Image processing methods, apparatus, devices, media, and programs

Abstract

Embodiments of the present disclosure relate to image processing methods and apparatus, devices and media, wherein the method includes receiving photo retouching demand information for a first image to be processed; analyzing the photo retouching demand information using a pre-configured model to identify at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions; and performing editing processing on the first image using the target photo retouching function to obtain a second image. Embodiments of the present disclosure can efficiently obtain a second image that satisfies the user's needs, effectively save the user's photo retouching time and effort, and guarantee a good user experience.

Description

Technical Field

[0001] [Cross - reference to Related Applications] This application claims priority based on a Chinese patent application with an application number of 202310967452.6, an invention title of "Image Processing Method, Apparatus, Device, and Medium", and a filing date of August 2, 2023, and all of the content of that application is incorporated herein by reference.

[0002] This disclosure relates to the technical field of image processing, and particularly to an image processing method, apparatus, device, and medium.

Background Art

[0003] The photo - retouch function is widely applied in various application scenarios such as image - clipping software, shooting software, and video live - streaming platforms, and users can adjust images according to their needs. However, through the inventor's research, it has been found that many of the user's photo - retouch needs are subjective, abstract, or ambiguous. For example, users hope that the photographed portrait is cuter or cooler, but they do not know how to use the existing photo - retouch function to edit the image to achieve the required effect. Therefore, in order to meet the photo - retouch needs, among the existing multiple photo - retouch functions, different photo - retouch functions must be repeatedly used to try image editing. However, such a method is cumbersome for users, has a high time cost, consumes the user's labor, and has a poor user experience.

Summary of the Invention

[0004] To solve the above technical problems or at least partially solve the above technical problems, this disclosure provides an image processing method, apparatus, device, and medium.

[0005] Embodiments of the present disclosure provide an image processing method which includes receiving photo retouching demand information for a first image to be processed, analyzing the photo retouching demand information using a pre-configured model to identify at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions, and performing editing processing on the first image using the target photo retouching function to obtain a second image.

[0006] Optionally, the method further includes displaying the second image according to a pre-configured scheme, wherein the pre-configured scheme includes displaying the image alone or together with the first image.

[0007] Optionally, the method further includes displaying information about the target photo retouching function.

[0008] Optionally, the method further includes, in response to receiving user feedback information regarding the second image, associating and storing the photo retouching request information, the target photo retouching function information, the feedback information, the first image, and the second image.

[0009] Optionally, the method further includes, in response to receiving an edit request for the second image, making the second image a new first image.

[0010] Selectively analyzing the photo retouching demand information using the aforementioned pre-configured model and identifying at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions includes analyzing the photo retouching demand information using a pre-configured model based on information from a plurality of pre-configured photo retouching functions and identifying at least one target photo retouching function corresponding to the photo retouching demand information from the plurality of photo retouching functions.

[0011] Selectively analyzing the photo retouching demand information using a pre-configured model based on the information of the pre-configured plurality of photo retouching functions, and identifying at least one target photo retouching function corresponding to the photo retouching demand information from the plurality of photo retouching functions, includes generating model prompt information including a second name for each of the plurality of photo retouching functions based on pre-configured information of the plurality of photo retouching functions, including a first name for each of the plurality of photo retouching functions, and inputting the model prompt information and the photo retouching demand information into a pre-configured model, causing the model to analyze the photo retouching demand information based on the model prompt information, and outputting information for at least one target photo retouching function corresponding to the photo retouching demand information.

[0012] Optionally, the method further includes inputting a first name of the photo retouching function into the pre-configured model and obtaining function definition information output by the model for the first name of the photo retouching function; determining that the second name of the photo retouching function be the same as the first name if the function definition information matches the photo retouching function; and obtaining the second name of the photo retouching function if the function definition information does not match the photo retouching function, wherein the function definition information output by the model for the second name of the photo retouching function matches the photo retouching function.

[0013] Optionally, the information for the multiple photo retouching functions and the model prompt information each include the respective sequence numbers of the multiple photo retouching functions.

[0014] Optionally, the model prompt information further includes output format information corresponding to the model.

[0015] Optionally, the model may include a large-scale language model.

