Search and filter videos based on objects that appear in them using convolutional neural networks

Overview

Thingscoop: Utility for searching and filtering videos based on their content

Description

Thingscoop is a command-line utility for analyzing videos semantically - that means searching, filtering, and describing videos based on objects, places, and other things that appear in them.

When you first run thingscoop on a video file, it uses a convolutional neural network to create an "index" of what's contained in the every second of the input by repeatedly performing image classification on a frame-by-frame basis. Once an index for a video file has been created, you can search (i.e. get the start and end times of the regions in the video matching the query) and filter (i.e. create a supercut of the matching regions) the input using arbitrary queries. Thingscoop uses a very basic query language that lets you to compose queries that test for the presence or absence of labels with the logical operators ! (not), || (or) and && (and). For example, to search a video the presence of the sky and the absence of the ocean: thingscoop search 'sky && !ocean' <file>.

Right now two models are supported by thingscoop: vgg_imagenet uses the architecture described in "Very Deep Convolutional Networks for Large-Scale Image Recognition" to recognize objects from the ImageNet database, and googlenet_places uses the architecture described in "Going Deeper with Convolutions" to recognize settings and places from the MIT Places database. You can specify which model you'd like to use by running thingscoop models use <model>, where <model> is either vgg_imagenet or googlenet_places. More models will be added soon.

Thingscoop is based on Caffe, an open-source deep learning framework.

Installation

  1. Install ffmpeg, imagemagick, and ghostscript: brew install ffmpeg imagemagick ghostscript (Mac OS X) or apt-get install ffmpeg imagemagick ghostscript (Ubuntu).
  2. Follow the installation instructions on the Caffe Installation page.
  3. Make sure you build the Python bindings by running make pycaffe (on Caffe's directory).
  4. Set the environment variable CAFFE_ROOT to point to Caffe's directory: export CAFFE_ROOT=[Caffe's directory].
  5. Install thingscoop: easy_install thingscoop or pip install thingscoop.

Usage

thingscoop search <query> <files...>

Print the start and end times (in seconds) of the regions in <files> that match <query>. Creates an index for <file> using the current model if it does not exist.

Example output:

$ thingscoop search violin waking_life.mp4
/Users/anastasis/Downloads/waking_life.mp4 148.000000 162.000000
/Users/anastasis/Downloads/waking_life.mp4 176.000000 179.000000
/Users/anastasis/Downloads/waking_life.mp4 180.000000 186.000000
/Users/anastasis/Downloads/waking_life.mp4 189.000000 190.000000
/Users/anastasis/Downloads/waking_life.mp4 192.000000 200.000000
/Users/anastasis/Downloads/waking_life.mp4 211.000000 212.000000
/Users/anastasis/Downloads/waking_life.mp4 222.000000 223.000000
/Users/anastasis/Downloads/waking_life.mp4 235.000000 243.000000
/Users/anastasis/Downloads/waking_life.mp4 247.000000 249.000000
/Users/anastasis/Downloads/waking_life.mp4 251.000000 253.000000
/Users/anastasis/Downloads/waking_life.mp4 254.000000 258.000000

####thingscoop filter <query> <files...>

Generate a video compilation of the regions in the <files> that match <query>. Creates index for <file> using the current model if it does not exist.

Example output:

thingscoop sort <file>

Create a compilation video showing examples for every label recognized in the video (in alphabetic order). Creates an index for <file> using the current model if it does not exist.

Example output:

thingscoop describe <file>

Print every label that appears in <file> along with the number of times it appears. Creates an index for <file> using the current model if it does not exist.

thingscoop preview <file>

Create a window that plays the input video <file> while also displaying the labels the model recognizes on every frame.

$ thingscoop describe koyaanisqatsi.mp4 -m googlenet_places
sky 405
skyscraper 363
canyon 141
office_building 130
highway 78
lighthouse 66
hospital 64
desert 59
shower 49
volcano 45
underwater 44
airport_terminal 43
fountain 39
runway 36
assembly_line 35
aquarium 34
fire_escape 34
music_studio 32
bar 28
amusement_park 28
stage 26
wheat_field 25
butchers_shop 25
engine_room 24
slum 20
butte 20
igloo 20
...etc

thingscoop index <file>

Create an index for <file> using the current model if it does not exist.

thingscoop models list

List all models currently available in Thingscoop.

