An NFT image generation method, apparatus and device

Abstract

The present invention discloses a method, apparatus, and device for generating NFT images. The method includes: obtaining a layer material library folder, where the layer material library folder includes a plurality of layer files, and each layer file includes a plurality of NFT layer elements with numbers and / or names; binding the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination, where each NFT layer element appears in only one element combination; setting the binding relationship between each layer file as the highest priority, setting the image generation parameters of each layer file, and automatically generating NFT images according to the random principle. The present invention enables each layer file to be bound into groups according to the preset matching rule, and randomly generates NFT images with the binding relationship as the highest priority during the image generation process, so as to quickly and accurately generate NFT pictures of complex layers, and further avoid the generation of incorrect NFT image content due to chaotic layer attributes.

Description

Technical Field

[0001] The present invention belongs to the technical field of blockchain NFT, and specifically relates to a method, device, and equipment for generating NFT images. Background Art

[0002] Blockchain technology is based on a decentralized peer-to-peer network. By using open-source software to combine cryptographic principles, time-series data, and consensus mechanisms, it ensures the coherence and continuity of each node in a distributed database, enabling information to be instantaneously verified, traceable, difficult to tamper with, and impossible to block, thus creating a set of privacy, efficient, and secure shared value systems. In the Ethereum blockchain system, the ERC721 protocol is proposed for NFT (Non-Fungible Token). This protocol enables each NFT image to have a unique token in the blockchain. When combining image elements to form an NFT image and finally storing it in the blockchain, an NFT image generator is required to generate the NFT image.

[0003] However, most existing NFT image generators randomly generate NFT images by simply overlaying layers. Although they can generate images with basic layer relationships, there are still certain defects. Specifically, when existing NFT image generators generate complex layer relationships and randomly match layers, matching errors may occur, leading to confusion in the belonging relationship and further causing errors in the generated image content. For example, when generating an NFT image of a portrait, the portrait has multiple different attributes, including hair, headdress, ears, eyes, nose, mouth, face, clothing, and earrings. When a certain attribute consists of two or more layers, there is a front-back relationship between the layers, and there is an occlusion relationship between the attributes. For example, the headdress is divided into left and right parts, the hair is divided into three layers: front, middle, and back, and the earrings are divided into left and right parts, but the left part is behind and the right part is in front. When existing NFT image generators perform random generation, they may match the left half of headdress 1 with the right half of headdress 2, or combine the front part of hair 1, the middle part of hair 2, and the back part of hair 3, or combine the left part of earring 1 with the right part of earring 2. However, this kind of matching does not meet the requirements of image generation, resulting in confusion in the attribute relationship. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device, and equipment for generating NFT images to solve at least one technical problem existing in the prior art.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] In the first aspect, the present invention provides a method for generating NFT images, including:

[0007] Obtain a layer material library folder, where the layer material library folder includes multiple layer files, and each layer file includes multiple NFT layer elements with numbers and / or names;

[0008] Bind the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination, where each NFT layer element appears only in one element combination;

[0009] Set the binding relationship between each layer file to the highest priority, set the image generation parameters of each layer file, and automatically generate NFT images according to the random principle.

[0010] In a possible design, binding the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination includes:

[0011] Bind one NFT layer element in a certain layer file to one NFT layer element in at least one layer file according to a preset layer element matching rule to generate at least one first element combination.

[0012] In a possible design, binding the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination includes:

[0013] Bind at least two layer files, then each NFT layer element in each layer file will be matched and bound in the order of the element numbers to generate at least one second element combination.

[0014] In a possible design, setting the image generation parameters of each layer file includes:

[0015] Set the scarcity of the attributes of each layer file, the appearance frequency of the attributes, the stacking order of the layers, and / or the number of NFT images to be generated.

[0016] In a possible design, before binding the relationships of different layer files according to a preset layer element matching rule, the method further includes:

[0017] Edit, invert, and / or delete the NFT layer elements in the layer file.

[0018] In a possible design, before automatically generating NFT images, the method further includes:

[0019] Add a dot texture layer and / or a mask layer to the NFT image to be generated.

[0020] In a second aspect, the present invention provides an NFT image generation device, including:

[0021] A folder acquisition module for acquiring a layer material library folder, where the layer material library folder includes multiple layer files, and each layer file includes multiple NFT layer elements with numbers and / or names;

[0022] A layer element binding module for binding different layer files according to a preset layer element matching rule to generate at least one element combination, where each NFT layer element appears only in one element combination;

[0023] An NFT image generation module for setting the binding relationship between each layer file as the highest priority, setting the image generation parameters of each layer file, and automatically generating NFT images according to the random principle.

