Image processing method and device, equipment and medium

Abstract

The invention provides an image processing method and device, equipment and a medium. A specific implementation mode of the method comprises the following steps: acquiring an original image comprising a target object; obtaining guide information input through the target interface; the guide information is used for describing a modeling intention for the target object; generating analysis suggestion information and a modeling effect image corresponding to the original image at least according to the guide information; and outputting the modeling effect image and the analysis suggestion information. According to the embodiment, the image does not need to be manually processed, the image modeling processing efficiency is improved, the image modeling processing way is expanded, and the user experience is improved.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of image processing technology, and more particularly to image processing methods, apparatus, devices, and media. Background Technology

[0002] With the continuous development and improvement of network technology and digital media technology, image processing technology is increasingly being applied to people's lives, providing entertainment services, bringing numerous conveniences, and adding more fun. To make images, including real people and virtual cartoon characters, more aesthetically pleasing, it is often necessary to enhance the appearance of figures in existing images. For example, this might involve replacing makeup or changing clothing. Currently, there is a need for an image processing solution to improve the appearance of figures in existing images. Summary of the Invention

[0003] Embodiments of this disclosure describe an image processing method, apparatus, device, and medium.

[0004] According to a first aspect, an image processing method is provided, the method comprising: acquiring an original image including a target object; acquiring guidance information input through a target interface; the guidance information being used to describe a styling intention for the target object; generating analysis suggestion information and a styling effect image corresponding to the original image based at least on the guidance information; and outputting the styling effect image and the analysis suggestion information.

[0005] According to a second aspect, an image processing apparatus is provided, the apparatus comprising: a first acquisition unit configured to acquire an original image including a target object; a second acquisition unit configured to acquire guidance information input through a target interface; the guidance information being used to describe a styling intention for the target object; a first generation unit configured to generate analysis suggestion information and a styling effect image corresponding to the original image, at least based on the guidance information; and a first output unit configured to output the styling effect image and the analysis suggestion information.

[0006] According to a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed in a computer, causes the computer to perform any of the methods described in the first aspect.

[0007] According to a fourth aspect, an electronic device is provided, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method described in any one of the first aspects.

[0008] According to the image processing scheme provided in this disclosure, by acquiring an original image including a target object, and acquiring guidance information input through a target interface, the guidance information describes the styling intention for the target object. Based at least on the guidance information, a styling effect image and analysis suggestion information corresponding to the original image are generated, and the styling effect image and analysis suggestion information are output. Thus, based on the guidance information, styling processing can be performed on the target object in the original image, and a styling effect image and analysis suggestion information for styling the target object in the original image can be generated. This eliminates the need for manual image processing, improves the efficiency of styling processing in images, expands the methods for styling processing in images, and enhances the user experience.

[0009] Because this embodiment introduces shape information related to the shape of the target object, and generates analysis suggestion information and shape effect images based on this shape information, the image processing effect is improved, making the analysis suggestion information and shape effect images more in line with the personalized needs of the target object.

[0010] By generating analysis suggestion information through the first model and guiding information, and then matching multiple modeling reference images from the pre-built knowledge base based on the analysis suggestion information, the selected target reference image is determined, thereby adding modeling effect to the target object in the original image based on the target reference image, further improving the modeling effect.

[0011] Since this embodiment stores multiple candidate images through a knowledge base, and the candidate images include objects with shaping effects, and the candidate images correspond to tags related to shaping effects, multiple shaping reference images can be selected from the candidate images, thereby making the quality of the shaping reference images higher and helping to improve the shaping effect.

[0012] Because this embodiment can generate a description of the styling effect, it can provide users with more accurate information on styling modifications, thereby improving the user experience. Attached Figure Description

[0013] Figure 1 This is a scene illustration of an image processing scheme according to an exemplary embodiment of the present disclosure;

[0014] Figure 2 This is a schematic diagram of an exemplary system architecture for applying embodiments of this disclosure;

[0015] Figure 3 This is a flowchart illustrating an image processing method according to an exemplary embodiment of the present disclosure;

[0016] Figure 4A This is a schematic diagram of an interactive scenario of an image processing scheme according to an exemplary embodiment of the present disclosure;

[0017] Figure 4B This is a schematic diagram of an interactive scenario of another image processing scheme according to an exemplary embodiment of the present disclosure; Figure 4C This is a schematic diagram of an interactive scenario of another image processing scheme according to an exemplary embodiment of the present disclosure; Figure 5 This is a block diagram illustrating an image processing apparatus according to an exemplary embodiment of the present disclosure; Figure 6 This is a schematic block diagram of an electronic device provided in some embodiments of this disclosure. Detailed Implementation

[0018] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0019] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as electronic devices, applications, servers, or storage media, that perform the operations of the technical solutions disclosed herein, based on the prompt message.

