Image Processing Method, Computer-Readable Storage Medium, and Computing Device

Abstract

The present application discloses an image processing method, a computer-readable storage medium, and a computing device. Among them, the method includes: obtaining a first image and a second image, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; performing a matte extraction operation on the first image based on the second image to obtain an image of the target object. The present application solves the technical problem that in the matte extraction solution in the related art, due to the interference of the content played on the screen, the accuracy of segmenting the image of the target object is relatively low.

Description

Technical Field

[0001] This application relates to the field of image processing, and more particularly, to an image processing method, a computer-readable storage medium, and a computing device. Background Art

[0002] Currently, the background of a live broadcast scene is a large screen. Limited by the clarity of the camera device, the captured screen will have a problem of being unclear. Therefore, it is necessary to segment the target object in front of the screen and then synthesize it with the screen to achieve the purpose of high-definition live broadcast. Due to the interference of the content played on the screen, the existing matting methods cannot accurately segment the image of the target object.

[0003] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of this application provide an image processing method, a computer-readable storage medium, and a computing device to at least solve the technical problem that the accuracy of segmenting the image of the target object is relatively low due to the interference of the content played on the screen in the existing matting scheme.

[0005] According to one aspect of the embodiments of this application, an image processing method is provided, including: obtaining a first image and a second image, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; performing a matting operation on the first image based on the second image to obtain the image of the target object.

[0006] According to another aspect of the embodiments of this application, an image processing method is further provided, including: displaying the first image on an interaction interface; in response to a matting instruction sensed on the interaction interface, displaying the image of the target object on the interaction interface, where the matting instruction is used to obtain the second image and perform a matting operation on the first image based on the second image, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen.

[0007] According to another aspect of the embodiments of this application, an image processing method is further provided, including: obtaining a teaching image and a screenshot image, where the teaching image is an image obtained by photographing a teacher and a target screen, and the screenshot image is an image obtained by performing a screenshot operation on the teaching content displayed on the target screen; performing a matting operation on the teaching image based on the screenshot image to obtain the image of the target object.

[0008] According to another aspect of the embodiments of this application, an image processing method is further provided, including: photographing a target object and a target screen to obtain a first image; performing a screenshot operation on the target screen to obtain a second image; performing a matting operation on the first image based on the second image to obtain the image of the target object.

[0009] According to another aspect of the embodiments of the present application, there is also provided an image processing method, including: the server receives a first image and a second image sent by a client, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; the server performs a matting operation on the first image based on the second image to obtain an image of the target object; the server sends the image of the target object to the client.

[0010] According to another aspect of the embodiments of the present application, there is also provided an image processing method, including: the server receives a first image and a second image sent by a client through a first interface, where the first interface includes: a first parameter and a second parameter, the parameter value of the first parameter is the first image, the parameter value of the second parameter is the second image, the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; the server performs a matting operation on the first image based on the second image to obtain an image of the target object; the server sends the image of the target object to the client through a second interface, where the second interface includes: a second parameter, and the parameter value of the second parameter is the output image.

[0011] In the embodiments of the present application, first, a first image and a second image are obtained, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen. A matting operation is performed on the first image based on the second image to obtain an image of the target object, achieving the use of the second image as a reference image to improve the accuracy of the matting operation on the first image.

[0012] It is easy to think that the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the screen. Therefore, the clarity of the second image is relatively higher than that of the first image. By matching the second image that is the same as the content displayed on the target screen in the first image and using the second image with higher clarity as a reference image to perform matting on the first image, the accuracy of the matting operation on the first image can be improved.

[0013] The embodiments of the present application solve the technical problem that in the matting scheme in the related art, due to the interference of the screen playback content, the accuracy of segmenting the image of the target object is relatively low. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0015] Figure 1 It is a hardware structure block diagram of a computer terminal (or mobile device) for an image processing method according to an embodiment of the present application;

[0016] Figure 2 It is a flowchart of an image processing method according to an embodiment of the present application;

[0017] Figure 3 It is a flowchart of another image processing method according to an embodiment of the present application;

[0018] Figure 4 It is a flowchart of another image processing method according to an embodiment of the present application;

[0019] Figure 5 It is a flowchart of another image processing method according to an embodiment of the present application;

[0020] Figure 6 It is a flowchart of another image processing method according to an embodiment of the present application;

[0021] Figure 7 It is a flowchart of another image processing method according to an embodiment of the present application;

