{"id":36276,"date":"2019-10-31T22:10:37","date_gmt":"2019-10-31T19:10:37","guid":{"rendered":"https:\/\/prohoster.info\/blog\/poyavilas-utilita-dlya-udaleniya-dvizhushhihsya-obektov-s-video\/"},"modified":"2019-10-31T22:10:37","modified_gmt":"2019-10-31T19:10:37","slug":"poyavilas-utilita-dlya-udaleniya-dvizhushhihsya-obektov-s-video","status":"publish","type":"post","link":"https:\/\/prohoster.info\/fr\/blog\/news\/poyavilas-utilita-dlya-udaleniya-dvizhushhihsya-obektov-s-video","title":{"rendered":"Une utilitaire pour supprimer des objets en mouvement de la vid\u00e9o est apparue","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Aujourd'hui, pour beaucoup, il n'est plus probl\u00e9matique de supprimer un \u00e9l\u00e9ment g\u00eanant d'une photo. Les comp\u00e9tences de base en Photoshop ou les r\u00e9seaux neuronaux \u00e0 la mode peuvent r\u00e9soudre le probl\u00e8me. Cependant, dans le cas de la vid\u00e9o, la situation se complique, car il faut traiter au moins 24 images par seconde.<\/p>\n

\"Une<\/p>\n

Et maintenant sur Github est d\u00e9sormais<\/a><\/noindex> un utilitaire qui automatise ces actions, permettant de supprimer tout objet en mouvement dans la vid\u00e9o. Il suffit de mettre en surbrillance l'objet ind\u00e9sirable avec le curseur, et le syst\u00e8me fera le reste. L'utilitaire s'appelle simplement video-object-removal. Cependant, il repose sur des technologies avanc\u00e9es.<\/p>\n

Le syst\u00e8me utilise un r\u00e9seau neuronale qui traite la vid\u00e9o image par image, rempla\u00e7ant l'objet ou la personne ind\u00e9sirable par un fond. Le programme peut modifier jusqu'\u00e0 55 images par seconde, reconstruisant le fond sur la base de l'image environnante. Bien qu'\u00e0 premi\u00e8re vue, il soit \u00e9vident que la m\u00e9thode de suppression des objets n'est pas parfaite, les r\u00e9sultats sont impressionnants.<\/p>\n

<\/center><\/p>\n

Dans certaines images, on peut voir qu'\u00e0 la place de la personne \u00ab supprim\u00e9e \u00bb, on laisse une trace fantomatique transparente ou semi-transparente. En effet, le syst\u00e8me analyse uniquement l'arri\u00e8re-plan disponible et n'est pas toujours capable de le reconstruire de mani\u00e8re ad\u00e9quate pour l'instant. Cela d\u00e9pend de la complexit\u00e9 de l'arri\u00e8re-plan : plus il est simple et homog\u00e8ne, meilleur sera le r\u00e9sultat final.<\/p>\n

Pour les tests, le syst\u00e8me d'exploitation utilis\u00e9 \u00e9tait Ubuntu 16.04, Python 3.5, Pytorch 0.4.0, CUDA 8.0, et le traitement se faisait sur la carte graphique NVIDIA GeForce GTX 1080 Ti. Les sources sont ouvertes et peuvent \u00eatre utilis\u00e9es par tous. Il est \u00e0 noter que cette technologie peut \u00e9galement \u00eatre utilis\u00e9e \u00e0 des fins malveillantes. Par exemple, pour \u00ab cacher \u00bb des violations du code de la route ou d'autres crimes capt\u00e9s par des cam\u00e9ras.<\/p>\n

<\/p>\n

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Source : 3dnews.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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