[0020] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0021] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0022] The technical solutions provided in this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0023] With the continuous development and improvement of network technology and digital media technology, image processing technology is increasingly being applied to people's lives, providing entertainment services, bringing numerous conveniences, and adding more fun. To make images, including real people and virtual cartoon characters, more aesthetically pleasing, people often need to enhance the appearance of people in existing images, such as replacing makeup or changing clothing. Currently, this technology generally requires the use of specialized software and manual processing of the appearance of people in existing images. Therefore, it has significant limitations, making it difficult to promote to the general public and meet user needs.

[0024] This disclosure provides an image processing solution that acquires an original image including a target object, obtains guidance information input through a target interface (the guidance information describes the design intent for the target object), and generates a design effect image and analysis suggestion information corresponding to the original image based at least on the guidance information. The solution then outputs the design effect image and analysis suggestion information. This enables design processing of the target object in the original image based on the guidance information, generating a design effect image and analysis suggestion information that represent the design modification of the target object in the original image. It eliminates the need for manual image processing, improving the efficiency of design processing in images, expanding the avenues for design processing in images, and enhancing the user experience.

[0025] See Figure 1 This is a scene diagram illustrating an image processing scheme according to an exemplary embodiment.

[0026] like Figure 1 As shown, firstly, the user can import the original image to be processed into the image processing client through their terminal device. The original image may include, for example, a person or cartoon character to be processed. Then, the user can input a guiding message, which can be text describing the intended style for the object to be processed; for example, the guiding message could be "Please recommend outfits for traveling to tropical regions."

[0027] The image processing client can input the original image, guidance information, and the shape information of the object to be processed into the planning module (planner). The planner can invoke analysis tools to provide analysis suggestions regarding the shape of the object to be processed based on the original image, guidance information, and the shape information of the object. Then, based on the analysis suggestions, it retrieves multiple shape reference images from a pre-built knowledge base. Each shape reference image includes a reference object, which may have a specific shape (e.g., a specific makeup style or specific clothing). The image processing client can display the analysis suggestions and multiple shape reference images to the user for reference. The user can select a target reference image from the multiple shape reference images through the image processing client's display interface as a reference for processing the original image. In addition, the knowledge base can also store shape description information corresponding to the shape reference images. This shape description information can be detailed information about the shape involved in the shape reference image. The target reference image and its corresponding shape description information can also be displayed to the user through the image processing client's display interface for reference.

[0028] Next, Planner can invoke the generation tool to generate a styling effect image based on the original image and the target reference image. For example, it can apply the clothing or makeup of the reference object in the target reference image to the object to be processed in the original image, giving the object in the original image the same clothing or makeup as the reference object in the target reference image. Finally, Planner can invoke the suggestion tool to generate a styling effect description based on the styling information, guidance information, and the styling effect image. The styling effect description can include styling suggestions for the object to be processed in the original image, as well as detailed descriptions of the styling of the processed object. The image processing client can then display the styling effect image and the styling effect description to the user through the image processing client's display interface.

[0029] It should be noted that, in Figure 1 In this embodiment, image processing is described using the image processing client directly performing image processing as an example. In other embodiments, the image processing client can also transmit the original image and guidance information via the network to the image processing server deployed on the service platform. The image processing server obtains the modeling information and, based on the original image, modeling information, and guidance information, generates a modeling effect image and analysis suggestion information. Then, the modeling effect image and analysis suggestion information are transmitted to the image processing client via the network to provide the modeling effect image and analysis suggestion information to the user. See details below. Figure 2 Example.

[0030] Figure 2 This is a schematic diagram of an exemplary system architecture for applying embodiments of this disclosure.

[0031] like Figure 2 As shown, system architecture 200 may include terminal device 202, network 203, and server 204. It should be understood that... Figure 2 The number or type of terminal devices, networks, and servers shown in the diagram is merely illustrative. Any number or type of terminal devices, networks, and servers can be included depending on actual needs.

