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A powerful, customisable, <img> component that simulates a shimmer effect while loading. (with zero dependencies!) Currently compatible with React, but RN compatibility is also on the way. Feel free to send PRs.
A React Native component for drawing by touching on both iOS and Android. To use an image as background, localSourceImage(see below) reqires an object, which consists of filename, directory(optional) and mode(optional). Note: Because native module cannot read the file in JS bundle, file path cannot be relative to JS side. For example, '../assets/image/image.png' will fail to load image.
There’s beauty in the breakdown of bitmap image data. A command line tool to measure the efficiency of your responsive image markup across viewport sizes and device pixel ratios. Works out-of-the-box with img (of course), img[srcset], img[srcset][sizes], picture, picture [srcset], picture [srcset][sizes]. Ignores .svg files. No support for background images (yet?).
This library started as a basic bridge of the native iOS image picker, and I want to keep it that way. As such, functionality beyond what the native UIImagePickerController supports will not be supported here. Multiple image selection, more control over the crop tool, and landscape support are things missing from the native iOS functionality - not issues with my library. If you need these things, react-native-image-crop-picker might be a better choice for you. To use this library you need to ensure you match up with the correct version of React Native you are using.
v-img is a plugin for Vue.js that allows you to show images in full-screen gallery by adding only one directive to the <img> tag. *in this snippet all settings has its default value. No need to specify them unless you want to change default behavior. Unfortunately if you used CDN way to include plugin you can't set up these options, but still can set them up inline.
This library started as a basic bridge of the native iOS image picker, and I want to keep it that way. As such, functionality beyond what the native UIImagePickerController supports will not be supported here. Multiple image selection, more control over the crop tool, and landscape support are things missing from the native iOS functionality - not issues with my library. If you need these things, react-native-image-crop-picker might be a better choice for you. IMPORTANT NOTE: You'll still need to perform step 4 for iOS and steps 2 and 5 for Android of the manual instructions below.
This is a photo management application based on web technologies. Run it on your home server and it will let you find what you want from your photo collection using any device. Smart filtering is made possible automatically by object recognition, location awareness, color analysis and other algorithms. This project is currently in development and not feature complete for a version 1.0 yet. If you don't mind putting up with broken parts or want to help out, run the Docker image and give it a go. I'd love for other contributors to get involved.
A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.
This is an implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" (CVPR 2016 Oral Paper) in caffe. VDSR (Very Deep network for Super-Resolution) is an end-to-end network with 20 convolutional layers for single image super-resolution. The performance of VDSR is better than other state-of-the-art SISR methods, such as SRCNN, A+ and CSCN (My implementation of CSCN).
Implementation of Image Super Resolution CNN in Keras from the paper Image Super-Resolution Using Deep Convolutional Networks. Also contains models that outperforms the above mentioned model, termed Expanded Super Resolution, Denoiseing Auto Encoder SRCNN which outperforms both of the above models and Deep Denoise SR, which with certain limitations, outperforms all of the above.
Extend HTML image tags with srcset and sizes attributes to leverage native responsive images. The responsive_images_extender task will scan your source files for HTML <img> tags and extend them with srcset and optional sizes attributes to leverage native responsive images as described in Yoav Weiss' article.