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
ExCon: Explanation-driven Supervised Contrastive Learning

ExCon: Explanation-driven Supervised Contrastive Learning Link to the paper: https://arxiv.org/pdf/2111.14271.pdf Contributors of this repo: Zhibo Zha

Zhibo (Darren) Zhang 18 Nov 01, 2022
[AI6122] Text Data Management & Processing

[AI6122] Text Data Management & Processing is an elective course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6122 of Semester 1, AY2021-2022, starting from 08/2021. The instruc

HT. Li 1 Jan 17, 2022
BisQue is a web-based platform designed to provide researchers with organizational and quantitative analysis tools for 5D image data. Users can extend BisQue by implementing containerized ML workflows.

Overview BisQue is a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for up to 5D

Vision Research Lab @ UCSB 26 Nov 29, 2022
Share a benchmark that can easily apply reinforcement learning in Job-shop-scheduling

Gymjsp Gymjsp is an open source Python library, which uses the OpenAI Gym interface for easily instantiating and interacting with RL environments, and

134 Dec 08, 2022
[NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"

Qu-ANTI-zation This repository contains the code for reproducing the results of our paper: Qu-ANTI-zation: Exploiting Quantization Artifacts for Achie

Secure AI Systems Lab 8 Mar 26, 2022
PyTorch reimplementation of hand-biomechanical-constraints (ECCV2020)

Hand Biomechanical Constraints Pytorch Unofficial PyTorch reimplementation of Hand-Biomechanical-Constraints (ECCV2020). This project reimplement foll

Hao Meng 59 Dec 20, 2022
This is the official implementation of "One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval".

CORA This is the official implementation of the following paper: Akari Asai, Xinyan Yu, Jungo Kasai and Hannaneh Hajishirzi. One Question Answering Mo

Akari Asai 59 Dec 28, 2022
Supporting code for "Autoregressive neural-network wavefunctions for ab initio quantum chemistry".

naqs-for-quantum-chemistry This repository contains the codebase developed for the paper Autoregressive neural-network wavefunctions for ab initio qua

Tom Barrett 24 Dec 23, 2022
code for CVPR paper Zero-shot Instance Segmentation

Code for CVPR2021 paper Zero-shot Instance Segmentation Code requirements python: python3.7 nvidia GPU pytorch1.1.0 GCC =5.4 NCCL 2 the other python

zhengye 86 Dec 13, 2022
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
NAS-FCOS: Fast Neural Architecture Search for Object Detection (CVPR 2020)

NAS-FCOS: Fast Neural Architecture Search for Object Detection This project hosts the train and inference code with pretrained model for implementing

Ning Wang 180 Dec 06, 2022
A toolkit for making real world machine learning and data analysis applications in C++

dlib C++ library Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real worl

Davis E. King 11.6k Jan 01, 2023
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Phil Wang 12.6k Jan 09, 2023
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding

Rot-Pro : Modeling Transitivity by Projection in Knowledge Graph Embedding This repository contains the source code for the Rot-Pro model, presented a

Tewi 9 Sep 28, 2022
Image Data Augmentation in Keras

Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset.

Grace Ugochi Nneji 3 Feb 15, 2022
A containerized REST API around OpenAI's CLIP model.

OpenAI's CLIP — REST API This is a container wrapping OpenAI's CLIP model in a RESTful interface. Running the container locally First, build the conta

Santiago Valdarrama 48 Nov 06, 2022
Human Pose estimation with TensorFlow framework

Human Pose Estimation with TensorFlow Here you can find the implementation of the Human Body Pose Estimation algorithm, presented in the DeeperCut and

Eldar Insafutdinov 1.1k Dec 29, 2022
Advanced Signal Processing Notebooks and Tutorials

Advanced Digital Signal Processing Notebooks and Tutorials Prof. Dr. -Ing. Gerald Schuller Jupyter Notebooks and Videos: Renato Profeta Applied Media

Guitars.AI 115 Dec 13, 2022
This repository contains a set of codes to run (i.e., train, perform inference with, evaluate) a diarization method called EEND-vector-clustering.

EEND-vector clustering The EEND-vector clustering (End-to-End-Neural-Diarization-vector clustering) is a speaker diarization framework that integrates

45 Dec 26, 2022
Corgis are the cutest creatures; have 30K of them!

corgi-net This is a dataset of corgi images scraped from the corgi subreddit. After filtering using an ImageNet classifier, the training set consists

Alex Nichol 6 Dec 24, 2022