[0032] Network 203 is a medium used to provide communication links between terminal devices and servers. Network 203 can include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0033] The terminal device 202 is equipped with an image processing client. The terminal device 202 can interact with the server via network 203 to receive or send requests or information. The terminal device 202 can be various electronic devices, including but not limited to smartphones, tablets, laptops, desktop computers, and smart wearable devices.

[0034] Server 204 houses an image processing server. Server 204 can store, analyze, and process received data, and can also send control commands or requests to terminal devices or other servers. The server can provide image processing services in response to user service requests. It is understood that a single server can provide one or more services, and the same service can be provided by multiple servers.

[0035] based on Figure 2 In the system architecture shown in this embodiment, user 201 can input an original image and guidance information through terminal device 202. Terminal device 202 can transmit the original image and guidance information to server 204 via network 203. After receiving the original image and guidance information, server 204 can generate analysis suggestion information based on the original image and guidance information, find at least one modeling reference image from the knowledge base, and then return the analysis suggestion information and at least one modeling reference image to terminal device 202 via network 203, allowing user 201 to view the analysis suggestion information and modeling reference image through terminal device 202. User 201 can select one of the at least one modeling reference image as a target reference image through terminal device 202, and terminal device 202 can transmit the target reference image to server 204 via network 203. Server 204 can generate a modeling effect image based on the target reference image and the original image. Finally, server 204 can return the modeling effect image to terminal device 202 via network 203, allowing user 201 to view and save the modeling effect image through terminal device 202.

[0036] The present disclosure will now be described in detail with reference to specific embodiments.

[0037] Figure 3 This is a flowchart illustrating an image processing method according to an exemplary embodiment. The method can be applied to an image processing client or an image processing server. In this embodiment, the image processing client is installed on a terminal device, which may include, but is not limited to, mobile terminal devices such as smartphones, smart wearable devices, tablets, laptops, and desktop computers. The image processing server is deployed in a service platform, which can be any device, server, or device cluster with computing and processing capabilities. The method may include the following steps:

[0038] like Figure 3 As shown, in step 301, the original image including the target object is obtained.

[0039] In this embodiment, the original image can be an image including the target object, which can be a person or a virtual character. The user needs to enhance the appearance of the target object in the original image; for example, the user can enhance the target object's appearance with makeup or clothing. The user can upload the original image through a client installed on the terminal device; for example, an image from the local photo album or a currently captured image can be uploaded as the original image. It should be noted that the target object in the original image can be the user, or it can be a person or a virtual character other than the user.

[0040] In step 302, the guidance information input through the target interface is obtained.

[0041] In this embodiment, the user can input guidance information through the input interface on the client. This guidance information can describe the styling intention for the target object. For example, the guidance information could be "Please recommend an outfit for a trip to a tropical region." Another example is "What style of makeup suits me?" Yet another example is "I just bought this sweater, how should I style my bottoms?" etc.

[0042] In addition, multiple images of the target object, shape data, color test information, etc. can be pre-transmitted to the image processing client. The image processing client can perform a simple analysis of the uploaded data to obtain the shape information of the target object.

[0043] Because this embodiment introduces shape information related to the shape of the target object, and generates analysis suggestion information and shape effect images based on this shape information, the image processing effect is improved, making the analysis suggestion information and shape effect images more in line with the personalized needs of the target object.

[0044] In this embodiment, if the target object is the user who logged into the image processing client, its shape information can be obtained directly using the user. If the target object is not the user who logged into the image processing client, an interface for inputting the target object's identification information can be provided. The user can input the target object's identification information through this interface and obtain its shape information based on the target object's identification information.

[0045] In step 303, at least the modeling effect image and analysis suggestion information corresponding to the original image are generated based on the guidance information, and in step 304, the modeling effect image and analysis suggestion information are output.

[0046] In one implementation, analysis suggestion information and a modeling effect image corresponding to the original image can be generated based on the guidance information. In another implementation, modeling information corresponding to the target object can be further obtained, and analysis suggestion information and a modeling effect image corresponding to the original image can be generated based on the guidance information and modeling information.

[0047] In this embodiment, a first model can be used to generate analysis suggestion information based on the guidance information. The first model can be a pre-trained small model, into which guidance information, or guidance information and shape information, can be input. The first model then generates analysis suggestion information based on the guidance information, or guidance information and shape information. This analysis suggestion information can be a suggestion for shape modification of the target object in the original image.

