ProcessedPiRecorder 0.2.0

Multiprocessed picamera class for simpler and faster computer vision

# ProcessedPiRecorder A multiprocessed class of picamera for simplified deployment of high framerate computer vision on raspberry pi.

Saves frames to a BigTif.

## Installation

pip3 install ProcessedPiRecorder

## Requires

• tifffile - 2019.7.26

• picamera - 1.13

• opencv-contrib-python - 3.4.4.19

• numpy - 1.17.0

## Basic Usage You have to initialize the recorder and then tell it when to start recording. ### Initialize:

While I do not use any positional arguments, I would recommend setting everything at initialization.

from ProcessedPiRecorder import ProcessedPiRecorder as pcr

myRecorder = pcr(tif_path=’’, x_resolution=0, y_resolution=0, scale_factor=1, framerate=0,

rec_length=0, display=True, stereo=False, timestamp=False, reportHz=False, monitor_qs= False, callback=None, cb_type=None, blocking=True, write_tif=True, tif_compression=6)

• tif_path - file to the output big tif file

• (x_resolution, y_resolution) - pixel dimensions to acquired by the sensor(s)

• scale_factor - sets the resize parameter at resolultion*scale_factor

• framerate - desired framerate in Hz

• rec_length - number of seconds to record

• display - if True, display video stream immediately prior to saving

• stereo - if True, hflip=True, stereo_mode=’side-by-side’, stereo_decimate=False

• timestamp - if True, all frames are timestapmed at aquisition

• report_Hz - if True, all frames have the current frame rate stamped at aquisition

• monitor_qs - if True, all frames have all queue lengths stamped at aquisition

• callback - if True, execute a callback function

• cb_type - if executing a callback, dictates if using the 2 process (=’2Proc’) or 3 process (=’3Proc’) workflow

• blocking - if True, block the main thread after spawning processes

• write_tif - if True, saves the video stream into tif_path

• tif_compression - specifies the degress of image compression used by tifffile

### Record

myRecorder.recordVid()

## Queues and Callbacks

ProcessedPiRecorder works by separating the acquisition, computer vision (optional), and file encoding tasks across multiple processes (cores) using the standard python multiprocessing library. These processes pass frames using multiprocessing.Queue objects, which are scoped to be inaccessible to the user so you don’t muck them up.

Computer vision can be easily added by means of a callback function. This function can be executed in same process as the file encoding (2 Process) or in its own process (3 Process). In either case the callback can communicate with the main process, if unblocked, using the cb_queue attached to the ProcessedPiRecorder object.

### Callback structure

cb_fucntion(frame, cb_queue):

#do some stuff top the frame frame = some_fn(frame)

#Communicate to the main process over the queue cb_queue.put(‘HiMom’)

#Return the processed frame return(frame)

### No callback

Just records video.

#### Parameters

Default.

### 2 Process callback

Executes callback in second process prior to file encoding.

#### Parameters

callback=cb_function, cb_type=’2Proc’

### 3 Processes callback

Executes the callback in its’ own process.

#### Parameters

callback=cb_function, cb_type=’2Proc’

## StereoPi

The StereoPi is cool, but using standard PiCamera you can’t save a highframerate video to file without dropping tons of frames, ProcessedPiRecorder fixes that. Be aware that the scale_factor parameter must be used to down sample the frames. I use the following parameters as a starting point for high framerate acquisition:

x_resolution=1280, y_resolution=480, scale_factor=0.3, framerate=25

## Contributors This code was written and is maintained by [Matt Davenport](https://github.com/mattisabrat) ([e-mail]).