System and program

Abstract

An object of the present invention is to provide a system and a program capable of easily determining whether or not an image is suitable for use in a moving image.SOLUTION: The data acquisition means acquires a plurality of material data related to images used for a moving image. The plurality of material data include one or more non-color texture data which are texture data other than color texture data representing a color of an object of the moving image. The data output module is configured to output, to the determination device, one or more pieces of unselected material data that are texture data excluding the one or more pieces of non-color texture data among the plurality of pieces of material data. The determination device is configured to determine a possibility that the one or more pieces of unselected material data are usable in the video.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a system and a program. [Background technology]

[0002] As an invention related to the conventional system, for example, the determination system described in Patent Document 1 is known. This determination system determines the authenticity of a product based on whether or not there is a correct correspondence between the product itself and a surface-finished part that is attached to the product and whose authenticity has been confirmed. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2016 / 035774 Summary of the Invention [Problem to be solved by the invention]

[0004] In the field of moving images, there is a demand for determining whether an image is appropriate for use in a moving image.

[0005] Therefore, an object of the present invention is to provide a system and a program that can easily determine whether an image is appropriate for use in a video. [Means for solving the problem]

[0006] The first aspect is The system comprises one or more computers, the one or more computer control devices include a data acquisition means and a data output means; the data acquisition means acquires a plurality of pieces of material data relating to images to be used in the video; the plurality of material data include one or more non-color texture data that are texture data other than color texture data that represent colors of the object of the video; the data output means can output, to a determination device, one or more non-selected material data, which is texture data excluding the one or more non-color texture data, from the plurality of material data; the determination device is capable of determining the possibility that the one or more non-selected material data items can be used in the video; It is a system.

[0007] The second aspect is the one or more non-chromatic texture data include bump map data, normal map data, specular map data, or displacement map data; 1 is a system according to the first aspect.

[0008] The third aspect is The one or more computers include the determination device. A system according to the first or second aspect.

[0009] The fourth aspect is the one or more computer controlled devices further include a processing means; the processing means performs image processing to enlarge at least a part of the one or more non-selected material data. The system according to any one of the first to third aspects.

[0010] The fifth aspect is the determination device is equipped with a machine learning model that is a trained model that has been trained using a plurality of determination data related to marks as training data; The machine learning model can determine the possibility that the one or more unselected material data can be used in the video. A system according to any one of the first to fourth aspects.

[0011] The sixth aspect is The machine learning model can calculate the relevance between the one or more non-selected material data and the plurality of judgment data as a possibility of being used in the video. A system according to a fifth aspect.

[0012] The seventh aspect is The plurality of determination data are data relating to marks that cannot be used in the video. A system according to the fifth or sixth aspect.

[0013] The eighth aspect is The program causes the control device of the computer to execute a data acquisition process and a data output process; In the data acquisition process, a plurality of pieces of material data relating to images to be used in the video are acquired, the plurality of material data include one or more non-color texture data that are texture data other than color texture data that represent colors of the object of the video; In the data output process, one or more non-selected material data, which is data excluding the one or more non-color texture data from the plurality of material data, can be output to a determination device, the determination device is capable of determining the possibility that the one or more non-selected material data items can be used in the video; It is a program. [Effects of the Invention]

[0014] According to the present disclosure, it is possible to easily determine whether an image is appropriate for use in a video. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is a block diagram of the system 1. [Figure 2] FIG. 2 is a block diagram of computer 10. [Figure 3] FIG. 3 is a block diagram of the determination device 110. As shown in FIG. [Figure 4] FIG. 4 is an explanatory diagram of the operations performed by the computer 10 and the determination device 110. As shown in FIG. [Figure 5] FIG. 5 is an explanatory diagram of the operations performed by the computer 10 and the determination device 110. DETAILED DESCRIPTION OF THE INVENTION

[0016] (Embodiment) A system 1 according to an embodiment of the present disclosure will be described with reference to the drawings.

[0017] [System Structure] First, the overall configuration of the system 1 will be described with reference to the drawings. Fig. 1 is a block diagram of the system 1. Fig. 2 is a block diagram of the computer 10. Fig. 3 is a block diagram of the determination device 110.

