Skip to main content

IMGStore houses your video frames

Project description

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.


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.

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())


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):

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

Extracting Metadata or Extra data

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


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

Once you have a conda 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

Installing from source and with all dependencies

  • git clone this repository
  • conda env create -f environment.yml

Note: conda will install Python3 by default. If you wish to install Python2 add python=2 to the command, e.g. conda env create -f environment-mac-windows.yml python=2

Installing only IMGStore and using system dependencies

We recommend installing IMGStore dependencies using the conda package manager, however it is possible to create a virtual env which uses your system OpenCV install. The

# generate virtual env
virtualenv ~/.envs/imgstore --system-site-packages
# activate the virtual env
source ~/.envs/imgstore/bin/activate
# install imgstore
pip install imgstore

Note: If you install in this manner you have to ensure that opencv is correct and has the required functionality (such as mp4 write support if required). Remember to run the tests imgstore-test after installing.

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.

Release Checklist

  • test with GPL opencv/ffmpeg
  • test with LGPL opencv/ffmpeg
  • test with Python2.7 and Python3
  • git clean -dfx
  • python sdist bdist_wheel
  • twine upload --repository-url dist/*
  • (test with pip, new env)
    • pip install --index-url imgstore

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

imgstore-0.2.9.tar.gz (899.9 kB view hashes)

Uploaded source

Built Distribution

imgstore-0.2.9-py2.py3-none-any.whl (904.1 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page