Image processing method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention discloses an image processing method and device, a storage medium and electronic equipment. A to-be-optimized first view image is extracted from a model window of a target model, and the target model is configured with a model map; performing image content optimization processing on the first view image through an image generation model to obtain a second view image; based on the image attribute information of the first view image, performing image adjustment on the second view image to obtain a target view image; and mapping the target view image to a target model in the model window, and triggering a model map of the target model to update based on a mapping result to obtain a target model map. In this way, the image generated through optimization can be directly applied to the model map of the three-dimensional model, the generation efficiency of the model map is effectively improved, and then the image processing efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of image processing, and particularly relates to an image processing method, apparatus, storage medium, and electronic device. Background Art

[0002] With the rapid development of life and technology, people often entertain themselves through game applications. Traditional game 3D models are made in digital content creation (DCC) software according to the three-view drawings of the original painting. During the model-making process, frequent modifications are required, which consume a lot of manpower and material resources. In existing image processing methods, an artificial intelligence (AI) model is used to assist in generating the model texture map of the 3D model.

[0003] In the research and practice of the existing technology, it is found that in the existing image processing methods, since the image generated by the AI model and the model texture map are two separate parts, the image generated by the AI model cannot be directly applied to the model texture map of the 3D model, and a large amount of manual painting optimization and adjustment is required in the later stage, resulting in a low generation efficiency of the model texture map and thus a poor image processing efficiency. Summary of the Invention

[0004] Embodiments of this application provide an image processing method, apparatus, storage medium, and electronic device, which can directly apply the optimized generated image to the model texture map of the 3D model, improve the generation efficiency of the model texture map, and thus improve the image processing efficiency.

[0005] Embodiments of this application provide an image processing method, including:

[0006] Extracting a first view image to be optimized in the model view window of the target model, where the target model is configured with a model texture map;

[0007] Performing image content optimization processing on the first view image through an image generation model to obtain a second view image;

[0008] Adjusting the second view image based on the image attribute information of the first view image to obtain a target view image;

[0009] Mapping the target view image to the target model in the model view window to trigger an update of the model texture map of the target model based on the mapping result to obtain a target model texture map.

[0010] Correspondingly, embodiments of this application provide an image processing apparatus, including:

[0011] An extraction unit, configured to extract a first view image to be optimized in the model view window of the target model, where the target model is configured with a model texture map;

[0012] A generating unit, configured to perform image content optimization processing on the first view image through an image generation model to obtain a second view image;

[0013] An adjusting unit, configured to perform image adjustment on the second view image based on the image attribute information of the first view image to obtain a target view image;

[0014] A mapping unit, configured to map the target view image to a target model in the model window, so as to trigger an update of the model texture map of the target model based on the mapping result to obtain a target model texture map.

[0015] In addition, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program is suitable for being loaded by a processor to execute the steps in any one of the image processing methods provided by the embodiments of the present application.

[0016] In addition, an embodiment of the present application further provides an electronic device, including a processor and a memory, where the memory stores an application program, and the processor is configured to run the application program in the memory to implement the image processing method provided by the embodiment of the present application.

[0017] An embodiment of the present application further provides a computer program product, which includes a computer program stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, so that the electronic device executes the steps in the image processing method provided by the embodiment of the present application.

[0018] In the embodiment of the present application, a first view image to be optimized is extracted in the model window of a target model, and the target model is configured with a model texture map; through an image generation model, image content optimization processing is performed on the first view image to obtain a second view image; based on the image attribute information of the first view image, image adjustment is performed on the second view image to obtain a target view image; the target view image is mapped to the target model in the model window, so as to trigger an update of the model texture map of the target model based on the mapping result to obtain a target model texture map. In this way, by extracting the first view image to be optimized in the model window of the target model for image optimization, and then, based on the image attribute information of the first view image, adjusting the optimized second view image, so that the adjusted target view image is mapped to the target model in the model window, and the target model texture map of the target model updated based on the optimized second view image is obtained, it can be realized that the optimized generated image is directly applied to the model texture map of the three-dimensional model, improving the generation efficiency of the model texture map, and further improving the image processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 FIG. 1 is a schematic diagram of an implementation scenario of an image processing method provided by an embodiment of the present application;

[0021] Figure 2 FIG. 2 is a schematic flowchart of an image processing method provided by an embodiment of the present application;

[0022] Figure 3a FIG. 3 is a schematic diagram of a model window of an image processing method provided by an embodiment of the present application;

[0023] Figure 3b FIG. 4 is a schematic diagram of image extraction of an image processing method provided by an embodiment of the present application;

[0024] Figure 3c FIG. 5 is a schematic diagram of image optimization of an image processing method provided by an embodiment of the present application;

[0025] Figure 3d FIG. 6 is another schematic diagram of image optimization of an image processing method provided by an embodiment of the present application;

[0026] Figure 4 FIG. 7 is a schematic diagram of image mapping of an image processing method provided by an embodiment of the present application;

[0027] Figure 5 FIG. 8 is a schematic structural diagram of an image processing apparatus provided by an embodiment of the present application;

[0028] Figure 6 FIG. 9 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0030] An embodiment of the present application provides an image processing method, apparatus, storage medium, and electronic device. Among them, the image processing apparatus can be integrated in the electronic device, and the electronic device can be a server or a terminal device, etc.

