Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Related tags

Deep LearningDeepImageSearch
Overview

Deep Image Search - AI-Based Image Search Engine

Brain+Machine

Deep Image Search is an AI-based image search engine that includes deep transfer learning features Extraction and tree-based vectorized search

Generic badge Generic badge Generic badge Generic badge Generic badgeGeneric badge

Brain+Machine Creators

Nilesh Verma

Features

  • Faster Search O(logN) Complexity.
  • High Accurate Output Result.
  • Best for Implementing on python based web application or APIs.
  • Best implementation for College students and freshers for project creation.
  • Applications are Images based E-commerce recommendation, Social media and other image-based platforms that want to implement image recommendation and search.

Installation

This library is compatible with both windows and Linux system you can just use PIP command to install this library on your system:

pip install DeepImageSearch

If you are facing any VS C++ 14 related issue in windows during installation, kindly refer to following solution: Pip error: Microsoft Visual C++ 14.0 is required

How To Use?

We have provided the Demo folder under the GitHub repository, you can find the example in both .py and .ipynb file. Following are the ideal flow of the code:

1. Importing the Important Classes

There are three important classes you need to load LoadData - for data loading, Index - for indexing the images to database/folder, SearchImage - For searching and Plotting the images

# Importing the proper classes
from DeepImageSearch import Index,LoadData,SearchImage

2. Loading the Images Data

For loading the images data we need to use the LoadData object, from there we can import images from the CSV file and Single/Multiple Folders.

# load the Images from the Folder (You can also import data from multiple folders in python list type)
image_list = LoadData().from_folder(['images','wiki-images'])
# Load data from CSV file
image_list = LoadData().from_csv(csv_file_path='your_csv_file.csv',images_column_name='column_name)

3. Indexing and Saving The File in Local Folder

For faster retrieval we are using tree-based indexing techniques for Images features, So for that, we need to store meta-information on the local path [meta-data-files/] folder.

# For Faster Serching we need to index Data first, After Indexing all the meta data stored on the local path
Index(image_list).Start()

3. Searching

Searching operation is performed by the following method:

# for searching, you need to give the image path and the number of the similar image you want
SearchImage().get_similar_images(image_path=image_list[0],number_of_images=5)

you can also plot some similar images for viewing purpose by following the code method:

# If you want to plot similar images you can use this method, It will plot 16 most similar images from the data index
SearchImage().plot_similar_images(image_path = image_list[0])

Complete Code

# Importing the proper classes
from DeepImageSearch import Index,LoadData,SearchImage
# load the Images from the Folder (You can also import data from multiple folder in python list type)
image_list = LoadData().from_folder(['images','wiki-images'])
# For Faster Serching we need to index Data first, After Indexing all the meta data stored on the local path
Index(image_list).Start()
# for searching you need to give the image path and the number of similar image you want
SearchImage().get_similar_images(image_path=image_list[0],number_of_images=5)
# If you want to plot similar images the you can use this method, It will plot 16 most similar images from the data index
SearchImage().plot_similar_images(image_path = image_list[0])

License

MIT License

Copyright (c) 2021 Nilesh Verma

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

More cool features will be added in future. Feel free to give suggestions, report bugs and contribute.

You might also like...
A hobby project which includes a hand-gesture based virtual piano using a mobile phone camera and OpenCV library functions
A hobby project which includes a hand-gesture based virtual piano using a mobile phone camera and OpenCV library functions

Overview This is a hobby project which includes a hand-gesture controlled virtual piano using an android phone camera and some OpenCV library. My moti

Abhinav Gupta 1 Nov 19, 2021
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

Softlearning Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. The implementation is

Robotic AI & Learning Lab Berkeley 997 Dec 30, 2022
A fast, dataset-agnostic, deep visual search engine for digital art history

imgs.ai imgs.ai is a fast, dataset-agnostic, deep visual search engine for digital art history based on neural network embeddings. It utilizes modern

Fabian Offert 5 Dec 14, 2022
This is a simple backtesting framework to help you test your crypto currency trading. It includes a way to download and store historical crypto data and to execute a trading strategy.

You can use this simple crypto backtesting script to ensure your trading strategy is successful Minimal setup required and works well with static TP a

Andrei 154 Sep 12, 2022
The Hailo Model Zoo includes pre-trained models and a full building and evaluation environment
The Hailo Model Zoo includes pre-trained models and a full building and evaluation environment

Hailo Model Zoo The Hailo Model Zoo provides pre-trained models for high-performance deep learning applications. Using the Hailo Model Zoo you can mea

Hailo 50 Dec 7, 2022
Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

Learning Structural Edits via Incremental Tree Transformations Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21) 1.