[0022] Figure 8 It is a flowchart of another image processing method according to an embodiment of the present application;

[0023] Figure 9 It is a schematic diagram of an image processing device according to an embodiment of the present application;

[0024] Figure 10 It is a schematic diagram of another image processing device according to an embodiment of the present application;

[0025] Figure 11 It is a schematic diagram of another image processing device according to an embodiment of the present application;

[0026] Figure 12 It is a schematic diagram of another image processing device according to an embodiment of the present application;

[0027] Figure 13 It is a schematic diagram of another image processing device according to an embodiment of the present application;

[0028] Figure 14 It is a schematic diagram of another image processing device according to an embodiment of the present application;

[0029] Figure 15 It is a structure block diagram of a computing device according to an embodiment of the present application. Detailed implementation manners

[0030] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0031] It should be noted that the terms "first", "second", etc. in the description and claims of this application and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0032] First, some nouns or terms that appear in the process of describing the embodiments of this application are applicable to the following explanations:

[0033] Matting: It can refer to segmenting a high-precision image;

[0034] Screen capture: It can refer to directly obtaining the screen image using software.

[0035] Embodiment 1

[0036] According to the embodiments of this application, an embodiment of an image processing method is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described here can be executed in a different order from here.

[0037] The method embodiments provided by the embodiments of this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing an image processing method is shown. As Figure 1As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (illustrated as 102a, 102b, ……, 102n in the figure) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than those Figure 1 shown in, or have a different configuration from that Figure 1 shown.

[0038] It should be noted that the above one or more processors 102 and / or other data processing circuits may generally be referred to as "data processing circuits" herein. The data processing circuit may be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit may be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the computer terminal 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is a processor control (such as the selection of a variable resistor terminal path connected to an interface).

[0039] The memory 104 may be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the image processing method in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the above-mentioned image processing method. The memory 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, a flash memory, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0040] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0041] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0042] It should be noted here that, in some alternative embodiments, the above Figure 1 illustrated computer device (or mobile device) may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. It should be pointed out that Figure 1 is only an example of a specific concrete example and is intended to illustrate the types of components that may exist in the above computer device (or mobile device).

[0043] Under the above operating environment, the present application provides an image processing method as Figure 2 illustrated. Figure 2 is a flowchart of the image processing method according to an embodiment of the present application. As Figure 2 illustrated, the method may include the following steps:

[0044] Step S202, obtaining a first image and a second image.

[0045] Among them, the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by taking a screenshot of the content displayed on the target screen.

[0046] The above first image can be an image obtained by a photographing device. The photographing device here can be a camera, a camera, a video recorder, etc. The present application does not make specific limitations on this.

[0047] The above second image can be an image obtained by taking a screenshot of the screen through a screenshot program or screenshot software. Among them, the second image is an image with a high-definition picture.

[0048] In the scenario of teaching videos, the above-mentioned target objects may include teachers and the items held by teachers. The above-mentioned target screen may be a screen that displays teaching content. The above-mentioned first image may be an image obtained by shooting a teacher, the items held by the teacher, and the target screen that displays teaching content with a shooting device. The above-mentioned second image may be an image obtained by taking a screenshot of the teaching content displayed on the target screen, and this image is a high-definition image. Among them, the items held by the teacher may be a device for controlling the screen, or teaching tools used during the teacher's lecture, such as a blackboard eraser, a pointer, etc., but not limited to this.

[0049] In the scenario of live conferences, the above-mentioned target objects may include the keynote speaker and the items held by the keynote speaker. The above-mentioned target screen may be a screen that displays conference content. The above-mentioned first image may be an image obtained by shooting the keynote speaker, the items held by the keynote speaker, and the target screen that displays conference content with a shooting device. The above-mentioned second image may be an image obtained by taking a screenshot of the conference content displayed on the target screen, and this image is a high-definition image. Among them, the items held by the keynote speaker may be a device for controlling the screen, or items displayed during the conference, but not limited to this.

[0050] Step S204, perform a matting operation on the first image based on the second image to obtain an image of the target object.

[0051] The above-mentioned matting operation can separate a certain part of the first image from the first image to become a separate image. It should be noted that the image of the target object may not only include the target object itself, but also the items held by the target object.

[0052] In an optional embodiment, the second image is an image directly obtained by taking a screenshot of the target screen. Therefore, the second image is clearer than the first image. A certain part different from the second image can be determined from the first image. By performing a matting operation on this part, an image of the target object can be obtained. By synthesizing this second image with the image of the target object, the image of the target object can be displayed while clear content is displayed on the target screen.