[0031] Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, network acceleration services (Content Delivery Network, CDN), and big data and artificial intelligence platforms. The terminal can include, but is not limited to, mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals, aircraft, etc. The terminal and the server can be directly or indirectly connected through wired or wireless communication methods, and the present application does not limit this.

[0032] Please refer to Figure 1 , taking the image processing apparatus integrated in the electronic device as an example, Figure 1 is a schematic diagram of the implementation scenario of the image processing method provided by the embodiment of the present application. Among them, the electronic device can extract a first view image to be optimized in the model window of the target model, and the target model is configured with a model texture; through the image generation model, perform image content optimization processing on the first view image to obtain a second view image; based on the image attribute information of the first view image, perform image adjustment on the second view image to obtain a target view image; map the target view image onto the target model in the model window to trigger an update of the model texture of the target model based on the mapping result to obtain a target model texture.

[0033] It should be noted that Figure 1 the schematic diagram of the implementation environment scenario of the image processing method shown is only an example. The implementation environment scenario of the image processing method described in the embodiment of the present application is for more clearly explaining the technical solution of the embodiment of the present application, and does not constitute a limitation on the technical solution provided by the embodiment of the present application. Those of ordinary skill in the art know that with the evolution of data processing and the emergence of new business scenarios, the technical solution provided by the present application is equally applicable to similar technical problems.

[0034] The solution provided by the embodiment of the present application is specifically described through the following embodiments. It should be noted that the description order of the following embodiments does not limit the preferred order of the embodiments.

[0035] This embodiment will be described from the perspective of the image processing apparatus. The image processing apparatus can be specifically integrated in the electronic device, and the electronic device can be a terminal and / or a server, and the present application does not limit this.

[0036] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of an image processing method provided by an embodiment of the present application. The image processing method includes:

[0037] In step 101, a first view image to be optimized is extracted in the model view window of the target model.

[0038] Among them, the target model may be configured with a model texture map.

[0039] Among them, the target model may be a three-dimensional model and may be a model for texture painting. The model texture map may be a texture map of the target model. For example, it may be a base color map of the target model. The model view window may be a view window for displaying the target model and may be used to display views of the target model from angles such as the front view, left view, and right view. For example, the model view window may be a window in digital content creation (DCC) software for displaying the view of a three-dimensional model. In a specific example, the DCC software may be a UV mapping software (such as Bodypainter3D), which can be used for texture painting and UV editing of three-dimensional models. BodyPaint 3D is a professional 3D painting and texture production software mainly used for texture painting of three-dimensional models. The first view image may be an image extracted in the model view window and may include at least a partial view of the target model displayed in the model view window. For example, the first view image may include the entire area of the target model displayed in the model view window or may include a partial area of the target model that needs to be optimized and is displayed in the model view window.

[0040] For example, please refer to Figure 3a , Figure 3a which is a schematic diagram of the model view window of an image processing method provided by an embodiment of the present application. The model view window of the target model can be displayed in the screen window, and the view of the target model can be displayed in the model view window.

[0041] Among them, there are various ways to extract the first view image to be optimized in the model view window of the target model. For example, the position information and image size parameters corresponding to the first view image to be extracted can be obtained; based on the position information and image size parameters, image extraction is performed in the model view window of the target model to obtain the first view image.

[0042] Among them, the position information may be information indicating the position of the first view image to be extracted in the screen window. For example, the position information may include the regional position information of the first view image to be extracted in the screen window and the central position information. The regional position information may indicate the position of the region where the first view image to be extracted is located in the screen window, and may include the coordinates of the four vertices of the region where the first view image to be extracted is located in the model window in the screen window, or may also include the position coordinates of the bounding box of the region where the first view image to be extracted is located in the model window in the screen window. The central position information may be the coordinates of the center position of the model window in the screen window, or may be the coordinates of the center position of the region where the first view image to be extracted is located in the model window in the screen window. Optionally, for the convenience of image alignment in each image processing process, the coordinates of the position information corresponding to the first view image to be extracted may be coordinates in the coordinate system of the screen window. The image size parameter may be a parameter indicating the image size of the first view image to be optimized that needs to be extracted from the model window. For example, the image size parameter may include the resolution and image ratio of the image to be extracted, and may also include information such as the vertex coordinates of the region where the image to be extracted is located. The image size parameter may be determined based on the resolution of the model window, so that image extraction is performed on the model window based on the image size parameter, and the resolution of the extracted first view image can be the same as the resolution of the model window. The screen window may be a window corresponding to the screen of the electronic device, and the electronic device may be a device that executes the image processing method provided in the embodiments of the present application.

[0043] Optionally, the first view image may be an image corresponding to the part that needs to be optimized in the target model displayed in the model window, and may include all the contents in the model window of the target model, or may also include some contents in the model window of the target model.

[0044] Optionally, the position information may include the regional position information and the central position information corresponding to the first view image to be extracted in the screen window. Thus, for the step of performing image extraction in the model window of the target model based on the position information and the image size parameter to obtain the first view image, it may include: determining the target region where the first view image is located in the screen window based on the regional position information; performing image extraction in the model window based on the target region, the central position information, and the image size parameter to obtain the first view image.

