Image processing method and apparatus, device, and medium

Abstract

Embodiments of the present invention relate to an image processing method and apparatus, a device, and a medium. The method comprises: receiving image retouching demand information for a first image to be processed; using a preset model to analyze the image retouching demand information, so as to determine from among a plurality of preset image retouching functions, at least one target image retouching function corresponding to the image retouching demand information; and using the target image retouching function to edit the first image, so as to obtain a second image. According to the embodiments of the present invention, a second image satisfying user demands can be efficiently obtained, thereby effectively saving the image retouching time and effort of a user, and guaranteeing user experience well.

Description

Image processing method, device, equipment and medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese invention patent application number 202310967452.6, entitled “Image processing method, device, equipment and medium” and filed on August 2, 2023, and the entire application is incorporated herein by reference. Technical Field

[0003] The present disclosure relates to the field of image processing technology, and in particular to an image processing method, apparatus, device, and medium. Background Art

[0004] Photo editing functions are widely used in various applications such as image editing software, photo taking software, and video live streaming platforms, allowing users to adjust images according to their needs. However, the inventors have found that users' photo editing needs are often subjective, abstract, or vague. For example, users may want to make their portraits more cute or cooler, but they often don't know how to use existing photo editing functions to achieve the desired effect. Therefore, they can only repeatedly try using different photo editing functions from the various existing ones to edit the image to meet their needs. However, this method is relatively cumbersome and complex for users, requiring a high time cost and consuming a lot of user energy, resulting in a poor user experience.

[0005] Summary of the Invention

[0006] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides an image processing method, apparatus, device and medium.

[0007] An embodiment of the present disclosure provides an image processing method, which includes: receiving photo editing requirement information for a first image to be processed; parsing the photo editing requirement information using a preset model to determine at least one target photo editing function corresponding to the photo editing requirement information from a plurality of preset photo editing functions; and editing the first image using the target photo editing function to obtain a second image.

[0008] Optionally, the method further includes: displaying the second image in a preset manner; wherein the preset manner includes: displaying the second image alone, or displaying the second image simultaneously with the first image.

[0009] Optionally, the method further includes: displaying information of the target photo editing function.

[0010] Optionally, the method further includes: in response to receiving user feedback information regarding the second image, associating and storing the photo editing requirement information, the information of the target photo editing function, the feedback information, the first image and the second image.

[0011] Optionally, the method further includes: in response to receiving an editing request for the second image, using the second image as a new first image.

[0012] Optionally, the photo editing requirement information is parsed using a preset model to determine at least one target photo editing function corresponding to the photo editing requirement information from a preset plurality of photo editing functions, including: based on information of a preset plurality of photo editing functions, the photo editing requirement information is parsed using a preset model to determine at least one target photo editing function corresponding to the photo editing requirement information from the preset plurality of photo editing functions.

[0013] Optionally, the information based on the preset multiple photo editing functions is used to parse the photo editing requirement information using a preset model to determine at least one target photo editing function corresponding to the photo editing requirement information from the multiple photo editing functions, including: generating model prompt information based on the information of the preset multiple photo editing functions; wherein the information of the multiple photo editing functions includes the first name of each of the multiple photo editing functions, and the model prompt information includes the second name of each of the multiple photo editing functions; inputting the model prompt information and the photo editing requirement information into a preset model, so that the model parses the photo editing requirement information based on the model prompt information, and outputs information of at least one target photo editing function corresponding to the photo editing requirement information.

[0014] Optionally, the method further includes: inputting the first name of the photo editing function into the preset model to obtain function definition information output by the model for the first name of the photo editing function; if the function definition information matches the photo editing function, determining that the second name of the photo editing function is the same as the first name; if the function definition information does not match the photo editing function, obtaining the second name of the photo editing function; wherein, the function definition information output by the model for the second name of the photo editing function matches the photo editing function.

[0015] Optionally, the information of the multiple photo editing functions and the model prompt information both include the serial numbers of the multiple photo editing functions.

[0016] Optionally, the model prompt information also includes output format information corresponding to the model.

[0017] Optionally, the model includes a large language model.

