Search Results for "scale image" - Page 5
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Showing 178 open source projects for "scale image"
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Free Weighbridge Software
Imagic Solution Easy to Use Windows Based Weighbridge Software
Imagic Solution Provide Easy to Use Windows Based Weighbridge Software, its Support Win10/Win11/Win7, 32 and 64 Bit both OS. Facility to Create Multi User with Various Authorisation, it will Provide Secure Weighment with Various Facility Like CCTV Image Capture, RFID Reader Integration, Boom Barrier Integration, Traffic Light, PA Sound System, LED display etc... This Software Provide a secure and Fraud-Free-Operation of All Kind Of Weighbridge, its Design to Suitable for all Kind of Industries. It will use Configurable various Database as per requirement. Software Suitable for In Bellow Sector: Industries Weighbridge [LAN Based Software for Multi Weighbridge], Public Weighbridge, Cloud Based Online Weighbridge Reporting system, Label Printing Scale Software, Batching Scale Software, Barcode + QR Code printing, CCTV Weighbridge Software, Support Multi Language Selection Option, -
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Snoise
Command line tool for scaling and denoising images
Snoise is a command line tool for scaling and denoising images. Usage: snoise -i <input image> -o <output image> -s <scale amount> -d <denoise amount> -t Usage example: snoise -i in.jpg -o out.png -s 1.5 -d 10 Type "snoise -h" to display the help message. -
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hora
Efficient approximate nearest neighbor search algorithm collections
hora is an open-source high-performance vector similarity search library designed for large-scale machine learning and information retrieval systems. The project focuses on approximate nearest neighbor search, a fundamental technique used in modern AI applications such as recommendation systems, image search, and semantic search engines. Hora implements multiple efficient indexing algorithms that allow systems to rapidly search through high-dimensional vectors produced by machine learning models. ... -
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MagicalPdfEditor
This is a small PDF editor based on OpenPdf core and AndroidPdfViewer
This is a small PDF editor based on OpenPDF Core and AndroidPdfViewer. As there is not many open-source easy working PDF editors and PDF wizards, I decided to create a simple directory to resolve my issues. Here I have worked on two separate cores, add some functionality to them, and combined them together to achieve my target. I am working on this repo, any help will be appreciated. Just clone the project and trance the source code, It's really easy and clear. All functionality in... -
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aPhoto
aPhoto is a Batch Intuitive Program Makes Text and Image Watermarks
Introduction: aPhoto is a batch, intuitive and portable program that helps you to make text(eg. Copyright Notice) and image(eg. Logo) watermarks for the photos, and save the result photos to JPG, PNG, GIF, or BMP file formats. aPhoto is a lightweight application whose purpose is to help users embed text or image watermarks on their favorite pictures. This is a portable program, so it is important to mention that it doesn't leave any traces in the Windows Registry. You can copy it on... -
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textureAtlasTools
A set of tools for slicing a texture atlas to individual components
...Manually select some tiles in the texture atlas (that are placed in difficult positions) Add them to a json file following this pattern: json file; you can use any image editing tool such as GIMP to manually select and inspect the location, width and height of each selection. Run the tool with the split; this will automatically create a folder with separate files from the selections, as well a [basefilename]_sliced.png file containing the texture without the sliced files. Manually upscale all the resulting files with the correct transparency / seamless mode, including the [basefilename]_sliced.png from the previous step. it is important to use the same scale factor for all the files. you can leave some of the files unscaled (including the basefile). -
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Slazzer-Auto Image Background Remover
Slazzer-Android can help to remove image background automatically
...Now you can save an incredible amount of time as our AI is capable of handling hair, fur or any complex edges in just a few seconds. Install our desktop application to drag and drop 1000s of images at once. Click "Start" and watch as each image gets cut out automatically. Boost your efficiency and replace background from multiple images to get thousands of AI-powered cut outs for all your design needs. Looking to process millions of images? We've made it super easy to integrate our API into your project with just a few lines of code so you can remove bg at scale! We've also built plugins directly in the best design tools, programs, apps, and eCommerce platforms to improve your efficiency and workflow. -
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Opyrator
Turns your machine learning code into microservices with web API
Instantly turn your Python functions into production-ready microservices. Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images. Opyrator builds on open standards - OpenAPI, JSON Schema, and Python type hints - and is powered by FastAPI, Streamlit, and Pydantic. It cuts out all the pain for productizing and sharing your Python code - or anything you can wrap into a single Python... -
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GD Process
A CLI image file manipulation script
This script was inspired by the 'convert' utility that is provided with ImageMagic. It is designed to be a CLI script that provides some basic image file manipulations. ./gd_process.php can perform these image file manipulations :- none - so it can be used to convert image file types create scale crop mirror rotate merge adjoin PHP can only work with image file types (by extension) :- jpg, jpeg gif png bmp ./gd_process.php can work on a single filename, or a 'glob' of files within a dir. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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DensePose
A real-time approach for mapping all human pixels of 2D RGB images
DensePose is a computer vision system that maps all human pixels in an RGB image to the 3D surface of a human body model. It extends human pose estimation from predicting joint keypoints to providing dense correspondences between 2D images and a canonical 3D mesh (such as the SMPL model). This enables detailed understanding of human shape, motion, and surface appearance directly from images or videos. The repository includes the DensePose network architecture, training code, pretrained... -
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Image GPT
Large-scale autoregressive pixel model for image generation by OpenAI
Image-GPT is the official research code and models from OpenAI’s paper Generative Pretraining from Pixels. The project adapts GPT-2 to the image domain, showing that the same transformer architecture can model sequences of pixels without altering its fundamental structure. It provides scripts to download pretrained checkpoints of different model sizes (small, medium, large) trained on large-scale datasets and includes utilities for handling color quantization with a 9-bit palette. ... -
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MaLiang
iOS painting and drawing library based on Metal
MaLiang is a painting framework based on Metal. It supports drawing and handwriting with customized textures. The name of "MaLiang" comes from a boy who had a magical brush in a Chinese ancient fairy story. A Canvas is the basic component of MaLiang. You will paint all things on it. Canvas extends from MetalView, which extends from MTKView. MetalView handles all the logic with MetalKit and hides them from you. MaLiang supports the automatic adjustment of stroke size with painting force. 3D... -
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medical image analysis
Libraries and command line tools for medical image processing.
