The invention relates to the field of computer technology, in particular, to the field of formation of training data for neural networks at retail, including automated, sales points, as well as to the field of using artificial neural networks in computer vision, and more specifically to systems and methods of image processing received from image registration devices and can be used, at least, to determine the list of purchased goods through image processing using artificial neural networks. Currently, in supermarkets, on average, there can be more than fifteen thousand unique stock keeping unit SKU items that have unique designs. Every month, about ten percent of the products can change their design, so that the entire range can be completely renewed during the year, and some products change design several times a year. Retail chains are interested in keeping their shelves full, which is difficult to control. One solution is unmanned or guided aerial vehicles drones, multicopters, such as quadcopters, etc. Such product images and, if applicable, product name images can be used to create a dataset. The use of drones has a number of disadvantages, such as the mobility of drones, and, consequently, deterioration of the quality of the images recorded by the cameras installed on them due to the blurring of the "blurring" of images, especially when registering product names. To use stationary cameras, it is necessary to install a large number of cameras for a large number of showcases counters, etc. Also, with the removal of such cameras from the registered names of goods, image distortions occur, including at the boundaries of images, cameras with a higher resolution of the camera matrix are required, etc.
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OpenNebula is an open and extensible cloud platform allowing an easy automation of data centers operations. Simplicity of deployment and configuration of virtual machines without the help of specialists is the main advantage of the OpenNebula platform. Also, the main advantages of this cloud platform include the ability to dynamically change the size of the physical infrastructure by adding and removing nodes in real time and splitting the cluster into virtual partitions, which allows us to allocate only the necessary amount of resources for the operation of a certain service. OpenNebula provides a centralized interface for managing all elements of the infrastructure, both virtual and physical, and also has a high degree of utilization of available resources. Initially, OpenNebula does not support the OpenVZ virtualization technology, however its modular architecture allows using third-party drivers, in particular for OpenVZ. The main advantage of OpenVZ virtualization technology is the ability to run multiple isolated copies of the operating system on a single physical server. The authors of this article had to refactor the code of the existing OpenVZ driver for the OpenNebula cloud platform. The work was done using the Ruby software environment.
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