[0053] In another optional embodiment, a matting operation can be performed on the first image based on the second image through a matting network, so as to obtain an image of the target object. Among them, the matting network may be a pre-trained multi-layer convolutional neural network.

[0054] In the scenario of teaching videos, a matting operation can be performed on the captured teacher and the screen of the teaching content based on the screenshot of the teaching content to obtain an image of the keynote teacher and the items held by the keynote teacher.

[0055] In the scenario of a live conference, a matte extraction operation can be performed on the screen captures of the conference content, as well as the screen of the speaker being filmed, to obtain images of the speaker and the objects held by the speaker.

[0056] Optionally, after performing a matte extraction operation on the first image based on the second image to obtain an image of the target object, the image of the target object and the second image are synthesized to obtain an output image.

[0057] In an alternative embodiment, the target position of the target object in the first image can be obtained, and based on the target position, the image of the target object and the second image are synthesized to obtain an output image with a high-definition background.

[0058] In another alternative embodiment, a first rectangular coordinate system can be established with the lower left corner of the first image as the origin, the bottom side of the first image as the x-axis, and the side passing through the origin and perpendicular to the x-axis as the y-axis. A second rectangular coordinate system can be established with the lower left corner of the second image as the origin, the bottom side of the second image as the x-axis, and the side passing through the origin and perpendicular to the x-axis as the y-axis. The first set of coordinate points of the target object in the first rectangular coordinate system of the first image can be obtained, and based on this first set of coordinate points, the corresponding second set of coordinate points of the image of the target object in the second rectangular coordinate system of the second image can be determined. Based on this second set of coordinate points, the image of the target object and the second image are synthesized to obtain an output image.

[0059] In the scenario of a teaching video, the image of the teacher and the teaching content of the screenshot can be synthesized to obtain a live teaching image with a high-definition background of the teaching content, so that students can watch a live teaching class or a recorded teaching class that can clearly display the teaching content.

[0060] In the scenario of a live conference, the image of the speaker and the conference content of the screenshot can be synthesized to obtain a live conference image with a high-definition background of the conference content, so that remote participants can watch a live conference that can clearly display the conference content.

[0061] Through the above embodiments, first, a first image and a second image are obtained. Among them, the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by taking a screenshot of the content displayed on the target screen. Based on the second image, a matting operation is performed on the first image to obtain an image of the target object, realizing the use of the second image as a reference image to improve the accuracy of the matting operation on the first image. It is easy to think that the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by taking a screenshot of the content displayed on the screen. Therefore, the clarity of the second image is higher than that of the first image. By matching the second image with the same content as the target screen displayed in the first image and using the second image with higher clarity as a reference image to perform matting on the first image, the accuracy of the matting operation on the first image can be improved. The embodiments of the present application solve the technical problem that the accuracy of segmenting the image of the target object is relatively low due to the interference of the content played on the screen in the matting solution in the related art.

[0062] Optionally, in the case of continuously obtaining the second image multiple times, the method includes: determining a target image among the multiple second images obtained, where the similarity between the target image and the first image is greater than the similarity between other images and the first image, and the other images are any one of the multiple second images except the target image; performing a matting operation on the first image based on the target image to obtain an image of the target object.

[0063] It should be noted that since the content played on the target screen is changing in real time and the shooting device can only capture a certain frame of the picture, in order to ensure that the content displayed on the target screen in the first image is the same as that in the second image, the content displayed on the target screen can be continuously screenshot multiple times to obtain multiple second images, and further screen out the target image that is the same as the content displayed on the target screen in the first image from the multiple second images.

[0064] In an optional embodiment, different contents can be played on the target screen in real time. At this time, by taking screenshots of the target screen, multiple second images with different contents can be continuously obtained, and the second image with a higher similarity to the first image can be obtained from the multiple second images, and this second image is determined as the target image. Since the similarity between the target image and the image displayed in the background of the first image is relatively high, therefore, by performing a matting operation on the first image based on the target image, an image of the target object with relatively high accuracy can be obtained.