[0048] By generating analysis suggestions based on the first model and guidance information, and then matching multiple modeling reference images from a pre-built knowledge base based on the analysis suggestions, the target reference image selected by the user is determined. This results in an enhanced modeling effect on the target object in the original image based on the target reference image, further improving the modeling effect.

[0049] Next, based on the analysis suggestions, multiple modeling reference images are matched from a pre-built knowledge base. The knowledge base can include multiple candidate images, which can include objects with modeling effects. The candidate images correspond to tags related to modeling effects, and multiple modeling reference images can be selected from these candidate images. Specifically, when building the knowledge base, a large number of sample images can be prepared, and tags related to modeling effects can be assigned to these sample images to obtain multiple candidate images, which are then stored in the knowledge base.

[0050] Since this embodiment stores multiple candidate images through a knowledge base, and the candidate images include objects with shaping effects, and the candidate images correspond to tags related to shaping effects, multiple shaping reference images can be selected from the candidate images, thereby making the quality of the shaping reference images higher and helping to improve the shaping effect.

[0051] These tags can be categorized into multiple types, such as people, styles, scenes, and images. Style categories may include, but are not limited to, elements, style attributes, and combinations. Scene categories may include, but are not limited to, time, location, and purpose. Image categories may include, but are not limited to, content attributes and shooting attributes.

[0052] Then, based on the analysis suggestions, label retrieval information can be determined for matching the model reference image. For example, the analysis suggestions can be input into a pre-trained model, which will analyze and process the suggestions to generate label retrieval information for matching the model reference image.

[0053] Finally, based on the tag retrieval information and the tags corresponding to the candidate images in the knowledge base, multiple candidate images can be matched from the knowledge base as styling reference images. Specifically, based on the guidance information, the target type involved in the styling intent for the target object can be determined first. The type involved in the styling intent can correspond to the categories into which the tags are divided. For example, the categories into which the tags are divided can include, but are not limited to, people, styles, scenes, and images. The type involved in the styling intent can be one of the categories into which the tags are divided, that is, the type involved in the styling intent can be one of people, styles, scenes, and images. Then, candidate images related to the target type are obtained from the knowledge base, and multiple candidate images are matched from the candidate images related to the target type as styling reference images based on the tag retrieval information. For example, if the target category is people, candidate images related to the people category can be searched first, and then multiple candidate images can be matched from them as styling reference images based on the tag retrieval information.

[0054] Furthermore, based on the guidance information and the resulting image, a design effect description can be generated and output to the user. This description can include design suggestions for the object to be processed in the original image, as well as a detailed explanation of the processed object's design.

[0055] Because this embodiment can generate a description of the styling effect, it can provide users with more accurate information on styling modifications, thereby improving the user experience.

[0056] This disclosure provides an image processing solution that acquires an original image including a target object, obtains guidance information input by the user through a target interface (the guidance information describes the design intent for the target object), and generates a design effect image and analysis suggestions corresponding to the original image based at least on the guidance information. The design effect image and analysis suggestions are then output to the user. This allows for design processing of the target object in the original image based on the guidance information, generating a design effect image and analysis suggestions that modify the target object in the original image. It eliminates the need for manual image processing, improving the efficiency of design processing in images, expanding the avenues for design processing in images, and enhancing the user experience.

[0057] It should be noted that although the operations of the methods of this disclosure embodiment are described in a specific order in the above embodiments, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flowcharts may be executed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0058] The following is for reference. Figures 4A-4C The solution disclosed herein will be illustrated with a complete and specific application example.

[0059] like Figures 4A-4C As shown, firstly, the image processing client displays interface 401 to the user. Interface 401 includes a guidance information input interface 402, through which the user can input guidance information. For example, ... Figure 4A As shown, the user inputs the guidance information "I want to travel to tropical XX, what outfit should I wear to get good photos?" After completing the input, the user can send the guidance information by clicking button 403. After sending the guidance information, the image processing client can further display scene options to the user through the display interface 401. The user can select a scene through the display interface 401, for example, the user selects the "Tropical Rainforest / Botanical Garden" scene. Next, the image processing client can output the image input interface 404 through the display interface 401, and the user can upload the original image through interface 404.

