Search Results for "computer vision"
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Showing 4 open source projects for "computer vision"
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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OculiX
Visual Automation IDE — automate anything you see on screen
OculiX is the evolution of SikuliX, actively maintained with the full agreement of its original creator RaiMan. Automate any desktop application using image recognition (OpenCV) and OCR (Tesseract + PaddleOCR). No access to source code or DOM required — if you can see it, you can automate it. Key features: - Guided step-by-step recorder with live code preview - Image recognition via OpenCV 4.10 - Dual OCR: Tesseract (built-in) + PaddleOCR (neural, high precision) - Local and remote... -
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Eye
Process monitoring tool. Inspired from Bluepill and God
...Requires Ruby(MRI) >= 1.9.3-p194. Uses Celluloid and Celluloid::IO. Eye is an image processing and analysis library for quickly analyzing image patterns and features, often used in computer vision tasks. -
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HornetsEye Ruby Computer Vision Library
Ruby computer vision library
Video processing and computer vision library for GNU/Linux offering interfaces to do image- and video-I/O with ImageMagick/Magick++, Xine, firewire digital camera (DC1394), and video for linux (V4L2). Note that this version of HornetsEye is deprecated. HornetsEye now is released as multiple packages on RubyGems.org. The source code is available on Github.com (see https://wedesoft.github.io/hornetseye-doc/ for more information). -
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A tool for segmenting objects of interest in images, namely creating masks, and storing them for a set of images. It may provide some automatic/interactive segmentation for certain classes of objects. For computer vision / machine learning applications.
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