian.

For Python 2

sudo pip install -U hcsr04sensor

or

sudo apt-get upgrade python-rpi.gpio

For Python 3

sudo pip3 install -U hcsr04sensor

or

sudo apt-get upgrade python3-rpi.gpio

Contributing

Contributions to hcsr04sensor are welcome. Please open an issue in the issue tracker prior to a pull request.

New features and bug fixes should be applied against the devel branch and not master. Contributions against the master branch will be rejected.

Nose is currently used for testing. All tests should pass before issuing the pull request.

About

Python module for measuring distance and depth (metric or imperial) with a Raspberry Pi and HC-SR04 sensor

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 100.0%