1. An image processing method, comprising:

acquiring an expression image;

adjusting the expression in the expression image based on a preset image processing model to generate a video with an expression change process;

and displaying the video.

2. The method of claim 1, wherein the adjusting the expression in the expression image based on a preset image processing model to generate a video with an expression change process comprises:

and adjusting the smiling degree and/or the eye opening and closing degree in the expression image based on a preset image processing model to generate a video with a smiling degree and/or eye opening and closing degree change process.

3. The method of claim 1, wherein the image processing model is trained based on an expression image of a sample object and an expression change video of the sample object.

4. The method according to claim 3, wherein the expression change video of the sample object is obtained by transferring an expression change process in a preset video to the expression image of the sample object by using a preset transfer model.

5. The method of claim 4, wherein the migration model is trained based on images of a plurality of facial regions and expression differences between the images of the plurality of facial regions, wherein the images of the plurality of facial regions are images of the same type of expression, and the expression degrees of the same type of expression in different images are different.

6. The method of claim 5, wherein the image of the face region is extracted based on facial key points on a preset face image.

7. An image processing apparatus characterized by comprising:

the expression image acquisition unit is used for acquiring expression images;

the video generation unit is used for adjusting the expression in the expression image based on a preset image processing model to generate a video with an expression change process;

and the video display unit is used for displaying the video.

8. The apparatus according to claim 7, wherein the video generation unit adjusts the degree of smiling and/or the degree of open/close eyes in the expression image based on a preset image processing model, and generates the video having a process of changing the degree of smiling and/or the degree of open/close eyes.

9. The apparatus of claim 7, wherein the image processing model is trained based on an expression image of a sample object and an expression change video of the sample object.

10. The apparatus of claim 9, wherein the expression change video of the sample object is obtained by migrating an expression change process in a preset video to an expression image of the sample object by using a preset migration model.

11. The apparatus of claim 10, wherein the migration model is trained based on images of a plurality of facial regions, and expression differences between the images of the plurality of facial regions, wherein the images of the plurality of facial regions are images of the same type of expression, and the expression degrees of the same type of expression in different images are different.

12. The apparatus of claim 11, wherein the image of the face region is extracted based on facial key points on a preset face image.

13. A terminal device, comprising:

memory and a processor, wherein the memory has stored therein a computer program which, when executed by the processor, implements the method of any of claims 1-6.

14. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.

Background

In the related art, a user can record life by means of videos, photos and the like and upload the life to a video application for other video consumers to watch. However, with the development of video applications, the increasing user demands cannot be met by simple video or picture sharing, and therefore how to process videos or images to improve the interestingness of the videos or images is a technical problem which needs to be solved at present.

Disclosure of Invention

In order to solve the technical problem or at least partially solve the technical problem, embodiments of the present disclosure provide an image processing method, apparatus, device and storage medium.

A first aspect of the present disclosure provides an image processing method, including:

acquiring an expression image;

adjusting the expression in the expression image based on a preset image processing model to generate a video with an expression change process;

and displaying the video.

Optionally, the adjusting the expression in the expression image based on a preset image processing model to generate a video with an expression change process includes:

and adjusting the smiling degree and/or the eye opening and closing degree in the expression image based on a preset image processing model to generate a video with a smiling degree and/or eye opening and closing degree change process.

Optionally, the image processing model is obtained by video training based on expression images of the sample object and expression changes of the sample object.

Optionally, the expression change video of the sample object is obtained by migrating an expression change process in a preset video to the expression image of the sample object by using a preset migration model.

Optionally, the migration model is trained based on images of a plurality of facial regions and expression differences between the images of the plurality of facial regions, wherein the images of the plurality of facial regions are images of the same type of expression, and the expression degrees of the same type of expression in different images are different.

Optionally, the image of the face region is extracted based on a face key point on a preset face image.

A second aspect of the present disclosure provides an image processing apparatus comprising:

the expression image acquisition unit is used for acquiring expression images;

the video generation unit is used for adjusting the expression in the expression image based on a preset image processing model to generate a video with an expression change process;

and the video display unit is used for displaying the video.

Optionally, the video generating unit adjusts the smiling degree and/or the eye opening and closing degree in the expression image based on a preset image processing model, and generates a video having a process of changing the smiling degree and/or the eye opening and closing degree.

