Here is the situation:
Raspberry pi 2 running raspbian running python 3.5.2 with Opencv,Numpy,pyusb
Orange pi pc running Armbian.
Both boards are quad-core arm.
So the question is can i somehow transfer the libraries from one distro to another without recompiling them? It would save me hours.
If not can i just transfer Opencv as this is the main culprit.
Thanks in advance.
Related
I have been trying for a day to install Pandas_ta on my raspberry py with little succes. I first made a Python script on my PC and then placed it on my Raspberry pi, after installing some packages, the last one I had to so was Pandas_ta. I searched op documentary, I tried out some youtube video's. But it feels harder then it should. Does someone had a guide for me ?
I am trying to install OpenCV in raspberry pi 3B but every time I try to install it gives error during compilation. I have been trying this for a long time and I don't know how to do it. I have followed mainly all the instruction given in these articles :
Install opencv 4 on raspberry pi
Install guide raspberry pi 3 raspbian jessie opencv 3
I had the same error a few weeks ago and for me it works when I just use
make -j
instead of
make -j4
-j4 simply means that the raspberry pi uses all for cpu cores instead of just one with make -j
Hope I could help and please write if that works for you as well ;)
I am very new to Python, but this does not make sense to me, here's an example script:
import pygame as py
import time
py.init()
song = py.mixer.Sound("pineSiskin.wav") # c1Mb/20sec long
song.play(0 , 9000)
time.sleep(8)
song.fadeout(800)
py.quit()
print("quit")
Running this on a Raspberry Pi*, the CPU goes up to 75% and stays there until I restart the Python shell. This soon leads to overheating on the RPi.
Other questions (like this one & other mentioned in link), are dissimilar as they refer to scripts which have not completed.
This link does hint that what I'm seeing is not "normal" behaviour.
Any help to track this problem/diagnostic advice would be useful.
Apologies if I've made a mistake about which forum; tell me and I'll move it!
*Hardware/Software:
Raspberry Pi 3 Model B running
Raspbian Jessie Pixel
Python 3.4.2
accessed via IDLE3 Python 3.4.2 as bundled with scipi & matplotlib
added
this problem turned out to be an OS problem. recreating the Raspbian OS from a new disk image solved it. Now scripts are behaving as I'd expect.
I'm trying to connect raspberry pi with MatLab. I use the toolbox within matlab to configure raspberry pi. It automatically connects through network however it also gives you OS within that setup procedure, you can't connect to raspberry pi without it. Although the system it gives is technically Raspbian Jessie it comes with limited package.
When it launches and I input dir into console it only shows files such as: satkin_ws install ros_indigo.sh install_ros_package.sh and ros_catkin_ws. No other folders or files are pre-loaded onto the system. I tried to install some packages for display manager such as gdm3 and lightdm but I still have a problem with loading desktop environment. Can someone give me suggestions on how to resolve this issue?
Alright after doing some research I found that the system installed by Matlab is Raspbian Jessie Lite which does not come with GUI/Desktop environment by default but you can install it you want. Here is the link to get you going:
https://www.raspberrypi.org/forums/viewtopic.php?f=66&t=133691
I'm really not familiar with make. So I don't understand the meaning of those steps in 2 tutorials ("TensorFlow Makefile" and "TensorFlow Raspberry Pi Examples") to make my project on laptop work on raspberry pi 2
TensorFlow Makefile:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/makefile
TensorFlow Raspberry Pi Examples:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/pi_examples
As far as i know,"TensorFlow Makefile" will convert the tensorflow source,lib to executable file. "TensorFlow Raspberry Pi Examples" means convert your project to executable file then run the file in your_project/gen/bin/your_project
tensorflow/contrib/pi_examples/label_image/gen/bin/label_image
Hence,if I have a project on my laptop and want to load my project into Pi 2 ("TensorFlow Makefile" is already done). I just copy it to my pi 2 and do :
make -f tensorflow/my_project/Makefile
then run :
tensorflow/my_project/gen/bin/my_project
This is how to make your project work on raspberry pi 2, right ?
This will only work for Makefiles which have the option to cross-compile from a (presumably) x86(_64?)-architecture laptop to an ARM-architecture Raspberry Pi.
You're in luck, though: Google's included options in TensorFlow's Makefile to cross-compile between architectures!
If you look at line 123 of the Makefile, you'll see a comment about this.
Default to running on the same system we're compiling on.
You should override TARGET on the command line if you're cross-compiling, e.g.
make -f tensorflow/contrib/makefile/Makefile TARGET=ANDROID
So, simply make the project like so:
$ make -f tensorflow/contrib/makefile/Makefile TARGET=PI
This will compile TensorFlow for the RPi.
Incidentally, if you can, it may be easier to simply compile TensorFlow on the RPi by either pulling the sources from git or transferring it onto the RPi via USB, and building there.