[0018] The embodiment of the present disclosure also provides an image processing device, including: an information receiving module for receiving photo retouching requirement information for a first image to be processed; a function determination module for parsing the photo retouching requirement information using a preset model to determine at least one target photo retouching function corresponding to the photo retouching requirement information from a plurality of preset photo retouching functions; and an image editing module for editing the first image using the target photo retouching function to obtain a second image.

[0019] An embodiment of the present disclosure also provides an electronic device, which includes: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the image processing method provided by the embodiment of the present disclosure.

[0020] An embodiment of the present disclosure further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the image processing method provided by the embodiment of the present disclosure.

[0021] =

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0024] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0025] FIG1 is a schematic flow chart of an image processing method provided by an embodiment of the present disclosure;

[0026] FIG2 is a schematic diagram of an interface provided by an embodiment of the present disclosure;

[0027] FIG3 is a schematic diagram of an interface provided by an embodiment of the present disclosure;

[0028] FIG4 is a schematic flow chart of an image processing method provided by an embodiment of the present disclosure;

[0029] FIG5 is a schematic structural diagram of an image processing device provided by an embodiment of the present disclosure;

[0030] FIG6 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0033] FIG1 is a flow chart of an image processing method provided by an embodiment of the present disclosure. The method can be performed by an image processing device, wherein the device can be implemented using software and / or hardware and can generally be integrated into an electronic device. As shown in FIG1 , the method mainly includes the following steps S102 to S106:

[0034] Step S102: receiving image retouching requirement information for a first image to be processed.

[0035] In actual applications, the target interface may provide the user with an information input port for inputting information on photo editing requirements and an upload port for the first image. The information input port may be, for example, a text input box, a voice input control, etc., and is not limited here. The user may upload the first image to be processed through the upload port, and input photo editing requirements for the first image through the information input port. The photo editing requirements information may be presented in text form or voice form, and the disclosed embodiments do not restrict the expression of the photo editing requirements information. The user does not need to express it in a specific format, and the user may also use colloquial expressions according to their needs, such as "I want to look cooler," "I want to look more sunny," "make my facial features more three-dimensional," etc., and are not limited here.

[0036] Step S104 , parsing the photo editing requirement information using a preset model to determine at least one target photo editing function corresponding to the photo editing requirement information from a plurality of preset photo editing functions.

[0037] When receiving retouching request information for a first image through an information input port, the disclosed embodiment inputs the retouching request information into a preset model, uses the preset model to parse the retouching request information, and searches for a target retouching function that satisfies the retouching request information from a plurality of preset retouching functions. The plurality of preset retouching functions may, for example, be retouching functions available in current image editing software. The model may be a neural network model. The disclosed embodiment does not impose any restrictions on the model structure. In practical applications, the model may be obtained through pre-training.

[0038] For example, the initial neural network model can be comprehensively trained using the sample information of the retouching requirements and the information of the retouching functions that meet the sample information of the retouching requirements among the various retouching functions until the function prediction result output by the initial neural network model meets the expectations, such as when the retouching function information output by the neural network model is consistent with the information of the retouching function that actually meets the sample information of the retouching requirements or the difference is less than a preset threshold, then the training ends. The neural network model that has completed the training is the model preset in the embodiment of the present disclosure, which can accurately parse the retouching requirements information, thereby having the ability to accurately and reliably determine the target retouching function corresponding to the retouching requirements information from the preset multiple retouching functions. The above is only an example of a training model. In actual applications, the model training can also be comprehensively carried out in combination with the first image sample to be adjusted, the sample information of the retouching requirements, the information of the retouching functions that meet the sample information of the retouching requirements among the various retouching functions, and the second image sample obtained after retouching the first image sample based on the retouching function that meets the sample information of the retouching requirements, so as to fully ensure the reliability of the trained model.

[0039] In summary, in the disclosed embodiments, artificial intelligence technology can be used to utilize a pre-trained neural network model to directly determine the target retouching function corresponding to the retouching request information from a variety of preset retouching functions. In other words, by utilizing a neural network model with strong learning and processing capabilities to analyze the retouching request information, it is possible to efficiently and reliably determine the target retouching function that meets the user's needs from a variety of retouching functions. In practical applications, the number of target retouching functions can be one or more, and this is not limited here.

[0040] Step S106: Edit the first image using the target image editing function to obtain a second image.

