Implementation of GGB color space

Related tags

Deep Learningpythonopencvimage-processingpillowmedical-image-processingskimageggb-color
Overview

GGB Color Space

PyPI license Travis CI docker codecov

This package is implementation of GGB color space from Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Image.

Installation

Install GGB

This package could be installed via PyPI.

pip3 install ggb

or manually:

python3 setup.py install

Building Docker Image

Dockerfile is also provided in this project. To build the image:

cd docker/
bash docker.build.sh

or pull it directly from Docker Hub:

docker pull reshalfahsi/ggb

Building the Documentation

The documentations to this package could be built using Sphinx.

cd docs/
pip3 install -r requirements.txt
make html

The HTML pages are in docs/build/html.

Quick Demo

This package supports various computer vision libraries such as OpenCV and PIL. Complete examples for these computer vision libraries are provided in here. For the short example in Python3:

# import the package and its necessary components
from ggb import GGB, ColorSpace

# we are using OpenCV
import cv2

import urllib.request as urllib
import numpy as np

# load image from internet
req = urllib.urlopen('https://github.com/reshalfahsi/GGB/raw/master/docs/img/leukocytes.png')
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
img = cv2.imdecode(arr, -1)
    
# convert to GGB Color
ggb_image = GGB(image=img, input_color=ColorSpace.BGR).process()

# show the result    
ggb_image.show()

# save the image to OpenCV format
img = ggb_image.write()

This package also could be run through CLI:

ggb /path/to/image --output /path/to/output

Result

Leukocytes

alt text

Fundus

alt text

Car

alt text

You might also like...
Fast Soft Color Segmentation
Fast Soft Color Segmentation

Fast Soft Color Segmentation

null 3 Oct 29, 2022
Spatial color quantization in Rust
Spatial color quantization in Rust

rscolorq Rust port of Derrick Coetzee's scolorq, based on the 1998 paper "On spatial quantization of color images" by Jan Puzicha, Markus Held, Jens K

Collyn O'Kane 37 Dec 22, 2022
Rendering color and depth images for ShapeNet models.
Rendering color and depth images for ShapeNet models.

Color & Depth Renderer for ShapeNet This library includes the tools for rendering multi-view color and depth images of ShapeNet models. Physically bas

Yinyu Nie 41 Dec 19, 2022
3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans.
3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans.

3DMV 3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This work is based on our ECCV'18 p

Владислав Молодцов 0 Feb 6, 2022
A very simple baseline to estimate 2D & 3D SMPL-compatible keypoints from a single color image.
A very simple baseline to estimate 2D & 3D SMPL-compatible keypoints from a single color image.

Minimal Body A very simple baseline to estimate 2D & 3D SMPL-compatible keypoints from a single color image. The model file is only 51.2 MB and runs a

Yuxiao Zhou 49 Dec 5, 2022
A minimal solution to hand motion capture from a single color camera at over 100fps. Easy to use, plug to run.
A minimal solution to hand motion capture from a single color camera at over 100fps. Easy to use, plug to run.

Minimal Hand A minimal solution to hand motion capture from a single color camera at over 100fps. Easy to use, plug to run. This project provides the

Yuxiao Zhou 824 Jan 7, 2023
An end-to-end image translation model with weight-map for color constancy
An end-to-end image translation model with weight-map for color constancy

CCUnet An end-to-end image translation model with weight-map for color constancy 1. Download the dataset (take Colorchecker_recommended dataset as an

Jianhui Qiu 1 Dec 21, 2021
Blender Add-on that sets a Material's Base Color to one of Pantone's Colors of the Year
Blender Add-on that sets a Material's Base Color to one of Pantone's Colors of the Year

Blender PCOY (Pantone Color of the Year) MCMC (Mid-Century Modern Colors) HG71 (House & Garden Colors 1971) Blender Add-ons That Assign a Custom Color

Don Schnitzius 15 Nov 20, 2022
PyTorch implementation of paper
PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 20

Zhengqi Li 585 Jan 4, 2023
Comments
  • v1.1.2 release

    v1.1.2 release

    • GGBImage can not be called directly from ggb module.
    • Add ggb.testing module.
    • Move ggb.utils.test module to ggb.testing.base module.
    • Add get_image_from_url function in the ggb.testing.base module.
    • Add process method in the ggb.backend module.
    • Rewrite ggb.ggb module.
    • Add ComputerVisionLibraryError exception in the ggb.utils.error module.
    • Tidying up code for documentation.
    opened by reshalfahsi 0
Releases(v1.1.4)
Owner
Resha Dwika Hefni Al-Fahsi
AI Engineer at Techbros Group
Resha Dwika Hefni Al-Fahsi
GitHub Repository https://ggb.readthedocs.io
A collection of differentiable SVD methods and also the official implementation of the ICCV21 paper "Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?"

