CN111158647B - Creative material self-adaptive generation method and device based on structural theory - Google Patents
Creative material self-adaptive generation method and device based on structural theory Download PDFInfo
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Abstract
The invention discloses a creative material self-adaptive generation method and a creative material self-adaptive generation device based on a structural theory, wherein the method comprises the following steps: carrying out structuralized marking on an input material to be processed, and carrying out layer splitting on the material to be processed according to the attribute of a layer element; counting the feature vectors of all layers of the material to be processed, and calculating the similarity between the feature vectors of all layers and the feature vectors under corresponding calculation frames in a material library; and carrying out element replacement on the layer elements in the material to be processed according to the characteristic layout indicated by the target characteristic vector in the material library to obtain the target creative material, wherein the target characteristic vector is the characteristic vector with the highest similarity to the characteristic vector of the material to be processed under the corresponding computing frame. By adopting the method and the device, intelligent conversion in material design can be realized in the process of generating the target material, and the quality of the output material is ensured on the premise of ensuring the material conversion efficiency.
Description
Technical Field
The invention relates to the technical field of advertisement design, in particular to a creative material self-adaptive generation method and device based on a structural theory.
Background
The everywhere visible advertising banner has high-level design purposes, the presentation style and the displayed content of the advertising banner are extremely rich and changeable, and in the process of manufacturing the advertising banner, in order to enable the banner to be suitable for a real scene, a designer needs to solve the problem of design originality and needs to spend much time for repeated size extension, position adjustment, picture changing and character changing. In actual production, the repeated simple secondary processing operations can be completed by primary designers, but the primary designers are inevitable to make mistakes, such as material processing, exceeding the bottom line specified by advertising laws, design commonsense errors and the like, and the output of low-quality advertising banners directly influences the later business model of products.
Disclosure of Invention
The embodiment of the invention provides a creative material self-adaptive generation method and device based on a structural theory, which can realize intelligent conversion in material design and ensure the quality of output materials on the premise of ensuring the material conversion efficiency.
The first aspect of the embodiment of the invention provides a creative material adaptive generation method based on a structural theory, which may include:
carrying out structuralized marking on an input material to be processed, and carrying out layer splitting on the material to be processed according to the attribute of a layer element;
counting the feature vectors of all layers of the material to be processed, and calculating the similarity between the feature vectors of all layers and the feature vectors under corresponding calculation frames in a material library;
and carrying out element replacement on the layer elements in the material to be processed according to the characteristic layout indicated by the target characteristic vector in the material library to obtain the target creative material, wherein the target characteristic vector is the characteristic vector with the highest similarity to the characteristic vector of the material to be processed under the corresponding computing frame.
A second aspect of the embodiments of the present invention provides a creative material adaptive generation apparatus based on a structural theory, which may include:
the material marking and splitting module is used for carrying out structured marking on the input material to be processed and carrying out layer splitting on the material to be processed according to the attribute of the layer element;
the similarity calculation module is used for counting the feature vectors of all layers of the material to be processed and calculating the similarity between the feature vectors of all layers and the feature vectors under corresponding calculation frames in the material library;
and the target material generation module is used for carrying out element replacement on layer elements in the material to be processed according to the characteristic layout indicated by the target characteristic vector in the material library to obtain a target creative material, wherein the target characteristic vector is a characteristic vector with the highest similarity to the characteristic vector of the material to be processed under the corresponding calculation frame.
A third aspect of the embodiments of the present invention provides a computer device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the method for adaptively generating creative materials based on structured theory according to the above aspect.
A fourth aspect of the embodiments of the present invention provides a computer storage medium, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the computer storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the method for adaptively generating creative material based on structured theory in the foregoing aspect.
The invention has the following beneficial effects:
the similarity is calculated by matching the newly input material after marking, splitting and counting the characteristics with a material library, and the new material is subjected to equal-scale scaling replacement according to the corresponding characteristic layout based on the similarity, so that the desired target material is obtained. In the process of generating the target material, the historical design layout is learned, the position and the size of the new material are changed in a diversity mode, intelligent conversion in material design is achieved, and the quality of the output material is guaranteed on the premise that the material conversion efficiency is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a creative material adaptive generation method based on a structural theory according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an adaptive creative material generation apparatus based on a structural theory according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "including" and "having," and any variations thereof, in the description and claims of this invention and the above-described drawings are intended to cover a non-exclusive inclusion, and the terms "first" and "second" are used for distinguishing designations only and do not denote any order or magnitude of a number. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The creative material self-adaptive generation method based on the structural theory provided by the embodiment of the invention can be applied to the advertising banner production process and can realize the application scenes without creative design, such as size extension, position adjustment, picture changing and word changing, and the like.