Optionally, the image processing model is obtained by video training based on expression images of the sample object and expression changes of the sample object.

Optionally, the expression change video of the sample object is obtained by migrating an expression change process in a preset video to the expression image of the sample object by using a preset migration model.

Optionally, the migration model is trained based on images of a plurality of facial regions and expression differences between the images of the plurality of facial regions, wherein the images of the plurality of facial regions are images of the same type of expression, and the expression degrees of the same type of expression in different images are different.

Optionally, the image of the face region is extracted based on a face key point on a preset face image.

A third aspect of the present disclosure provides a terminal device, comprising:

a memory and a processor, wherein the memory has stored therein a computer program which, when executed by the processor, implements the image processing method as described above.

A fourth aspect of the present disclosure provides a computer-readable storage medium having stored therein a computer program which, when executed by a processor, implements the image processing method as described above.

Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:

the image processing method, the image processing device, the image processing equipment and the storage medium, which are provided by the embodiment of the disclosure, can adjust the expression in the expression image based on the preset image processing model after the expression image is obtained, so as to generate a video with an expression change process, and further enable a user to watch the video more interestingly by displaying the video to the user, so that the user experience is improved.

Drawings

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 disclosure.

In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.

Fig. 1 is a flowchart of an image processing method provided by an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of an expression image provided by one embodiment of the present disclosure;

FIG. 3 is a schematic diagram of an adjusted expression image generated based on FIG. 2;

FIG. 4 is a schematic diagram of another adjusted expression image generated based on FIG. 2;

FIG. 5 is a schematic diagram of yet another adjusted expression image generated based on FIG. 2;

FIG. 6 is an image of a face region provided by one embodiment of the present disclosure;

FIG. 7 is an image of a face region provided by another embodiment of the present disclosure;

FIG. 8 is an image of a face region provided by yet another embodiment of the present disclosure;

fig. 9 is a schematic structural diagram of an image processing apparatus provided in an embodiment of the present disclosure;

fig. 10 is a schematic structural diagram of a terminal device according to an embodiment of the present disclosure.

Detailed Description

In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.

Fig. 1 is a flowchart of an image processing method provided by an embodiment of the present disclosure, which may be executed by a terminal device having an image processing capability. In practical applications, the terminal device may be at least a mobile phone, a tablet computer, a desktop computer, a all-in-one machine, and the like, but is not limited to these devices listed here.

As shown in fig. 1, an image processing method provided by the embodiment of the present disclosure includes steps S101 to S103.

Step S101: and acquiring an expression image.

An expression image may be understood as an image of an object containing a certain expression. The expression of the object may be, for example but not limited to, smiling, serious, crying, obsessional, etc. The expression of the subject may be presented by the morphology of the subject's facial organs, which may include the subject's eyes, nose, mouth, eyebrows, etc.

In the embodiment of the present disclosure, the expression image may be understood as an expression image of a real person or an animal, or may also be understood as an expression image of a cartoon person or a cartoon animal, but the expression image in the embodiment is not limited thereto, and actually, the expression image referred to in the embodiment may be an expression image of an object having an expression.

In the embodiment of the disclosure, the expression image to be processed can be acquired in a preset mode; the preset mode may include photographing, downloading, drawing, or extracting. It should be noted that the preset manner is not limited to the aforementioned photographing, downloading, drawing or extracting manner.

The shooting mode is to shoot a subject by adopting a shooting device configured by the terminal equipment to obtain an expression image of the subject.

The downloading mode refers to finding and downloading the expression images from a remote database.

The drawing method is to draw a face image containing a certain expression by using a drawing tool, and to take the drawn face image as an expression image referred to in this embodiment, where the face image may be a realistic face image or a cartoon face image.

The extraction method is to extract a frame image containing a certain expression from a video as an expression image referred to in this embodiment.

In some embodiments of the present disclosure, if a frame image is extracted from a video as an expression image, and the foregoing operation is automatically performed by a terminal device, the terminal device needs to determine whether a certain expression of an object is included in the frame image. In this case, the terminal device may deploy a face recognition model to recognize whether a frame image in the video has a certain expression using the face recognition model, and then determine whether to extract the frame image as an expression image. The face recognition model can be obtained by training face images of a large number of objects, and in practical application, the face recognition model can be a deep learning model with various known architectures.