[0060] After the user uploads the original image, the image processing client can generate analysis suggestions based on the guidance information and the original image, and display the analysis suggestions in area 405. When the user triggers area 405, the image processing client can further display at least one modeling reference image and an explanation of the modeling reference image to the user in area 406 through display interface 401. The user can select a target reference image by triggering one of the modeling reference images. For example, in... Figure 4B In the image, the user selected a target reference image that corresponds to the French resort style.

[0061] After the user selects a target reference image, the original image can be processed based on the target reference image to obtain the modeling effect image 407. The image processing client can also provide an interface 408 to the user through the display interface 401. The user can trigger the interface 408 to instruct the image processing client to generate a modeling effect description and display the modeling effect description in area 409.

[0062] Corresponding to the foregoing embodiments of the image processing method, this disclosure also provides embodiments of the image processing apparatus.

[0063] like Figure 5 As shown, Figure 5 This disclosure is a block diagram of an image processing apparatus according to an exemplary embodiment. The apparatus may include: a first acquisition unit 501, a second acquisition unit 502, a first generation unit 503, and a first output unit 504.

[0064] The first acquisition unit 501 is configured to acquire the original image including the target object.

[0065] The second acquisition unit 502 is configured to acquire guidance information input through the target interface, the guidance information being used to describe the styling intent for the target object.

[0066] The first generation unit 503 is configured to generate analysis suggestion information and modeling effect image corresponding to the original image, based at least on the guidance information.

[0067] The first output unit 504 is configured to output modeling effect images and analysis suggestion information.

[0068] In some embodiments, the device may further include a third acquisition unit (not shown in the figure).

[0069] The third acquisition unit is configured to acquire shape information related to the shape of the target object.

[0070] The first generation unit 503 is configured to generate analysis suggestion information and modeling effect image based on guidance information, original image and modeling information.

[0071] In other embodiments, the first generation unit 503 is configured to: generate analysis suggestion information using a first model, at least based on guidance information; match multiple modeling reference images from a pre-built knowledge base based on the analysis suggestion information; output the multiple modeling reference images and determine the selected target reference image; and generate a modeling effect image based on the target reference image and the original image, wherein the modeling effect image is an image in which a modeling effect has been added to the target object in the original image based on the target reference image.

[0072] In other embodiments, the knowledge base includes multiple candidate images, which include objects with shaping effects. The candidate images correspond to tags related to shaping effects, and multiple shaping reference images are selected from the candidate images.

[0073] In other embodiments, the first generation unit 503 can match multiple modeling reference images from a pre-built knowledge base based on analysis suggestion information in the following manner: Based on the analysis suggestion information, it determines tag retrieval information for matching the modeling reference images. According to the tag retrieval information and the tags corresponding to candidate images in the knowledge base, it matches multiple candidate images from the knowledge base as modeling reference images.

[0074] In other embodiments, the first generation unit 503 may match multiple candidate images from the knowledge base as modeling reference images based on the tag retrieval information and the tags corresponding to the candidate images in the knowledge base in the following manner: based on the guidance information, determine the target type involved in the modeling intention of the target object, obtain candidate images related to the target type from the knowledge base, and match multiple candidate images from the candidate images related to the target type as modeling reference images based on the tag retrieval information.

[0075] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the embodiments of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0076] The following is for reference. Figure 6 , Figure 6This is a schematic block diagram of an electronic device provided for some embodiments of this disclosure. The electronic device 920 is, for example, suitable for implementing the image processing method provided in the embodiments of this disclosure. The electronic device 920 can be a terminal device, etc., and can be used to implement a client or server. The electronic device 920 can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), wearable electronic devices, etc., as well as fixed terminals such as digital TVs, desktop computers, smart home devices, etc. It should be noted that... Figure 6 The illustrated electronic device 920 is merely an example and does not impose any limitation on the functionality and scope of use of the embodiments of this disclosure.

[0077] like Figure 6 As shown, the electronic device 920 may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 921, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 922 or a program loaded from a storage device 928 into a random access memory (RAM) 923. The RAM 923 also stores various programs and data required for the operation of the electronic device 920. The processing unit 921, ROM 922, and RAM 923 are interconnected via a bus 924. An input / output (I / O) interface 925 is also connected to the bus 924.