[0016] Embodiments of the present disclosure further provide an image processing apparatus which includes an information receiving module for receiving photo retouching demand information for a first image to be processed; a function identification module for analyzing the photo retouching demand information using a pre-configured model and identifying at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions; and an image editing module for performing editing processing on the first image using the target photo retouching function to obtain a second image.

[0017] Embodiments of the present disclosure further provide electronic equipment comprising a processor and a memory for storing instructions executable by the processor, wherein the processor is used to read the executable instructions from the memory and execute the instructions to implement an image processing method according to embodiments of the present disclosure.

[0018] Embodiments of the present disclosure further provide a computer-readable storage medium in which a computer program is stored, and the computer program is used to execute an image processing method according to embodiments of the present disclosure.

[0019] The content described in this part is not intended to identify the key points or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will be readily understood from the following description.

Brief Description of the Drawings

[0020] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments suitable for the present disclosure, and are used in conjunction with the specification to interpret the principles of the present disclosure.

[0021] To more clearly explain the technical solutions in the embodiments of the present disclosure or the prior art, the drawings necessary for the description of the embodiments or the prior art will be briefly introduced below. It is self-evident that those skilled in the art can obtain other drawings based on these drawings without creative efforts. [Figure 1] It is a flowchart of an image processing method according to an embodiment of the present disclosure. [Figure 2] It is a schematic diagram of an interface according to an embodiment of the present disclosure. [Figure 3] It is a schematic diagram of an interface according to an embodiment of the present disclosure. [Figure 4] It is a flowchart of an image processing method according to an embodiment of the present disclosure. [Figure 5] It is a schematic structural diagram of an image processing apparatus according to an embodiment of the present disclosure. [Figure 6] It is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

Modes for Carrying Out the Invention

[0022] To more clearly understand the above objects, features, and advantages of the present disclosure, the solutions of the present disclosure will be further described below. Unless there is no conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other.

[0023] To fully understand the present disclosure, many details are specifically described in the following description. However, the present disclosure may be implemented in forms different from those described herein. Clearly, the embodiments in the specification are only some embodiments of the present disclosure, not all embodiments.

[0024] FIG. 1 is a flowchart of an image processing method according to an embodiment of the present disclosure. The method may be executed by an image processing apparatus, where the apparatus may be implemented by adopting software and / or hardware and may generally be integrated into an electronic device. As shown in FIG. 1, the method mainly includes the following steps S102 to S106.

[0025] In step S102, receive photo retouch requirement information for the first image to be processed.

[0026] In actual applications, an information input port for the user to input photo retouch requirement information and an upload port for the first image may be provided at the target interface. The information input port may be, for example, a text input box or a voice input control, etc., and is not limited here. The user can upload the first image to be processed through the upload port and input the photo retouch requirement information for the first image through the information input port. The photo retouch requirement information may be expressed in the form of text or voice, etc. The embodiments of the present disclosure do not limit the description of the photo retouch requirement information. The user does not need to describe it according to a specific format. The user may also adopt an oral expression method according to the need. For example, "want to look cooler", "want to look brighter", "want to enhance the three-dimensional effect of the face", etc., and is not limited here.

[0027] In step S104, utilize a preset model to analyze the photo retouch requirement information and identify at least one target photo retouch function corresponding to the photo retouch requirement information from a plurality of preset photo retouch functions.

[0028] An embodiment of this disclosure receives photo retouching demand information for a first image via an information input port, inputs the photo retouching demand information into a pre-configured model, analyzes the photo retouching demand information using the pre-configured model, and searches for a target photo retouching function that can satisfy the photo retouching demand information among a plurality of pre-configured photo retouching functions. Here, the plurality of pre-configured photo retouching functions may be, for example, photo retouching functions of current image editing software. Here, the model may be a neural network model, and the embodiment of this disclosure does not limit the model structure, and in actual applications, the model may be obtained by pre-training.

