Author: Phil Birch

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Piping numpy arrays to other processes in python

To pipe data from one process to another as a stream in python we need to pickle the object and pass it to the pipe stream.
In this example I’ve used Numpy arrays but this could be applied to any object that can be pickled in Python.
This took far too long to get working and I could find little information online on how to put it all together so here it is.
This code is Python 3 only, I’ve only run this on a Mac.

I’ve used binary as the stream rather than text purley becuase of effiencies. Numpy arrays can get huge! This means readline() is not
going to work. Instead, I send a single control byte , 1, for data and 0 for stop. This could be extended to include other control operations.
I then send the length of the data as a 8 byte int, followed by the data itself.

simpleSend.py

import numpy as np
import pickle
import sys
import io
import time

#define some control bytes
control_data=bytes([1])
control_stop=bytes([0])

def send_data(arr):
    dataStr=pickle.dumps(arr)  #pickle the data array into a byte array
    dlen=len(dataStr).to_bytes(8, byteorder='big') #find the length of the array and
    print(control_data.decode('latin-1'),end='',flush=True)  #convert this to a byte array
    print(dlen.decode('latin-1'), end='', flush=True)   #encode the data and write it
    print(dataStr.decode('latin-1'), end='', flush=True)  # end='' will remove that extra \r\n

def send_stop():
    print(control_stop.decode('latin-1'), end='', flush=True) 

#set the stdout such that it prints in latin-1,   sys.stdout.detach() is a binary stream
sys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding='latin-1')

for p in range(10):
    arr=np.ones((5000,500))*p  #generate some data
    send_data(arr)
    #the sleep is purely for testing and can be removed, ie does the reader fall over after a long delay
    time.sleep(.1)
send_stop()

simpleReceiver.py

import numpy as np
import sys
import pickle

#define some control bytes
control_data=bytes([1])
control_stop=bytes([0])

while True:
    data=sys.stdin.buffer.read(1)   #read the control byte
    if data==control_data:
        data=sys.stdin.buffer.read(8)  #read the data length
        dlen=int.from_bytes(data, byteorder='big')
        print('data lenght %d'%dlen)        
        data=sys.stdin.buffer.read(dlen) #read the data        
        npd=pickle.loads(data)  #unpickle
        print(npd.shape)
        print(npd.max())
    elif data==control_stop:
        print('stopped')
        break
    else:
        print('Oh no')

to run this
python simpleSend.py | python simpleReceiver.py

If we want to use Python’s subprocess module to start simpleReceiver.py we basically need to write to the STDIN instead of print

import numpy as np
import pickle
import sys
import subprocess as sp

#define some control bytes
control_data=bytes([1])
control_stop=bytes([0])

def send_data(arr,buff):
    dataStr=pickle.dumps(arr)  #pickle the data array into a byte array
    dlen=len(dataStr).to_bytes(8, byteorder='big') #find the length of the array and
    mp.stdin.write(control_data)
    mp.stdin.write(dlen)
    mp.stdin.write(dataStr)
    mp.stdin.flush() #not sure this needed
     
def send_stop(mp):
    mp.stdin.write(control_stop)
    mp.stdin.flush()
     
try:
    mp = sp.Popen("python3 simpleReceiver.py",  shell = True,stdin=sp.PIPE)   
except sp.CalledProcessError as err:
    print('ERROR:', err)
    sys.exit(-1)

for p in range(10):
    arr=np.ones((5000,5000))*p  #generate some data
    send_data(arr,mp)
send_stop(mp)

With such a large array 5000×5000 this takes sometime. Running it through the python profiler indicates about 75% of the time is taken by pickle.dumps and most of the rest of the remaining 25% is taken by the write operation. Numpy’s own method gives a speed increase. Replacing dataStr=pickle.dumps(arr) with dataStr=arr.tobytes() and npd=pickle.loads(data) with npd=np.frombuffer(data) more than halves the time taken but lose the shape and dtype information. This would have to be sent along with the data.

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numpyPipe.md

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Finding webcams

List all devices on windows using ffmpeg

ffmpeg -list_devices true -f dshow -i dummy

and on Linux

v4l2-ctl –list-devices

To get the device capabilites

ffmpeg -f dshow -list_options true -i video="Mobius"

where “Mobius” is the name of the camera.

On the Mac use

ffmpeg -f avfoundation -list_devices true -i ""
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How to write lossless video in Python

OpenCV does a reasonable job of reading videos from file or webcams. It’s simple and mostly works. When it comes to writing videos,
it however leaves a lot to be desired. There is little control over the codecs and it is almost impossible to know which codecs are installed. It also wants to know things like the frame size at intailisation. This isn’t always a problem, but if you don’t know it yet it means you have to set up the video writer inside your main processing loop.

To make something as cross-platform compatible as possible it would be nice to use FFmpeg. There are a few python wrappers around, but as far as I can tell they are mainly used for transcoding type applications. One solution is run FFmpeg as a subprocess and set its input to accept a pipe. Then every video frame is passed through the pipe. You write this yourself, in fact it’s only a few lines of code. However, the scikit-video package will do this for us, with some nice boilerplate to make life easier.

The steps are:

  1. install FFmpeg — if you running on Linux use your system’s package manager if it’s not already installed. If you’re unlucky enough to be using Windows you need to download the zip file from here, and add the bin directory to your system’s path.
  2. install scikit-video –I tried installing scikit-video via pip on my Anaconda distro but the version was too old. Instead, I cloned the github version and installed that. Instructions are provided on github.

Below is a simple example the grabs from your webcam and records lossless video.

#test recording of video
import cv2
import skvideo.io


capture=cv2.VideoCapture(0) #open the default webcam
outputfile = "test.mp4"   #our output filename
writer = skvideo.io.FFmpegWriter(outputfile, outputdict={
  '-vcodec': 'libx264',  #use the h.264 codec
  '-crf': '0',           #set the constant rate factor to 0, which is lossless
  '-preset':'veryslow'   #the slower the better compression, in princple, try 
                         #other options see https://trac.ffmpeg.org/wiki/Encode/H.264
}) 
while True:
    ret,frame=capture.read()
    if ret==False:
        print("Bad frame")
        break
    cv2.imshow('display',frame)
    writer.writeFrame(frame[:,:,::-1])  #write the frame as RGB not BGR
    ret=cv2.waitKey(10)
    if ret==27: #esc
        break

writer.close() #close the writer
capture.release()
cv2.destroyAllWindows()

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pythonVideoCapture.md

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