Library for converting from RGB / GrayScale image to base64 and back.

Overview

Library for converting RGB / Grayscale numpy images from to base64 and back.

Installation

pip install -U image_to_base_64

Conversion

RGB to base 64

base64 = rgb2base64(rgb_image, image_format)

where image format is JPEG, PNG

Grayscale to base 64

base64 = grayscale2base64(grayscale_image)

Base64 to RGB image

rgb_image = base64_to_rgb(base64)

Base64 to Grayscale image

grayscale_image = base64_to_grayscale(base64)

Issues

For some reason I cannot convert RGB image to JPEG representation in base 64 and back without losses. => test only for PNG and not JPEG

You might also like...
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose (CVPR 2021)
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose (CVPR 2021)

Back to the Feature with PixLoc We introduce PixLoc, a neural network for end-to-end learning of camera localization from an image and a 3D model via

(CVPR 2021) Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds
(CVPR 2021) Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

BRNet Introduction This is a release of the code of our paper Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds,

meProp: Sparsified Back Propagation for Accelerated Deep Learning
meProp: Sparsified Back Propagation for Accelerated Deep Learning

meProp The codes were used for the paper meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (ICML 2017) [pdf]

meProp: Sparsified Back Propagation for Accelerated Deep Learning (ICML 2017)
meProp: Sparsified Back Propagation for Accelerated Deep Learning (ICML 2017)

meProp The codes were used for the paper meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (ICML 2017) [pdf]

Tweesent-back - Tweesent backend uses fastAPI as the web framework

TweeSent Backend Tweesent backend. This repo uses fastAPI as the web framework.

[CVPR 2022] Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement
[CVPR 2022] Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement

Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement Announcement 🔥 We have not tested the code yet. We will fini

Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

scc4onnx Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel

A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

ManhattanSLAM Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.
Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera. This project prepares training and testing data for various deep learning projects such as 6D object pose estimation projects singleshotpose, as well as object detection and instance segmentation projects.

Comments
  • CHanged package (Sourcery refactored)

    CHanged package (Sourcery refactored)

    Pull Request #2 refactored by Sourcery.

    If you're happy with these changes, merge this Pull Request using the Squash and merge strategy.

    NOTE: As code is pushed to the original Pull Request, Sourcery will re-run and update (force-push) this Pull Request with new refactorings as necessary. If Sourcery finds no refactorings at any point, this Pull Request will be closed automatically.

    See our documentation here.

    Run Sourcery locally

    Reduce the feedback loop during development by using the Sourcery editor plugin:

    Review changes via command line

    To manually merge these changes, make sure you're on the fixed_package branch, then run:

    git fetch origin sourcery/fixed_package
    git merge --ff-only FETCH_HEAD
    git reset HEAD^
    

    Help us improve this pull request!

    opened by sourcery-ai[bot] 1
  • CHanged package (Sourcery refactored)

    CHanged package (Sourcery refactored)

    Pull Request #2 refactored by Sourcery.

    If you're happy with these changes, merge this Pull Request using the Squash and merge strategy.

    NOTE: As code is pushed to the original Pull Request, Sourcery will re-run and update (force-push) this Pull Request with new refactorings as necessary. If Sourcery finds no refactorings at any point, this Pull Request will be closed automatically.

    See our documentation here.

    Run Sourcery locally

    Reduce the feedback loop during development by using the Sourcery editor plugin:

    Review changes via command line

    To manually merge these changes, make sure you're on the fixed_package branch, then run:

    git fetch origin sourcery/fixed_package
    git merge --ff-only FETCH_HEAD
    git reset HEAD^
    

    Help us improve this pull request!

    opened by sourcery-ai[bot] 1
  • About the JPEG encoding, decoding

    About the JPEG encoding, decoding

    Referring to the issue in the README, saving the image as JPEG will inevitably apply some level of compression. So decoding the image to retrieve the exact pixel intensities of the original image may not be possible unless using lossless formats like PNG

    However, I found a little parameter of PIL save subsampling while doing the JPEG compression which tries to maintain the pixel intensity, still incurring some loss but not as big as without this parameter.

    Given codebase without subsampling

    Original implementation: https://github.com/ternaus/base64ToImageConverters/blob/main/image2base64/converters.py Line number: 12 format: 'JPEG', quality=100

    im.save(buffered, format=image_format, quality=quality)
    

    RGB Image: download

    Converted Image: download

    Mean difference of the assertion test: 125.74196689386562

    With Subsampling parameter set to 0

    im.save(buffered, format=image_format, quality=quality, subsampling=0)
    

    RGB Image: download

    Converted Image: download

    Mean difference of the assertion test: 53.83414475819539

    The visual as well as numerical difference in clear. JPEG will definitely lead to losses. But subsampling=0 can preserve more details.

    opened by Nachimak28 0
Owner
Vladimir Iglovikov
Ph.D. in Physics. Kaggle GrandMaster. Co-creator of Albumentations.
Vladimir Iglovikov
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
DSAC* for Visual Camera Re-Localization (RGB or RGB-D)

DSAC* for Visual Camera Re-Localization (RGB or RGB-D) Introduction Installation Data Structure Supported Datasets 7Scenes 12Scenes Cambridge Landmark

Visual Learning Lab 141 Nov 27, 2022
Some simple programs built in Python: webcam with cv2 that detects eyes and face, with grayscale filter

Programas en Python Algunos programas simples creados en Python: ?? Webcam con c

Madirex 1 Feb 15, 2022
This project helps to colorize grayscale images using multiple exemplars.

Multiple Exemplar-based Deep Colorization (Pytorch Implementation) Pretrained Model [Jitendra Chautharia](IIT Jodhpur)1,3, Prerequisites Python 3.6+ N

jitendra chautharia 3 Aug 5, 2022
This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models are Pix2Pix, Pix2PixHD, CycleGAN and PointWise.

RGB2NIR_Experimental This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models

null 3 Oct 29, 2022
Optimizes image files by converting them to webp while also updating all references.

About Optimizes images by (re-)saving them as webp. For every file it replaced it automatically updates all references. Works on single files as well

Watermelon Wolverine 15 Sep 29, 2022
CoReNet is a technique for joint multi-object 3D reconstruction from a single RGB image.

CoReNet CoReNet is a technique for joint multi-object 3D reconstruction from a single RGB image. It produces coherent reconstructions, where all objec

Google Research 78 Dec 6, 2022
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

NonCuboidRoom Paper Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image Cheng Yang*, Jia Zheng*, Xili Dai, Rui Tang, Yi Ma, Xiao

null 66 Nov 12, 2022
Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

PoseNet of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image" Introduction This repo is official Py

Gyeongsik Moon 675 Dec 8, 2022
Single-stage Keypoint-based Category-level Object Pose Estimation from an RGB Image

CenterPose Overview This repository is the official implementation of the paper "Single-stage Keypoint-based Category-level Object Pose Estimation fro

NVIDIA Research Projects 187 Dec 1, 2022