[0045] Among them, the target region may be the region where the first view image to be extracted is located in the screen window.

[0046] In one embodiment, the first view image to be optimized may be extracted from the model window of the target model by taking a screenshot.

[0047] Among them, there are various ways to obtain the position information and image size parameters corresponding to the first view image to be extracted. For example, the screenshot parameters configured in advance can be obtained through a target screenshot software. The screenshot parameters include the image size parameters, regional position information, and central position information corresponding to the first view image to be extracted. Correspondingly, there are various ways to extract the image in the model window based on the target region, central position information, and image size parameters to obtain the first view image. For example, based on the central position information, target region, and image size parameters, the model window can be screenshot by the target screenshot software to obtain the first view image to be optimized.

[0048] Among them, the target screenshot software can be software for taking screenshots. For example, the target screenshot software can be screenshot software such as PixPin, which can be used to quickly capture and edit screen screenshots, improving the efficiency of image processing and sharing. The screenshot parameters can be the parameters required for the target screenshot software to take screenshots, and the target region can be the region where the first view image to be captured is located in the model window.

[0049] For example, based on the screenshot parameters and the property that the target screenshot software can set a fixed constraint screenshot ratio, taking the full-screen mode of the model window as the standard, using the central position information as the screenshot center, setting the screenshot ratio based on the image size parameters, and setting the regional position information of the model window of the target model in advance. Then, software such as BodyPaint 3D for UV mapping can be entered, the angle of the 3D model to be optimized can be selected and displayed through the model window, and the model window can be switched to full-screen mode. Thus, based on the pre-configured screenshot parameters, the target screenshot software can be used to perform positioning screenshots in the screen window to extract the first view image. In this way, the pre-set screenshot ratio and coordinate positioning can ensure that the image generated after optimizing the first view image can be mapped in place to the target model later, so as to realize directly applying the optimized generated image to the model texture of the 3D model and improving the generation efficiency of the model texture.

[0050] Optionally, the model window can be displayed in full-screen mode or in non-full-screen mode. Due to the paste image projection characteristics of BodyPaint 3D, for the image obtained by screenshot to be mapped to the original position of the target model when pasted, it is necessary to constrain the resolution and coordinate position pasted back to be the same as those during screenshot. Therefore, for the model window, it is not necessarily required to be in full-screen mode, but a fixed window position needs to be set as the standard for image extraction and mapping. At the same time, it is necessary to ensure that the central position during screenshot is consistent with the central position of the model window, so as to maintain consistency when taking screenshots of the model window and when projecting and mapping the optimized image to the target model.

[0051] In one embodiment, the central position information of the model window can be compared with the screenshot center of the screenshot of the first view image to determine whether the constrained screenshot center is consistent with the center of the model window. Specifically, by performing a texture mapping transformation operation on the screen displayed in the model window, the central position information of the model window can be obtained through the transformation tool in the DCC software. Then, by invoking the auxiliary line during screenshotting using the target screenshot software, the position information of the screenshot center can be obtained. When the screenshot center and the center of the model window are in the same place and there is no change in the resolution of the first view image and the image to be mapped to the target model, the image information optimized by the AI model can be matched back to the corresponding position of the target model in the model window. Therefore, the higher the resolution of the model window of the target model and the screenshot, the clearer the details of the optimized image can be.

[0052] In one embodiment, please refer to Figure 3b , Figure 3b FIG. is a schematic diagram of image extraction of an image processing method provided by an embodiment of the present application. Taking the model window in full-screen mode as an example, the target area where the first view image to be optimized is located can be located in the screen window according to the screenshot parameters, so that the target area can be screenshot to obtain the first view image.

[0053] In step 102, the first view image is subjected to image content optimization processing through an image generation model to obtain a second view image.

[0054] Among them, the second view image can be an image obtained by optimizing the first view image. The image generation model can be an artificial intelligence model for image generation, and can generate a second view image with optimized image content based on the first view image. The image content optimization processing can include processing such as style conversion and image detail optimization of the image.

[0055] For example, an image generation model provided by an image generation software based on artificial intelligence (AI) technology can be used to perform AI image generation on the first view image to perform image content optimization processing on the first view image to obtain a second view image.

[0056] For example, please refer to Figure 3c , Figure 3c FIG. is a schematic diagram of image optimization of an image processing method provided by an embodiment of the present application. The image on the left can be optimized through an image generation software to obtain the optimized image on the right, so as to improve the display effect of the image and further improve the generation effect of the model texture.

[0057] In one embodiment, in order to make the effect of the optimized second view image better, the first view image extracted in the model window can be set as a square image.

[0058] Optionally, before performing image content optimization processing on the first view image through an image generation model to obtain a second view image, the first view image can be stored in a shared storage space; correspondingly, the step of performing image content optimization processing on the first view image through an image generation model to obtain a second view image can include: reading the first view image stored in the shared storage space through image generation software; calling an image generation model corresponding to at least one preset image optimization direction to perform image content optimization processing on the first view image through the image generation model to obtain a second view image; in addition, the second view image can also be stored in the shared storage space.