[0029] For example, an initial neural network model may be comprehensively trained by employing sample photo retouching demand information and information on photo retouching functions that satisfy the sample photo retouching demand information from among multiple photo retouching functions. Training is performed until the function prediction results output by the initial neural network model match the prediction. For example, training is terminated when the photo retouching function information output by the neural network model matches the information on photo retouching functions that actually satisfy the sample photo retouching demand information, or when the difference is smaller than a preset threshold. The neural network model after training is a preset model of the embodiment of this disclosure and is capable of accurately analyzing photo retouching demand information, thereby accurately and reliably identifying a target photo retouching function corresponding to the photo retouching demand information from among multiple preset photo retouching functions. The above is merely one example of model training. In actual applications, model training may be carried out comprehensively by linking sample information such as the first image sample to be adjusted, the photo retouching demand sample information, information on a photo retouching function among multiple photo retouching functions that satisfies the photo retouching demand sample information, and the second image sample obtained by photo retouching the first image sample based on the photo retouching function that satisfies the photo retouching demand sample information, thereby ensuring sufficient reliability of the trained model.

[0030] In summary, the embodiments of this disclosure utilize a pre-trained neural network model based on artificial intelligence technology to directly identify a target photo retouching function corresponding to photo retouching demand information from a set of pre-configured photo retouching functions. That is, by using a neural network model with high learning and processing capabilities and analyzing photo retouching demand information, a target photo retouching function that satisfies the user's needs can be efficiently and reliably identified from a set of photo retouching functions. In actual applications, the number of target photo retouching functions may be one or more, and is not limited thereto.

[0031] In step S106, the first image is edited using the target photo retouching function to obtain the second image.

[0032] In the embodiments of this disclosure, the target photo retouching function can be called directly to perform editing on the first image, eliminating the need for the user to search for and use the target photo retouching function each time they want to edit an image, making the process very fast and easy.

[0033] In summary, the above-described technology according to the embodiment of this disclosure, upon receiving photo retouching demand information, analyzes the photo retouching demand information using a model, automatically identifies a target photo retouching function corresponding to the demand information from multiple photo retouching functions, and directly uses the target photo retouching function to perform editing processing on the first image, thereby efficiently obtaining a second image that satisfies the user's demands. Since the user no longer needs to repeatedly try editing images using different photo retouching functions among multiple existing photo retouching functions to meet their photo retouching demands, the time and effort of the user in photo retouching can be effectively saved, and the user experience can be guaranteed.

[0034] In actual applications, embodiments of this disclosure may display the second image according to a pre-defined method, where the pre-defined method includes displaying it alone or together with the first image. That is, the second image may be displayed alone, or the second image and the first image may be displayed simultaneously. Note that displaying the second image alone means that the only image displayed on the interface is the second image, and does not mean that no other content is displayed on the interface; for example, other text or controls may be displayed on the interface. The method of displaying the second image and the first image simultaneously may be a parallel display method or a vertical alignment method, and is not limited herein. The method of displaying the first image and the second image simultaneously helps the user to clearly compare the differences between the first image and the second image.

[0035] Furthermore, to facilitate the user's clear understanding of which photo retouching function to employ to achieve the effect of the second image, the methods according to embodiments of the present disclosure further include displaying information about the target photo retouching function. For example, the information about the target photo retouching function may include the name of the target photo retouching function and may further include an identifier such as the sequence number of the target photo retouching function. Displaying information about the target photo retouching function on the interface helps the user to clearly understand which photo retouching function to employ to edit the first image in order to obtain the second image.

[0036] Furthermore, the method according to the embodiments of this disclosure further includes, in response to receiving user feedback information on a second image, associating and storing photo retouching demand information, target photo retouching function information, feedback information, the first image, and the second image. In actual applications, the interface may provide a feedback control for the user to input feedback information, which may be in the form of a text input box or a score option, but is not limited herein. Acquiring the feedback information and associating and storing the photo retouching demand information, target photo retouching function information, feedback information, the first image, and the second image facilitates subsequent analysis. For example, based on the photo retouching demand information, target photo retouching function information, feedback information, the first image, and the second image, the parameters of a neural network model for identifying a target photo retouching function corresponding to the photo retouching demand information from a set of pre-configured photo retouching functions can be optimized and adjusted so that the optimized neural network model can later accurately and reliably identify a target photo retouching function that satisfies the user's demand from the set of photo retouching functions, thus further improving the accuracy of target photo retouching function identification.

[0037] Considering that the user may still have a need to adjust the second image, the method according to the embodiments of the present disclosure further includes making the second image a new first image in response to receiving an edit request for the second image. In a practical application, the target interface may provide the user with an edit control for the second image, and upon detecting that such edit control has been triggered, the second image is made a new first image, that is, the original first image is replaced with the second image to create a new first image, and photo retouching is performed based on the photo retouching need information obtained from the user for the new first image (i.e., the previously generated second image). The above method ensures that the user can constantly adjust the photo retouching effect of the image as needed, and that the user can ultimately obtain an image that meets their needs.