[0024] In a possible design, the layer element binding module includes:

[0025] A first layer element binding unit for binding one of the NFT layer elements in a certain layer file with one of the NFT layer elements in at least one layer file according to a preset layer element matching rule to generate at least one first element combination.

[0026] In a possible design, the layer element binding module includes:

[0027] A second layer element binding unit for binding at least two layer files, and then each NFT layer element in each layer file will be matched and bound in the order of the element numbers to generate at least one second element combination.

[0028] In a possible design, when setting the image generation parameters of each layer file, the NFT image generation module specifically is used for:

[0029] Setting the scarcity of the attributes of each layer file, the occurrence frequency of the attributes, the stacking order of the layers, and / or the number of NFT images to be generated.

[0030] In a possible design, the device further includes:

[0031] An element editing module for editing, inverse selecting, and / or deleting the NFT layer elements in the layer file.

[0032] In a possible design, the device further includes:

[0033] An image processing module for adding a dot texture layer and / or a mask layer to the NFT image to be generated.

[0034] In a third aspect, the present invention provides a computer device, including a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the NFT image generation method described in any possible design of the first aspect.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium, on which instructions are stored. When the instructions are run on a computer, the NFT image generation method described in any possible design of the first aspect is executed.

[0036] In a fifth aspect, the present invention provides a computer program product containing instructions. When the instructions are run on a computer, the computer is made to execute the NFT image generation method described in any possible design of the first aspect.

[0037] Beneficial effects:

[0038] In the present invention, the layer material library folder is set as multiple layer files, and each layer file is provided with multiple NFT layer elements with numbers and / or names. Then, different layer files are relationally bound according to a preset layer element matching rule to generate at least one element combination. Finally, the binding relationship between the layer files is set as the highest priority, the image generation parameters of each layer file are set, and NFT images are automatically generated using the random principle. As a result, the layer files can be bound into groups according to the preset matching rules, and NFT images are randomly generated with the binding relationship as the highest priority during the image generation process, so that NFT pictures of complex layers can be generated quickly and accurately, thereby avoiding errors in the content of the generated NFT images caused by chaotic layer attributes. Description of the drawings

[0039] Figure 1 It is a schematic flowchart of the NFT image generation method in this embodiment. Detailed implementation manners

[0040] To make the objectives, technical solutions, and advantages of the embodiments of this specification clearer, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are part of the embodiments of this specification, rather than all of them. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0041] Embodiment

[0042] To solve the technical problem that when the existing NFT image generators generate complex layer relationships and randomly match the layers, matching errors may occur, leading to confusion in the belonging relationship and further causing errors in the generated image content, the embodiments of the present application provide an NFT image generation method. The invention sets the layer material library folder as multiple layer files, and each layer file is provided with multiple NFT layer elements with numbers and / or names. Then, different layer files are bound in relationship according to the preset layer element matching rules to generate at least one element combination. Finally, the binding relationship between each layer file is set as the highest priority, the image generation parameters of each layer file are set, and NFT images are automatically generated according to the random principle, so that each layer file can be bound into groups according to the preset matching rules and randomly generate NFT images with the binding relationship as the highest priority during the image generation process, thereby being able to quickly and accurately generate NFT pictures of complex layers and further avoiding errors in the generated NFT image content due to layer attribute confusion. The following will elaborate on this method through specific embodiments.

[0043] As Figure 1 shown, in the first aspect, the present embodiment provides an NFT image generation method, including but not limited to being implemented by steps S101 to S103:

[0044] Step S101. Obtain a layer material library folder, where the layer material library folder includes multiple layer files, and each layer file includes multiple NFT layer elements with numbers and / or names;

[0045] Here, it should be noted that in step S101, the user can manually create a layer material library folder, correspondingly set several layer files in this folder, and correspondingly set several layer elements in each layer file, and can number and / or name the layer elements, such as No. 001, No. 002, No. 003,..., No. 00N. For example: if the image to be generated is a human image or an animal image, the layer material library folder can be named as human image materials. Then, in the human image material library folder, there may be a hair attribute folder (including three folders: front hair, middle hair, and back hair), and each folder is provided with multiple layer elements, such as multiple hair styles, and each hair style is a layer element; for another example: the headdress attribute includes two folders: left headdress and right headdress, and there are multiple headdresses under each headdress folder, that is, multiple layer elements; similarly, for layer attributes such as eyes, ears, clothes, and ear ornaments, they can all be numbered and named in the above manner. Of course, it can be understood that in this embodiment, the material library of the image to be generated can also be obtained from various resource libraries, a material library folder can be generated, and automatic numbering and naming can be performed, which is not limited here.