[0059] Among them, the shared storage space can be the storage space of shared resources. For example, it can be the clipboard. The image optimization direction can be the direction for optimizing the first view image. For example, it can include optimizing the image into a specific style, such as comic, ink painting, abstraction, etc. It can include different detail effects and can also include different optimization degrees. The image generation model can be a model for image drawing based on AI technology. For example, it can be a Low-Rank Adaptation of Large Language Models (Lora model), which can be used to perform style conversion on the input image and generate an optimized image. Each image generation model can optimize the input image in at least one image optimization direction.

[0060] Optionally, the optimized second view image can be stored in the shared storage space through image generation software, so that UV mapping software such as BodyPaint 3D can obtain the second view image through the shared storage space.

[0061] Among them, there are various ways to call an image generation model corresponding to at least one preset image optimization direction to perform image content optimization processing on the first view image through the image generation model to obtain a second view image. For example, the user can select image generation images corresponding to multiple image optimization directions, so as to call the image generation models corresponding to each image optimization direction to perform image content optimization processing on the first view image to generate second view images corresponding to each image optimization direction. Then, the user can select the second view image that meets the requirements from the generated second view images corresponding to multiple image optimization directions.

[0062] For example, please refer to Figure 3d , Figure 3dIt is another schematic diagram of image optimization for an image processing method provided by an embodiment of this application. By calling the second view images corresponding to multiple image optimization directions, the second view images corresponding to each image optimization direction can be generated. Users can select the second view images that meet their requirements from the generated second view images corresponding to multiple image optimization directions, thereby improving the image processing efficiency.

[0063] In step 103, based on the image attribute information of the first view image, the second view image is adjusted to obtain the target view image.

[0064] Among them, the image attribute information can be the attribute information of the first view image. For example, it can be the image size parameters corresponding to the first view image, specifically including information such as resolution and image ratio.

[0065] Among them, there are various ways to adjust the second view image based on the image attribute information of the first view image to obtain the target view image. For example, the image attribute information can include image size parameters. Based on this, the second view image can be scaled according to the image size parameters of the first view image so that the resolution of the scaled second view image is the same as that of the first view image, thereby obtaining the target view image.

[0066] Optionally, to enable the optimized image to be directly projected onto the target model, it is necessary to scale the optimized second view image according to the resolution of the first view image during extraction, so that the resolution of the target view image is the same as that of the first view image. This can facilitate mapping the target view image to the target model and adjusting the model texture map of the target model based on the optimized target view image to obtain the optimized model texture map of the target model. The model texture map can be the texture map of the target model. For example, it can be the base color map of the target model.

[0067] In step 104, the target view image is mapped onto the target model in the model window to trigger the update of the model texture map of the target model based on the mapping result to obtain the target model texture map.

[0068] Among them, the mapping result can be the result of mapping the target view image onto the target model in the model window. The model texture map can be the texture map of the target model. For example, it can be the base color map of the target model. The target model texture map can be the model texture map adjusted based on the target view image.

[0069] Optionally, based on UV projection mapping, the target view image can be mapped onto the target model in the model window, triggering an update of the model texture map of the target model to obtain the target model texture map of the target model. Among them, UV projection mapping can be used to project a two-dimensional texture image onto the surface of a three-dimensional model using a virtual projection source (such as a camera or a plane). Different from traditional UV mapping, UV projection mapping can directly cover the texture onto the three-dimensional model from a specific angle or position, which is a flexible and precise texture mapping technology suitable for detail processing and texture coverage at specific angles. Specifically, by using the projection mapping tool provided in the DCC software, the coverage effect of the texture in the target model can be controlled, improving the efficiency of texture mapping.

[0070] Among them, there are various ways to map the target view image onto the target model in the model window and trigger an update of the model texture map of the target model based on the mapping result to obtain the target model texture map. For example, in response to a projection mapping operation on the target model, the model window can be set to the mapping state; in the mapping state, based on the position information, the target view image is pasted into the model window, triggering the mapping of the target view image onto the target model, so that the model texture map of the target model is updated to the target model texture map of the target model based on the target view image.

[0071] Among them, the projection mapping operation can be an operation to enable the projection mapping function for the target model, and the mapping state can be a state to be subjected to UV projection mapping.

[0072] Optionally, after obtaining the target view image, the target view image can be copied to the shared storage space; thus, the step of pasting the target view image into the model window based on the position information can include: pasting the target view image in the shared storage space into the model window based on the position information.

[0073] Thus, through the shared storage space, it is convenient to optimize the image content of the first view image after screenshotting, and adjust the optimized second view image to obtain the target view image. Therefore, through the shared storage space, it is convenient to paste the target view image into the model window of the target model, so that the model texture map of the target model is updated to the target model texture map of the target model based on the optimized target view image, realizing the direct application of the optimized generated image to the model texture map of the three-dimensional model and improving the generation efficiency of the model texture map.

[0074] Optionally, the model window of the target model can be captured by a target screenshot software, and the first view image obtained by the capture is stored in the clipboard. Then, the first view image stored in the clipboard can be read by an image generation software and the image content can be optimized, resulting in a second view image. The second view image is scaled according to the resolution of the first view image to obtain a target view image. Then, the target view image is stored in the clipboard by the image generation software. Thus, the target view image in the clipboard can be pasted by DCC software such as BodyPaint 3D, triggering the mapping of the target view image to the target model. In this way, by integrating 3D software, screenshot software, and AI tools, multiple tools are used in combination to achieve the direct application of the optimized generated image to the model texture of the 3D model, effectively improving the generation efficiency of the model texture.