[0038] For ease of understanding, the embodiments of this disclosure provide one application example of the embodiments of this disclosure, specifically referring to schematic diagrams of the interfaces shown in Figures 2 and 3, respectively. Figure 2 shows an input box for photo retouching request information, below which a first image uploaded by the user is shown. Figure 2 also shows a clear control and a submit control, and the user may delete the first image using the clear control or submit the first image to the background using the submit control for automatic editing processing. In Figure 3, the obtained second image is displayed in parallel with the first image, and information on the adopted target photo retouching function is clearly shown. For example, if the photo retouching request information is "I want to make it look cooler," the information on the target photo retouching function may be as follows. {"Ability": "Thin facial features", "Sequence number": "8"}{"Ability": "Adjust jaw length", "Sequence number": "21"}{"Ability": "Adjust cheekbones", "Sequence number": "24"}{"Ability": "Adjust eyebrow height", "Sequence number": "50"}{"Ability": "Remove dark circles", "Sequence number": "79"}. Here, "Ability" represents the photo retouching function, and "Sequence number" is the number of the photo retouching function. Furthermore, in the interface shown in Figure 3, a "Move to input" button is also shown. This "Move to input" button is a control to instruct the user to continue editing the second image. When the user clicks this button, the second image can be directly made into the new first image, and the user returns to the interface shown in Figure 2, where the user's photo retouching request information for the first image at this time (i.e., the second image obtained previously) is re-obtained. This is by analogy, and the explanation is omitted here. This method allows users to quickly obtain photo retouching results that meet their needs, and enables them to make multiple adjustments, effectively guaranteeing a positive user experience.Furthermore, Figure 3 shows three level rating buttons (good, average, bad) and a comment input box for users to provide feedback information. Users can directly rate the second image using the level rating buttons and also add text comments to the second image using the comment input box. The above is merely an example and should not be considered a limitation. For example, in actual applications, more or fewer rating levels may be set, or direct scoring by the user may be implemented. Specifically, the settings can be configured flexibly.

[0039] Figures 2 and 3 are merely interfaces illustrating application examples of the image processing method according to the embodiments of this disclosure. In actual applications, the interface format and elements can be flexibly configured and are not limited thereto.

[0040] Based on the above, the embodiments of this disclosure provide embodiments for analyzing photo retouching demand information using a pre-configured model and identifying at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions. For example, based on information from a plurality of pre-configured photo retouching functions, the photo retouching demand information may be analyzed using a pre-configured model to identify a target photo retouching function corresponding to the photo retouching demand information from the plurality of photo retouching functions. The above model may be a neural network model, that is, by using a neural network model with high learning and processing capabilities, a target photo retouching function that satisfies the user's demand can be efficiently and reliably identified from a plurality of photo retouching functions. The embodiments of this disclosure do not limit the structure of the neural network model, and for example, the neural network model may be a language model, and furthermore, the neural network model may be a large-scale language model. A large-scale language model refers to a deep learning model trained using a large amount of data, capable of processing tasks such as multiple types of natural language tasks, possessing high information analysis capabilities, and capable of analyzing photo retouching demand information well. Furthermore, the embodiments of this disclosure can provide the model with information on multiple existing photo retouching functions, and by supporting the model's further analysis of photo retouching demand information, it is possible to identify target photo retouching functions that satisfy the photo retouching demand information.

[0041] In order to enable the model to identify target photo retouching functions more accurately and efficiently, or to reduce the difficulty of the model in identifying target photo retouching functions, a specific example is given below in which, based on information of a plurality of pre-configured photo retouching functions, a pre-configured model is used to analyze the photo retouching demand information, and at least one target photo retouching function corresponding to the photo retouching demand information is identified from the plurality of photo retouching functions, and this may be performed by referring to steps A and B below.