In the embodiment of the invention, the creative material self-adaptive generation method based on the structural theory can be applied to computer equipment, and the computer equipment can be a computer and can also be other terminal equipment with computing processing capacity.
It should be noted that, the computer device in the present application is provided with a local database, a large number of raw materials collected in advance, that is, historical banner materials, are stored in the database, and each banner material is classified according to size, for example, in the real estate industry, each size generally corresponds to an application scenario, and the common material size is as shown in table 1:
TABLE 1
Further, the layout design mode of the raw materials in the database needs to be converted into frame data which can be understood by a computer, and the frame data can include information such as material types, sizes, size ratios, areas and the like. The raw materials classified and framed by scenes in the industry form the material library of the application, and the common formats of the material library include psd, svg, ai and the like.
In actual design, designers can label the layers according to the customary naming mode, most designers cannot label each element, and a uniform naming format does not exist in the industry. In the embodiment of the application, the layers of the materials in the material library are labeled according to a unified structural labeling rule, and it is to be understood that the labeling rule of each industry is different, but the labeling rule of the same industry is unified. For example, for the property industry, the labeling rules for psd structured hierarchies are as follows: 1. a background; 2. paragraph characters; 3. a title; 4. a subtitle; 5. a telephone number; 6. a project logo; 7. a developer logo; 8. an address; 9. sales information; 10. exempt from the terms of liability; 11. a substrate; 12. graph sets (a graph 1, b graph 2, c graph 3); 13. and others. Wherein, the rule 13 in the labeling rule may include information that cannot be included in other rules, such as: bottom bar information, fine decorative elements, two-dimensional codes and the like. Further, the graph 1, the graph 2, and the graph 3 in the rule 12 are essential visual graph elements that are important in parallel, and may be a combination of several elements that are relatively concentrated in position, and a combination of more dispersed graph elements that are not combined, but dispersed graph elements that are placed everywhere may be listed in the rule 13. Note that, if the necessary rules, such as the above-described rules 1, 3, 6, and 12.a, are missing from the labeling rules, the marking cannot be successful.
As shown in fig. 1, the creative material adaptive generation method based on the structural theory at least includes the following steps:
s101, conducting structuralized marking on the input material to be processed, and conducting layer splitting on the material to be processed according to the attribute of the layer element.
It can be understood that the material to be processed of the input device can be designed banner advertisements, and in order to adapt to various real scenes, the material needs to be subjected to design transformation such as size extension, position adjustment or image and character changing without creative labor.
In the specific implementation, the equipment can perform structured marking on the material to be processed according to the marking rule of the industry to which the material to be processed belongs, and simultaneously needs to perform layer splitting on the material to be processed, so that the material to be processed is split into a series of layers. Preferably, layer splitting can be performed according to attributes of layer elements. It should be noted that, the marking rules adopted for the materials in the same industry, whether the newly input materials to be processed or the original materials in the material library, are the same.
S102, counting the feature vectors of all layers of the material to be processed, and calculating the similarity between the feature vectors of all the layers and the feature vectors under corresponding calculation frames in the material library.
In specific implementation, the device may record information such as the size, position, area, pixel area, alignment mode, etc. of each layer, and store the information in the database in the form of a feature vector.
Further, the device may obtain a target size of the user input as a basis for material transformation, and may determine one or more computing frames in the material library that match the material to be processed according to the size. Before the feature similarity calculation, a calculation frame meeting the requirements of similarity and target size of the material to be processed can be preliminarily selected in the material library, and then the similarity between the feature vector of each layer of the material to be processed and the feature vector in the material library can be calculated in the data range corresponding to the frame.
Further, the device may rank the calculated similarity, determine a target feature vector matched with the feature vector of the material to be processed when the similarity is maximum, and further determine a feature layout corresponding to the target feature vector.
And S103, carrying out element replacement on the layer elements in the material to be processed according to the characteristic layout indicated by the target characteristic vector in the material library to obtain a target creative material.
In specific implementation, the device may perform element equal-scale scaling, replacement, and layer merging on layer elements in the material to be processed according to the feature layout indicated by the target feature vector, and finally obtain the transformed target creative material.
It should be noted that the feature layout may correspond to the following design rules: the method comprises the following steps of layer matching rules, layer alignment rules, layer welting rules and general design rules. The above design rules need to be followed when making element replacements.
Under the layer matching rule, after the device is positioned to a corresponding calculation frame according to the target size, the device can calculate the layer feature matching degree of the layer feature in the frame and the layer feature of the input material, such as: the width of the target is matched with the width of the frame element, the height of the target above the frame element, the effective pixel occupied area, the area of the frame element and other various layer characteristics. It should be noted that if some layers of the material to be processed do not have a corresponding layer in the calculation frame, the layers may be sorted according to the similarity, and matching is continued from the calculation frame corresponding to the target feature vector that is slightly behind the sorting.