[0041] In the embodiment of the present disclosure, the target photo editing function can be directly called to edit the first image, without the user having to search one by one and use the target photo editing function to edit the image, which is very convenient and fast.

[0042] In summary, the above-mentioned technical solution provided by the embodiment of the present disclosure can, when receiving photo retouching requirement information, automatically determine the target photo retouching function corresponding to the photo retouching requirement information from a variety of photo retouching functions by adopting a model to parse the photo retouching requirement information, and directly use the target photo retouching function to edit the first image, so as to efficiently obtain the second image that meets the user's needs. The user does not need to repeatedly try to use different photo retouching functions from the existing multiple photo retouching functions to edit the image in order to meet the photo retouching needs. Therefore, it can effectively save the user's time and energy in photo retouching, and can better guarantee the user experience.

[0043] In actual applications, the embodiment of the present disclosure can display the second image in a preset manner; wherein the preset manner includes: displaying it alone, or displaying it simultaneously with the first image. That is, the second image can be displayed alone, or the second image can be displayed simultaneously with the first image. It should be noted that displaying the second image alone means that the image displayed on the interface is only the second image, and does not mean that other content is not displayed on the interface, such as other text, controls, etc. can also be displayed on the interface. The way in which the second image and the first image are displayed simultaneously can be in a side-by-side manner or in a top-to-bottom alignment manner, which is not limited here. Displaying the first image and the second image simultaneously helps users to clearly compare the difference between the first image and the second image.

[0044] Furthermore, to help users clearly understand which retouching function was used to achieve the effect of the second image, the method provided in the embodiments of the present disclosure further includes: displaying information about the target retouching function. For example, the information about the target retouching function may include the name of the target retouching function and, in addition, an identifier such as a sequence number of the target retouching function. By displaying the information about the target retouching function on the interface, it is also helpful for users to clearly understand which retouching function was used to edit the first image to obtain the second image.

[0045] In addition, the method provided by the embodiment of the present disclosure also includes: in response to receiving feedback information from the user for the second image, associating and storing the photo retouching requirement information, the information of the target photo retouching function, the feedback information, the first image, and the second image. In actual applications, a feedback control for the user to input feedback information can be provided on the interface. The feedback control can be in the form of a text input box or in the form of a rating option, which is not limited here. By obtaining feedback information, the photo retouching requirement information, the information of the target photo retouching function, the feedback information, the first image, and the second image are associated and stored for subsequent analysis. For example, based on the photo retouching requirement information, the information of the target photo retouching function, the feedback information, the first image, and the second image, the parameters of the neural network model used to determine the target photo retouching function corresponding to the photo retouching requirement information from the preset multiple photo retouching functions are optimized and adjusted, so that the optimized neural network model can accurately and reliably determine the target photo retouching function that meets the user's needs from the multiple photo retouching functions in the future, which also helps to further improve the accuracy of determining the target photo retouching function.

[0046] Taking into account that the user may have a need to continue adjusting the second image, the method provided in the embodiment of the present disclosure also includes: in response to receiving an edit request for the second image, using the second image as the new first image. In actual applications, an editing control for the second image can be provided to the user on the target interface. When it is detected that the editing control is triggered, the second image is used as the new first image, that is, the original first image is replaced with the second image as the new first image, and then the user's retouching requirement information for the new first image (that is, the second image generated previously) is obtained, so that the image is further retouched based on the retouching requirement information. In this way, the user can continuously adjust the retouching effect of the image according to the needs, fully ensuring that the user can finally obtain an image that meets the needs.