In other embodiments, a certain frame in the video may also be selected by the user as the expression image in this embodiment. At this time, the terminal extracts the designated image as the expression image according to the user's execution operation.

Step S102: and adjusting the expression in the expression image based on a preset image processing model to generate a video with an expression change process.

The preset image processing model is a model which is specially used for adjusting the characteristics of facial organs in the expression image and realizing the change of facial expressions by changing the characteristics of the facial organs to obtain a video with specific expression changes.

In the embodiment of the disclosure, after the expression image is input into the preset image processing model, the preset image processing model adjusts pixels of an image area where at least one facial organ in the expression image is located, so as to obtain a plurality of adjusted expression images, where the forms and/or positions of some facial organ in each adjusted expression image are different from each other.

For example, in one embodiment, the smile degree of the mouth in the expression image may be adjusted based on a preset image processing model to generate expression images with different smile degrees; in another embodiment, the degree of opening and closing eyes in the expression image can be adjusted based on a preset image processing model, and expression images with different degrees of opening and closing eyes are generated; in another embodiment, eyebrow features of the expression image can be adjusted based on a preset image processing model, and expression images with different eyebrow features are generated; in still another embodiment, the nose features in the expression image may be adjusted based on a preset image processing model, so as to generate expression images with different nose features.

It is to be understood that the above description is only by way of example and not meant to be the only limitation on the object and manner of expression adjustment, and in fact, the object and manner of expression adjustment may be set as required and not limited to a specific object or a specific manner, for example, in some embodiments, at least some of the above manners may be combined to obtain an expression image with an expression change process of combined facial organ representation, for example, the degree of mouth smile and the degree of eyes open and closed in the expression image may be adjusted at the same time to generate an expression image with both the degree of smile and the degree of eyes open and closed.

In the embodiment of the present disclosure, after the adjusted images are generated, the generated plurality of expression images may be sequentially ordered based on the order from small to large of the expression amplitude, or from large to small, so as to generate a video with a specific frame frequency.

FIG. 2 is a schematic diagram of an expression image provided by one embodiment of the present disclosure; fig. 3 is a schematic diagram of one adjusted expression image generated based on fig. 2, fig. 4 is a schematic diagram of another adjusted expression image generated based on fig. 2, and fig. 5 is a schematic diagram of still another adjusted expression image generated based on fig. 2.

As can be seen from fig. 2 to 5, in an embodiment of the present disclosure, in step S102, the expression in the expression image is adjusted based on the preset image processing model, which is to adjust the form of the mouth 02 of the object 01, specifically, to adjust the upwarping position of the mouth corner 021 of the object, so as to obtain a plurality of adjusted images with different mouth corner 021 variation characteristics; subsequently, in step S102, the three adjusted images are combined according to the sorting order of fig. 3-5, so as to obtain a video with a changed mouth corner 021, so as to represent the change of smiling degree of the object by using the video with a changed mouth corner.

Of course, this is merely an example and is not limited to the above, and in other embodiments, the generated multiple expression images may also include different expressions, in which case, the multiple expression images may be sorted according to a specific expression change sequence to generate a video with a specific expression change process, for example, based on a smile expression image, a serious expression image and a difficult expression image are generated, and then the expression images may be sequentially arranged according to an order of smile first and then serious and then difficult to group to generate a video with a change process from smile to difficult.

Step S103: and displaying the video.

After generating the video with the expression change process, the video can be displayed by using a display device. For example, when the terminal device referred to in this embodiment is specifically a smartphone, the smartphone executes the foregoing steps S101 and S102, and displays the video obtained based on the steps S101 and S102 on the display screen.

By adopting the image processing method provided by the embodiment of the disclosure, after the expression image is obtained, the video with the expression change process can be generated according to the expression image. The methods in steps S101 to S103 may be integrated into a specific application program or software tool, and by installing the application program or software tool on the terminal device, the terminal device can generate a video with expression feature changes based on a certain expression image, so that the interest of the video is improved, and the user experience is further improved.

