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.github/workflows/main.yml

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# This is a basic workflow to help you get started with Actions
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name: CI
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# Controls when the action will run. Triggers the workflow on push or pull request
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# events but only for the master branch
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on:
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push:
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branches: [ master ]
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pull_request:
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branches: [ master ]
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# A workflow run is made up of one or more jobs that can run sequentially or in parallel
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jobs:
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# This workflow contains a single job called "build"
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build:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8]
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# Steps represent a sequence of tasks that will be executed as part of the job
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v1
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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pip install --no-binary :all: nmslib
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- name: Test with nose2
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run: |
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pip install nose2
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nose2

README.md

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# imSearch: A generic framework to build your own reverse image search engine
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![CI](https://github.com/rikenmehta03/imsearch/workflows/CI/badge.svg?branch=master)
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imsearch helps to create your own custom, robust & scalable reverse image search engine. This project uses state of the art object detection algorithm ([yolov3](https://pjreddie.com/darknet/yolo/)) at its core to extract the features from an image. It uses an efficient cross-platform similarity search library [NMSLIB](https://github.com/nmslib/nmslib) for similarity search. [Redis](https://redis.io/) is used as a messaging queue between feature extractor and core engine. [MongoDB](https://www.mongodb.com/) is used to store the meta-data of all the indexed images. HD5 file system is used to store the feature vectors extracted from indexed images.
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## Installation

requirements.txt

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Cython
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requests
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numpy
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torch>=1.4.0
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matplotlib
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wget
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opencv_python
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boto3
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redis
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pandas
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torchvision
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pymongo
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google-cloud-storage

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