[0075] For example, please refer to Figure 4 , Figure 4 FIG. is a schematic diagram of image mapping of an image processing method provided by an embodiment of the present application. In DCC software such as BodyPaint 3D, the model window of the target model can be displayed. Then, the model window is captured to obtain a first view image and stored in the clipboard. Thus, the first view image in the clipboard can be read by an image generation software and optimized and adjusted to obtain a target view image. The target view image is stored in the clipboard. Thus, it can be returned to the DCC software. In the state where the display state of the model window of the target model is maintained, the projection mapping function provided by the DCC software is enabled. In the state where the projection mapping function is enabled, a paste operation is performed on the target view image in the clipboard to achieve the direct application of the optimized generated image to the model texture of the 3D model, and then the complete matching combination of the target model and the target view image is achieved, obtaining the target model texture of the target model updated based on the optimized second view image. The updated target model can be displayed in the model window of the target model.

[0076] Optionally, after the target view image is mapped to the target model in the model window, a model texture layer corresponding to the target model can be created in the DCC software to display the image information corresponding to the target view image, thereby triggering the update of the model texture of the target model to obtain the target model texture of the target model.

[0077] Traditional 3D models of games are made in DCC software based on the three-view drawings of the original paintings. During the model-making process, frequent modifications are required, which consume a great deal of manpower and material resources. In existing image processing methods, an artificial intelligence (AI) model is used to assist in generating the model texture maps of 3D models. However, since the images generated by the AI model and the model texture maps are two separate parts, only local mapping can be performed manually, and there is a large gap between the images generated by the AI model and the original body, which greatly limits the use of UV mapping. Moreover, a large amount of manual drawing optimization and adjustment is required in the later stage, and the final texture map drawing effect is not ideal, time-consuming and laborious, and the effect is not as good as direct manual drawing. Therefore, in existing image processing methods, the images generated by the AI model cannot be directly applied to the model texture maps of 3D models, and a large amount of manual drawing optimization and adjustment is required in the later stage, resulting in a low generation efficiency of model texture maps and thus a poor image processing efficiency.

[0078] To this end, in the embodiments of the present application, by combining a 3D DCC software, a screenshot software, and an AI image generation tool, multiple tools are integrated and used. The screenshot software is used to extract the images to be optimized corresponding to the 3D models and save the screen positions of the images to be optimized in the model viewport of the model, so that the effect images optimized by the AI based on the images to be optimized can be aligned according to the original screen positions. Furthermore, UV projection mapping can be directly performed in the DCC software to directly modify some texture maps of the model, realizing the direct application of the optimized generated images to the model texture maps of 3D models, optimizing the long feedback modification process in the 3D model texture map production process, enabling the AI tool to directly be integrated into the DCC software to assist in the production of model texture maps, realizing the complete combination of the image effects generated by the AI into the model texture maps, making the optimized images more fitting with the 3D models, effectively reducing the workload of later manual drawing optimization and adjustment. At the same time, by optimizing the extracted images in multiple image optimization directions, multiple image content optimization effects can be provided for selection, increasing the flexibility and selectivity of texture map drawing, and effectively improving the generation efficiency of model texture maps.

[0079] As can be seen from the above, in the embodiment of the present application, a first view image to be optimized is extracted from the model window of the target model, and the target model is configured with a model texture map; through an image generation model, image content optimization processing is performed on the first view image to obtain a second view image; based on the image attribute information of the first view image, image adjustment is performed on the second view image to obtain a target view image; the target view image is mapped onto the target model in the model window, so as to trigger an update of the model texture map of the target model based on the mapping result to obtain a target model texture map. In this way, by extracting the first view image to be optimized in the model window of the target model for image optimization, and then, based on the image attribute information of the first view image, adjusting the optimized second view image, so that the adjusted target view image is mapped onto the target model in the model window, and the target model texture map of the target model updated based on the optimized second view image is obtained, it is possible to directly apply the optimized generated image to the model texture map of the three-dimensional model, improve the generation efficiency of the model texture map, and further improve the image processing efficiency.

[0080] To better implement the above method, an embodiment of the present invention further provides an image processing apparatus, which can be integrated in an electronic device, and the electronic device can be a terminal or a server.

[0081] For example, as Figure 5 shown, it is a schematic structural diagram of the image processing apparatus provided by the embodiment of the present application. The image processing apparatus may include an extraction unit 201, a generation unit 202, an adjustment unit 203, and a mapping unit 204, as follows:

[0082] The extraction unit 201 is configured to extract a first view image to be optimized from the model window of the target model, and the target model is configured with a model texture map;

[0083] The generation unit 202 is configured to perform image content optimization processing on the first view image through an image generation model to obtain a second view image;

[0084] The adjustment unit 203 is configured to perform image adjustment on the second view image based on the image attribute information of the first view image to obtain a target view image;

[0085] The mapping unit 204 is configured to map the target view image onto the target model in the model window, so as to trigger an update of the model texture map of the target model based on the mapping result to obtain a target model texture map.