[0047] For ease of understanding, the present disclosure provides an application example of the present disclosure. Specifically, please refer to the interface diagrams shown in Figures 2 and 3, respectively. Figure 2 illustrates an input box for photo editing request information, and below the input box, the first image uploaded by the user is illustrated. Figure 2 also illustrates a clear control, allowing the user to delete the first image using the clear control or submit the first image to the backend for automatic editing. Figure 3 not only displays the resulting second image alongside the first image, but also clearly indicates the target photo editing function being used. For example, if the photo editing request is "I want to look cooler," the target photo editing function might be: {"Ability":"Light Face Shape", "Serial Number":"8"} {"Ability":"Adjust Chin Length", "Serial Number":"21"} {"Ability":"Adjust Cheekbones", "Serial Number":"24"} {"Ability":"Adjust Eyebrow Height", "Serial Number":"50"} {"Ability":"Remove Dark Circles", "Serial Number":"79"}. The "capability" above represents the photo editing function, and the "serial number" is the number of the photo editing function. In addition, the interface shown in Figure 3 also shows a "Move to Input" button. This "Move to Input" button is a control used to indicate that editing of the second image should continue. When the user clicks this button, the second image can be directly used as the new first image, returning to the interface shown in Figure 2 again, and the user's photo editing request information for the current first image (i.e., the last second image obtained) is again obtained, and so on. In this way, users can quickly obtain photo editing results that meet their needs and can also make multiple adjustments, effectively ensuring user experience. In addition, Figure 3 also shows three rating buttons (Excellent, Average, and Poor) for users to provide feedback information and a comment input box. Users can directly rate the second image using the rating buttons and can also leave text comments on the second image using the comment input box. The above is for simple illustration only and should not be considered as limiting. For example, in actual applications, more or fewer rating levels can be set or users can directly provide ratings, etc., and the specific settings are flexible.

[0048] It should be noted that FIG. 2 and FIG. 3 are merely example application interfaces of the image processing method provided by an embodiment of the present disclosure. In actual applications, the interface style and interface elements can be flexibly set, which is not limited here.

[0049] Based on the foregoing, embodiments of the present disclosure provide an implementation method for parsing photo editing request information using a preset model to determine at least one target photo editing function corresponding to the photo editing request information from a plurality of preset photo editing functions. For example, the preset model can be used to parse the photo editing request information based on the information of the plurality of preset photo editing functions to determine the target photo editing function corresponding to the photo editing request information from the plurality of preset photo editing functions. The aforementioned model can be a neural network model, that is, a neural network model with strong learning and processing capabilities can efficiently and reliably determine the target photo editing function that meets the user's needs from the plurality of photo editing functions. Embodiments of the present disclosure do not limit the structure of the neural network model. For example, the neural network model can be a language model, and further, the neural network model can be a large language model. A large language model refers to a deep learning model trained using a large amount of data, capable of handling a variety of natural language tasks, and possessing strong information parsing capabilities, which can effectively parse photo editing request information. Furthermore, embodiments of the present disclosure can also provide the model with existing information on a variety of photo editing functions to assist the model in further parsing the photo editing request information, thereby determining the target photo editing function that meets the photo editing request information.

[0050] To facilitate the model to more accurately and efficiently determine the target photo retouching function, or to reduce the difficulty of the model in determining the target photo retouching function, the following provides a specific example of using a preset model to parse the photo retouching requirement information based on information of multiple preset photo retouching functions to determine at least one target photo retouching function corresponding to the photo retouching requirement information from the multiple photo retouching functions. This example can be performed with reference to the following steps A and B:

[0051] Step A: Generate model prompt information based on information about multiple preset photo editing functions; wherein the information about the multiple photo editing functions includes the first names of each of the multiple photo editing functions, and the model prompt information includes the second names of each of the multiple photo editing functions. The first name is the name of the photo editing function itself, and the embodiments of the present disclosure fully take into account that the model may not accurately understand the name of the photo editing function. In order to avoid errors in the functions recommended by the model due to misunderstanding, the embodiments of the present disclosure can generate model prompt information for assisting the model in determining the target photo editing function, wherein the model prompt information includes at least the second name of each of the multiple photo editing functions. The first name and the second name of the photo editing function may be the same or different. If the model can accurately understand the first name, such as based on the first name of a photo editing function, the function of the photo editing function can be determined, then the second name is the same as the first name; if the model cannot accurately understand the first name, such as based on the first name of a photo editing function, the function of the photo editing function cannot be accurately determined, then the second name is different from the first name, and the second name is a name that the model can accurately understand. In addition, the information about the photo editing function may also include description information of the photo editing function, etc., which is not limited here. For ease of understanding, the embodiment of the present disclosure provides a method for determining the second name of the photo editing function, which can be performed with reference to the following steps (1) to (3):

[0052] In step (1), the first name of the photo editing function is input into a preset model to obtain function definition information output by the model for the first name of the photo editing function. The function definition information is a function description of the photo editing function output by the model based on the first name.