[0046] After constructing the layer material library folder, import it into the NFT image generation system for subsequent execution of the NFT image generation method, which is specifically described as follows.

[0047] Step S102. Bind the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination, where each NFT layer element appears in only one element combination;

[0048] It should be noted that each NFT layer element appears in only one element combination means that the same NFT layer element is only allowed to exist in one element combination and cannot appear in multiple element combinations at the same time, that is, the same layer element can only have a unique binding relationship. However, it should be noted that the number of elements in the same element combination is not limited, that is, there can be an unlimited number of layer elements in an element combination, which is specifically set according to user needs.

[0049] In a possible implementation manner of step S102, binding the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination includes:

[0050] Bind one NFT layer element in a certain layer file with one NFT layer element in at least one layer file according to a preset layer element matching rule to generate at least one first element combination.

[0051] It should be noted that in this embodiment, when binding the relationships of each layer element, it can be automatically bound based on the preset matching rules of the system. For example: bind the layer elements with the same number in each layer file. For example, name the elements 001, 002, and 003 in the front hair folder, name the elements in the middle hair folder with the names 001, 002, and 003, and name the elements in the back hair folder with the names 001, 002, and 003. Automatically bind the 001 elements of each folder into a group, the 002 elements into a group, and the 003 elements into a group, thus realizing quick grouping. Preferably, for the automatically grouped element combinations, they can be clicked again for manual editing or deletion.

[0052] Of course, it can be understood that in this embodiment, the layer elements in each layer file can also be manually selected for relationship binding. For example, bind the layer element 001 in the front hair folder with the layer element 002 in the middle hair folder and the layer element 003 in the back hair folder. This is not limited here.

[0053] In a possible implementation manner of step S102, binding the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination includes:

[0054] Bind at least two layer files in relation, so that each NFT layer element in each layer file will be matched and bound in the order of the element numbers, generating at least one second element combination.

[0055] It should be noted that in this embodiment, when binding each layer element in relation, each layer file can be automatically bound based on the preset matching rules in the system. For example, when the folders of the front hair, middle hair, and back hair are set as the binding relationship, after binding each folder, the 001 elements of each folder will also be automatically bound into a group, the 002 elements will be automatically bound into a group, and the 003 elements will be bound into a group, thus realizing quick grouping. Preferably, for the automatically grouped element combinations, they can be manually edited or deleted after being clicked again.

[0056] In a possible design, before binding different layer files in relation according to the preset layer element matching rules, the method further includes:

[0057] Edit, invert the selection, and / or delete the NFT layer elements in the layer file.

[0058] It should be noted that the "invert the selection" means that after importing the material resource library folder, all the element files in the folder are defaulted to the selected state. When the user is previewing, if they don't want a certain element to participate in the generation process but don't want to directly delete it from the folder (it can be assumed that the user is testing the combined effects of different NFT elements during preparation), they can click on the element in the file list interface to change it to the unselected state, and then this element will not appear in the subsequent generation process. It should be noted that preferably, if the elements selected or inverted by the user have a binding relationship, the other elements in the same group will be selected or inverted simultaneously.

[0059] Step S103, set the binding relationship between each layer file to the highest priority, set the image generation parameters of each layer file, and automatically generate NFT images using the random principle.

[0060] Among them, it should be noted that setting the binding relationship between each layer file as the highest priority serves to make the binding relationship between each layer file the most precondition for all parameter changes, that is, the highest priority, which can ensure the accuracy of the finally generated NFT image. The reason is that since the logic for generating the subsequent NFT image is random generation, and the binding relationship between each layer file is the highest priority, when a randomly selected element has a binding relationship, other elements in the same group as it will appear together, that is, the elements with a binding relationship with it will appear together. Even if the parameters are adjusted again, the logic of the simultaneous appearance of the matching elements in this link remains unchanged. According to this logic, the composition of the attributes of the generated NFT image conforms to the matching principle. For example, for the earrings of the NFT portrait generated according to the above logic, the left earring is located behind the face, and the right earring is located in front of the face, and the styles are the same. It will not happen that the left earring is of style A and the right earring is of style B.