[0065] In another alternative embodiment, multiple second images with different contents can be obtained continuously for multiple times, and a preset number of second images with a relatively high similarity to the first image can be obtained from the multiple second images. Then, a second image is randomly selected from the preset number of second images as the target image, and a matting operation is performed on the first image based on the target image. Further, after obtaining the preset number of second images, the preset number of second images can be fed back to the client, so that the user can select a second image from the preset number of second images as the target image through the client, and a matting operation is performed on the first image based on the target image to obtain an image of the target object with relatively high accuracy.

[0066] Optionally, determining the target image among the multiple obtained second images includes: obtaining the similarity between each second image and the first image; determining the second image corresponding to the maximum similarity as the target image.

[0067] In an alternative embodiment, the similarity between each second image and the first image can be obtained through a similarity algorithm, and the similarities between each second image and the first image are sorted from large to small. The second image corresponding to the maximum similarity with the first image is determined, and this second image is determined as the target image.

[0068] The above similarity algorithm can be Euclidean distance, Pearson correlation coefficient, cosine similarity, Tanimoto coefficient, etc. There is no limitation on the similarity algorithm adopted here.

[0069] Optionally, a similarity estimation network is used to process the multiple second images and the first image to obtain the target image.

[0070] The above similarity estimation network can be a multi-layer convolutional neural network.

[0071] In an alternative embodiment, the multiple second images and the first image can be input into the similarity estimation network. The similarity estimation network can obtain the similarity between the multiple second images and the first image, then select the second image with the highest similarity from the multiple second images according to the similarity between the multiple second images and the background of the first image as the target image, and finally the similarity estimation network outputs the target image.

[0072] Optionally, before determining the target image among the multiple obtained second images, the method further includes: performing an alignment operation on the multiple second images based on the first image to obtain the aligned multiple second images; determining the target image among the aligned multiple second images.

[0073] The above alignment operation refers to processing the second image through rotation, scaling, translation, etc., so that the angle, size, displacement, etc. of the second image are the same as those of the first image.

[0074] In an alternative embodiment, since there are differences in angle, size, displacement, etc. between the first image obtained by shooting and the second image obtained by screenshot, in order to ensure that the target image can be accurately selected from multiple second images, it is necessary to perform an alignment operation on multiple second images based on the first image to obtain the aligned multiple second images. Among them, the aligned multiple second images are the same as the first image in terms of displacement, size, etc., and the second image with the highest similarity to the first image can be selected from the aligned multiple second images as the target image.

[0075] Optionally, performing an alignment operation on multiple second images based on the first image to obtain the aligned multiple second images includes: using an alignment network to process the first image and multiple second images to obtain alignment coefficients, where the alignment coefficients include at least one of the following: rotation coefficient, scaling coefficient, and translation coefficient; performing an alignment operation on multiple second images based on the alignment coefficients to obtain the aligned multiple second images.

[0076] The above alignment network can be a multi-layer convolutional neural network.

[0077] In an alternative embodiment, an alignment network can be used to process the first image and multiple second images, and the differences in angle, size, and displacement between the first image and the multiple second images can be obtained. Through the obtained differences in angle, size, and displacement between the first image and the multiple second images, the rotation coefficient, scaling coefficient, and translation coefficient of the multiple second images can be determined.

[0078] Specifically, by performing an alignment operation on multiple second images through the rotation coefficient, the angles between the aligned multiple second images and the first image can be made the same. By performing an alignment operation on multiple second images through the scaling coefficient, the sizes between the aligned multiple second images and the first image can be made the same. By performing an alignment operation on multiple second images through the translation coefficient, the displacements between the aligned multiple second images and the first image can be made the same.

[0079] Optionally, performing a matting operation on the first image based on the target image to obtain the image of the target object includes: using a matting network to process the target image and the first image to obtain the mask matrix of the target object; using the mask matrix of the target object to process the first image to obtain the image of the target object.

[0080] The above matting network can be a multi-layer convolutional neural network.

[0081] The above mask matrix is used to globally or locally occlude the processed image to control the area or process of image processing.

[0082] In an alternative embodiment, a matting network can be used to process the target image and the first image. Specifically, similar parts in the first image can be intercepted according to the target image, and the remaining part is the area of the target object. At this time, a mask matrix corresponding to the area of the target object can be obtained, and the first image can be cropped and discarded except for the area corresponding to the mask matrix using the mask matrix. The remaining image is the image of the target object. Synthesizing the image of the target object with the clear second image can output an image with a high-definition background.

[0083] The following will combine Figure 3 A preferred embodiment of the present application will be described in detail. This method can be executed by a mobile terminal or a server. In the embodiments of the present application, it is described by taking the method being executed by the server as an example.

