Picture generation method and system for refitted vehicle, storage medium and equipment

Abstract

The invention provides a picture generation method and system for a refitted vehicle, a storage medium and equipment, and the method comprises the steps: obtaining system parameters and picture parameters of a target vehicle, and enabling the system parameters to be a plurality of subsystem parameters forming the target vehicle; modeling the plurality of subsystem parameters and the picture parameters to obtain a vehicle model; obtaining at least one first modification parameter; judging whether the first refitting parameter meets one of the subsystem parameters or the picture parameters or not; when the first refitting parameter meets one of the subsystem parameters or the picture parameters, a preset stable diffusion model algorithm is called to refine the vehicle model according to the first refitting parameter to obtain a refitted vehicle model, at least one piece of picture information corresponding to the refitted vehicle model is generated, and the picture information corresponding to the refitted vehicle model is obtained by introducing an image stable diffusion model algorithm. The problems in the aspects of image stability, real-time performance, user experience and the like are solved, and therefore more stable, natural and efficient vehicle refitting effect picture preview is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of picture generation, and in particular to a method, system, storage medium and device for generating a picture of a modified vehicle. Background Art

[0002] A car refers to a vehicle that moves on wheels on land. Currently, in order to meet the personalized needs of vehicles, some users will modify the vehicles, such as changing the exterior color of the vehicle, the front bumper or rear bumper of the vehicle, etc.

[0003] However, although the existing modification methods can meet personalized needs, users cannot observe the modified vehicle before the modification is completed, so that users cannot immediately see the modification effect. When the vehicle is modified and the required effect does not meet customer satisfaction, it will need to be modified again. Therefore, the existing vehicle modification efficiency is low, and the effect cannot be predicted before the vehicle is modified, which greatly reduces the user experience of vehicle modification. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a method, system, storage medium and device for generating images of modified vehicles, so as to solve the technical problems in the background technology that the existing vehicle modification efficiency is low and the effect cannot be predicted before the vehicle is modified, which greatly reduces the user's experience of vehicle modification.

[0005] A first aspect of the present invention is to provide a method for generating a picture of a modified vehicle, the method comprising:

[0006] Acquire system parameters and image parameters of a target vehicle, wherein the system parameters are parameters of multiple subsystems constituting the target vehicle;

[0007] Modeling the plurality of subsystem parameters and the image parameters to obtain a vehicle model corresponding to the target vehicle;

[0008] Obtaining at least one first modification parameter;

[0009] Determining whether the first modification parameter satisfies one of the subsystem parameters or the picture parameters;

[0010] When the first modification parameter satisfies one of the subsystem parameters or the image parameters, the preset stable diffusion model algorithm is called to modify the vehicle model according to the first modification parameter to obtain a modified vehicle model, and generate at least one image information corresponding to the modified vehicle model.

[0011] Furthermore, in the step of obtaining at least one first modification parameter, it also includes:

[0012] When a plurality of first modification parameters are obtained;

[0013] Determining whether each of the first modification parameters satisfies one of the subsystem parameters or the image parameters;

[0014] If so, a preset stable diffusion model algorithm is called to modify the vehicle model according to each of the first modification parameters to obtain a modified vehicle model.

[0015] Further, after the step of determining whether each of the first modification parameters satisfies any one of the subsystem parameters or the picture parameters, the method further includes:

[0016] When a part of the first modification parameters among the plurality of the first modification parameters does not satisfy any one of the subsystem parameters or the picture parameters;

[0017] Then the part of the first modification parameters is marked as other parameters.

[0018] Furthermore, after the step of generating at least one piece of picture information corresponding to the modified vehicle model, the method further includes:

[0019] Determining whether the picture effect corresponding to the picture information meets the preset picture effect;

[0020] If so, the modified vehicle model is determined as the final modified vehicle.

[0021] Further, after the step of determining whether the picture effect corresponding to the picture information satisfies a preset picture effect, the method further includes:

[0022] If not, obtaining at least one second modification parameter;

[0023] Determining whether the second modification parameter satisfies one of the subsystem parameters or the picture parameters;

[0024] When the second modification parameter satisfies one of the subsystem parameters or the image parameters, calling a preset stable diffusion model algorithm to modify the current vehicle model again according to the second modification parameter;

[0025] The first modification parameter is different from the second modification parameter.