[0018] 1 includes a computer 10 (one or more computers) and a determination device 110 (one or more computers). The computer 10 and the determination device 110 can communicate with each other via a communication network. The network may be the Internet, an intranet, or the like.

[0019] The computer 10 is an information processing device used by a user. The computer 10 may be, for example, a smartphone, a tablet terminal, or a personal computer. As shown in Fig. 2, the computer 10 includes a control device 12, a storage device 14, a network interface 16, a graphics processing unit 18, a display 20, and an operation unit 26.

[0020] The storage device 14 stores programs and data and is, for example, a combination of a read-only memory (ROM), a random access memory (RAM), and a storage (for example, a flash memory or a hard disk).

[0021] The programs include, for example, the following programs: OS (Operating System) programs - Programs for applications that process information (e.g., web browsers or target apps described below)

[0022] The data includes, for example, the following data: Databases referenced in information processing Data obtained by performing information processing (i.e., the results of performing information processing)

[0023] The control device 12 realizes the functions of the computer 10 by executing a program stored in the storage device 14. The control device 12 is, for example, at least one of the following: ·CPU(Central Processing Unit) ·GPU(Graphic Processing Unit) ·ASIC(Application Specific Integrated Circuit) ·FPGA(Field Programmable Array)

[0024] The control device 12 includes a data acquisition means 120, a data selection means 122, a data output means 124, and a processing means 126 as functional blocks.

[0025] The network interface 16 controls communication between the computer 10 and an external device, which is the determination device 110. The network interface 16 is, for example, a network interface card, an on-board network interface, or a USB network adapter.

[0026] The graphics processing unit 18 displays an image on the display 20 based on the image data generated by the control device 12. The display 20 is a liquid crystal display or an organic EL (Electro Luminescence) display.

[0027] The operation unit 26 generates an operation signal based on a user's operation and outputs the operation signal to the control device 12. The operation unit 26 is, for example, a touch panel, a keyboard, a mouse, or a combination of these.

[0028] The determination device 110 is a server. As shown in FIG. 3, the determination device 110 includes a control device 112, a storage device 114, and a network interface .

[0029] The storage device 114 stores programs and data and is, for example, a combination of a read-only memory (ROM), a random access memory (RAM), and a storage device (for example, a flash memory or a hard disk).

[0030] The control device 112 implements the functions of the determination device 110 by executing a program stored in the storage device 114. The control device 112 is, for example, at least one of the following: ·CPU(Central Processing Unit) ·GPU(Graphic Processing Unit) ·ASIC(Application Specific Integrated Circuit) ·FPGA(Field Programmable Array)

[0031] The control device 112 includes an acquiring means 130, a determining means 132, and a transmitting means 134 as functional blocks.

[0032] The network interface 116 controls communication between the determination device 110 and an external device, which is the computer 10. The network interface 116 is, for example, a network interface card, an on-board network interface, or a USB network adapter.

[0033] [System 1 operation] Next, the operation of the system 1 will be described with reference to the drawings. Figures 4 and 5 are explanatory diagrams of the operations performed by the computer 10 and the determination device 110.

[0034] When the control device 12 of the computer 10 reads out the programs stored in the storage device 14, these programs cause the control device 12 of the computer 10 to execute the operations described below. The programs then cause the control device 12 of the computer 10 to function as data acquisition means 120, data selection means 122, data output means 124, and processing means 126.

[0035] When the control device 112 of the determination device 110 reads out the programs stored in the storage device 114, these programs cause the control device 112 of the determination device 110 to execute the operations described below. The programs then cause the control device 112 of the determination device 110 to function as an acquisition means 130, a determination means 132, and a transmission means 134.

[0036] System 1 determines whether an image is appropriate for use in a game (video). In this specification, data related to an image used in a game (video) is referred to as material data D0. The multiple pieces of material data D0 include texture data. Texture data is data for expressing the detailed appearance of the surface of a 3D object or a 2D object. Texture data is used to reproduce visual characteristics such as the color, pattern, and texture of an object. Texture data includes, for example, color texture data, bump map data, normal map data, specular map data, and displacement map data.