[0042] In step A, model prompt information is generated based on information of a set of multiple photo retouching functions, where the information of the multiple photo retouching functions includes the first name of each of the multiple photo retouching functions, and the model prompt information includes the second name of each of the multiple photo retouching functions. The first name is the name of the photo retouching function itself, and the embodiments of the disclosure take into full consideration that the model may not be able to accurately understand the name of the photo retouching function, and in order to avoid errors in the model's function recommendations due to misunderstandings, the embodiments of the disclosure can generate model prompt information to help the model identify the target photo retouching function. Here, the model prompt information includes at least the second name of each of the multiple photo retouching functions, and the first and second names of the photo retouching functions may be the same or different. If the model can accurately understand the first name and, for example, recognize the role of a photo retouching function based on its first name, then the second name is the same as the first name. If the model cannot accurately understand the first name, for example, if it cannot accurately recognize the role of a photo retouching function based on the first name of that photo retouching function, the second name is different from the first name and is a name that the model can accurately understand. The information about the photo retouching function may also include, but is not limited to, descriptive information about the photo retouching function. For ease of understanding, the embodiments of this disclosure provide a method for determining the second name of a photo retouching function, which may be carried out by referring to the following steps (1) to (3).

[0043] In step (1), the first name of the photo retouching function is input to a pre-configured model, and the function definition information output by the model for the first name of the photo retouching function is obtained. The function definition information is a function description of the photo retouching function output by the model based on the first name.

[0044] In step (2), if the function definition information matches the photo retouching function, it is decided that the second name of the photo retouching function will be the same as the first name. When the function definition information matches the photo retouching function, that is, the model's definition of the photo retouching function matches the actual role of the photo retouching function, it indicates that the model can accurately understand the first name, and in the model prompt information, the second name of the photo retouching function entered into the model is equal to the first name.

[0045] In step (3), if the function definition information does not match the photo retouching function, a second name for the photo retouching function is obtained, and the function definition information output by the model for the second name of the photo retouching function matches the photo retouching function. That is, if the model's definition of the photo retouching function does not match the actual role of the photo retouching function, a new name can be obtained for the photo retouching 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 retouching function matches the photo retouching function. For example, if the model does not understand "forehead adjustment" in the first name, it may be changed to "adjust the area from the hairline to the space between the eyebrows" in the second name.

[0046] In actual applications, steps (1) to (3) above are applied to each of the pre-configured photo retouching functions. This method ensures that the model can accurately understand the meaning of each photo retouching function, thereby guaranteeing the accuracy of the ultimately determined target photo retouching function.

[0047] Furthermore, considering that "hallucinatory phenomena" may occur in the model, that is, in the case of a "hallucinatory phenomenon" in the model, the target photo retouching function output by the model may not exist or may be inaccurate. For example, if a total of 80 photo retouching functions are pre-set, and most of them are photo retouching functions related to human faces, but the photo retouching demand information entered by the user is a demand for body shape correction, in this case the target photo retouching function output by the model may be a photo retouching function other than the 80 photo retouching functions mentioned above, that is, it may fabricate a function that does not exist among the pre-set multiple photo retouching functions. To improve this problem, the embodiments of this disclosure include the sequence number of each of the multiple photo retouching functions in both the information of the multiple photo retouching functions and the model prompt information. That is, for each photo retouching function, there is a one-to-one correspondence between the name of the photo retouching function and its sequence number. By using the above method, the name and sequence number of the photo retouching function are simultaneously included in the model prompt information, which not only mitigates the hallucinatory phenomenon in the model and improves the reliability of the model's output results, but also helps detect whether the name and sequence number of the target photo retouching function output by the model correspond to each other, thus providing a double guarantee of the accuracy of the model's output results.

[0048] Furthermore, to ensure that the model can directly output a format that meets the requirements, the model prompt information may also include output format information corresponding to the model. That is, the model needs to output the name and sequence number of the target photo retouching function according to the output format information. For example, the output format information may include enclosing each key-value field in double quotes, such as “Capability”: “Capability Name”, “Sequence Number”: “Sequence Number Corresponding to the Capability”, as shown in the following JSON format, and may also include, but are not limited to, other fields that may be required and their corresponding field interpretations, such as the recommended capability strength (0-100 or light / medium / strong, etc.).

[0049] In step B, model prompt information and photo retouching demand information are input to a pre-configured model, causing the model to analyze the photo retouching demand information based on the model prompt information and output information for at least one target photo retouching function corresponding to the photo retouching demand information.