In the foregoing step S102, the expression in the expression image needs to be adjusted by using a preset image processing model, in some embodiments of the present disclosure, the preset image processing model may be obtained by video training based on the expression image of the sample object and the expression change of the sample object, where the sample object refers to an object capable of displaying a specific expression and a continuously changing expression corresponding to the specific expression.

In some embodiments of the present disclosure, the expression image of the sample object and the corresponding expression change video may be obtained by a sample acquisition method.

For example, in some embodiments, one expression change video of the sample object may be obtained first, and then an image of a certain frame having a specific expression in the expression change video is adopted as the expression image.

For another example, in other embodiments, an expression image of an object may be collected first, and then prompt information may be output to prompt the object how to change the expression and collect a collected video of the user when performing the expression change; and finally, taking the collected video as an expression change video corresponding to the expression image.

In still other embodiments of the present disclosure, the expression image of the sample object may be determined first, and then the expression change process in the preset video is migrated to the expression image of the sample object by using a preset migration model, so as to obtain the expression change video of the sample object.

The preset video is a video with an expression change process; for example, it may be a video with a change in the degree of smiling, a video with a change in the degree of opening and closing eyes, or a video with both a change in the degree of smiling and a change in the degree of opening and closing eyes.

By adopting the migration model, the expression change process in the preset video is migrated to the plurality of sample objects to obtain expression change videos of the plurality of sample objects, so that each expression change video has the same expression change trend, for example, the smiling degree/the eye opening and closing degree is gradually increased. The video with the expression change trend can be obtained by processing the image processing model obtained based on the expression change videos and the expression image training.

By adopting the migration model based on the preset video and the expression images of the sample objects, expression trend videos corresponding to the expression images and having the same expression change characteristics can be obtained, the difficulty in obtaining the expression change videos is simplified, and the consistency of expression change trends in the expression change videos for training the image processing model is also ensured.

In some embodiments of the present disclosure, the migration model for obtaining the expression change video may be trained based on images of a plurality of facial regions and expression differences between the images of the plurality of facial regions; the aforementioned images of the plurality of facial regions are images of the same type of expression, and the degree of expression of the same type of expression differs among the different images. The image of the face region may be obtained based on a face key point on a preset face image; wherein the facial keypoints may include one or more of eye keypoints, mouth keypoints, eyebrow keypoints, and forehead keypoints.

Fig. 6 is an image of a face region provided in one embodiment of the present disclosure, fig. 7 is an image of a face region provided in another embodiment of the present disclosure, and fig. 8 is an image of a face region provided in yet another embodiment of the present disclosure.

As shown in fig. 6 to 8, in one embodiment of the present disclosure, the images of the plurality of facial regions are images of the subject 03 having different degrees of opening of the eyes 031, and the expression difference between the images is a difference in the degree of opening of the eyes 031.

If the migration model is trained by using fig. 6-8, one image in fig. 6-8 is taken as an input image a, the other image is taken as an output image B, the difference value of the eye opening degrees of the input image a and the output image B is taken as an expression difference a-B, parameters in the migration model F are optimized and trained, and the migration model F for obtaining the expression change video is obtained.

In some embodiments of the present disclosure, the degree of opening of the eyes 030 in each of the facial region images of fig. 6-8 described above may be manually labeled, and the model calculates the difference in the degree of opening or closing of the eyes between the images based on the labels; for example, if the eye opening degree is characterized by a numerical value between 0.0 and 1.0, the opening degree of the eye 03 corresponding to fig. 6 may be labeled as 0.5, the opening degree of the eye 03 corresponding to fig. 7 may be labeled as 0.2, the opening degree of the eye 03 corresponding to fig. 8 may be labeled as 0.0, and the difference in the eye opening degree between the three images may be calculated by the migration model from the labeling information.

In some other embodiments of the present disclosure, the expression difference used to train the migration model may also be derived by processing the facial region images. For example, for the images of the facial regions shown in fig. 6 to 8, key points of the eyes 03 may be extracted, expression feature parameters of each facial image may be determined based on the key points, and expression differences may be determined based on the expression feature parameters of each facial image.