[0037] Color texture data is data that represents the color of an object in a game (video). In other words, color texture data contains RGB values. Bump map data is data used to simulate the unevenness of an object's surface. Bump maps contain height information. Normal map data is data used to realistically recreate the feeling of unevenness by changing the normal vector of the object's surface. Specular map data is data used to represent the reflective properties of an object's surface. Displacement map data is data used to deform the geometry of a model.

[0038] In this specification, texture data other than color texture data is defined as non-color texture data. Non-color texture data includes bump map data, normal map data, specular map data, and displacement map data. Therefore, as shown in Figure 4, the multiple material data D0 includes color texture data D1-1 to D1-n and non-color texture data D2-1 to D2-m.

[0039] First, the user operates the operation unit 26 of the computer 10 to store multiple pieces of material data D0 in the storage device 14. Then, the user operates the operation unit 26 of the computer 10 to start a program. As a result, the control device 12 (data acquisition means 120) acquires the multiple pieces of material data D0 stored in the storage device 14 (step S1: data acquisition process).

[0040] Next, the control device 12 (data selection means 122) selects one or more non-color texture data D2-1 to D2-m from the plurality of material data D0 (step S2: data selection process). Here, the texture data from the plurality of material data D0 excluding one or more non-color texture data D2-1 to D2-m is defined as non-selected material data D3. In this embodiment, the non-selected material data D3 is color texture data D1-1 to D1-n.

[0041] Next, the control device 12 (processing means 126) performs image processing to enlarge at least a portion of one or more pieces of non-selected material data D3 (step S3: processing processing). More specifically, the size of the color texture data D1-3 is smaller than the sizes of the color texture data D1-1 and D1-2. In such a case, the control device 112 of the determination device 110 performs image processing to enlarge the color texture data D1-3 in the determination processing described below. In this embodiment, the control device 12 enlarges the color texture data D1-1 to D1-n that is smaller than a predetermined size to the predetermined size.

[0042] Next, the control device 12 (data output means 124) outputs one or more pieces of unselected material data D3 to the determination device 110 (step S4—data output process). In this embodiment, the control device 12 transmits the color texture data D1-1 to D1-n to the determination device 110 via the network interface 16. In response, the network interface 116 of the determination device 110 receives the color texture data D1-1 to D1-n (one or more pieces of unselected material data D3) and outputs the color texture data D1-1 to D1-n (one or more pieces of unselected material data D3) to the control device 112. As a result, the control device 112 acquires the color texture data D1-1 to D1-n (one or more pieces of unselected material data D3). In other words, the color texture data D1-1 to D1-n (one or more pieces of unselected material data D3) is input to the machine learning model 300.

[0043] Here, the machine learning model 300 will be described. The determination device 110 includes the machine learning model 300, as shown in Fig. 5. Specifically, the storage device 114 stores the machine learning model 300. The control device 112 of the determination device 110 performs a determination, which will be described later, using the machine learning model 300.

[0044] As shown in FIG. 5, the machine learning model 300 is a trained model trained using a plurality of pieces of assessment data D10-1 to D10-l related to marks as training data. l is a natural number. In this embodiment, the marks are logos. The plurality of pieces of assessment data D10-1 to D10-l are data related to marks that are inappropriate for use in games (videos). Such marks include, for example, other people's registered trademarks, other people's copyrighted works, and marks related to ideas that are deemed to have a dangerous impact on the existence of the nation or society.

[0045] The machine learning program is a program for executing a machine learning algorithm to find certain rules from training data and generate a trained machine learning model 300 that expresses the found rules. When the control device 112 of the determination device 110 executes the machine learning program, multiple training data are machine-learned and the parameters of the inference program are adjusted. As a result, the trained machine learning model 300 is generated.

[0046] The machine learning algorithm is not particularly limited as long as it is supervised learning, and may be, for example, a decision tree, a nearest neighbor algorithm, a naive Bayes classifier, a support vector machine, or a neural network. Therefore, the trained machine learning model 300 includes a decision tree, a nearest neighbor algorithm, a naive Bayes classifier, a support vector machine, or a neural network. Backpropagation may be used in the machine learning used to generate the trained machine learning model 300.

[0047] For example, a neural network includes an input layer, one or more hidden layers, and an output layer. Specifically, the neural network is a deep neural network, a recurrent neural network, or a convolutional neural network, and performs deep learning. The deep neural network includes, for example, an input layer, multiple hidden layers, and an output layer.