NeuLab 40 Dec 23, 2022
Code for Graph-to-Tree Learning for Solving Math Word Problems (ACL 2020)

Graph-to-Tree Learning for Solving Math Word Problems PyTorch implementation of Graph based Math Word Problem solver described in our ACL 2020 paper G

Jipeng Zhang 66 Nov 23, 2022
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

RGF-team 364 Dec 28, 2022
nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.

What is nnDetection? Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of hi

MIC-DKFZ 365 Jan 9, 2023
Comments
  • Similar images

    Similar images

    The function to plot similar images plot 16 images, how do we know which image is related to or similar to the which image according to the algorithm?

    I mean like it should say these two are similar and the other two are similar to each other, no?

    opened by amrrs 3
  • TypeError: show() takes 1 positional argument but 2 were given

    TypeError: show() takes 1 positional argument but 2 were given

    Classification.py:

    from DeepImageSearch import Index, LoadData, SearchImage

    folders = [] folders.append("monos_segmented") image_list = LoadData().from_folder(folders)

    print (image_list)

    Index(image_list).Start()

    SearchImage().get_similar_images(image_path=image_list[0],number_of_images=5)

    SearchImage().plot_similar_images(image_path = image_list[0])

    Running...

    Traceback (most recent call last): File "Classification.py", line 13, in SearchImage().plot_similar_images(image_path = image_list[0]) File "/home/mike/.local/lib/python3.8/site-packages/DeepImageSearch/DeepImageSearch.py", line 132, in plot_similar_images plt.show(fig) File "/home/mike/.local/lib/python3.8/site-packages/matplotlib/pyplot.py", line 378, in show return _backend_mod.show(*args, **kwargs) TypeError: show() takes 1 positional argument but 2 were given

    opened by mikedorin 1
  • Single thread.

    Single thread.

    Hello,

    What i want to ask is, cant we make extracting features parallel? I'm using 3060 Ti and it seems a little bit slow for this GPU.

    Or am i wrong?

    1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3070/242451 [02:25<3:08:09, 21.20it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step                                                                                                                    | 3073/242451 [02:25<3:07:27, 21.28it/s]
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3076/242451 [02:25<3:07:21, 21.29it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3079/242451 [02:25<3:06:30, 21.39it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3082/242451 [02:26<3:07:04, 21.33it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3085/242451 [02:26<3:08:38, 21.15it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step                                                                                                                    | 3088/242451 [02:26<3:09:21, 21.07it/s]
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 15ms/step                                                                                                                    | 3091/242451 [02:26<3:09:04, 21.10it/s]
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step
1/1 [==============================] - 0s 13ms/step                                                                                                                    | 3094/242451 [02:26<3:11:12, 20.86it/s]
1/1 [==============================] - 0s 13ms/step
1/1 [==============================] - 0s 14ms/step

    Best regards.

    opened by ucyildirim 0
  • Problems with TensorFlow

    Problems with TensorFlow

    Hello,

    when trying to install DeepImageSearch on a Windows machine I got this:

    ERROR: Cannot install deepimagesearch==1.0, deepimagesearch==1.1, deepimagesearch==1.2, deepimagesearch==1.3 and deepimagesearch==1.4 because these package versions have conflicting dependencies.

The conflict is caused by:
    deepimagesearch 1.4 depends on tensorflow
    deepimagesearch 1.3 depends on tensorflow
    deepimagesearch 1.2 depends on tensorflow
    deepimagesearch 1.1 depends on tensorflow
    deepimagesearch 1.0 depends on tensorflow`

    I tried to install it like stated here: https://stackoverflow.com/questions/69751318/i-had-trouble-installing-python-deepimagesearch-library but also same error as mentioned there by using this.

    ERROR: Could not find a version that satisfies the requirement tensorflow==2.3.2 (from versions: none)
ERROR: No matching distribution found for tensorflow==2.3.

    Digging into TensorFlow itself, it seems that it is not running on windows properly anymore beginning from version 2.11 - that would not matter, if the version required by your library would still be available

    Using Windows 10 with Python 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] on win32

    Installing https://pypi.org/project/tensorflow-intel/ and changing requirements in your library did not help either.

    So, what else I can do ?

