🎇 Quickly search over billions of images
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Updated
Oct 25, 2019 - 401 commits
- Python
🎇 Quickly search over billions of images
An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)
Ruby wrapper around pHash, the perceptual hash library for detecting duplicate multimedia files
OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
Extracts data points from images of graphs
pytorch structural similarity (SSIM) loss
SimpleITK: a simplified layer build on top of the Insight Toolkit (ITK), intended to facilitate its use in rapid prototyping, education and interpreted languages.
A working prototype for capturing frames off of a live MJPEG video stream, identifying objects in near real-time using deep learning, and triggering actions based on an objects watch list.
PHP Exif Library - library for reading and writing Exif headers in JPEG and TIFF files using PHP.
Interactive Jupyter widgets to visualize images, point sets, and meshes in 2D and 3D
Detect source resolution of upscaled images
Image processing and manipulation in JavaScript
Imago is a python tool that extract digital evidences from images.
A Pi Zero and Motion based webcamera that forwards images to Amazon Web Services for Image Processing
A K-means algorithm for detecting image colours
Image segmentation - general superpixel segmentation & center detection & region growing
Automated anatomical brain label/shape analysis software (+ website)
GammaCV is a WebGL accelerated Computer Vision library for browser
Vision Framework IOS WWDC 2017
A C++ Qt GUI desktop program to calculate Harris, FAST, SIFT and SURF image features with OpenCV
Interactive Image similarity and Visual Search and Retrieval application
Let's update the documentation to capture that the library can be installed as a module. We should include an explanation of the pip install -e . or python setup.py develop syntax to capture that many developers may want to install in place so they can continue to update and develop the library code.
PixLab Resources & Sample Set
Describe the bug
Specifications of the machine learning models that the different applications (e.g. survival) are expecting are not provided in the documentation.
Expected behavior
A clear and concise description of the model specification should be provided, e.g. files types, file formats, number of files.
CaPTk Version
1.6.1
Image Segmentation using Texture and Color features in C++
Collection of Blind Image Quality Metrics in Matlab
Description
Explicitly test the use of
itk::VectorImage withitk::ExpandImageFilterinitkExpandImageFilterTest`.Expected behavior
The testing coverage of this use case is explicitly tested.
Actual behavior
It is only implicitly tested by its used in
itk::FEMRegistrationFilter.Additional Information
This issue was identified in #1054