[0026] Further, after the step of when the picture effect corresponding to the picture information does not meet the preset picture effect, it also includes:

[0027] When a plurality of second modification parameters are obtained;

[0028] Determining whether each of the second modification parameters satisfies one of the subsystem parameters or the picture parameters;

[0029] If yes, calling a preset stable diffusion model algorithm to modify the vehicle model again according to each of the second modification parameters to obtain a modified vehicle model;

[0030] If not, the unsatisfied second modification parameter is marked as other parameters.

[0031] A second aspect of the present invention is to provide a picture generation system, the system comprising:

[0032] An acquisition module, used to acquire system parameters and image parameters of a target vehicle, wherein the system parameters are parameters of multiple subsystems constituting the target vehicle;

[0033] The acquisition module is also used to acquire at least one first modification parameter;

[0034] A modeling module, used for modeling the plurality of subsystem parameters and the image parameters to obtain a vehicle model corresponding to the target vehicle;

[0035] A determination module, used for determining whether the first modification parameter satisfies one of the subsystem parameters or the image parameters;

[0036] When the first modification parameter satisfies one of the subsystem parameters or the image parameters, a preset stable diffusion model algorithm is called to modify the vehicle model according to the first modification parameter to obtain a modified vehicle model, and at least one image information corresponding to the modified vehicle model is generated;

[0037] The picture generation module is used to generate at least one picture information corresponding to the modified vehicle model.

[0038] Further, the acquisition module includes a vehicle parameter acquisition submodule and a modification parameter acquisition submodule;

[0039] The vehicle parameter acquisition submodule is used to acquire system parameters and image parameters of the target vehicle, wherein the system parameters are multiple subsystem parameters constituting the target vehicle;

[0040] The modification parameter acquisition submodule is used to acquire at least one first modification parameter.

[0041] A third aspect of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for generating a picture of a modified vehicle.

[0042] The fourth aspect of the present invention is to provide a picture generating device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for generating a picture of a modified vehicle when executing the program.

[0043] Compared with the prior art, the method, system, storage medium and device for generating a modified vehicle image shown in the present invention have the following beneficial effects:

[0044] In a method for generating a modified vehicle image provided by the present invention, the system parameters and image parameters of a target vehicle are first obtained, wherein the system parameters are multiple subsystem parameters constituting the target vehicle; then the multiple subsystem parameters and image parameters are modeled to obtain a vehicle model corresponding to the target vehicle; then at least one first modification parameter is obtained; it is determined whether the first modification parameter satisfies one of the subsystem parameters or the image parameters; when the first modification parameter satisfies one of the subsystem parameters or the image parameters, a preset stable diffusion model algorithm is called to modify the vehicle model according to the first modification parameter to obtain a modified vehicle model, and at least one image information corresponding to the modified vehicle model is generated, thereby solving problems in image stability, real-time performance and user experience by introducing an image stable diffusion model algorithm, thereby providing a more stable, natural and efficient preview of a vehicle modification effect image, solving the technical problems of low vehicle modification efficiency and failure to predict the effect before vehicle modification in the prior art, which greatly reduces the user's experience of vehicle modification, and before modifying the vehicle, the matching degree between the first modification parameter and the vehicle model can be judged, thereby further improving the efficiency of vehicle model modification. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of a method for generating a picture of a modified vehicle in a first embodiment of the present invention;

[0046] Figure 2 A schematic diagram of a picture generation system in a third embodiment of the present invention;

[0047] Figure 3 A schematic diagram of the structure of an acquisition module in a third embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of a picture generating device in the fourth embodiment of the present invention.

[0049] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0050] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0051] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0053] First embodiment

[0054] See also Figure 1 , which is a method for generating a picture of a modified vehicle in a first embodiment of the present invention, and the generating method comprises:

[0055] S01, obtaining system parameters and image parameters of a target vehicle, where the system parameters are parameters of multiple subsystems constituting the target vehicle.

[0056] To facilitate understanding of the present case and modification of the vehicle, the target vehicle in step S01 is an initial vehicle that has been determined, and it should be noted that multiple subsystem parameters in the system parameters may constitute the target vehicle, specifically, may include hardware subsystem parameters, software subsystem parameters, etc. of the vehicle. Hardware subsystem parameters may be the front bumper, rear bumper, body, wheels, interior, etc. of the car. Picture parameters may be the color of the vehicle itself, the external contour, etc. The examples are not limiting. The subsystem parameters and picture parameters shown in this embodiment are not limiting on the target vehicle, and may also include other parameters that constitute the target vehicle. Since other parameters belong to conventional prior art in this field, they are not specifically explained here.