Under the layer alignment rule, the layout alignment mode of element replacement can be judged from the title, the subtitle and the paragraph characters, and the method can be divided into left alignment, right alignment and middle alignment.
Under the layer welting rule, welting placing of the corresponding layer can be realized by adding a welting label and utilizing the label. For example, the welt label may be left welt: "graphic 1_ left", right welt: "graphic 1_ right", left top border: "graph 1_ left _ top", bottom left border: "graphic 1_ left _ bottom", right top border "", "graphic 1_ right _ top", right bottom border "", and so on.
Under the common rules of the industry, element replacement is required to follow the design rules commonly known in the industry, as shown in table 2:
TABLE 2
In an alternative embodiment, the device may employ an evaluation model to evaluate the final output composite target size target creative material. The evaluation model can be obtained by deep neural network DNN training, the evaluation rules in the model can calculate the welting of elements, the alignment mode among the elements and the margin area of the layout through an image algorithm, so that a comprehensive evaluation score is obtained, and the design with common sense errors is effectively avoided through the evaluation criteria.
In the embodiment of the application, the similarity is calculated by matching the newly input material after marking, splitting and counting the characteristics with a material library, and the new material is subjected to equal-scale scaling replacement according to the corresponding characteristic layout based on the similarity, so that the desired target material is obtained. In the process of generating the target material, the historical design layout is learned, the position and the size of the new material are changed in a diversity mode, intelligent conversion in material design is achieved, and the quality of the output material is guaranteed on the premise that the material conversion efficiency is guaranteed.
The creative material adaptive generation device based on the structural theory provided by the embodiment of the invention will be described in detail with reference to fig. 2. It should be noted that, the creative material adaptive generation apparatus shown in fig. 2 is used for executing the method of the embodiment shown in fig. 1 of the present invention, for convenience of description, only the portion related to the embodiment of the present invention is shown, and details of the technology are not disclosed, please refer to the embodiment shown in fig. 1 of the present invention.
As shown in fig. 3, the creative material adaptive generation apparatus 10 of an embodiment of the present invention may include: the system comprises a material marking and splitting module 101, a similarity calculation module 102, a target material generation module 103, a calculation frame determination module 104 and a feature vector determination module 105.
The material marking and splitting module 101 is configured to perform structured marking on an input material to be processed, and perform layer splitting on the material to be processed according to attributes of layer elements.
It should be noted that, the structured standard marking rule for the material to be processed is consistent with the marking rule of each original material in the material library.
The similarity calculation module 102 is configured to count feature vectors of each layer of the material to be processed, and calculate a similarity between the feature vector of each layer and a feature vector under a corresponding calculation frame in the material library.
Optionally, the feature vector determining module 105 is configured to store the size, the position, the area, the pixel area, and the alignment manner of each layer in the material to be processed as the feature vector of each layer.
And the target material generation module 103 is configured to perform element replacement on layer elements in the material to be processed according to a feature layout indicated by a target feature vector in the material library to obtain a target creative material, where the target feature vector is a feature vector having the highest similarity with the feature vector of the material to be processed in a corresponding calculation frame.
And the calculation frame determining module 104 is used for determining a calculation frame matched with the material to be processed in the material library according to the target size input by the user.
It can be understood that the design rules corresponding to the feature layout include layer matching rules, layer alignment rules, layer bordering rules, and general design rules.
It should be noted that, for the execution process of each module and unit in this embodiment, reference may be made to the description in the foregoing method embodiment, and details are not described here again.
In the embodiment of the application, the similarity is calculated by matching the newly input material after marking, splitting and counting the characteristics with a material library, and the new material is subjected to equal-scale scaling replacement according to the corresponding characteristic layout based on the similarity, so that the desired target material is obtained. In the process of generating the target material, the historical design layout is learned, the position and the size of the new material are changed in a diversity mode, intelligent conversion in material design is achieved, and the quality of the output material is guaranteed on the premise that the material conversion efficiency is guaranteed.
An embodiment of the present invention further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiment shown in fig. 1, and a specific execution process may refer to a specific description of the embodiment shown in fig. 1, which is not described herein again.
The embodiment of the application also provides computer equipment. As shown in fig. 3, the computer device 20 may include: the at least one processor 201, e.g., CPU, the at least one network interface 204, the user interface 203, the memory 205, the at least one communication bus 202, and optionally, a display 206. Wherein a communication bus 202 is used to enable the connection communication between these components. The user interface 203 may include a touch screen, a keyboard or a mouse, among others. The network interface 204 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), and a communication connection may be established with the server via the network interface 204. The memory 205 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory, and the memory 205 includes a flash in the embodiment of the present invention. The memory 205 may optionally be at least one memory system located remotely from the processor 201. As shown in fig. 3, memory 205, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.