[0086] In some embodiments, the extraction unit 201 includes:

[0087] An acquisition subunit, configured to acquire the position information and image size parameters corresponding to the first view image to be extracted;

[0088] An extraction subunit, configured to perform image extraction in a model viewport of a target model based on position information and image size parameters, so as to obtain a first view image.

[0089] In some embodiments, the position information includes region position information and center position information corresponding to the first view image to be extracted in a screen window, and the extraction subunit is configured to:

[0090] Determine a target region where the first view image is located in the screen window based on the region position information;

[0091] Perform image extraction in the model viewport based on the target region, the center position information, and the image size parameters, so as to obtain a first view image.

[0092] In some embodiments, the acquisition subunit is configured to:

[0093] Obtain pre-configured screenshot parameters through a target screenshot software, where the screenshot parameters include image size parameters, region position information, and center position information corresponding to the first view image to be extracted;

[0094] The above-mentioned performing image extraction in the model viewport based on the target region, the center position information, and the image size parameters to obtain a first view image is specifically configured to:

[0095] Perform screenshot processing on the model viewport through the target screenshot software based on the center position information, the target region, and the image size parameters, so as to obtain a first view image to be optimized.

[0096] In some embodiments, the mapping unit 204 includes:

[0097] A setting subunit, configured to set the model viewport to a mapping state in response to a projective mapping operation on the target model;

[0098] A mapping subunit, configured to paste a target view image into the model viewport based on the position information in the mapping state, trigger mapping the target view image to the target model, so that the model texture map of the target model is updated to a target model texture map based on the target view image.

[0099] In some embodiments, the image processing device further includes a copying unit, configured to:

[0100] Copy the target view image to a shared storage space;

[0101] The mapping subunit is configured to:

[0102] Paste the target view image in the shared storage space into the model viewport based on the position information.

[0103] In some embodiments, the image processing apparatus further includes a first storage unit for:

[0104] Store the first view image into the shared storage space;

[0105] The generation unit 202 is for:

[0106] Read the first view image stored in the shared storage space through image generation software;

[0107] Invoke at least one image generation model corresponding to a preset image optimization direction to perform image content optimization processing on the first view image through the image generation model to obtain a second view image;

[0108] The image processing apparatus further includes a second storage unit for:

[0109] Store the second view image into the shared storage space.

[0110] In some embodiments, the image attribute information includes image size parameters, and the adjustment unit 203 is for:

[0111] Perform image scaling processing on the second view image based on the image size parameters of the first view image so that the resolution of the scaled second view image is the same as that of the first view image to obtain a target view image.

[0112] In specific implementation, each of the above units can be implemented as an independent entity, or can be combined arbitrarily to be implemented as the same or several entities. For the specific implementation of each of the above units, reference can be made to the foregoing method embodiments, which will not be elaborated herein.

[0113] As can be seen from the above, in the embodiment of the present application, the extraction unit 201 extracts the first view image to be optimized in the model window of the target model, and the target model is configured with a model texture map; the generation unit 202 performs image content optimization processing on the first view image through an image generation model to obtain a second view image; the adjustment unit 203 adjusts the second view image based on the image attribute information of the first view image to obtain a target view image; the mapping unit 204 maps the target view image to the target model in the model window, so as to trigger an update of the model texture map of the target model based on the mapping result to obtain a target model texture map. In this way, by extracting the first view image to be optimized in the model window of the target model for image optimization, and then adjusting the optimized second view image based on the image attribute information of the first view image, so as to map the adjusted target view image to the target model in the model window, and obtain the target model texture map of the target model updated based on the optimized second view image, it is possible to directly apply the optimized generated image to the model texture map of the three-dimensional model, improve the generation efficiency of the model texture map, and further improve the image processing efficiency.

[0114] The embodiment of the present application further provides an electronic device, as Figure 6 shown, which shows a schematic structural diagram of the electronic device involved in the embodiment of the present application. The electronic device may be a terminal or a server. Specifically:

[0115] The electronic device 300 includes a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, and a computer program stored on the memory 302 and executable on the processor. Among them, the processor 301 is electrically connected to the memory 302. Those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the electronic device, and it may include more or fewer components than shown in the figure, or combine some components, or arrange different components.

[0116] The processor 301 is the control center of the electronic device 300, connects various parts of the entire electronic device 300 through various interfaces and lines, executes various functions of the electronic device 300 and processes data by running or loading software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, so as to monitor the entire electronic device 300.

[0117] In the embodiment of the present application, the processor 301 in the electronic device 300 will load the instructions corresponding to the processes of one or more application programs into the memory 302 according to the following steps, and the processor 301 will run the application programs stored in the memory 302 to implement various functions:

[0118] Extract a first view image to be optimized in the model view window of the target model, where the target model is configured with a model texture map;

[0119] Through an image generation model, perform image content optimization processing on the first view image to obtain a second view image;

[0120] Based on the image attribute information of the first view image, perform image adjustment on the second view image to obtain a target view image;

[0121] Map the target view image onto the target model in the model view window to trigger an update of the model texture map of the target model based on the mapping result to obtain a target model texture map.