[0053] In step (2), if the function definition information matches the photo editing function, then the second name of the photo editing function is determined to be the same as the first name. If the function definition information matches the photo editing function, that is, the model's definition of the photo editing function is consistent with the actual function of the photo editing function, then it indicates that the model can accurately understand the first name. In the model prompt information, the second name of the photo editing function input to the model is equal to the first name.

[0054] In step (3), if the function definition information does not match the photo editing function, a second name of the photo editing function is obtained; wherein the function definition information output by the model for the second name of the photo editing function matches the photo editing function. In other words, if the model's definition of the photo editing function does not match the actual function of the photo editing function, a new name can be obtained for the photo editing function, and the obtained second name can be accurately understood by the model, that is, the function definition information output by the model for the second name of the photo editing function matches the photo editing function. For example, assuming that the model does not understand the first name "adjust the upper face", it can be changed to the second name "adjust the hairline area to the eyebrow area".

[0055] In practical applications, the above steps (1) to (3) are used to execute each of the preset multiple photo editing functions. In this way, it can be fully ensured that the model can accurately understand the meaning of each photo editing function and guarantee the accuracy of the final target photo editing function.

[0056] Furthermore, considering that the model may experience "hallucination phenomenon", that is, in the "hallucination phenomenon" of the model, the target photo retouching function output by the model may not exist or be inaccurate. For example, assuming that there are 80 preset photo retouching functions, and most of them are face-related photo retouching functions, and the photo retouching requirement information input by the user is a body beauty requirement, then the target photo retouching function output by the model may be other photo retouching functions other than the above 80 photo retouching functions, that is, a function that does not exist in the preset multiple photo retouching functions may be fabricated. To improve this problem, the embodiment of the present disclosure includes the serial numbers of the multiple photo retouching functions in the information of the multiple photo retouching functions and the model prompt information. That is, for each photo retouching function, the name of the photo retouching function corresponds to the serial number one by one. In this way, the name and serial number of the photo retouching function are included in the model prompt information at the same time, which not only alleviates the hallucination phenomenon of the model and improves the reliability of the model output results, but also helps to detect whether the name and serial number of the target photo retouching function output by the model correspond, thereby doubly ensuring the accuracy of the model output results.

[0057] Furthermore, in order to ensure that the model can directly output a format that meets the requirements, the model prompt information can also include the output format information corresponding to the model. That is, the model needs to output the name, serial number, etc. of the target photo editing function in accordance with the output format information. For example, the output format information includes: in accordance with the following json format, each key-value field (key value field) is enclosed in double quotes - "capability": "capability name", "serial number": "serial number corresponding to the capability", etc. In addition, it can also include other fields that may be needed and corresponding field explanations, such as capability recommendation strength (0-100 or mild / moderate / severe, etc.), which are not restricted here.

[0058] In step B, the model prompt information and the photo editing requirement information are input into a preset model, so that the model parses the photo editing requirement information based on the model prompt information and outputs information of at least one target photo editing function corresponding to the photo editing requirement information.

[0059] Through the above method, the model can accurately determine the target photo retouching function that matches the photo retouching requirement information based on the various photo retouching functions provided by the model prompt information, and determine the name, serial number and other information of the target photo retouching function according to the output format information provided by the model prompt information.

[0060] In actual applications, model prompt information and photo editing requirement information can be described in a preset language format and presented in the form of a preset script, and finally input into the model so that the model can convert the user's abstract requirements into specific photo editing solutions.

[0061] For ease of understanding, the present disclosure provides a flow chart of an image processing method as shown in FIG4 . In this method, the aforementioned model is a large language model as an example for explanation. Specifically, the user's photo editing requirement information can be input into the prompt project, and the prompt project outputs model prompt information. The prompt project is mainly used to generate model prompt information that can be input into the large language model based on the existing multiple photo editing functions, and input the model prompt information into the large language model. The large language model outputs a target photo editing function list (name and serial number), and the input first image is edited based on the target photo editing function list to obtain a second image. The user can evaluate based on the target photo editing function list and the second image to obtain user feedback information. The photo editing requirement information, feedback information, target photo editing function list, first image and second image can be further saved and can be used for subsequent data analysis and model training, thereby further optimizing the large language model.

