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7.[Performing vector search](#external)
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8.[Support for cloud storage](#s3)
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9.[Working with tar/zip files as input](#tar)
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10.[Debugging fastdup]
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10.[Working with video](#video)
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11.[Debugging fastdup](#debug)
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## Detailed Python API documentation <aname="run"/>
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The main function of fastdup is `run`. It works by extracting short feature vectors from each image, clsutering the images together using a nearest neighbor model which computes similarities of pairs of images. Then a graph is formed to deduce the network structure of local similarities. The input/ outputs are described below in the section Input/Output.
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## Running video <aname="video"/>
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fastdup supports video in mp4 and avi formats. For other formats please reach out. For running on video you need to install ffmpeg.
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On Ubuntu
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```bash
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sudo apt install ffmpeg
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```
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On Mac
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```bash
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brew install ffmpeg
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```
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Note: on Mac 10.14 we encountered brew error, you can download statically compile ffmpeg [here](https://evermeet.cx/ffmpeg/).
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Currently we extract frame 1 per sec, please reach out if you need other support. Our video tutorial is found here:
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-[🔥Analyzing video of the MEVA dataset - Google Colab](https://colab.research.google.com/github/visualdatabase/fastdup/blob/main/examples/fastdup_video.ipynb)
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## Debugging fastdup <aname="debug"/>
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- It is recommneded to debug in a python shell (and not in a Jupyter notebook)
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- Run with `verbose=1` to get additional traces
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- Run with `num_images=10` to run on a small subset of your data before running on the full dataset.
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- If the issue persist please join our slack channel, we would love to support!
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- If the issue persist please join our [Slack Channel]("https://join.slack.com/t/visualdatabase/shared_invite/zt-19jaydbjn-lNDEDkgvSI1QwbTXSY6dlA")
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