|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Easyocr||18,098||37||9 days ago||30||June 02, 2022||230||apache-2.0||Python|
|Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.|
|Albumentations||12,082||64||155||19 hours ago||52||June 15, 2022||368||mit||Python|
|Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125|
|Cvpr2023 Papers With Code||12,053||6 days ago||10|
|CVPR 2023 论文和开源项目合集|
|Caire||10,175||1||2||2 months ago||53||April 05, 2022||3||mit||Go|
|Content aware image resize library|
|Computervision Recipes||8,950||4 months ago||65||mit||Jupyter Notebook|
|Best Practices, code samples, and documentation for Computer Vision.|
|Kornia||8,240||58||3 days ago||31||May 17, 2022||250||apache-2.0||Python|
|Differentiable Computer Vision Library|
|Darkflow||6,082||2 months ago||644||gpl-3.0||Python|
|Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices|
|Deeplake||6,067||52||1||2 hours ago||149||June 28, 2022||53||mpl-2.0||Python|
|Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai|
|Opencvsharp||4,579||6||74||3 hours ago||38||June 08, 2022||72||apache-2.0||C#|
|OpenCV wrapper for .NET|
|Dali||4,438||3 days ago||266||apache-2.0||C++|
|A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.|
Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper.
|Original image||Energy map||Seams applied|
Key features which differentiates this library from the other existing open source solutions:
First, install Go, set your
GOPATH, and make sure
$GOPATH/bin is on your
$ go install github.com/esimov/caire/cmd/[email protected]
The library can also be installed via Homebrew.
$ brew install caire
$ caire -in input.jpg -out output.jpg
$ caire --help
The following flags are supported:
||true||Show GUI window|
||false||Reduce image by percentage|
||false||Reduce image to square dimensions|
||2||Sobel filter threshold|
||false||Use face detection|
||float||Plane rotated faces angle|
||string||Mask file path|
||string||Remove mask file path|
||string||Seam color (default
||string||Shape type used for debugging:
The library is capable of detecting human faces prior resizing the images by using the lightweight Pigo (esimov/pigo) face detection library.
The image below illustrates the application capabilities for human face detection prior resizing. It's clearly visible that with face detection activated the algorithm will avoid cropping pixels inside the detected faces, retaining the face zone unaltered.
|Original image||With face detection||Without face detection|
A GUI preview mode is also incorporated into the library for in time process visualization. The Gio GUI library has been used because of its robustness and modern architecture. Prior running it please make sure that you have installed all the required dependencies noted in the installation section (https://gioui.org/#installation) .
The preview window is activated by default but you can deactivate it any time by setting the
-preview flag to false. When the images are processed concurrently from a directory the preview mode is deactivated.
In order to detect faces prior rescaling, use the
-face flag. There is no need to provide a face classification file, since it's already embedded into the generated binary file. The sample code below will resize the provided image with 20%, but checks for human faces in order tot avoid face deformations.
For face detection related settings please check the Pigo documentation.
$ caire -in input.jpg -out output.jpg -face=1 -perc=1 -width=20
You can also use
$ cat input/source.jpg | caire -in - -out - >out.jpg
out default to
- so you can also use:
$ cat input/source.jpg | caire >out.jpg $ caire -out out.jpg < input/source.jpg
You can provide also an image URL for the
-in flag or even use curl or wget as a pipe command in which case there is no need to use the
$ caire -in <image_url> -out <output-folder> $ curl -s <image_url> | caire > out.jpg
The library can also process multiple images from a directory concurrently. You have to provide only the source and the destination folder and the new width or height in this case.
$ caire -in <input_folder> -out <output-folder>
There is no need to define the output file type, just use the correct extension and the library will encode the image to that specific type. You can export the resized image even to a Gif file, in which case the generated file shows the resizing process interactively.
In case you wish to scale down the image by a specific percentage, it can be used the
-perc boolean flag. In this case the values provided for the
height are expressed in percentage and not pixel values. For example to reduce the image dimension by 20% both horizontally and vertically you can use the following command:
$ caire -in input/source.jpg -out ./out.jpg -perc=1 -width=20 -height=20 -debug=false
Also the library supports the
-square option. When this option is used the image will be resized to a square, based on the shortest edge.
When an image is resized on both the X and Y axis, the algorithm will first try to rescale it prior resizing, but also will preserve the image aspect ratio. The seam carving algorithm is applied only to the remaining points. Ex. : given an image of dimensions 2048x1536 if we want to resize to the 1024x500, the tool first rescale the image to 1024x768 and then will remove only the remaining 268px.
-mask: The path to the protective mask. The mask should be in binary format and have the same size as the input image. White areas represent regions where no seams should be carved.
-rmask: The path to the removal mask. The mask should be in binary format and have the same size as the input image. White areas represent regions to be removed.
$ snap run caire --h
Copyright © 2018 Endre Simo
This project is under the MIT License. See the LICENSE file for the full license text.