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Search and filter videos based on objects that appear in them using convolutional neural networks
| Date | Stars |
|---|---|
| 2026-07-31 | 358 |
| 2026-08-06 | 358 |
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<img width=800 src="http://i.imgur.com/gyVNlgX.jpg"></img> ## 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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/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"](http://arxiv.org/abs/1409.1556) to recognize objects from the [ImageNet](http://www.image-net.org/) database, and `googlenet_places` uses the architecture described in ["Going Deeper with Convolutions"](http://arxiv.org/abs/1409.4842) to recognize settings and places from the [MIT Places](http://places.csail.mit.edu/) 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](http://caffe.berkeleyvision.org/), 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). 1. Follow the installation instructions on the [Caffe Installation page](http://caffe.berkeleyvision.org/installation.html). 2. Make sure you build the Python bindings by running `make pycaffe` (on Caffe's directory). 3. Set the environment variable CAFFE_ROOT to point to Caffe's directory: `export CAFFE_ROOT=[Caffe's directory]`. 4. 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: <a href="https://www.youtube.com/watch?v=qe9GjrUJipY"><img width=600 src="resources/filter.png"></img></a> #### `thingscoop so
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matched fp:a60cd94de9d463d1, llm:description: 'Search and filter videos based on objects that appear in them using convolutional neural networks'
matched fp:a60cd94de9d463d1, llm:description: 'Search and filter videos based on objects that appear in them using convolutional neural networks'
matched fp:a60cd94de9d463d1, llm:description: 'Search and filter videos based on objects that appear in them using convolutional neural networks'