[0122] This solution can extract a first view image to be optimized in the model view window of the target model, where the target model is configured with a model texture map; through an image generation model, perform image content optimization processing on the first view image to obtain a second view image; based on the image attribute information of the first view image, perform image adjustment on the second view image to obtain a target view image; map the target view image onto the target model in the model view window to trigger an update of the model texture map of the target model based on the mapping result to obtain a target model texture map. In this way, by extracting the first view image to be optimized in the model view window of the target model for image optimization, and then, based on the image attribute information of the first view image, adjusting the optimized second view image, so as to map the adjusted target view image onto the target model in the model view window, and obtain the target model texture map of the target model updated based on the optimized second view image, it can achieve directly applying the optimized generated image to the model texture map of the three-dimensional model, improving the generation efficiency of the model texture map, and further improving the image processing efficiency.

[0123] For the specific implementation of each of the above operations, reference may be made to the previous embodiments, which will not be elaborated here.

[0124] Optionally, as Figure 6 shown, the electronic device 300 further includes: a touch display screen 303, a radio frequency circuit 304, an audio circuit 305, an input unit 306, and a power supply 307. Among them, the processor 301 is electrically connected to the touch display screen 303, the radio frequency circuit 304, the audio circuit 305, the input unit 306, and the power supply 307 respectively. Those skilled in the art can understand that Figure 6 the structure of the electronic device shown in

[0125] The touch display screen 303 can be used to display a graphical user interface and receive operation instructions generated by a user acting on the graphical user interface. The touch display screen 303 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device. These graphical user interfaces can be composed of graphics, text, icons, videos, and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. The touch panel can be used to collect touch operations of the user on or near it (such as operations of the user using any suitable object or accessory such as a finger or a stylus on the touch panel or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute the corresponding program. Optionally, the touch panel can include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch orientation of the user, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 301, and can receive and execute the commands sent by the processor 301. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 301 to determine the type of touch event. Subsequently, the processor 301 provides a corresponding visual output on the display panel according to the type of touch event. In the embodiments of the present application, the touch panel and the display panel can be integrated into the touch display screen 303 to implement input and output functions. However, in some embodiments, the touch panel and the touch panel can be implemented as two independent components to implement input and output functions. That is, the touch display screen 303 can also be used as part of the input unit 306 to implement the input function.

[0126] The radio frequency circuit 304 can be used to transmit and receive radio frequency signals to establish wireless communication with a network device or other electronic devices through wireless communication, and transmit and receive signals with the network device or other electronic devices.

[0127] The audio circuit 305 can be used to provide an audio interface between the user and the electronic device through a speaker and a microphone. The audio circuit 305 can transmit the electrical signal after converting the received audio data to the speaker, and the speaker converts it into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 305 and then converted into audio data. After the audio data is output to the processor 301 for processing, it is transmitted through the radio frequency circuit 304 to, for example, another electronic device, or the audio data is output to the memory 302 for further processing. The audio circuit 305 may also include an earphone jack to provide communication between the peripheral earphone and the electronic device.

[0128] The input unit 306 can be used to receive input digital, character information or user characteristic information (such as fingerprint, iris, facial information, etc.), and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0129] The power supply 307 is used to supply power to each component of the electronic device 300. Optionally, the power supply 307 can be logically connected to the processor 301 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 307 can also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0130] Although Figure 6 not shown in the figure, the electronic device 300 may also include a camera, a sensor, a Wi-Fi module, a Bluetooth module, etc., which will not be elaborated here.

[0131] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. It should be noted that the electronic device provided in the embodiments of the present application and the image processing method in the above embodiments belong to the same concept. The specific implementation process is detailed in the above method embodiments and will not be elaborated here.

[0132] As can be seen from the above, the electronic device provided in the embodiments of the present application can extract a first view image to be optimized in the model view window of the target model, and the target model is configured with a model texture; through an image generation model, perform image content optimization processing on the first view image to obtain a second view image; based on the image attribute information of the first view image, perform image adjustment on the second view image to obtain a target view image; map the target view image to the target model in the model view window to trigger an update of the model texture of the target model based on the mapping result to obtain a target model texture. In this way, by extracting the first view image to be optimized in the model view window of the target model for image optimization, and then, based on the image attribute information of the first view image, adjusting the optimized second view image, so as to map the adjusted target view image to the target model in the model view window, and obtain the target model texture of the target model updated based on the optimized second view image, it is possible to directly apply the optimized generated image to the model texture of the 3D model, improve the generation efficiency of the model texture, and further improve the image processing efficiency.

[0133] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by a computer program or by controlling relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0134] Therefore, an embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor to execute the steps in any one of the image processing methods provided by the embodiments of the present application. For example, the computer program can execute the following steps:

[0135] Extract a first view image to be optimized in the model window of the target model, where the target model is configured with a model texture;

[0136] Through an image generation model, perform image content optimization processing on the first view image to obtain a second view image;

[0137] Based on the image attribute information of the first view image, perform image adjustment on the second view image to obtain a target view image;

[0138] Map the target view image onto the target model in the model window to trigger an update of the model texture of the target model based on the mapping result to obtain a target model texture.