[0050] According to the above method, the model can accurately identify a target photo retouching function that matches the photo retouching demand information from among the multiple photo retouching functions provided, based on the model prompt information, and determine information such as the name and sequence number of the target photo retouching function according to the output format information provided by the model prompt information.

[0051] In practical applications, model prompt information and photo retouching demand information may be written according to a pre-configured language format, presented in the form of a pre-configured script, and ultimately input into the model, thereby allowing the model to translate the user's abstract demands into concrete photo retouching solutions.

[0052] For ease of understanding, the embodiments of this disclosure provide a flowchart of an image processing method shown in Figure 4, in which the aforementioned model is described as a large-scale language model. Specifically, user photo retouching request information may be input to a prompt project, which outputs model prompt information. Here, the prompt project generates model prompt information that can be input to the large-scale language model, mainly based on a plurality of existing photo retouching functions. This model prompt information is used to input the model prompt information to the large-scale language model. The large-scale language model outputs a target photo retouching function list (name and sequence number), and based on the target photo retouching function list, performs image editing on the input first image to obtain a second image. The user can perform an evaluation based on the target photo retouching function list and the second image to obtain user feedback information. Furthermore, the photo retouching request information, feedback information, target photo retouching function list, first image, and second image can be saved and used for subsequent data analysis and model training, thereby further optimizing the large-scale language model.

[0053] In summary, the image processing method according to the embodiment of this disclosure, upon receiving photo retouching demand information, analyzes the photo retouching demand information using a model, automatically identifies a target photo retouching function corresponding to the photo retouching demand information from multiple photo retouching functions, and directly uses the target photo retouching function to perform editing processing on the first image, thereby efficiently obtaining a second image that satisfies the user's demands. Since the user does not need to repeatedly try editing images using different photo retouching functions among multiple existing photo retouching functions to meet their photo retouching demands, the time and effort of the user in photo retouching can be effectively saved, and the user experience can be guaranteed.

[0054] Corresponding to the image processing method described above, the embodiments of this disclosure further provide an image processing apparatus, Figure 5 being a schematic diagram of the structure of an image processing apparatus according to an embodiment of this disclosure, the apparatus can be implemented by software and / or hardware and may generally be integrated into electronic equipment, as shown in Figure 5, the image processing apparatus is An information receiving module 502 for receiving photo retouching request information for the first image to be processed, A function identification module 504 for analyzing the photo retouching demand information using a pre-configured model and identifying at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions, It includes an image editing module 506 for performing editing processing on a first image using a target photo retouching function to obtain a second image.

[0055] In summary, the apparatus according to the embodiments of this disclosure can meet the demands of photo retouching by eliminating the need for users to repeatedly try different photo retouching functions in multiple existing photo retouching functions, thereby effectively saving users time and effort in photo retouching and ensuring a good user experience.

[0056] In some embodiments, the apparatus further includes an image display module for displaying the second image according to a preset scheme, wherein the preset scheme includes displaying the image alone or together with the first image.

[0057] In some embodiments, the device further includes a function information display module for displaying information about the target photo retouching function.

[0058] In some embodiments, the device further includes an association storage module for associating and storing the photo retouching request information, the target photo retouching function information, the feedback information, the first image, and the second image in response to receiving user feedback information on the second image.

[0059] In some embodiments, the apparatus further includes an update module for making the second image a new first image in response to receiving an edit request for the second image.

[0060] In some embodiments, the function identification module 504 is specifically used to analyze the photo retouching demand information using a pre-configured model based on information of a plurality of pre-configured photo retouching functions, and to identify at least one target photo retouching function corresponding to the photo retouching demand information from the plurality of photo retouching functions.

[0061] In some embodiments, the function identification module 504 is used to generate model prompt information including a second name for each of the plurality of photo retouching functions, based on preset information of a plurality of photo retouching functions including a first name for each of the plurality of photo retouching functions, input the model prompt information and the photo retouching demand information into a preset model, and have the model analyze the photo retouching demand information based on the model prompt information and output information for at least one target photo retouching function corresponding to the photo retouching demand information.

[0062] In some embodiments, the device inputs a first name for the photo retouching function to a preset model, obtains function definition information output by the model for the first name of the photo retouching function, and if the function definition information matches the photo retouching function, it decides to make the second name of the photo retouching function the same as the first name. If the function definition information does not match the photo retouching function, the device further includes a name determination module for obtaining the second name of the photo retouching function, wherein the function definition information output by the model for the second name of the photo retouching function matches the photo retouching function.