Differentiable SVD Introduction This repository contains: The official Pytorch implementation of ICCV21 paper Why Approximate Matrix Square Root Outpe

YueSong 32 Dec 25, 2022
sense-py-AnishaBaishya created by GitHub Classroom

Compute Statistics Here we compute statistics for a bunch of numbers. This project uses the unittest framework to test functionality. Pass the tests T

1 Oct 21, 2021
A MatConvNet-based implementation of the Fully-Convolutional Networks for image segmentation

MatConvNet implementation of the FCN models for semantic segmentation This package contains an implementation of the FCN models (training and evaluati

VLFeat.org 175 Feb 18, 2022
Hierarchical Uniform Manifold Approximation and Projection

HUMAP Hierarchical Manifold Approximation and Projection (HUMAP) is a technique based on UMAP for hierarchical non-linear dimensionality reduction. HU

Wilson Estécio Marcílio Júnior 160 Jan 06, 2023
Align and Prompt: Video-and-Language Pre-training with Entity Prompts

ALPRO Align and Prompt: Video-and-Language Pre-training with Entity Prompts [Paper] Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, Steven C.H

Salesforce 127 Dec 21, 2022
MEDS: Enhancing Memory Error Detection for Large-Scale Applications

MEDS: Enhancing Memory Error Detection for Large-Scale Applications Prerequisites cmake and clang Build MEDS supporting compiler $ make Build Using Do

Secomp Lab at Purdue University 34 Dec 14, 2022
Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in Pytorch

Retrieval-Augmented Denoising Diffusion Probabilistic Models (wip) Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in P

Phil Wang 55 Jan 01, 2023
Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.

Face Detect MQTT Face or Pose detector that emits MQTT events when a face or human body is detected and not detected. I built this as an alternative t

Jacob Morris 38 Oct 21, 2022
Implementation of Pix2Seq in PyTorch

pix2seq-pytorch Implementation of Pix2Seq paper Different from the paper image input size 1280 bin size 1280 LambdaLR scheduler used instead of Linear

Tony Shin 9 Dec 15, 2022
Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

IIC2233 - Programación Avanzada Evaluación Las evaluaciones serán efectuadas por medio de actividades prácticas en clases y tareas. Se calculará la no

IIC2233 @ UC 0 Dec 15, 2022
CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels Accurate pressure drop estimat

Alejandro Montanez 0 Jan 21, 2022
PyTorch implementation of PSPNet

PSPNet with PyTorch Unofficial implementation of "Pyramid Scene Parsing Network" (https://arxiv.org/abs/1612.01105). This repository is just for caffe

Kazuto Nakashima 52 Nov 16, 2022
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers Authors: Jaemin Cho, Abhay Zala, and Mohit Bansal (

Jaemin Cho 98 Dec 15, 2022
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation By Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang SenseTime, Tsinghua Unive

33 Oct 14, 2022
A Joint Video and Image Encoder for End-to-End Retrieval

Frozen️ in Time ❄️ ️️️️ ⏳ A Joint Video and Image Encoder for End-to-End Retrieval project page | arXiv | webvid-data Repository containing the code,

225 Dec 25, 2022
PyTorch implementation of Convolutional Neural Fabrics http://arxiv.org/abs/1606.02492

PyTorch implementation of Convolutional Neural Fabrics arxiv:1606.02492 There are some minor differences: The raw image is first convolved, to obtain

Anuvabh Dutt 25 Dec 22, 2021
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Adversarial Machine Learning 9 Nov 27, 2022
《Towards High Fidelity Face Relighting with Realistic Shadows》(CVPR 2021)

Towards High Fidelity Face-Relighting with Realistic Shadows Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu. In CVPR, 2021. T

114 Dec 10, 2022
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth, in ICCV 2021 (oral)

RINDNet RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth Mengyang Pu, Yaping Huang, Qingji Guan and Haibin Lin

Mengyang Pu 75 Dec 15, 2022
On Evaluation Metrics for Graph Generative Models

On Evaluation Metrics for Graph Generative Models Authors: Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham Taylor This is the offic

13 Jan 07, 2023