$ thingscoop models list
googlenet_imagenet            Model described in the paper "Going Deeper with Convolutions" trained on the ImageNet database
googlenet_places              Model described in the paper "Going Deeper with Convolutions" trained on the MIT Places database
vgg_imagenet                  16-layer model described in the paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets" trained on the ImageNet database

thingscoop models info <model>

Print more detailed information about <model>.

$ thingscoop models info googlenet_places
Name: googlenet_places
Description: Model described in the paper "Going Deeper with Convolutions" trained on the MIT Places database
Dataset: MIT Places

thingscoop models freeze

List all models that have already been downloaded.

$ thingscoop models freeze
googlenet_places
vgg_imagenet

thingscoop models current

Print the model that is currently in use.

$ thingscoop models current
googlenet_places

thingscoop models use <model>

Set the current model to <model>. Downloads that model locally if it hasn't been downloaded already.

thingscoop models download <model>

Download the model <model> locally.

thingscoop models remove <model>

Remove the model <model> locally.

thingscoop models clear

Remove all models stored locally.

thingscoop labels list

Print all the labels used by the current model.

$ thingscoop labels list
abacus
abaya
abstraction
academic gown
accessory
accordion
acorn
acorn squash
acoustic guitar
act
actinic radiation
action
activity
adhesive bandage
adjudicator
administrative district
admiral
adornment
adventurer
advocate
...

thingscoop labels search <regexp>

Print all the labels supported by the current model that match the regular expression <regexp>.

$ thingscoop labels search instrument$
beating-reed instrument
bowed stringed instrument
double-reed instrument
free-reed instrument
instrument
keyboard instrument
measuring instrument
medical instrument
musical instrument
navigational instrument
negotiable instrument
optical instrument
percussion instrument
scientific instrument
stringed instrument
surveying instrument
wind instrument
...

Full usage options

thingscoop - Command-line utility for searching and filtering videos based on their content

Usage:
  thingscoop filter <query> <files>... [-o <output_path>] [-m <model>] [-s <sr>] [-c <mc>] [--recreate-index] [--gpu-mode] [--open]
  thingscoop search <query> <files>... [-o <output_path>] [-m <model>] [-s <sr>] [-c <mc>] [--recreate-index] [--gpu-mode] 
  thingscoop describe <file> [-n <words>] [-m <model>] [--recreate-index] [--gpu-mode] [-c <mc>]
  thingscoop index <files> [-m <model>] [-s <sr>] [-c <mc>] [-r <ocr>] [--recreate-index] [--gpu-mode] 
  thingscoop sort <file> [-m <model>] [--gpu-mode] [--min-confidence <ct>] [--max-section-length <ms>] [-i <ignore>] [--open]
  thingscoop preview <file> [-m <model>] [--gpu-mode] [--min-confidence <ct>]
  thingscoop labels list [-m <model>]
  thingscoop labels search <regexp> [-m <model>]
  thingscoop models list
  thingscoop models info <model>
  thingscoop models freeze
  thingscoop models current
  thingscoop models use <model>
  thingscoop models download <model>
  thingscoop models remove <model>
  thingscoop models clear

Options:
  --version                       Show version.
  -h --help                       Show this screen.
  -o --output <dst>               Output file for supercut
  -s --sample-rate <sr>           How many frames to classify per second (default = 1)
  -c --min-confidence <mc>        Minimum prediction confidence required to consider a label (default depends on model)
  -m --model <model>              Model to use (use 'thingscoop models list' to see all available models)
  -n --number-of-words <words>    Number of words to describe the video with (default = 5)
  -t --max-section-length <ms>    Max number of seconds to show examples of a label in the sorted video (default = 5)
  -r --min-occurrences <ocr>      Minimum number of occurrences of a label in video required for it to be shown in the sorted video (default = 2)
  -i --ignore-labels <labels>     Labels to ignore when creating the sorted video video
  --title <title>                 Title to show at the beginning of the video (sort mode only)
  --gpu-mode                      Enable GPU mode
  --recreate-index                Recreate object index for file if it already exists
  --open                          Open filtered video after creating it (OS X only)

CHANGELOG

0.2 (8/16/2015)