[0048] The control device 112 of the determination device 110 determines the possibility that the color texture data D1-1 to D1-n (one or more non-selected material data D3) can be used in the game (video) (step S11—determination process). In this embodiment, the machine learning model 300 determines the possibility that the color texture data D1-1 to D1-n (one or more non-selected material data D3) can be used in the game (video). At this time, the machine learning model 300 calculates the association between the color texture data D1-1 to D1-n (one or more non-selected material data D3) and the multiple determination data D10-1 to D10-l as the possibility that the color texture data D1-1 to D1-n can be used in the game (video). In this embodiment, as shown in FIG. 5 , the machine learning model 300 calculates the similarity between the color texture data D1-1 to D1-n and the multiple determination data D10-1 to D10-l. The higher the similarity, the lower the possibility that the color texture data D1-1 to D1-n can be used in the game.

[0049] Next, the control device 112 of the determination device 110 transmits the determination result R to the computer 10 via the network interface 116 (step S12, transmission process). In response, the network interface 16 of the computer 10 receives the determination result R and outputs the determination result R to the control device 12. As a result, the control device 12 of the computer 10 acquires the determination result R (step S5, acquisition process).

[0050] Finally, the control device 12 of the computer 10 displays the determination result R on the display 20 (step S6: display process). This allows the user to determine whether or not the color texture data D1-1 to D1-n can be used in the game while looking at the determination result R displayed on the display 20.

[0051] [effect] The system 1 can easily determine whether an image is appropriate for use in a video. More specifically, the control device 12 acquires multiple pieces of material data D0 related to images to be used in the video. Here, the multiple pieces of material data D0 include one or more pieces of non-color texture data D2-1 to D2-m, which are texture data other than color texture data D1-1 to D1-n that represent the colors of objects in the video. The non-color texture data D2-1 to D2-m do not contain data that represent the colors of logos, etc., and therefore do not require a determination of whether the non-color texture data D2-1 to D2-m can be used in the video. Therefore, the control device 12 outputs one or more pieces of non-selected material data D3, which are texture data excluding the one or more pieces of non-color texture data D2-1 to D2-m from the multiple pieces of material data D0, to the determination device 110. This allows the determination device 110 to acquire data that requires a determination of whether the non-selected material data D3 can be used in the video. The determination device 110 then determines the possibility that the one or more pieces of non-selected material data D3 can be used in the video. As a result, a judgment result R is generated for data that needs to be judged as to whether it can be used in a video, thereby reducing the processing load of the system 1. In other words, the system 1 makes it easy to judge whether an image is appropriate for use in a video. Furthermore, the user determines whether color texture data D1-1 to D1-n can be used in a video while looking at the judgment result R for data that needs to be judged as to whether it can be used in a video. Therefore, the user does not need to look at unnecessary judgment results, and can easily judge whether an image is appropriate for use in a video.

[0052] According to the system 1, it is possible to easily determine whether an image is appropriate for use in a game during the development stage of the game. More specifically, the control device 12 acquires a plurality of pieces of material data D0 related to images to be used in the game. That is, the plurality of pieces of material data D0 is not an executable file for the game generated by compiling a plurality of pieces of data, but data that exists before the executable file for the game is generated. The control device 112 of the determination device 110 then determines the possibility that one or more pieces of non-selected material data D3 from the plurality of pieces of material data D0 can be used in the game. Therefore, according to the system 1, it is possible to easily determine whether an image is appropriate for use in a game before the executable file for the game is generated.

[0053] Furthermore, the system 1 can easily determine whether an image that is not displayed during game play is appropriate for use in the game for the following reasons. When a computer determines whether an image is appropriate for use in the game using a game executable file, the computer must execute the executable file and play the game. In this case, the computer cannot determine images that are not displayed during game play. Images that are not displayed during game play are image data that were determined not to be used during the game development stage and remain in the executable file. Meanwhile, in the system 1, the control device 12 acquires multiple pieces of material data D0 related to images to be used in the game. That is, the multiple pieces of material data D0 are not the game executable file generated by compiling multiple pieces of data, but are data that existed before the game executable file was generated. The control device 112 of the determination device 110 then determines the possibility that one or more pieces of non-selected material data D3 from the multiple pieces of material data D0 can be used in the game. Therefore, the system 1 can easily determine whether an image that is not displayed during game play is appropriate for use in the game.