[0057] S02, modeling multiple subsystem parameters and image parameters to obtain a vehicle model corresponding to the target vehicle;

[0058] Specifically, the vehicle model is a virtual model generated in three-dimensional software, and the virtual model is used to facilitate subsequent prediction of vehicle modification effects.

[0059] S03, obtaining at least one first modification parameter;

[0060] It should be noted that the first modification parameter is the modification parameter proposed by the user for the target vehicle, such as changing the front bumper or rear bumper of the vehicle, etc. The example is not limiting, and the front bumper or rear bumper shown in this embodiment does not limit the first modification parameter.

[0061] S04: Determine whether the first modification parameter satisfies any one of the subsystem parameters or the picture parameters.

[0062] When the first modification parameter satisfies one of the subsystem parameters or the picture parameters, the first modification parameter is input into the picture generation system, and the preset stable diffusion model algorithm is called to modify the vehicle model to obtain a modified vehicle model, and at least one picture information corresponding to the modified vehicle model is generated through the picture generation system.

[0063] It should be noted that in order to quickly modify the vehicle model, when the first modification parameter is obtained, the first modification parameter is judged to determine whether the first modification parameter is the modification parameter corresponding to the vehicle model. When it is determined that it is the modification parameter corresponding to the vehicle model, the vehicle model is modified. Otherwise, when the first modification parameter does not correspond to the vehicle model, the vehicle model is not modified, thereby reducing the modification error rate before modification, or when the first modification parameter does not match, an error is directly reported, thereby greatly improving the efficiency of vehicle model modification.

[0064] Specifically, in actual situations, when the vehicle model is a small vehicle model and the first modification parameter is the front bumper of a small vehicle, the corresponding modification is performed; when the first modification parameter is the front bumper of a pickup truck, an error is reported or the small vehicle model is not modified.

[0065] It should be noted that the stable diffusion model algorithm in this embodiment is an image generation and processing algorithm based on a diffusion process, and its core principle involves generating and optimizing images by simulating a physical diffusion process.

[0066] The following are the main principles and steps of the stable diffusion model algorithm:

[0067] 1. Basis of the Diffusion Process

[0068] The diffusion model draws on the diffusion process in physics, which is the phenomenon that matter diffuses from high-concentration areas to low-concentration areas. The diffusion process in image generation can be understood as gradually diffusing the information in the image from high-density (clear areas) to low-density (blurred areas) until the entire image is stable.

[0069] 2. Noise initialization

[0070] In the initial stage of image generation, we usually start with a random noise image. This is because the random noise image contains all possible image information, and through the subsequent diffusion process, the target image can be gradually extracted and stabilized.

[0071] 3. Diffusion Steps

[0072] The diffusion process is divided into multiple iterative steps, each of which diffuses some of the information in the current image outward. The specific steps include:

[0073] Forward diffusion: diffuse the current image information outward to increase the blurriness of the image.

[0074] Reverse diffusion: After each step of forward diffusion, the blur is gradually reduced through the reverse diffusion process to extract clear image information.

[0075] 4. Loss Function and Optimization

[0076] In order to ensure the quality of image generation during the diffusion process, a loss function is introduced to measure the difference between the current generated image and the target image. Common loss functions include:

[0077] Mean Squared Error (MSE): measures the squared difference between the pixel values ​​of the generated image and the target image.

[0078] Perceptual loss: Use a pre-trained neural network model (such as VGG) to extract image features and compare the difference between the generated image and the target image in the feature space.

[0079] By continuously optimizing the loss function, the diffusion process can be guided to gradually generate high-quality images that are closer to the target.

[0080] 5. Noise and Image Fusion

[0081] In each diffusion process, part of the information of the generated image may be affected by noise. Therefore, it is necessary to suppress noise and fuse image information during the diffusion process to ensure the stability and consistency of the generated image. This is usually achieved through noise filtering and image reconstruction technology.

[0082] 6. Stability Control

[0083] In order to ensure the stability of the image generation process, the diffusion process needs to be controlled to avoid distortion or inconsistency in the generated image. Stability control techniques include:

[0084] Gradient constraint: By constraining the image gradient, the image can avoid detail loss or excessive blurring during the diffusion process.