[0084] As Figure 3 shown, multiple alignment coefficients of the second images can be obtained first, and then the multiple second images can be aligned using the alignment coefficients to obtain the aligned multiple second images. Inputting the first image and the aligned multiple second images into a similarity estimation network, a second image with a high similarity to the first image among the aligned multiple second images can be obtained, and this second image is used as the target image. After obtaining the target image, the first image and the target image can be input into a matting network, and the first image can be matted using the target image to obtain a mask matrix of the target object. Then, the mask matrix of the target object is synthesized with the first image and the target image. Specifically, the first image can be processed using the mask matrix of the target object to obtain the image of the target object, and then the image of the target object is synthesized with the target image, and an image with a high-definition background can be output.

[0085] As Figure 3 shown, the above alignment coefficients can be obtained by inputting the first image and the second image into an alignment network. The second image can be processed using the alignment coefficients to obtain the aligned second image, so as to eliminate differences such as angles, displacements, and sizes between the first image and the second image.

[0086] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be in other sequences or carried out simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0087] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present application.

[0088] Embodiment 2

[0089] According to an embodiment of the present application, an embodiment of an image processing method is further provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from here.

[0090] Figure 4 is a flowchart of an image processing method according to an embodiment of the present invention. As Figure 4 shown, the method may include the following steps:

[0091] Step S402, display a first image on the interaction interface.

[0092] The above interaction interface may be a display interface of a mobile terminal or a computer terminal.

[0093] Step S404, in response to a matte extraction instruction sensed on the interaction interface, display an image of a target object on the interaction interface.

[0094] Among them, the matte extraction instruction is used to obtain a second image and perform a matte extraction operation on the first image based on the second image. The second image is an image obtained by performing a screenshot operation on the content displayed on the target screen.

[0095] The above matte extraction instruction may be generated by the user by clicking on a preset space on the display interface.

[0096] Optionally, after displaying the image of the target object on the interaction interface, an output image may also be displayed on the interaction interface.

[0097] Among them, the output image is an image obtained by synthesizing the image of the target object and the second image.

[0098] In the above embodiments of the present application, when the second images are obtained continuously for multiple times, generating an image of the target object includes: determining a target image among the multiple obtained second images, where the similarity between the target image and the first image is greater than the similarity between other images and the first image, and the other images are any one of the multiple second images except the target image; performing a matte extraction operation on the first image based on the target image to obtain an image of the target object.

[0099] In the above embodiments of the present application, before performing a matte extraction operation on the first image based on the target image to obtain an image of the target object, the method further includes: aligning the multiple second images based on the first image to obtain the aligned multiple second images; determining the target image among the aligned multiple second images.

[0100] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0101] Embodiment 3

[0102] According to an embodiment of the present application, there is also provided an embodiment of an image processing method. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0103] Figure 5 is a flowchart of an image processing method according to an embodiment of the present invention. As Figure 5 shown, the method may include the following steps:

[0104] Step S502, obtain a teaching image and a screenshot image.

[0105] Among them, the teaching image is an image obtained by photographing a teacher and a target screen, and the screenshot image is an image obtained by taking a screenshot of the teaching content displayed on the target screen.

[0106] Step S504, perform a matte extraction operation on the teaching image based on the screenshot image to obtain an image of the teacher.

[0107] Optionally, after obtaining the image of the teacher, the image of the teacher and the screenshot image may also be synthesized to obtain an output image.

[0108] Optionally, after obtaining the output image, the output image may be displayed in the live broadcast interface.

[0109] In the above embodiments of the present application, in the case of continuously obtaining screenshot images multiple times, the method includes: determining a target image among the multiple obtained screenshot images, where the similarity between the target image and the teaching image is greater than the similarity between other images and the teaching image, and the other images are any one of the multiple screenshot images except the target image; performing a matte extraction operation on the teaching image based on the target image to obtain an image of the target object.

[0110] In the above embodiments of the present application, before determining the target image among the multiple obtained screenshot images, the method further includes: performing an alignment operation on the multiple screenshot images based on the teaching image to obtain the aligned multiple screenshot images; determining the target image among the aligned multiple screenshot images.

[0111] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0112] Embodiment 4

[0113] According to an embodiment of the present application, there is also provided an embodiment of an image processing method. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0114] Figure 6 is a flowchart of an image processing method according to an embodiment of the present invention. As Figure 6 shown, the method may include the following steps:

[0115] Step S602, photographing the target object and the target screen to obtain a first image.