[0061] In a possible design, set the image generation parameters of each layer file, including:

[0062] Set the scarcity of the attributes of each layer file, the appearance frequency of the attributes, the stacking order of the layers, and / or the number of generated NFT images.

[0063] Among them, it should be noted that all attributes of the entire NFT image in this embodiment have a scarcity classification. Each attribute can be divided into a common style and a scarce style. Then, the frequency parameters of the appearance of the common style and the scarce style can be set. At the same time, the overall appearance frequency of a certain attribute can also be set. For example, if the appearance frequency of the attribute of the headdress is set to 100%, then every randomly generated NFT picture will definitely have a headdress. Or if the frequency parameter of the appearance of the common style in the headdress attribute is set to 70% and the frequency parameter of the appearance of the scarce style is set to 30%, then among the randomly generated pictures, the proportion of NFT pictures with scarce-style headdresses will be less, and the proportion of NFT pictures with common-style headdresses will be more.

[0064] In a possible design, before automatically generating the NFT image, the method further includes:

[0065] Add a polka dot texture layer and / or a mask layer to the NFT image to be generated.

[0066] Among them, preferably, this embodiment has the function of batch simple editing of the combined NFT pictures. For example, add a layer of polka dot texture or a mask layer to all the upcoming generated NFT pictures, so as to provide more design possibilities for users and meet more creative needs.

[0067] Based on the above - disclosed content, in this embodiment, the layer material library folder is set as multiple layer files, and each layer file is provided with multiple NFT layer elements with numbers and / or names. Then, different layer files are relationally bound according to a preset layer element matching rule to generate at least one element combination. Finally, the binding relationship between the layer files is set to the highest priority, the image generation parameters of each layer file are set, and NFT images are automatically generated using the random principle, so that the layer files can be bound into groups according to the preset matching rule, and the binding relationship is the highest priority during the image generation process to randomly generate NFT images, thus being able to quickly and accurately generate NFT pictures of complex layers, and further avoiding errors in the content of the generated NFT images caused by chaotic layer attributes.

[0068] In a second aspect, the present invention provides an NFT image generation device, including:

[0069] A folder acquisition module, configured to acquire a layer material library folder, where the layer material library folder includes multiple layer files, and each layer file includes multiple NFT layer elements with numbers and / or names;

[0070] A layer element binding module, configured to relationally bind different layer files according to a preset layer element matching rule to generate at least one element combination, where each NFT layer element appears in only one element combination;

[0071] An NFT image generation module, configured to set the binding relationship between the layer files to the highest priority, set the image generation parameters of each layer file, and automatically generate NFT images using the random principle.

[0072] In a possible design, the layer element binding module includes:

[0073] A first layer element binding unit, configured to relationally bind one of the NFT layer elements in a certain layer file with one of the NFT layer elements in at least one layer file according to a preset layer element matching rule to generate at least one first element combination.

[0074] In a possible design, the layer element binding module includes:

[0075] A second layer element binding unit, configured to relationally bind at least two layer files, and then each NFT layer element in each layer file will be matched and bound in the order of the element numbers to generate at least one second element combination.

[0076] In a possible design, when setting the image generation parameters of each layer file, the NFT image generation module specifically is used for:

[0077] Set the scarcity of the attributes of each layer file, the occurrence frequency of the attributes, the stacking order of the layers, and / or the number of NFT images generated.

[0078] In a possible design, the device further includes:

[0079] An element editing module for editing, inverse selecting, and / or deleting NFT layer elements in the layer file.

[0080] In a possible design, the device further includes:

[0081] An image processing module for adding a dot texture layer and / or a mask layer to the NFT image to be generated.

[0082] For the working process, working details, and technical effects of the foregoing device provided in the second aspect of this embodiment, reference may be made to the method described in the first aspect above or any possible design in the first aspect, and details are not described herein again.

[0083] In a third aspect, the present invention provides a computer device, including a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the NFT image generation method described in any possible design in the first aspect.