Specifically, the eye region images obtained by processing fig. 6 to 8 by using the eye region recognition model can be processed, and the eye region images are processed by using an edge recognition algorithm to recognize the inner canthus 031, the outer canthus 032, the upper eyelid 033 pole and the lower eyelid 034 pole of the eye 03 as the eye key points; the eye transverse length is then determined based on the inner corner 031 and the outer corner 032, and the longitudinal width of the eye 03 is determined based on the upper eyelid 033 pole and the lower eyelid 034 pole; and finally, the ratio of the longitudinal width and the transverse length of the eyes is used as an expression characteristic parameter of the facial image, and the expression difference is determined based on the expression characteristic parameter of the facial image.

The foregoing embodiments of the present disclosure have been described with an example in which three input face region images are used for training the migration model, and in other applications, the number of input face region images used for training the migration model may not be limited to three.

Fig. 9 is a schematic structural diagram of an image processing apparatus provided in an embodiment of the present disclosure, where the processing apparatus may be understood as the terminal device or a part of functional modules in the terminal device. As shown in fig. 9, the image processing apparatus 90 includes an expression image acquisition unit 901, a video generation unit 902, and a video presentation unit 903.

The expression image acquisition unit 901 is used for acquiring expression images; the video generating unit 902 is configured to adjust an expression in the expression image based on a preset image processing model, and generate a video with an expression change process; the video presentation unit 903 is used to present video.

In some embodiments of the present disclosure, the video generating unit 902 adjusts the smile degree and/or the open-and-close eye degree in the representation image based on a preset image processing model, and generates a video having a process of changing the smile degree and/or the open-and-close eye degree.

In some embodiments of the present disclosure, the image processing model is trained based on the expression image of the sample object and the expression change video of the sample object.

In still other embodiments of the present disclosure, the expression change video of the sample object is obtained by migrating an expression change process in a preset video to an expression image of the sample object by using a preset migration model.

In some embodiments of the present disclosure, the migration model is trained based on images of a plurality of facial regions, and expression differences between the images of the plurality of facial regions, wherein the images of the plurality of facial regions are images of the same type of expression, and the expression degrees of the same type of expression in different images are different.

In some embodiments of the present disclosure, the image of the face region is extracted based on facial key points on a preset face image.

The apparatus provided in this embodiment can execute the method of any of the above method embodiments, and the execution manner and the beneficial effect are similar, which are not described herein again.

The embodiment of the present disclosure further provides a terminal device, which includes a processor and a memory, where the memory stores a computer program, and when the computer program is executed by the processor, the method of any one of the foregoing embodiments in fig. 1 to fig. 8 may be implemented.

For example, fig. 10 is a schematic structural diagram of a terminal device provided in an embodiment of the present disclosure. Referring now specifically to fig. 10, a schematic diagram of a terminal device 1000 suitable for implementing embodiments of the present disclosure is shown. The terminal apparatus 1000 in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The terminal device shown in fig. 10 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 10, terminal apparatus 1000 can include a processing device (e.g., central processing unit, graphics processor, etc.) 1001 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)1002 or a program loaded from a storage device 1008 into a Random Access Memory (RAM) 1003. In the RAM 1003, various programs and data necessary for the operation of the terminal apparatus 1000 are also stored. The processing device 1001, the ROM 1002, and the RAM 1003 are connected to each other by a bus 1004. An input/output (I/O) interface 1005 is also connected to bus 1004.

Generally, the following devices may be connected to the I/O interface 1005: input devices 1006 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 1007 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 1008 including, for example, magnetic tape, hard disk, and the like; and a communication device 1009. The communication means 1009 may allow the terminal apparatus 1000 to perform wireless or wired communication with other apparatuses to exchange data. While fig. 10 illustrates a terminal apparatus 1000 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 1009, or installed from the storage means 1008, or installed from the ROM 1002. The computer program, when executed by the processing device 1001, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be included in the terminal device; or may exist separately without being assembled into the terminal device.

The computer readable medium carries one or more programs which, when executed by the terminal device, cause the terminal device to: acquiring an expression image; adjusting the expression in the expression image based on a preset image processing model to generate a video with an expression change process; and displaying the video.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The embodiments of the present disclosure also provide a computer-readable storage medium, where a computer program is stored in the storage medium, and when the computer program is executed by a processor, the method of any of the above method embodiments can be implemented, and the execution manner and the beneficial effect are similar, and are not described herein again.

It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.