[0054] (Other embodiments) The system according to the present invention is not limited to System 1, and can be modified within the scope of the gist thereof.

[0055] The control device 12 selects one or more non-color texture data D2-1 to D2-m from the plurality of material data D0. However, the control device 12 may also select color texture data D1-1 to D1-n from the plurality of material data D0. This is because, when the plurality of material data D0 includes only color texture data D1-1 to D1-n and non-color texture data D2-1 to D2-m, selecting color texture data D1-1 to D1-n is synonymous with selecting non-color texture data D2-1 to D2-m.

[0056] The marks indicated by the plurality of determination data D10-1 to D10-l may be something other than logos. The marks indicated by the plurality of determination data D10-1 to D10-l may be, for example, character information or audio information.

[0057] In the processing of step S3, the control device 12 may enlarge or reduce the size of the color texture data D1-1 to D1-n so that the size of the color texture data D1-1 to D1-n becomes a predetermined size.

[0058] The computer 10 may include the determination device 110. That is, the computer 10 may include the machine learning model 300.

[0059] The plurality of material data D0 may include only color texture data, or may include color texture data and data other than color texture data. Furthermore, the material data D0 may include data other than texture data. Data other than texture data may be, for example, icon images related to a UI (User Interface).

[0060] The system 1 may include an additional determination device in addition to the determination device 110. That is, the machine learning model 300 may determine the plurality of unselected material data D3, and a machine learning model different from the machine learning model 300 may further determine the plurality of unselected material data D3.

[0061] The video is not limited to a game, but may also be a metaverse or a video work that does not have game elements. [Explanation of symbols]

[0062] 1: System 10: Computer 12: Control device 14:Storage device 16: Network interface 18: Graphics processing unit 20: Display 26:Operation section 110: Judgment device 112: Control device 114: Storage device 116: Network interface 120: Data acquisition method 122: Data selection means 124: Data output means 126: Processing means 130: Acquisition means 132: Judgment means 134: Transmission means 300: Machine learning models D0: Material data D1-1~D1-n: Color texture data D10-1 to D10-m: Data for judgment D2-1~D2-m: Non-color texture data D3: Unselected material data R: Judgment result

Claims

1. The system comprises one or more computers, the one or more computer control devices include a data acquisition means and a data output means; the data acquisition means acquires a plurality of pieces of material data relating to images to be used in the video; the plurality of material data include one or more non-color texture data that are texture data other than color texture data that represent colors of objects in the video; the data output means can output, to a determination device, one or more non-selected material data, which is texture data excluding the one or more non-color texture data, from the plurality of material data; the determination device is capable of determining a possibility that the one or more non-selected material data items can be used in the video; system.

2. the one or more non-chromatic texture data include bump map data, normal map data, specular map data, or displacement map data; The system of claim 1 .

3. the one or more computers include the determination device; 3. The system according to claim 1 or claim 2.

4. the one or more computer controlled devices further include processing means; the processing means performs image processing to enlarge at least a part of the one or more non-selected material data.

3. The system according to claim 1 or claim 2.

5. the determination device is equipped with a machine learning model that is a trained model that has been trained using a plurality of determination data related to marks as training data; The machine learning model can determine the possibility that the one or more unselected material data can be used in the video.

3. The system according to claim 1 or claim 2.

6. The machine learning model can calculate the relevance between the one or more non-selected material data and the plurality of judgment data as a possibility of being used in the video. The system of claim 5.

7. The plurality of pieces of determination data are data relating to marks that are not suitable for use in the video. The system of claim 5.

8. The program causes the control device of the computer to execute a data acquisition process and a data output process; In the data acquisition process, a plurality of pieces of material data relating to images to be used in the video are acquired, the plurality of material data include one or more non-color texture data that are texture data other than color texture data that represent colors of objects in the video; In the data output process, one or more non-selected material data, which is data of the plurality of material data excluding the one or more non-color texture data, can be output to a determination device, the determination device is capable of determining a possibility that the one or more non-selected material data items can be used in the video; program.

Images