{"id":54815,"date":"2020-01-05T00:00:00","date_gmt":"2020-01-04T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/vypusk-biblioteki-kompyuternogo-zreniya-opencv-4-2"},"modified":"2020-02-18T14:02:52","modified_gmt":"2020-02-18T11:02:52","slug":"vypusk-biblioteki-kompyuternogo-zreniya-opencv-4-2","status":"publish","type":"post","link":"https:\/\/prohoster.info\/es\/blog\/news\/vypusk-biblioteki-kompyuternogo-zreniya-opencv-4-2","title":{"rendered":"Lanzamiento de la biblioteca de visi\u00f3n por computadora OpenCV 4.2","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><noindex><a rel=\"nofollow\" href=\"https:\/\/opencv.org\/opencv-4-2-0\/\">Se realiz\u00f3<\/a><\/noindex> liberaci\u00f3n de la biblioteca de c\u00f3digo abierto <noindex><a rel=\"nofollow\" href=\"http:\/\/opencv.org\/\">OpenCV 4.2<\/a><\/noindex> (Open Source Computer Vision Library), que proporciona herramientas para el procesamiento y an\u00e1lisis de contenido de im\u00e1genes. OpenCV ofrece m\u00e1s de 2500 algoritmos, tanto cl\u00e1sicos como que reflejan los \u00faltimos avances en visi\u00f3n por computadora y sistemas de aprendizaje autom\u00e1tico. El c\u00f3digo de la biblioteca est\u00e1 escrito en C++ y <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/opencv\/opencv\/\">se distribuye<\/a><\/noindex> bajo licencia BSD. Se han preparados enlaces para varios lenguajes de programaci\u00f3n, incluidos Python, MATLAB y Java.<\/p>\n<p>La biblioteca se puede utilizar para el reconocimiento de objetos en fotograf\u00edas y videos (por ejemplo, reconocimiento de rostros y figuras humanas, texto, etc.), seguimiento del movimiento de objetos y c\u00e1maras, clasificaci\u00f3n de acciones en videos, transformaci\u00f3n de im\u00e1genes, extracci\u00f3n de modelos 3D, formaci\u00f3n de un espacio 3D a partir de im\u00e1genes de c\u00e1maras estereosc\u00f3picas, creaci\u00f3n de im\u00e1genes de alta calidad a trav\u00e9s de la combinaci\u00f3n de im\u00e1genes de menor calidad, b\u00fasqueda de objetos en una imagen similares a un conjunto presentado, aplicaci\u00f3n de m\u00e9todos de aprendizaje autom\u00e1tico, colocaci\u00f3n de marcadores, identificaci\u00f3n de elementos comunes en diferentes im\u00e1genes, eliminaci\u00f3n autom\u00e1tica de defectos como el efecto de ojos rojos.<\/p>\n<p>En <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/opencv\/opencv\/wiki\/ChangeLog#version420\">nuevo<\/a><\/noindex> <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/opencv\/opencv\/releases\/tag\/4.2.0\">lanzamiento<\/a><\/noindex>:<\/p>\n<ul>\n<li class=\"l\"> En el m\u00f3dulo DNN (Deep Neural Network) con implementaci\u00f3n de algoritmos de aprendizaje autom\u00e1tico basados en redes neuronales se agreg\u00f3 un backend para usar CUDA y se implement\u00f3 soporte experimental para la API <noindex><a rel=\"nofollow\" href=\"http:\/\/docs.openvinotoolkit.org\/\">nGraph OpenVINO<\/a><\/noindex>;\n<li class=\"l\"> Con el uso de instrucciones SIMD se ha optimizado el rendimiento del c\u00f3digo para la salida estereosc\u00f3pica (StereoBM\/StereoSGBM), redimensionamiento, superposici\u00f3n de m\u00e1scara, rotaci\u00f3n, c\u00e1lculo de componentes de color faltantes y muchas otras operaciones;\n<li class=\"l\"> Se ha a\u00f1adido una implementaci\u00f3n multihilo de la funci\u00f3n <noindex><a rel=\"nofollow\" href=\"https:\/\/docs.opencv.org\/3.4\/d4\/d86\/group__imgproc__filter.html#gaf9bba239dfca11654cb7f50f889fc2ff\">pyrDown<\/a><\/noindex>;\n<li class=\"l\"> Se ha a\u00f1adido la posibilidad de extraer flujos de video de contenedores multimedia (demuxing) mediante el backend videoio basado en FFmpeg;\n<li class=\"l\"> Se ha a\u00f1adido un algoritmo para la reconstrucci\u00f3n r\u00e1pida selectiva en frecuencia de im\u00e1genes da\u00f1adas <noindex><a rel=\"nofollow\" href=\"https:\/\/docs.opencv.org\/4.2.0\/dc\/d2f\/tutorial_xphoto_inpainting.html\">FSR<\/a><\/noindex> (Reconstrucci\u00f3n