It should be noted that the network interface 204 may be connected to a receiver, a transmitter or other communication module, and the other communication module may include, but is not limited to, a WiFi module, a bluetooth module, etc., and it is understood that the computer device in the embodiment of the present invention may also include a receiver, a transmitter, other communication module, etc.
carrying out structuralized marking on an input material to be processed, and carrying out layer splitting on the material to be processed according to the attribute of a layer element;
counting the feature vectors of all layers of the material to be processed, and calculating the similarity between the feature vectors of all layers and the feature vectors under corresponding calculation frames in a material library;
and carrying out element replacement on the layer elements in the material to be processed according to the characteristic layout indicated by the target characteristic vector in the material library to obtain a target creative material, wherein the target characteristic vector is the characteristic vector with the highest similarity with the characteristic vector of the material to be processed under the corresponding computing frame.
In some embodiments, apparatus 20 is further configured to:
and determining a calculation frame matched with the material to be processed in the material library according to the target size input by the user.
In some embodiments, the structured standard marking rule for the material to be processed is consistent with the marking rule of each original material in the material library.
In some embodiments, apparatus 20 is further configured to:
and storing the size, position, area, pixel area and alignment mode of each layer in the material to be processed as the feature vector of each layer.
In some embodiments, the design rules corresponding to the feature layout include layer matching rules, layer alignment rules, layer bordering rules, and general design rules.
In the embodiment of the application, the similarity is calculated by matching the newly input material after marking, splitting and counting the characteristics with a material library, and the new material is subjected to equal-scale scaling replacement according to the corresponding characteristic layout based on the similarity, so that the desired target material is obtained. In the process of generating the target material, the historical design layout is learned, the position and the size of the new material are changed in a diversity mode, intelligent conversion in material design is achieved, and the quality of the output material is guaranteed on the premise that the material conversion efficiency is guaranteed.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A creative material self-adaptive generation method based on a structural theory is characterized by comprising the following steps:
carrying out structuralized marking on an input material to be processed, and carrying out layer splitting on the material to be processed according to the attribute of a layer element;
counting the feature vectors of all layers of the material to be processed, and calculating the similarity between the feature vectors of all layers and the feature vectors under corresponding calculation frames in a material library, wherein the corresponding calculation frames are one or more calculation frames which are matched with the material to be processed and used for acquiring a target size input by a user as a material transformation basis and determining in the material library according to the size;
and carrying out element replacement on the layer elements in the material to be processed according to the characteristic layout indicated by the target characteristic vector in the material library to obtain a target creative material, wherein the target characteristic vector is the characteristic vector with the highest similarity to the characteristic vector of the material to be processed under the corresponding computing frame.
2. The method of claim 1, further comprising:
and determining a calculation frame matched with the material to be processed in the material library according to the target size input by the user.
3. The method of claim 1, wherein the structured standard marking rule for the material to be processed is consistent with the marking rule of each raw material in the material library.
4. The method of claim 1, further comprising:
and storing the size and position of each layer, the occupied area of effective pixels, the area of frame elements and the alignment mode in the material to be processed as the feature vector of each layer.
5. The method according to claim 1, wherein the design rules corresponding to the feature layout include layer matching rules, layer alignment rules, layer bordering rules, and general design rules.
6. A creative material self-adaptive generation device based on a structural theory is characterized by comprising:
the material marking and splitting module is used for carrying out structured marking on an input material to be processed and carrying out layer splitting on the material to be processed according to the attribute of a layer element;
the similarity calculation module is used for counting the feature vectors of all layers of the material to be processed and calculating the similarity between the feature vectors of all layers and the feature vectors under corresponding calculation frames in a material library, wherein the corresponding calculation frames are one or more calculation frames which are matched with the material to be processed and are used for acquiring a target size input by a user as a material transformation basis and determining in the material library according to the size;
and the target material generation module is used for carrying out element replacement on the layer elements in the material to be processed according to the characteristic layout indicated by the target characteristic vector in the material library to obtain a target creative material, wherein the target characteristic vector is a characteristic vector with the highest similarity to the characteristic vector of the material to be processed under a corresponding calculation frame.
7. The apparatus of claim 6, further comprising:
and the calculation frame determining module is used for determining a calculation frame matched with the material to be processed in the material library according to the target size input by the user.
8. The apparatus of claim 6, wherein the structured standard marking rule for the material to be processed is consistent with the marking rule of each original material in the material library.
9. The apparatus of claim 6, further comprising:
and the characteristic vector determining module is used for storing the size, the position, the effective pixel occupied area, the area of the frame element and the alignment mode of each layer in the material to be processed as the characteristic vector of each layer.
10. A computer-readable storage medium, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the storage medium, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by a processor to implement the method for adaptively generating creative material based on structured theory as claimed in any one of claims 1 to 5.
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