[0063] In some embodiments, both the information on the multiple photo retouching functions and the model prompt information include the respective sequence numbers of the multiple photo retouching functions.

[0064] In some embodiments, the model prompt information further includes output format information corresponding to the model.

[0065] In some embodiments, the model includes a large-scale language model.

[0066] The image processing apparatus according to the embodiments of this disclosure can perform an image processing method according to any embodiment of this disclosure and has a functional module and beneficial effects corresponding to the performance of the method.

[0067] For the convenience and brevity of the description, and so that it can be clearly understood by those skilled in the art, the specific operating processes of the embodiments of the apparatus described above may be described by referring to the corresponding processes in the method embodiments, and are therefore omitted here.

[0068] Embodiments of the present disclosure provide electronic devices comprising a storage device in which a computer program is stored, and a processing device for executing the computer program in the storage device to perform a step of any one of the methods of the present disclosure.

[0069] Referring below to Figure 6, which shows a schematic diagram of the structure of an electronic device 600 suitable for realizing an embodiment of the present disclosure. The terminal devices in the embodiment 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), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. The electronic device shown in Figure 6 is merely an example and does not impose any limitations on the functions and scope of use of the embodiment of the present disclosure.

[0070] As shown in Figure 6, the electronic device 600 may include a processing unit (e.g., a central processor, graphics processor, etc.) 601, which can perform various appropriate operations and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage device 608 into random access memory (RAM) 603. RAM 603 further stores various programs and data necessary for the operation of the electronic device 600. The processing unit 601, ROM 602, and RAM 603 are connected to each other via bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0071] Generally, devices such as input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, and gyroscopes; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, and vibrators; storage devices 608 including, for example, magnetic tape and hard disks; and communication devices 609 can be connected to the I / O interface 605. The communication device 609 can allow the electronic device 600 and other devices to exchange data via wireless or wired communication. Figure 6 shows an electronic device 600 with various devices, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have all of the devices instead.

[0072] In particular, according to embodiments of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product which includes a computer program placed on a non-temporary computer-readable medium, the computer program including program code for performing the method shown in the flowchart. In such embodiments, the computer program may be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, the above-described functions, limited by the methods of embodiments of the present disclosure, are performed.

[0073] In addition to the methods and apparatus described above, embodiments of the present disclosure may also be computer program products including computer program instructions, which, when executed by a processor, cause the processor to execute the image processing method according to the embodiments of the present disclosure. The computer program product may be composed of program code for executing the operations of the embodiments of the present disclosure using any combination of one or more programming languages, and the programming languages ​​may include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user computing device, as a single independent software pack, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0074] The embodiments of this disclosure may also be computer-readable storage media in which computer program instructions are stored, and when the computer program instructions are executed by a processor, the processor is made to execute the image processing method according to the embodiments of this disclosure.

[0075] The computer-readable storage medium may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0076] Embodiments of the present disclosure further provide a computer program product including a computer program / instruction, which, when executed by a processor, realizes the image processing method of the embodiment of the present disclosure.

[0077] To ensure understanding, before using any of the technical proposals disclosed in each embodiment of this disclosure, users should be informed in an appropriate manner, in accordance with applicable laws and regulations, of the type of personal information related to this disclosure, its scope of use, and usage scenarios, and their permission should be obtained.

[0078] For example, in response to receiving a voluntary request from the user, prompt information is sent to the user to clearly inform the user that the operation requested to be performed requires the acquisition and use of the user's personal information. This allows the user to choose whether or not to provide personal information to software or hardware such as electronic devices, application programs, servers, or storage media that perform the operation of the proposed technical method of this disclosure based on the prompt information.

[0079] As a selective but non-restrictive implementation, a method for sending prompt information to a user in response to receiving a voluntary request from the user may be, for example, a pop-up window, and the prompt information may be presented in text format within the pop-up window. Furthermore, the pop-up window may include a selection control that allows the user to choose whether to "agree" or "disagree" to providing personal information to the electronic device.