[0084] Specifically, for example, the memory may include, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a flash memory, a first in first out memory (FIFO), and / or a first in last out memory (FILO), etc.; the processor may not be limited to using a microprocessor of the STM32F105 series; the transceiver may include, but is not limited to, a WiFi (Wireless Fidelity) wireless transceiver, a Bluetooth wireless transceiver, a GPRS (General Packet Radio Service) wireless transceiver, and / or a ZigBee (a low-power local area network protocol based on the IEEE802.15.4 standard) wireless transceiver, etc. In addition, the computer device may further include, but is not limited to, a power module, a display screen, and other necessary components.

[0085] For the working process, working details and technical effects of the aforementioned computer device provided in the third aspect of this embodiment, reference may be made to the method described in the first aspect above or any possible design in the first aspect, which will not be elaborated herein.

[0086] In a fourth aspect, the present invention provides a computer-readable storage medium, on which instructions are stored. When the instructions run on a computer, they execute the NFT image generation method described in any possible design in the first aspect.

[0087] Among them, the computer-readable storage medium refers to a carrier for storing data, which may but is not limited to include floppy disks, optical discs, hard disks, flash memories, USB flash drives, and / or memory sticks, etc. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0088] For the working process, working details and technical effects of the aforementioned readable storage medium provided in the fourth aspect of this embodiment, reference may be made to the method described in the first aspect above or any possible design in the first aspect, which will not be elaborated herein.

[0089] In a fifth aspect, the present invention provides a computer program product containing instructions. When the instructions run on a computer, the computer is made to execute the NFT image generation method described in any possible design in the first aspect.

[0090] For the working process, working details and technical effects of the aforementioned computer program product provided in the fifth aspect of this embodiment, reference may be made to the method described in the first aspect above or any possible design in the first aspect, which will not be elaborated herein.

[0091] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An NFT image generation method, characterized in that, it includes: Obtain a layer material library folder, where the layer material library folder includes multiple layer files, and each layer file includes multiple NFT layer elements with numbers and / or names; Bind the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination, where each NFT layer element appears in only one element combination; binding the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination includes: binding one NFT layer element in a certain layer file with one NFT layer element in at least one layer file according to a preset layer element matching rule to generate at least one first element combination, or binding at least two layer files, then each NFT layer element in each layer file will be matched and bound in the order of the element numbers to generate at least one second element combination; Set the binding relationship between each layer file to the highest priority, set the image generation parameters of each layer file, and automatically generate an NFT image using the random principle.

2. The NFT image generation method according to claim 1, characterized in that, Setting the image generation parameters of each layer file includes: Setting the scarcity of the attributes of each layer file, the appearance frequency of the attributes, the stacking order of the layers, and / or the number of NFT images to be generated.

3. The NFT image generation method according to claim 1, characterized in that, Before binding the relationships of different layer files according to a preset layer element matching rule, the method further includes: Editing, inverse selecting, and / or deleting the NFT layer elements in the layer file.

4. The NFT image generation method according to claim 1, characterized in that, Before automatically generating an NFT image, the method further includes: Adding a dot texture layer and / or a mask layer to the NFT image to be generated.

5. An NFT image generation device, characterized in that, it includes: A folder acquisition module for acquiring a layer material library folder, where the layer material library folder includes multiple layer files, and each layer file includes multiple NFT layer elements with numbers and / or names; A layer element binding module for binding the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination, where each NFT layer element appears in only one element combination; binding the relationships of different layer files according to a preset layer element matching rule to generate at least one element combination includes: binding one NFT layer element in a certain layer file with one NFT layer element in at least one layer file according to a preset layer element matching rule to generate at least one first element combination, or binding at least two layer files, then each NFT layer element in each layer file will be matched and bound in the order of the element numbers to generate at least one second element combination; The NFT image generation module is used to set the binding relationship between each layer file to the highest priority, set the image generation parameters of each layer file, and automatically generate NFT images according to the principle of randomness.

6. The NFT image generation device according to claim 5, wherein, the layer element binding module includes: The first layer element binding unit is used to bind the relationship between one NFT layer element in a certain layer file and one NFT layer element in at least one layer file according to a preset layer element matching rule to generate at least one first element combination.

7. The NFT image generation device according to claim 5, wherein, the layer element binding module includes: The second layer element binding unit is used to bind the relationship between at least two layer files, and then the NFT layer elements in the at least two layer files are matched and bound in the order of element numbers to generate at least one second element combination.

8. A computer device, wherein, it includes a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the NFT image generation method according to any one of claims 1-4.

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