[0085] Multi-scale diffusion: Diffusion is performed at different scales to ensure that both the overall structure and details of the image are well preserved.

[0086] 7. Iterative Optimization

[0087] Diffusion model algorithms usually use iterative optimization methods to gradually improve the generated image through multiple iterations. In each iteration, a loss function is used to guide the diffusion process, and combined with noise suppression and stability control techniques, a high-quality stable image is finally generated.

[0088] Neural network assistance: Use deep neural networks to extract and generate image features to enhance the effect of the diffusion process.

[0089] Adaptive adjustment: Adaptively adjust the parameters of the diffusion process according to the image content and user needs to improve the personalization and customization of the generated image.

[0090] Through the above principles and steps, the stable diffusion model algorithm can achieve high-quality, stable and real-time image generation in the process of generating vehicle modification renderings, meeting users' high requirements for renderings.

[0091] In summary, the method for generating a modified vehicle image shown in this embodiment has at least the following beneficial effects compared with the vehicle modification method in the prior art:

[0092] In a method for generating a modified vehicle image provided by the present invention, the system parameters and image parameters of a target vehicle are first obtained, wherein the system parameters are multiple subsystem parameters constituting the target vehicle; then the multiple subsystem parameters and image parameters are modeled to obtain a vehicle model corresponding to the target vehicle; then at least one first modification parameter is obtained; it is determined whether the first modification parameter satisfies one of the subsystem parameters or the image parameters; when the first modification parameter satisfies one of the subsystem parameters or the image parameters, a preset stable diffusion model algorithm is called to modify the vehicle model according to the first modification parameter to obtain a modified vehicle model, and at least one image information corresponding to the modified vehicle model is generated, thereby solving problems in image stability, real-time performance and user experience by introducing an image stable diffusion model algorithm, thereby providing a more stable, natural and efficient preview of a vehicle modification effect image, solving the technical problems of low vehicle modification efficiency and failure to predict the effect before vehicle modification in the prior art, which greatly reduces the user's experience of vehicle modification, and before modifying the vehicle, the matching degree between the first modification parameter and the vehicle model can be judged, thereby further improving the efficiency of vehicle model modification.

[0093] Second embodiment

[0094] The second embodiment of the present invention provides a method for generating a picture of a modified vehicle. The method for generating a picture of a modified vehicle in this embodiment is different from the method for generating a picture of a modified vehicle in the first embodiment in that, in this embodiment:

[0095] The step of obtaining at least one first modification parameter also includes:

[0096] When a plurality of first modification parameters are obtained;

[0097] Determining whether each first modification parameter satisfies one of the subsystem parameters or the image parameters;

[0098] If so, the preset stable diffusion model algorithm is called to modify the vehicle model according to each first modification parameter to obtain a modified vehicle model.

[0099] It should be noted that the multiple first modification parameters are modification parameters proposed by the user for the target vehicle, such as the front bumper, rear bumper and wheels of the vehicle can be changed at the same time. The example is not limiting, and the front bumper, rear bumper and wheels shown in this embodiment are not limiting on the multiple first modification parameters.

[0100] When the plurality of first modification parameters all satisfy one of the subsystem parameters or the picture parameters, the vehicle model is modified according to the plurality of first modification parameters to obtain a modified vehicle model.

[0101] Furthermore, after the step of determining whether each first modification parameter satisfies any one of the subsystem parameters or the picture parameters, the method further includes:

[0102] When a portion of the first modification parameters among the plurality of first modification parameters do not satisfy any one of the subsystem parameters or the picture parameters;

[0103] A portion of the first reconfiguration parameters is marked as other parameters.

[0104] It should be noted that other parameters are parameters that cannot be paired with the vehicle model and have errors.

[0105] Furthermore, after the step of generating at least one piece of picture information corresponding to the modified vehicle model, the method further includes:

[0106] Determine whether the picture effect corresponding to the picture information meets the preset picture effect;

[0107] If so, the modified vehicle model is determined as the final modified vehicle.

[0108] It should be noted that the picture effect is the user's satisfaction with the modified vehicle model. When the user is satisfied, the current modified vehicle model is determined as the final modified vehicle, otherwise the next step of modification is continued.