[0062] To sum up, the above-mentioned image processing method provided by the embodiment of the present disclosure can, when receiving photo retouching requirement information, automatically determine the target photo retouching function corresponding to the photo retouching requirement information from a variety of photo retouching functions by adopting a model to parse the photo retouching requirement information, and directly use the target photo retouching function to edit the first image, so as to efficiently obtain the second image that meets the user's needs. The user does not need to repeatedly try to use different photo retouching functions from the existing multiple photo retouching functions to edit the image in order to meet the photo retouching needs. Therefore, it can effectively save the user's time and energy in photo retouching, and can better guarantee the user experience.

[0063] Corresponding to the aforementioned image processing method, an embodiment of the present disclosure further provides an image processing device. FIG5 is a schematic structural diagram of an image processing device provided by an embodiment of the present disclosure. The device can be implemented by software and / or hardware and can generally be integrated into an electronic device. As shown in FIG5 , the image processing device includes:

[0064] An information receiving module 502 is configured to receive image retouching requirement information for a first image to be processed;

[0065] a function determination module 504 for analyzing the image retouching requirement information using a preset model to determine at least one target image retouching function corresponding to the image retouching requirement information from among a plurality of preset image retouching functions;

[0066] The image editing module 506 is configured to edit the first image using a target image editing function to obtain a second image.

[0067] In summary, the above-mentioned device provided by the embodiment of the present disclosure does not require users to repeatedly try different photo editing functions from the existing multiple photo editing functions to edit images in order to meet the photo editing needs. Therefore, it can effectively save users' photo editing time and energy and better guarantee user experience.

[0068] In some embodiments, the device further includes an image display module for displaying the second image in a preset manner; wherein the preset manner includes: displaying the second image alone, or displaying the second image simultaneously with the first image.

[0069] In some embodiments, the device further includes a function information display module for displaying information about the target photo editing function.

[0070] In some embodiments, the device further includes an associated storage module for associating and storing the photo editing requirement information, the target photo editing function information, the feedback information, the first image, and the second image in response to receiving user feedback information regarding the second image.

[0071] In some embodiments, the apparatus further comprises an updating module configured to, in response to receiving an editing request for the second image, use the second image as a new first image.

[0072] In some embodiments, the function determination module 504 is specifically used to: based on information of a plurality of preset photo editing functions, use a preset model to parse the photo editing requirement information to determine at least one target photo editing function corresponding to the photo editing requirement information from the plurality of photo editing functions.

[0073] In some embodiments, the function determination module 504 is specifically used to: generate model prompt information based on information of multiple preset photo editing functions; wherein the information of the multiple photo editing functions includes the first name of each of the multiple photo editing functions, and the model prompt information includes the second name of each of the multiple photo editing functions; input the model prompt information and the photo editing requirement information into a preset model, so that the model parses the photo editing requirement information based on the model prompt information, and outputs information of at least one target photo editing function corresponding to the photo editing requirement information.

[0074] In some embodiments, the device also includes a name determination module, which is used to input the first name of the photo editing function into the preset model to obtain function definition information output by the model for the first name of the photo editing function; if the function definition information matches the photo editing function, it is determined that the second name of the photo editing function is the same as the first name; if the function definition information does not match the photo editing function, the second name of the photo editing function is obtained; wherein, the function definition information output by the model for the second name of the photo editing function matches the photo editing function.

[0075] In some embodiments, the information of the multiple photo editing functions and the model prompt information both include serial numbers of the multiple photo editing functions.

[0076] In some implementations, the model prompt information further includes output format information corresponding to the model.

[0077] In some implementations, the model comprises a large language model.

[0078] The image processing device provided by the embodiments of the present disclosure can execute the image processing method provided by any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0079] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device embodiment can refer to the corresponding process in the method embodiment, and will not be repeated here.

[0080] An embodiment of the present disclosure provides an electronic device, which includes: a storage device storing a computer program; and a processing device configured to execute the computer program in the storage device to implement the steps of any one of the methods in the present disclosure.

[0081] 6, which shows a schematic diagram suitable for implementing the embodiment of the present disclosure.