[0139] This solution can extract a first view image to be optimized in the model window of the target model, where the target model is configured with a model texture; through an image generation model, perform image content optimization processing on the first view image to obtain a second view image; based on the image attribute information of the first view image, perform image adjustment on the second view image to obtain a target view image; map the target view image onto the target model in the model window to trigger an update of the model texture of the target model based on the mapping result to obtain a target model texture. In this way, by extracting the first view image to be optimized in the model window of the target model for image optimization, and then, based on the image attribute information of the first view image, adjusting the optimized second view image, so as to map the adjusted target view image onto the target model in the model window to obtain the target model texture of the target model updated based on the optimized second view image, it can be realized that the optimized generated image is directly applied to the model texture of the three-dimensional model, improving the generation efficiency of the model texture and further improving the image processing efficiency.

[0140] For the specific implementation of each of the above operations, reference can be made to the previous embodiments and will not be elaborated here.

[0141] Among them, the computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc.

[0142] Since the computer program stored in the computer-readable storage medium can execute the steps in any one of the image processing methods provided in the embodiments of the present application, the beneficial effects achievable by any one of the image processing methods provided in the embodiments of the present application can be realized. For details, refer to the previous embodiments and will not be elaborated here.

[0143] Among them, according to one aspect of the present application, a computer program product is provided. The computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, so that the electronic device executes the methods provided in the various optional implementation manners provided in the above embodiments.

[0144] The above has introduced in detail an image processing method, apparatus, storage medium, and electronic device provided in the embodiments of the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. An image processing method, characterized in that: include: Extracting a first view image to be optimized in a model window of a target model, wherein the target model is configured with a model map; By using an image generation model, performing image content optimization processing on the first view image to obtain a second view image; Based on the image attribute information of the first view image, performing image adjustment on the second view image to obtain a target view image; The target view image is mapped onto the target model in the model window, so as to trigger the model map update of the target model based on the mapping result to obtain the target model map.

2. The image processing method according to claim 1, characterized in that: The step of extracting the first view image to be optimized in the model window of the target model includes: Obtaining position information and image size parameters corresponding to the first view image to be extracted; Based on the position information and the image size parameter, image extraction is performed in the model window of the target model to obtain a first view image.

3. The image processing method according to claim 2, characterized in that: The position information includes the area position information and the center position information corresponding to the first view image to be extracted in the screen window; The step of extracting an image in a model window of the target model based on the position information and the image size parameter to obtain a first view image includes: Based on the area position information, determining a target area where the first view image is located in the screen window; Based on the target area, the center position information and the image size parameter, image extraction is performed in the model window to obtain a first view image.

4. The image processing method according to claim 3, characterized in that: The obtaining of the position information and image size parameters corresponding to the first view image to be extracted includes: Acquire pre-configured screenshot parameters through target screenshot software, wherein the screenshot parameters include image size parameters, region position information, and center position information corresponding to the first view image to be extracted; The step of extracting an image in the model window based on the target area, the center position information and the image size parameter to obtain a first view image includes: Based on the center position information, the target area and the image size parameters, the model window is screenshotted by the target screenshot software to obtain a first view image to be optimized.

5. The image processing method according to claim 2, characterized in that: Mapping the target view image onto the target model in the model window to trigger the model map update of the target model based on the mapping result to obtain the target model map includes: In response to a projection mapping operation on the target model, setting the model window to a mapping state; In the mapping state, based on the position information, the target view image is pasted into the model window, triggering mapping of the target view image to the target model, so that the model map of the target model is updated to the target model map based on the target view image.

6. The image processing method according to claim 5, characterized in that: Before mapping the target view image onto the target model in the model window to trigger the model map update of the target model based on the mapping result to obtain the target model map, the method further includes: Copying the target view image to a shared storage space; Pasting the target view image into the model window based on the position information includes: Based on the position information, the target view image in the shared storage space is pasted into the model window.

7. The image processing method according to claim 1, characterized in that: Before performing image content optimization processing on the first view image by using the image generation model to obtain the second view image, the method further includes: storing the first view image in a shared storage space; The step of performing image content optimization processing on the first view image by using the image generation model to obtain the second view image includes: Reading the first view image stored in the shared storage space through image generation software; Calling at least one image generation model corresponding to a preset image optimization direction to perform image content optimization processing on the first view image through the image generation model to obtain a second view image; The method further comprises: The second view image is stored in the shared storage space.

8. The image processing method according to any one of claims 1 to 7, characterized in that: The image attribute information includes an image size parameter, and the step of performing image adjustment on the second view image based on the image attribute information of the first view image to obtain a target view image includes: Based on the image size parameter of the first view image, the second view image is subjected to image scaling processing so that the resolution of the scaled second view image is the same as that of the first view image, thereby obtaining a target view image.

9. An image processing device, characterized in that: include: An extraction unit, configured to extract a first view image to be optimized in a model window of a target model, wherein the target model is configured with a model map; A generating unit, configured to perform image content optimization processing on the first view image by using an image generation model to obtain a second view image; an adjusting unit, configured to perform image adjustment on the second view image based on the image attribute information of the first view image to obtain a target view image; A mapping unit is used to map the target view image onto the target model in the model window, so as to trigger the model map update of the target model based on the mapping result to obtain the target model map.

10. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the methods of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: It includes a computer program. When the computer program is run on an electronic device, the computer program is used to enable the electronic device to execute the steps of any method described in claims 1 to 8.

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