[0109] Specifically, after the step of determining whether the picture effect corresponding to the picture information satisfies the preset picture effect, the method further includes:

[0110] If not, obtaining at least one second modification parameter;

[0111] Determining whether the second modification parameter satisfies one of the subsystem parameters or the image parameters;

[0112] When the second modification parameter satisfies one of the subsystem parameters or the image parameters, the preset stable diffusion model algorithm is called to modify the current vehicle model again according to the second modification parameter;

[0113] The first modification parameter is different from the second modification parameter.

[0114] It should be noted that the first modification parameter is such as a first type of front bumper, and the second modification parameter is such as a second type of front bumper. The first type of front bumper and the second type of front bumper may have differences in appearance.

[0115] Further, after the step of when the picture effect corresponding to the picture information does not meet the preset picture effect, the method further includes:

[0116] When a plurality of second modification parameters are obtained;

[0117] Determining whether each second modification parameter satisfies one of the subsystem parameters or the image parameters;

[0118] If yes, calling the preset stable diffusion model algorithm to modify the vehicle model again according to each second modification parameter to obtain a modified vehicle model;

[0119] If not, the unsatisfied second modification parameter is marked as other parameter.

[0120] In summary, the method for generating a picture of a modified vehicle shown in this embodiment has at least the following beneficial effects compared with the method for generating a picture of a modified vehicle in the first embodiment:

[0121] In the method for generating images of modified vehicles in the second embodiment of the present invention, by previewing the image effects and repeatedly adjusting the vehicle model according to the previewed image effects, the modified vehicle model can accurately match the customer's satisfaction, thereby further improving the user experience.

[0122] Third embodiment

[0123] Another aspect of the present invention is to provide a picture generation system. Figure 2-Figure 3 , which shows a picture generation system in a third embodiment of the present invention, and the picture generation system includes:

[0124] An acquisition module 11 is used to acquire system parameters and image parameters of a target vehicle, where the system parameters are multiple subsystem parameters constituting the target vehicle;

[0125] The acquisition module is also used to acquire at least one first modification parameter;

[0126] A modeling module 12, used for modeling a plurality of subsystem parameters and image parameters to obtain a vehicle model corresponding to a target vehicle;

[0127] A determination module 13, used to determine whether the first modification parameter satisfies one of the subsystem parameters or the image parameters;

[0128] When the first modification parameter satisfies one of the subsystem parameters or the picture parameters, a preset stable diffusion model algorithm is called to modify the vehicle model according to the first modification parameter to obtain a modified vehicle model, and at least one picture information corresponding to the modified vehicle model is generated;

[0129] The picture generation module 14 is used to generate at least one picture information corresponding to the modified vehicle model.

[0130] Further, the acquisition module 11 includes a vehicle parameter acquisition submodule 111 and a modification parameter acquisition submodule 112;

[0131] The vehicle parameter acquisition submodule 111 is used to acquire the system parameters and image parameters of the target vehicle, where the system parameters are multiple subsystem parameters constituting the target vehicle;

[0132] The modification parameter acquisition submodule 112 is used to acquire at least one first modification parameter.

[0133] Fourth embodiment

[0134] Another aspect of the present invention also provides a picture generating device, see Figure 4 , shown is a picture generating device in the fourth embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the method for generating a picture of a modified vehicle as described above is implemented.

[0135] Among them, the device of the method for generating pictures of modified vehicles can specifically be a vehicle, and the processor 10 in some embodiments can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, which is used to run the program code stored in the memory 20 or process data, such as executing access restriction programs.

[0136] Among them, the memory 20 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of the image generating device, such as the hard disk of the image generating device. In other embodiments, the memory 20 can also be an external storage device of the image generating device, such as a plug-in hard disk equipped on the image generating device, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDiɡital, SD) card, a flash card (FlashCard), etc. Further, the memory 20 can also include both an internal storage unit and an external storage device of the image generating device. The memory 20 can be used not only to store application software and various types of data installed in the image generating device, but also to temporarily store data that has been output or is to be output.

[0137] It should be pointed out that Figure 3 The structure shown does not constitute a limitation on the image generating device. In other embodiments, the image generating device may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0138] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for generating a picture of a modified vehicle as described above is implemented.

[0139] Those skilled in the art will appreciate that the logic or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable storage medium for use by an instruction execution system, device or apparatus (e.g., based on a computer system, including a processor system or other system that can fetch instructions from an instruction execution system, device or apparatus and execute an instruction system), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, a "computer-readable storage medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0140] More specific examples of computer-readable storage media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in another suitable manner as necessary, and then stored in a computer memory.