    Thanks in advance for any help !

    opened by Creat1veM1nd 6
Owner
Data Science Enthusiast & Digital Influencer
GitHub Repository
AdaFocus (ICCV 2021) Adaptive Focus for Efficient Video Recognition

AdaFocus (ICCV 2021) This repo contains the official code and pre-trained models for AdaFocus. Adaptive Focus for Efficient Video Recognition Referenc

Rainforest Wang 115 Dec 21, 2022
A Kaggle competition: discriminate gender based on handwriting

Gender discrimination based on handwriting See http://fastml.com/gender-discrimination/ for description. prep_data.py - a first step chunk_by_authors.

Zygmunt Zając 22 Jul 20, 2022
Generative Autoregressive, Normalized Flows, VAEs, Score-based models (GANVAS)

GANVAS-models This is an implementation of various generative models. It contains implementations of the following: Autoregressive Models: PixelCNN, G

MRSAIL (Mini Robotics, Software & AI Lab) 6 Nov 26, 2022
LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and

TuZheng 405 Jan 04, 2023
PERIN is Permutation-Invariant Semantic Parser developed for MRP 2020

PERIN: Permutation-invariant Semantic Parsing David Samuel & Milan Straka Charles University Faculty of Mathematics and Physics Institute of Formal an

ÚFAL 40 Jan 04, 2023
'A C2C E-COMMERCE TRUST MODEL BASED ON REPUTATION' Python implementation

Project description A library providing functionalities to calculate reputation and degree of trust on C2C ecommerce platforms. The work is fully base

Davide Bigotti 2 Dec 14, 2022
Weighted QMIX: Expanding Monotonic Value Function Factorisation

This repo contains the cleaned-up code that was used in "Weighted QMIX: Expanding Monotonic Value Function Factorisation"

whirl 82 Dec 29, 2022
Code release for Hu et al. Segmentation from Natural Language Expressions. in ECCV, 2016

Segmentation from Natural Language Expressions This repository contains the code for the following paper: R. Hu, M. Rohrbach, T. Darrell, Segmentation

Ronghang Hu 88 May 24, 2022
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)

Pop-Out Motion Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022) Jihyun Lee*, Minhyuk Sung*, Hyunjin Kim, Tae-Ky

Jihyun Lee 88 Nov 22, 2022
PolyTrack: Tracking with Bounding Polygons

PolyTrack: Tracking with Bounding Polygons Abstract In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segme

Gaspar Faure 13 Sep 15, 2022
Tensorflow implementation of DeepLabv2

TF-deeplab This is a Tensorflow implementation of DeepLab, compatible with Tensorflow 1.2.1. Currently it supports both training and testing the ResNe

Chenxi Liu 21 Sep 27, 2022
Talk covering the features of skorch

Skorch Talk Skorch - A Union of Scikit-learn and PyTorch Presentation The slides can be downloaded at: download link. Google Colab Part One - MNIST Pa

Thomas J. Fan 3 Oct 20, 2020
This is the official implementation of Elaborative Rehearsal for Zero-shot Action Recognition (ICCV2021)

Elaborative Rehearsal for Zero-shot Action Recognition This is an official implementation of: Shizhe Chen and Dong Huang, Elaborative Rehearsal for Ze

DeLightCMU 26 Sep 24, 2022
Semantic Segmentation Architectures Implemented in PyTorch

pytorch-semseg Semantic Segmentation Algorithms Implemented in PyTorch This repository aims at mirroring popular semantic segmentation architectures i

Meet Shah 3.3k Dec 29, 2022
Animate molecular orbital transitions using Psi4 and Blender

Molecular Orbital Transitions (MOT) Animate molecular orbital transitions using Psi4 and Blender Author: Maximilian Paradiz Dominguez, University of A

3 Feb 01, 2022
Rate-limit-semaphore - Semaphore implementation with rate limit restriction for async-style (any core)

Rate Limit Semaphore Rate limit semaphore for async-style (any core) There are t

Yan Kurbatov 4 Jun 21, 2022
Car Parking Tracker Using OpenCv

Car Parking Vacancy Tracker Using OpenCv I used basic image processing methods i

Adwait Kelkar 30 Dec 03, 2022
Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Clay Mullis 82 Oct 13, 2022
Multiple Object Tracking with Yolov5!

Tracking with yolov5 This implementation is for who need to tracking multi-object only with detector. You can easily track mult-object with your well

9 Nov 08, 2022
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022