[0080] To ensure clarity, the above notice and user permission acquisition process are general in nature and do not limit the forms in which this disclosure may be implemented. Other methods that comply with applicable laws and regulations are also applicable to the implementation of this disclosure.

[0081] In this specification, relational terms such as “first” and “second” are merely used to distinguish one entity or operation from another, and do not necessarily require or suggest that any such actual relationship or order exists between these entities or operations. Furthermore, the terms “include,” “incorporate,” or any other variations thereof are intended to cover non-exclusive inclusion, thereby meaning that a process, method, article, or apparatus containing a set of elements includes not only those elements but also other elements not explicitly listed, or further elements specific to that process, method, article, or apparatus. Unless further limited, an element limited by the phrase “includes one…” does not preclude the existence of other identical elements in a process, method, article, or apparatus containing such element.

[0082] The above description is merely a set of specific embodiments of the Disclosure intended to enable those skilled in the art to understand or implement the Disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can also be implemented in other embodiments without departing from the spirit or scope of the Disclosure. Therefore, the Disclosure is not limited to these embodiments described herein, but rather conforms to the broadest extent to which the principles and novel features disclosed herein are consistent.

Claims

1. An image processing method, The steps include receiving photo retouching request information for a first image to be processed, The steps include: analyzing the photo retouching demand information using a pre-configured model, and identifying at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions; An image processing method comprising the steps of performing editing processing on the first image using the target photo retouching function to obtain a second image.

2. The method according to claim 1, further comprising displaying the second image according to a pre-set method, wherein the pre-set method includes the steps of displaying the image alone or together with the first image.

3. The method according to claim 1, further comprising the step of displaying information of the target photo retouching function.

4. The method according to claim 1, further comprising the step of associating and storing the photo retouching request information, the target photo retouching function information, the feedback information, the first image, and the second image in response to receiving user feedback information regarding the second image.

5. The method according to claim 1, further comprising the step of making the second image a new first image in response to receiving an editing request for the second image.

6. The step of analyzing the photo retouching demand information using the aforementioned pre-configured model and identifying at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions is: The method according to any one of claims 1 to 5, comprising the step of analyzing the photo retouching demand information using a pre-configured model based on information of a plurality of pre-configured photo retouching functions, and identifying at least one target photo retouching function corresponding to the photo retouching demand information from the plurality of photo retouching functions.

7. The step of analyzing the photo retouching demand information using a pre-configured model based on the information of the aforementioned pre-configured multiple photo retouching functions, and identifying at least one target photo retouching function corresponding to the photo retouching demand information from the aforementioned multiple photo retouching functions, is: A step of generating model prompt information including a second name for each of the multiple photo retouching functions, based on information for multiple photo retouching functions, including a first name for each of the multiple photo retouching functions, which has been set in advance. The method according to claim 6, comprising the steps of inputting the model prompt information and the photo retouching demand information into a pre-configured model, causing the model to analyze the photo retouching demand information based on the model prompt information, and outputting information for at least one target photo retouching function corresponding to the photo retouching demand information.

8. The steps include: inputting the first name of the photo retouching function into the pre-configured model and obtaining the function definition information output by the model for the first name of the photo retouching function; If the function definition information matches the photo retouching function, the step of deciding to make the second name of the photo retouching function the same as the first name, The method according to claim 7, further comprising the step of obtaining a second name for the photo retouching function if the function definition information does not match the photo retouching function, wherein the function definition information output by the model for the second name for the photo retouching function matches the photo retouching function.

9. The method according to claim 7, wherein the information of the plurality of photo retouching functions and the model prompt information each include the sequence number of the plurality of photo retouching functions.

10. The method according to claim 7, further comprising the model prompt information and output format information corresponding to the model.

11. The method according to claim 6, wherein the model includes a large-scale language model.

12. An image processing device, An information receiving module for receiving photo retouching request information for the first image to be processed, A function identification module for analyzing the photo retouching demand information using a pre-configured model and identifying at least one target photo retouching function corresponding to the photo retouching demand information from a plurality of pre-configured photo retouching functions, An image processing apparatus including an image editing module for performing editing processing on the first image using the target photo retouching function to obtain a second image.

13. It is an electronic device, A memory device in which a computer program is stored, Electronic device comprising a processing unit for executing the computer program in the storage device to realize the steps of the image processing method described in any one of claims 1 to 11.

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

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