[0141] It should be understood that the various parts of the present invention can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PgA), a field programmable gate array (FpgA), etc.

[0142] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0143] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A method for generating a picture of a modified vehicle, characterized in that: The generation method comprises: Acquire system parameters and image parameters of a target vehicle, wherein the system parameters are parameters of multiple subsystems constituting the target vehicle; Modeling the plurality of subsystem parameters and the image parameters to obtain a vehicle model corresponding to the target vehicle; Obtaining at least one first modification parameter; Determining whether the first modification parameter satisfies one of the subsystem parameters or the picture parameters; When the first modification parameter satisfies one of the subsystem parameters or the image parameters, the preset stable diffusion model algorithm is called to modify the vehicle model according to the first modification parameter to obtain a modified vehicle model, and generate at least one image information corresponding to the modified vehicle model.

2. The method for generating a modified vehicle image according to claim 1, characterized in that: The step of obtaining at least one first modification parameter further includes: When a plurality of first modification parameters are obtained; Determining whether each of the first modification parameters satisfies one of the subsystem parameters or the image parameters; If so, a preset stable diffusion model algorithm is called to modify the vehicle model according to each of the first modification parameters to obtain a modified vehicle model.

3. The method for generating a modified vehicle image according to claim 2, characterized in that: After the step of determining whether each of the first modification parameters satisfies any one of the subsystem parameters or the picture parameters, the method further includes: When a part of the first modification parameters among the plurality of the first modification parameters does not satisfy any one of the subsystem parameters or the picture parameters; Then the part of the first modification parameters is marked as other parameters.

4. The method for generating a modified vehicle image according to claim 1, characterized in that: After the step of generating at least one piece of picture information corresponding to the modified vehicle model, the method further includes: Determining whether the picture effect corresponding to the picture information meets the preset picture effect; If so, the modified vehicle model is determined as the final modified vehicle.

5. The method for generating a modified vehicle image according to claim 4, characterized in that: After the step of determining whether the picture effect corresponding to the picture information satisfies the preset picture effect, the step further includes: If not, obtaining at least one second modification parameter; Determining whether the second modification parameter satisfies one of the subsystem parameters or the picture parameters; When the second modification parameter satisfies one of the subsystem parameters or the image parameters, calling a preset stable diffusion model algorithm to modify the current vehicle model again according to the second modification parameter; The first modification parameter is different from the second modification parameter.

6. The method for generating a modified vehicle image according to claim 4, characterized in that: After the step of when the picture effect corresponding to the picture information does not meet the preset picture effect, the method further includes: When a plurality of second modification parameters are obtained; Determining whether each of the second modification parameters satisfies one of the subsystem parameters or the picture parameters; If yes, calling a preset stable diffusion model algorithm to modify the vehicle model again according to each of the second modification parameters to obtain a modified vehicle model; If not, the unsatisfied second modification parameter is marked as other parameters.

7. A picture generation system, characterized in that: The system is used to implement the method for generating a picture of a modified vehicle according to any one of claims 1 to 6, and the system comprises: An acquisition module, used to acquire system parameters and image parameters of a target vehicle, wherein the system parameters are parameters of multiple subsystems constituting the target vehicle; The acquisition module is also used to acquire at least one first modification parameter; A modeling module, used for modeling the plurality of subsystem parameters and the image parameters to obtain a vehicle model corresponding to the target vehicle; A determination module, used for determining whether the first modification parameter satisfies one of the subsystem parameters or the image parameters; When the first modification parameter satisfies one of the subsystem parameter or the picture parameter, a preset stable diffusion model algorithm is called to modify the vehicle model according to the first modification parameter to obtain a modified vehicle model, and at least one picture information corresponding to the modified vehicle model is generated; The picture generation module is used to generate at least one picture information corresponding to the modified vehicle model.

8. The image generation system according to claim 7, characterized in that: The acquisition module includes a vehicle parameter acquisition submodule and a modification parameter acquisition submodule; The vehicle parameter acquisition submodule is used to acquire system parameters and image parameters of the target vehicle, wherein the system parameters are multiple subsystem parameters constituting the target vehicle; The modification parameter acquisition submodule is used to acquire at least one first modification parameter.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for generating a picture of a modified vehicle according to any one of claims 1 to 6 is implemented.

10. A picture generating device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for generating a picture of a modified vehicle as claimed in any one of claims 1 to 6 is implemented.

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