[0078] Typically, the following devices can be connected to I / O interface 925: input devices 926 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 927 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 928 including, for example, magnetic tapes, hard disks, etc.; and communication devices 929. Communication device 929 allows electronic device 920 to communicate wirelessly or wiredly with other electronic devices to exchange data. Although Figure 6 An electronic device 920 with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown, and the electronic device 920 may alternatively implement or have more or fewer devices. Figure 6 Each box shown can represent a device or multiple devices as needed.

[0079] According to embodiments of this disclosure, the image processing method described above can be implemented as a computer software program. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program including program code for performing the image processing method described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 929, or installed from a storage device 928, or installed from a ROM 922. When the computer program is executed by the processing device 921, the functions defined in the image processing method provided by embodiments of this disclosure can be implemented.

[0080] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods provided in this disclosure.

[0081] It should be noted that the computer-readable medium described in the embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the embodiments of this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the embodiments of this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (Radio Frequency), etc., or any suitable combination thereof.

[0082] Computer program code for performing the operations of embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0083] The various embodiments in this disclosure are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for storage media and computing devices are basically similar to the method embodiments, so they are described more simply; relevant parts can be referred to the descriptions of the method embodiments.

[0084] Those skilled in the art will recognize that the functions described in the embodiments of this disclosure in one or more of the examples above can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.

[0085] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this disclosure. It should be understood that the above descriptions are merely specific implementations of the embodiments of this disclosure and are not intended to limit the scope of protection of this invention. Any modifications, equivalent substitutions, improvements, etc., made based on the technical solutions of this disclosure should be included within the scope of protection of this invention.

Claims

1. An image processing method, the method comprising: Obtain the original image including the target object; Obtain the boot information input through the target interface; The guidance information is used to describe the design intent for the target object; At least based on the guidance information, generate analysis suggestion information and modeling effect image corresponding to the original image; Output the image showing the desired shape and the analysis suggestions.

2. The method according to claim 1, wherein, The method further includes: acquiring shape information related to the shape of the target object; The step of generating analysis suggestion information and styling effect image corresponding to the original image based at least on the guidance information includes: generating the analysis suggestion information and styling effect image based on the guidance information, the original image, and the styling information.

3. The method according to claim 1, wherein, The step of generating analysis suggestion information and modeling effect image corresponding to the original image based at least on the guidance information includes: Using the first model, at least based on the guidance information, the analysis suggestion information is generated; Based on the analysis and suggestion information, multiple modeling reference images are matched from a pre-built knowledge base; Output the multiple modeling reference images and determine the selected target reference image; The modeling effect image is generated based on the target reference image and the original image; the modeling effect image is an image in which the target object in the original image has been given a modeling effect based on the target reference image.

4. The method according to claim 3, wherein, The knowledge base includes multiple candidate images; the candidate images include objects with shaping effects; the candidate images correspond to tags related to shaping effects; the multiple shaping reference images are selected from the candidate images.

5. The method according to claim 4, wherein, Based on the analysis and suggestion information, multiple modeling reference images are matched from a pre-built knowledge base, including: Based on the analysis and suggestion information, tag retrieval information for matching the modeling reference image is determined; Based on the tag retrieval information and the tags corresponding to the candidate images in the knowledge base, multiple candidate images are matched from the knowledge base as modeling reference images.

6. The method according to claim 5, wherein, The step of matching multiple candidate images from the knowledge base as modeling reference images based on the tag retrieval information and the tags corresponding to the candidate images in the knowledge base includes: Based on the guidance information, the target type involved in the styling intent for the target object is determined; Obtain alternative images related to the target type from the knowledge base; Based on the tag retrieval information, multiple candidate images are matched from the candidate images related to the target type as modeling reference images.

7. The method according to claim 1, wherein, The method further includes: At least based on the guidance information and the styling effect image, generate a styling effect description; Output a description of the design effect.

8. An image processing apparatus, the apparatus comprising: The first acquisition unit is configured to acquire the original image including the target object; The second acquisition unit is configured to acquire the guidance information input through the target interface; The guidance information is used to describe the design intent for the target object; The first generation unit is configured to generate analysis suggestion information and modeling effect image corresponding to the original image based at least on the guidance information; The first output unit is configured to output the modeling effect image and the analysis suggestion information.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method of any one of claims 1-7.

10. An electronic device comprising a memory and a processor, wherein the memory stores executable code, and the processor, when executing the executable code, implements the method of any one of claims 1-7.

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