This software provides libraries and command line tools for the processing and analysis of gray scale medical images. -
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OpenAI Glow
Copy code in "Glow: Generative Flow with Invertible 1x1 Convolutions"
Glow is an open source generative model released by OpenAI that demonstrates flow-based generative modeling techniques. Unlike models that rely on approximate inference, Glow uses invertible transformations to directly learn the data distribution, allowing for exact likelihood computation and efficient sampling. The model is capable of producing high-quality synthetic images while maintaining interpretable latent spaces that enable meaningful manipulation of generated outputs. Glow’s... -
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Sparse Attention
"Generating Long Sequences with Sparse Transformers" examples
...It highlights both fixed and learnable sparsity patterns that trade off computational cost and model expressiveness. By enabling tractable training on longer contexts, the project opened the door to applications in large-scale text and image generation. Though archived, it remains a key reference for efficient transformer research, influencing many later architectures that aim to extend sequence length while reducing compute. -
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Super-résolution via CNN
Super resolution using a CNN, based on the work of the DGtal team
...This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. Low res images should be located in a "dataset/input" folder, and high res targets in a "dataset/target" folder, where each different quality image has the same name in both folders. -
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VGGFace2
VGGFace2 Dataset for Face Recognition
VGGFace2 is a large-scale face recognition dataset developed to support research on facial recognition across variations in pose, age, illumination, and identity. It consists of 3.31 million images covering 9,131 subjects, with an average of over 360 images per subject. The dataset was collected from Google Image Search, ensuring a wide diversity in ethnicity, profession, and real-world conditions. -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few... -
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imgaug
Image augmentation for machine learning experiments
...Affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions, hue/saturation changes, cropping/padding, blurring, etc. Rotate image and segmentation map on it by the same value sampled. Convert keypoints to distance maps, extract pixels within bounding boxes from images, clip polygon to the image plane, etc. Scale segmentation maps, average/max pool of images/maps, pad images to aspect ratios (e.g. to square them). Draw heatmaps, segmentation maps, keypoints, bounding boxes, etc. -
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ResNeXt
Implementation of a classification framework
...The design is modular and homogeneous, making it relatively easy to scale (by tuning cardinality, width, depth) and adopt in existing residual frameworks. The official repository offers a Torch (Lua) implementation with code for training, evaluation, and pretrained models on ImageNet. In practice, ResNeXt models often outperform standard ResNet models of comparable complexity. -
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RefineNet
RefineNet: Multi-Path Refinement Networks
...It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. While this codebase is specific to MATLAB and MatConvNet, a PyTorch implementation and lighter-weight variants are also available from the community. -
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PyTorch pretrained BigGAN
PyTorch implementation of BigGAN with pretrained weights
An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind. This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis. This PyTorch implementation of BigGAN is provided with the pretrained 128x128, 256x256 and 512x512 models by DeepMind. We also provide the scripts used to download and convert these models from the TensorFlow Hub models. This reimplementation was done from the raw computation graph of the Tensorflow version and behave similarly to the TensorFlow version (variance of the output difference of the order of 1e-5). ... -
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Go Graphics
Go Graphics - 2D rendering in Go with a simple API
...It is often desired to rotate or scale about a point that is not the origin. The functions RotateAbout, ScaleAbout, ShearAbout are provided as a convenience. -
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DeepMask
Torch implementation of DeepMask and SharpMask
DeepMask is an early, influential approach to class-agnostic object segmentation that learns to propose pixel-accurate masks directly from images. Instead of first generating boxes and then refining them, the network predicts a foreground mask and an “objectness” score for a given image patch, yielding high-quality segment proposals suitable for downstream detection or instance segmentation. The model is trained end-to-end to align mask shape with object extent, which markedly improves... -
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EvalAI
Evaluating state of the art in AI
EvalAI is an open-source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale. We allow the creation of an arbitrary number of evaluation phases and dataset splits, compatibility using any programming language, and organizing results in both public and private leaderboards. Certain large-scale challenges need special computing capabilities for evaluation. If the challenge needs extra computational power, challenge organizers can easily add their own cluster of worker nodes to process participant submissions while we take care of hosting the challenge, handling user submissions, and maintaining the leaderboard. ...