Selectiva en Frecuencia);\n<li class=\"l\"> Se a\u00f1adi\u00f3 el m\u00e9todo <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/opencv\/opencv_contrib\/pull\/2367\">RIC<\/a><\/noindex> para la interpolaci\u00f3n de \u00e1reas t\u00edpicamente vac\u00edas;\n<li class=\"l\"> Se ha a\u00f1adido un m\u00e9todo de normalizaci\u00f3n de desviaciones <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/short-circuitt\/LOGOS\">LOGOS<\/a><\/noindex>;\n<li class=\"l\"> En el m\u00f3dulo G-API (opencv_gapi), que realiza funciones de motor para un procesamiento eficiente de im\u00e1genes utilizando algoritmos basados en grafos, se ha implementado soporte para algoritmos h\u00edbridos m\u00e1s complejos de visi\u00f3n por computadora y aprendizaje autom\u00e1tico profundo. Se ha asegurado soporte para el backend Intel Inference Engine. Se ha a\u00f1adido soporte para el procesamiento de flujos de video en el modelo de ejecuci\u00f3n;\n<li class=\"l\"> Eliminados <noindex><a rel=\"nofollow\" href=\"https:\/\/blog.talosintelligence.com\/2020\/01\/opencv-buffer-overflow-jan-2020.html\">una vulnerabilidad<\/a><\/noindex>  (<noindex><a rel=\"nofollow\" href=\"https:\/\/www.talosintelligence.com\/vulnerability_reports\/TALOS-2019-0852\">CVE-2019-5063<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"https:\/\/www.talosintelligence.com\/vulnerability_reports\/TALOS-2019-0853\">CVE-2019-5064<\/a><\/noindex>), que potencialmente pueden llevar a la ejecuci\u00f3n de c\u00f3digo del atacante al procesar datos no verificados en formatos XML, YAML y JSON. Si durante el an\u00e1lisis de JSON se encuentra un car\u00e1cter con c\u00f3digo cero, el valor se copia completamente en el b\u00fafer, pero sin una verificaci\u00f3n adecuada de los l\u00edmites de la memoria asignada.\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Fuente: <a \ncontent=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=52134\">opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0421\u043e\u0441\u0442\u043e\u044f\u043b\u0441\u044f \u0440\u0435\u043b\u0438\u0437 \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0439 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 OpenCV 4.2 (Open Source Computer Vision Library), \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044e\u0449\u0435\u0439 \u0441\u0440\u0435\u0434\u0441\u0442\u0432\u0430 \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0438 \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u043c\u043e\u0433\u043e \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439. OpenCV \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0431\u043e\u043b\u0435\u0435 2500 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432, \u043a\u0430\u043a \u043a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u0438\u0445, \u0442\u0430\u043a \u0438 \u043e\u0442\u0440\u0430\u0436\u0430\u044e\u0449\u0438\u0445 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \u0434\u043e\u0441\u0442\u0438\u0436\u0435\u043d\u0438\u044f \u0432 \u043e\u0431\u043b\u0430\u0441\u0442\u0438 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u043d\u043e\u0433\u043e \u0437\u0440\u0435\u043d\u0438\u044f \u0438 \u0441\u0438\u0441\u0442\u0435\u043c \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f. \u041a\u043e\u0434 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 \u0421++ \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 BSD. \u0411\u0438\u043d\u0434\u0438\u043d\u0433\u0438 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u044b \u0434\u043b\u044f \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u044f\u0437\u044b\u043a\u043e\u0432 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-54815","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0421\u043e\u0441\u0442\u043e\u044f\u043b\u0441\u044f \u0440\u0435\u043b\u0438\u0437 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