Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of images as "pixels"

Related tags

Deep Learning picinpics
Overview

picinpics

Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of images as "pixels"

Requirements

  • Python 3.8
  • Poetry
  • Pyenv [OPTIONAL]

Install

poetry install

Usage

poetry run python main.py

Execution log:

2021-10-24 15:47:55.390 | DEBUG    | __main__:<module>:107 - to grid image read
2021-10-24 15:47:56.226 | INFO     | __main__:<module>:111 - reading files
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 1081.52it/s]
2021-10-24 15:47:56.318 | INFO     | __main__:<module>:113 - 20 retrieved files
2021-10-24 15:47:56.319 | INFO     | __main__:<module>:115 - images normalizing
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 458.17it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 597.57it/s]
2021-10-24 15:47:56.399 | INFO     | __main__:<module>:119 - quantizing color
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 857.52it/s]
2021-10-24 15:47:56.425 | INFO     | __main__:<module>:122 - building grid
2021-10-24 15:47:56.431 | INFO     | __main__:build_grid:82 - quantizing to grid image
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8160/8160 [00:06<00:00, 1257.16it/s]
2021-10-24 15:48:02.929 | INFO     | __main__:build_grid:85 - finding closests images
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8160/8160 [00:16<00:00, 500.46it/s]
2021-10-24 15:48:19.237 | INFO     | __main__:build_grid:92 - assemblying grid
2021-10-24 15:48:19.472 | INFO     | __main__:<module>:126 - done

Result

To increase the output quality change SUB_IMAGE_SIZE in code (W at image below), but this will dramatically increase the processing time.

Original Grid Image

Algorithm

Improves

There is two major bottlenecks regarding processing:

  1. Quantization

    Receives an image as input and returns a single pixel 3D Tuple of UINT8

  2. Match

    Find the closest Quantized Pixel from Pixel Images to a given Quantized Pixel from Original Image

You might also like...
Code for our CVPR 2021 Paper
Code for our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".

Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes (CVPR 2021) Project page | Paper | Colab | Colab for Drawing App Rethinking Style

CompVis Heidelberg 153 Jan 4, 2023
Bayesian Image Reconstruction using Deep Generative Models
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

Table of Content Introduction Getting Started Datasets Installation Experiments Training & Testing Pretrained models Texture fine-tuning Demo Toward R

VinAI Research 42 Dec 5, 2022
Official implementation of "SinIR: Efficient General Image Manipulation with Single Image Reconstruction" (ICML 2021)

SinIR (Official Implementation) Requirements To install requirements: pip install -r requirements.txt We used Python 3.7.4 and f-strings which are in

null 47 Oct 11, 2022
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Soubhik Sanyal 71 Dec 30, 2022
AI virtual gym is an AI program which can be used to exercise and can be used to see if we are doing the exercises

AI virtual gym is an AI program which can be used to exercise and can be used to see if we are doing the exercises

null 4 Feb 13, 2022
U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.
U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

Dennis Bappert 104 Nov 25, 2022
Using this codebase as a tool for my own research. Making some modifications to the original repo for my own purposes.

For SwapNet Create a list.txt file containing all the images to process. This can be done with the GNU find command: find path/to/input/folder -name '

Andrew Jong 2 Nov 10, 2021
OCR Streamlit App is used to extract text from images using python's easyocr, pytorch and streamlit packages
OCR Streamlit App is used to extract text from images using python's easyocr, pytorch and streamlit packages

OCR-Streamlit-App OCR Streamlit App is used to extract text from images using python's easyocr, pytorch and streamlit packages OCR app gets an image a

Siva Prakash 5 Apr 5, 2022
Owner
RodrigoCMoraes
Computing and electronics lover. Enjoys understand computational thinking and how to use it to solve society problems.
RodrigoCMoraes
GitHub
An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

SVM Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétrait

Achraf Rahouti 3 Nov 30, 2021
"MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction" (CVPRW 2022) & (Winner of NTIRE 2022 Challenge on Spectral Reconstruction from RGB)

MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022) Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Z

Yuanhao Cai 274 Jan 5, 2023
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial offici

Sicheng Xu 833 Dec 28, 2022
Automatically download the cwru data set, and then divide it into training data set and test data set

Automatically download the cwru data set, and then divide it into training data set and test data set.自动下载cwru数据集，然后分训练数据集和测试数据集

null 6 Jun 27, 2022
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022)

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR2022)[paper] Authors: Chenhang He, Ruihuang Li, Shuai Li, L

Billy HE 141 Dec 30, 2022
Activating More Pixels in Image Super-Resolution Transformer

HAT [Paper Link] Activating More Pixels in Image Super-Resolution Transformer Xiangyu Chen, Xintao Wang, Jiantao Zhou and Chao Dong BibTeX @article{ch

XyChen 270 Dec 27, 2022
Where2Act: From Pixels to Actions for Articulated 3D Objects

Where2Act: From Pixels to Actions for Articulated 3D Objects The Proposed Where2Act Task. Given as input an articulated 3D object, we learn to propose

Kaichun Mo 69 Nov 28, 2022
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

Computer Vision and Geometry Lab 610 Jan 5, 2023
PixelPick This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick."

PixelPick This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick." [Project page] [Paper

Gyungin Shin 59 Sep 25, 2022
MNIST, but with Bezier curves instead of pixels

bezier-mnist This is a work-in-progress vector version of the MNIST dataset. Samples Here are some samples from the training set. Note that, while the

Alex Nichol 15 Jan 16, 2022