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DeepImageSearch-2.5
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مشاهده بیشترتوضیحات
DeepImageSearch is a Python library for fast and accurate image search. It offers seamless integration with Python, GPU support, and advanced capabilities for identifying complex image patterns using the Vision Transformer models.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | DeepImageSearch-2.5 |
| نام | DeepImageSearch |
| نسخه کتابخانه | 2.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Nilesh Verma |
| ایمیل نویسنده | me@nileshverma.com |
| آدرس صفحه اصلی | https://github.com/TechyNilesh/DeepImageSearch |
| آدرس اینترنتی | https://pypi.org/project/DeepImageSearch/ |
| مجوز | MIT |
# Deep Image Search - AI-Based Image Search Engine
<p align="center"><img src="https://raw.githubusercontent.com/TechyNilesh/DeepImageSearch/786e96c48561d67be47dccbab2bc8debced414a3/images/deep%20image%20search%20logo%20New.png" alt="Deep+Image+Search+logo" height="218" width="350"></p>
**DeepImageSearch** is a powerful Python library that combines **state-of-the-art computer vision models** for feature extraction with **highly optimized algorithms for indexing and searching**. This enables fast and accurate similarity search and clustering of dense vectors, allowing users to build **scalable image search systems** capable of handling large-scale datasets. The library offers seamless integration with Python and provides **GPU support** for accelerated processing, delivering a comprehensive solution for researchers and developers working on image-based search and retrieval applications. By incorporating the **Vision Transformer (ViT) model**, DeepImageSearch further enhances its capabilities in identifying and understanding complex image patterns, making it an essential tool for advanced image search and analysis tasks.
  
   [](https://pepy.tech/project/deepimagesearch)
## Developed By
### [Nilesh Verma](https://nileshverma.com "Nilesh Verma")
## Features
- You can now load more than 500+ pre-trained state-of-the-art computer vision models available on [timm](https://timm.fast.ai/).
- Faster Search using [FAISS (Facebook AI Similarity Search)](https://github.com/facebookresearch/faiss).
- Highly Accurate Output Results.
- GPU & CPU based indexing and Searching Support.
- Best for implementing on Python-based web applications or APIs.
- Applications include image-based e-commerce recommendations, social media, and other image-based platforms that want to implement image recommendations 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:
```shell
pip install DeepImageSearch --upgrade
```
<span style="color:yellow"> If you're using a GPU, first uninstall the **faiss_cpu** version and then try installing the **faiss_gpu** version. The library installs the CPU version by default because not all systems support GPUs. </span>
## 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:
```python
from DeepImageSearch import Load_Data, Search_Setup
# Load images from a folder
image_list = Load_Data().from_folder(['folder_path'])
# Set up the search engine, You can load 'vit_base_patch16_224_in21k', 'resnet50' etc more then 500+ models
st = Search_Setup(image_list=image_list, model_name='vgg19', pretrained=True, image_count=100)
# Index the images
st.run_index()
# Get metadata
metadata = st.get_image_metadata_file()
# Add new images to the index
st.add_images_to_index(['image_path_1', 'image_path_2'])
# Get similar images
st.get_similar_images(image_path='image_path', number_of_images=10)
# Plot similar images
st.plot_similar_images(image_path='image_path', number_of_images=9)
# Update metadata
metadata = st.get_image_metadata_file()
```
This code demonstrates how to load images, set up the search engine, index the images, add new images to the index, and retrieve similar images.
<span style="color:red"> **Note:** Some models may not work properly due to resizing and normalization issues. By default, I have chosen a size of 224x244. Please try to select models that support this size or resized inputs. I have already tested many models, but testing over 500 is beyond my scope.</span>
## Documentation
This project aims to provide a powerful image search engine using deep learning techniques. To get started, please follow the link: [Read Full Documents](https://github.com/TechyNilesh/DeepImageSearch/blob/main/Documents/Document.md)
## Screenshot
<p align="center"><img src="https://github.com/TechyNilesh/DeepImageSearch/blob/c2a5e511662adade6ddece9be67167fe3f96cc4c/images/Deep-Image-Search-Demo-Screenshot.png?raw=true" alt="Brain+Machine" height="auto" width="auto"></p>
## Citaion
If you use DeepImageSerach in your Research/Product, please cite the following GitHub Repository:
```latex
@misc{TechyNilesh/DeepImageSearch,
author = {VERMA, NILESH},
title = {Deep Image Search - AI-Based Image Search Engine},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/TechyNilesh/DeepImageSearch}},
}
```
### Please do STAR the repository, if it helped you in anyway.
**More cool features will be added in future. Feel free to give suggestions, report bugs and contribute.**
نحوه نصب
نصب پکیج whl DeepImageSearch-2.5:
pip install DeepImageSearch-2.5.whl
نصب پکیج tar.gz DeepImageSearch-2.5:
pip install DeepImageSearch-2.5.tar.gz