[0082] Schematic diagram of the structure of electronic device 600. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The electronic device shown in FIG6 is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0083] As shown in Figure 6, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device 600 are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0084] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although FIG. 6 illustrates the electronic device 600 with various devices, it should be understood that not all of the illustrated devices are required to be implemented or present. More or fewer devices may alternatively be implemented or present.

[0085] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0086] In addition to the above-mentioned methods and devices, the embodiments of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, cause the processor to perform the image processing method provided by the embodiments of the present disclosure. The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present disclosure, the programming languages ​​including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0087] In addition, the embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor is enabled to execute the image processing method provided by the embodiment of the present disclosure.

[0088] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0089] The embodiment of the present disclosure further provides a computer program product, including a computer program / instruction, which implements the image processing method in the embodiment of the present disclosure when executed by a processor.

[0090] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0091] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested 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 electronic device, application, server, storage medium, or other software or hardware that performs the operation of the disclosed technical solution based on the prompt message.

[0092] As an optional but non-limiting implementation, in response to receiving a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0093] It is understandable that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0094] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0095] The foregoing description is intended only to provide specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments described herein, but rather to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. An image processing method, comprising: Receiving image retouching requirement information for a first image to be processed; Analyzing the image editing requirement information using a preset model to determine at least one target image editing function corresponding to the image editing requirement information from a plurality of preset image editing functions; The first image is edited using the target image editing function to obtain a second image.

2. The method according to claim 1, further comprising: The second image is displayed in a preset manner; wherein the preset manner includes: displaying the second image alone, or displaying the second image together with the first image.

3. The method according to claim 1, further comprising: Display information about the target image editing function.

4. The method according to claim 1, further comprising: In response to receiving user feedback information regarding the second image, the image retouching requirement information, the information of the target image retouching function, the feedback information, the first image, and the second image are stored in association.

5. The method according to claim 1, further comprising: In response to receiving an edit request for the second image, the second image is used as a new first image.

6. The method according to any one of claims 1 to 5, wherein: The using a preset model to analyze the image editing requirement information to determine at least one target image editing function corresponding to the image editing requirement information from a plurality of preset image editing functions includes: Based on information of a plurality of preset photo editing functions, the photo editing requirement information is parsed using a preset model to determine at least one target photo editing function corresponding to the photo editing requirement information from the plurality of photo editing functions.

7. The method according to claim 6, wherein: The step of parsing the picture retouching requirement information based on the information of the preset multiple picture retouching functions using a preset model to determine at least one target picture retouching function corresponding to the picture retouching requirement information from the multiple picture retouching functions includes: Generate model prompt information based on information of a plurality of preset photo editing functions; wherein the information of the plurality of photo editing functions includes first names of the plurality of photo editing functions, and the model prompt information includes second names of the plurality of photo editing functions; The model prompt information and the photo editing requirement information are input into a preset model, so that the model parses the photo editing requirement information based on the model prompt information, and outputs information of at least one target photo editing function corresponding to the photo editing requirement information.

8. The method according to claim 7, further comprising: Inputting the first name of the photo retouching function into the preset model to obtain function definition information output by the model for the first name of the photo retouching function; If the function definition information matches the photo editing function, determining that the second name of the photo editing function is the same as the first name; If the function definition information does not match the photo editing function, a second name of the photo editing function is obtained; wherein the function definition information output by the model for the second name of the photo editing function matches the photo editing function.

9. The method according to claim 7, wherein: The information of the multiple photo editing functions and the model prompt information both include the serial numbers of the multiple photo editing functions.

10. The method according to claim 7, wherein: The model prompt information also includes output format information corresponding to the model.

11. The method according to claim 6, wherein: The model includes a large language model.

12. An image processing device, comprising: An information receiving module, used for receiving image retouching requirement information for a first image to be processed; a function determination module, configured to analyze the image retouching requirement information by using a preset model, so as to determine at least one target image retouching function corresponding to the image retouching requirement information from among a plurality of preset image retouching functions; The image editing module is used to edit the first image using the target image editing function to obtain a second image.

13. An electronic device comprising: a storage device having a computer program stored thereon; A processing device, used to execute the computer program in the storage device to implement the steps of the image processing method according to any one of claims 1 to 11.

14. A computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program is used to execute the image processing method according to any one of claims 1 to 11.

Images