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IMGStore - Houses Your Video And Data

Imgstore is a container for video frames and metadata. It allows efficient storage and seeking through recordings from hours to weeks in duration. It supports compressed and uncompressed formats.

Imgstore allows reading (and writing) videos recorded with loopbio's Motif recording system.

Introduction

The Concept

Video data is broken into chunks, which can be individual video files VideoImgStore, or a directory full of images DirectoryImgStore. The format of the chunks determines if the store is compressed, uncompressed, lossless or lossy.

Along side the video data Imgstore can also record different types of metadata:

  • Recording (User) Metadata:
    This includes information set at the time of recording, such as 'genotype', that is constant for the entire duration of recording.
  • Frame Metadata:
    This is the frame_number and frame_timestamp for every frame in the imgstore
  • Extra Data:
    This is DAQ and Audio data recorded by Motif at a rate different, and often faster than, the video framerate.

See Extracting Metadata.

Basic API

There are only a few public API entry points exposed (most operations are done on ImgStore objects (see writing and reading examples below).

  • new_for_filename(path) - Open a store for reading
  • new_for_format(format, path, **kwargs)
    • Open a store for writing
    • You also need to pass imgshape= and imgdtype
    • Note: imgshape is the array shape, i.e. (h,w,d) and not (w,h)
  • get_supported_formats() - list supports formats (remember to test after install)
  • extract_only_frame(path, frame_index) - extract a single frame at given index from file

Example: Write a store

import imgstore
import numpy as np
import cv2
import time

height = width = 500
blank_image = np.zeros((height,width,3), np.uint8)

store = imgstore.new_for_format('npy',  # numpy format (uncompressed raw image frames)
                                mode='w', basedir='mystore',
                                imgshape=blank_image.shape, imgdtype=blank_image.dtype,
                                chunksize=1000)  # 1000 files per chunk (directory)

for i in range(40):
    img = blank_image.copy()
    cv2.putText(img,str(i),(0,300), cv2.FONT_HERSHEY_SIMPLEX, 4, 255)
    store.add_image(img, i, time.time())

store.close()

You can also add additional (JSON serialable) data at any time, and this will be stored with a reference to the current frame_number so that it can be retrieved and easily combined later.

store.add_extra_data(temperature=42.5, humidity=12.4)

Example: Read a store

from imgstore import new_for_filename

store = new_for_filename('mystore/metadata.yaml')

print 'frames in store:', store.frame_count
print 'min frame number:', store.frame_min
print 'max frame number:', store.frame_max

# read first frame
img, (frame_number, frame_timestamp) = store.get_next_image()
print 'framenumber:', frame_number, 'timestamp:', frame_timestamp

# read last frame
img, (frame_number, frame_timestamp) = store.get_image(store.frame_max)
print 'framenumber:', frame_number, 'timestamp:', frame_timestamp

Extracting frames: frame index vs frame number

Stores maintain two separate and distinct concepts, 'frame number', which is any integer value associated with a single frame, and 'frame index', which is numbered from 0 to the number of frames in the store. This difference is visible in the API with

class ImgStore
    def get_image(self, frame_number, exact_only=True, frame_index=None):
        pass

where 'frame index' OR 'frame number' can be passed.

Extracting Metadata

To get the Recording (user) metadata access the ImgStore.user_metadata property.

To get all the image metadata at once you can call ImgStore.get_frame_metadata() which will return a dictionary containing all frame_number and frame_timestamps.

To retrieve a pandas DataFrame of all extra data and associated frame_number and frame_timestamps call ImgStore.get_extra_data()

Command line tools

Some simple tools for creating, converting and viewing imgstores are provided

  • imgstore-view /path/to/store
    • view an imgstore
  • imgstore-save --format 'avc1/mp4' --source /path/to/input.mp4 /path/to/store/to/save
    • --source if omitted will be the first webcam
  • imgstore-test
    • run extensive tests to check opencv build has mp4 support and trustworthy encoding/decoding

Install

IMGStore depends on reliable OpenCV builds, and built with mp4/h264 support for writing mp4s.

You can install opencv from pip or conda if you wish, or on linux you can use the system apt-get installed opencv if you create a virtual environment with --system-site-packages.

Once you have a python (virtual) environment with a recent and reliable OpenCV build, you can install IMGStore from pip

$ pip install imgstore

After installing imgstore from any location, you should check it's tests pass to guarantee that you have a trustworthy OpenCV version

Post install testing

You should always run the command imgstore-test after installing imgstore. If your environment is working correctly you should see a lot of text printed, followed by the text ==== 66 passed, ..... ======

To test against the package without installing first, run python -m pytest

Note: by running pytest through it's python module interface, the interpreter adds pwd to top of PYTHONPATH, as opposed to running tests through py.test which doesn't.

Note: if you recieve many failed tests with the error message 'The opencv backend does not actually have write support' or 'Your opencv build does support writing this codec', this is not an imgestore bug - it is a warning that you have an OpenCV version that does not support Writing h264 encoded videos. This is often the case on windows.

Even if some write tests fail due to these issues, you can stil use the imgstore package to read h264 encoded video files.

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Store for video frames and metadata. Seekable & scaleable

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