  • Added sort option for creating a video compilation of all labels appearing in a video
  • Now using JSON for the index files

0.1 (8/5/2015)

  • Conception

License

MIT

Owner
Anastasis Germanidis
🎭
Anastasis Germanidis
Tools for robust generative diffeomorphic slice to volume reconstruction

RGDSVR Tools for Robust Generative Diffeomorphic Slice to Volume Reconstructions (RGDSVR) This repository provides tools to implement the methods in t

Lucilio Cordero-Grande 0 Oct 29, 2021
Augmented CLIP - Training simple models to predict CLIP image embeddings from text embeddings, and vice versa.

Train aug_clip against laion400m-embeddings found here: https://laion.ai/laion-400-open-dataset/ - note that this used the base ViT-B/32 CLIP model. S

Peter Baylies 55 Sep 13, 2022
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation E2EC: An End-to-End Contour-based Method for High-Quality H

zhangtao 146 Dec 29, 2022
Offical implementation for "Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation".

Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation (NeurIPS 2021) by Qiming Hu, Xiaojie Guo. Dependencies P

Qiming Hu 31 Dec 20, 2022
Rule-based Customer Segmentation

Rule-based Customer Segmentation Business Problem A game company wants to create level-based new customer definitions (personas) by using some feature

Cem Çaluk 2 Jan 03, 2022
Extreme Dynamic Classifier Chains - XGBoost for Multi-label Classification

Extreme Dynamic Classifier Chains Classifier chains is a key technique in multi-label classification, sinceit allows to consider label dependencies ef

6 Oct 08, 2022
This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies.

Deformable Neural Radiance Fields This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies. Project Page Paper Video This codebase conta

Google 1k Jan 09, 2023
PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

PointCloud Annotation Tools, support to label object bound box, ground, lane and kerb

halo 368 Dec 06, 2022
Y. Zhang, Q. Yao, W. Dai, L. Chen. AutoSF: Searching Scoring Functions for Knowledge Graph Embedding. IEEE International Conference on Data Engineering (ICDE). 2020

AutoSF The code for our paper "AutoSF: Searching Scoring Functions for Knowledge Graph Embedding" and this paper has been accepted by ICDE2020. News:

AutoML Research 64 Dec 17, 2022
Attention for PyTorch with Linear Memory Footprint

Attention for PyTorch with Linear Memory Footprint Unofficially implements https://arxiv.org/abs/2112.05682 to get Linear Memory Cost on Attention (+

11 Jan 09, 2022
Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021) Tensorflow implementation of Bridging the Gap between Label- and Reference-ba

huangqiusheng 8 Jul 13, 2022
Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features"

EDM-subgenre-classifier This repository contains the code for "Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Fea

11 Dec 20, 2022
MPI-IS Mesh Processing Library

Perceiving Systems Mesh Package This package contains core functions for manipulating meshes and visualizing them. It requires Python 3.5+ and is supp

Max Planck Institute for Intelligent Systems 494 Jan 06, 2023
CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energy Management, 2020, PikaPika team

Citylearn Challenge This is the PyTorch implementation for PikaPika team, CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energ

bigAIdream projects 10 Oct 10, 2022
An open source bike computer based on Raspberry Pi Zero (W, WH) with GPS and ANT+. Including offline map and navigation.

Pi Zero Bikecomputer An open-source bike computer based on Raspberry Pi Zero (W, WH) with GPS and ANT+ https://github.com/hishizuka/pizero_bikecompute

hishizuka 264 Jan 02, 2023
This repository builds a basic vision transformer from scratch so that one beginner can understand the theory of vision transformer.

vision-transformer-from-scratch This repository includes several kinds of vision transformers from scratch so that one beginner can understand the the

1 Dec 24, 2021
Compare GAN code.

Compare GAN This repository offers TensorFlow implementations for many components related to Generative Adversarial Networks: losses (such non-saturat

Google 1.8k Jan 05, 2023
Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search This is an implementation for our paper Contextual Non-Loca

Tencent YouTu Research 50 Dec 03, 2022
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

BUPT GAMMA Lab 519 Jan 02, 2023
Install alphafold on the local machine, get out of docker.

AlphaFold This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP

Kui Xu 73 Dec 13, 2022