[0116] Step S604, performing a screenshot operation on the target screen to obtain a second image.

[0117] Step S606, performing a matte extraction operation on the first image based on the second image to obtain an image of the target object.

[0118] Optionally, after obtaining the image of the target object, the image of the target object and the second image may also be synthesized to obtain an output image.

[0119] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0120] Embodiment 5

[0121] According to an embodiment of the present application, an embodiment of an image processing method is further provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0122] Figure 7 is a flowchart of an image processing method according to an embodiment of the present invention. As Figure 7 shown, the method may include the following steps:

[0123] Step S702, the server receives a first image and a second image sent by the client.

[0124] Among them, the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by taking a screenshot of the content displayed on the target screen.

[0125] The above-mentioned server may be a cloud server.

[0126] Step S704, the server performs a matting operation on the first image based on the second image to obtain an image of the target object.

[0127] Step S706, the server sends the image of the target object to the client.

[0128] Optionally, after obtaining the image of the target object, the server may synthesize the image of the target object and the second image to obtain an output image.

[0129] Optionally, after obtaining the output image, the server may send the output image to the client.

[0130] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0131] Embodiment 6

[0132] According to an embodiment of the present application, an embodiment of an image processing method is further provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0133] Figure 8 is a flowchart of an image processing method according to an embodiment of the present invention. As Figure 8 shown, the method may include the following steps:

[0134] Step S802, the server receives a first image and a second image sent by the client through a first interface.

[0135] Among them, the first interface includes: a first parameter and a second parameter. The parameter value of the first parameter is the first image, and the parameter value of the second parameter is the second image. The first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by taking a screenshot of the content displayed on the target screen.

[0136] The first interface in the above steps can be an interface for data interaction between the cloud server and the client. The client can pass the first image and the second image into the interface function and use them as the first parameter and the second parameter of the interface function to achieve the purpose of uploading the first image and the second image to the cloud server.

[0137] Step S804, the server performs a matte extraction operation on the first image based on the second image to obtain an image of the target object.

[0138] Step S806, the server sends the image of the target object to the client through a second interface.

[0139] Among them, the second interface includes: a third parameter, and the parameter value of the third parameter is the image of the target object.

[0140] The second interface in the above steps can be an interface for data interaction between the cloud server and the client. The cloud server can pass the image of the target object into the interface function and use it as the third parameter of the interface function to achieve the purpose of sending the image of the target object to the client.

[0141] Optionally, after obtaining the image of the target object, the server can synthesize the image of the target object and the second image to obtain an output image.

[0142] Optionally, after obtaining the output image, the server sends the output image to the client through the second interface.

[0143] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0144] Embodiment 7

[0145] According to an embodiment of the present application, there is also provided an image processing apparatus for implementing the above image processing method, as Figure 9 shown. The apparatus 900 includes: a first acquisition module 902 and a first operation module 904.

[0146] Among them, the first acquisition module is used to acquire a first image and a second image, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; the first operation module is used to perform a matting operation on the first image based on the second image to obtain an image of the target object.

[0147] It should be noted here that the above-mentioned first acquisition module 902 and first operation module 904 correspond to steps S202 to S204 in Embodiment 1. The examples and application scenarios implemented by the two modules and the corresponding steps are the same, but are not limited to the content disclosed in the above-mentioned Embodiment 1. It should be noted that the above modules, as part of the device, can run in the computer terminal 10 provided in Embodiment 1.

[0148] In the above embodiments of the present application, the device further includes: a first determination module and a second operation module.

[0149] Among them, the first determination module is used to determine a target image among multiple acquired second images, where the similarity between the target image and the first image is greater than the similarity between other images and the first image, and the other images are any one of the multiple second images except the target image; the second operation module is used to perform a matting operation on the first image based on the target image to obtain an image of the target object.

[0150] In the above embodiments of the present application, the first determination module includes: a first acquisition unit and a first determination unit.

[0151] Among them, the first acquisition unit is used to acquire the similarity between each second image and the first image; the first determination unit is used to determine that the second image corresponding to the maximum similarity is the target image.

[0152] In the above embodiments of the present application, multiple second images and the first image are processed by using a similarity estimation network to obtain the target image.

[0153] In the above embodiments of the present application, the device further includes: a third operation module and a second determination module.

[0154] Among them, the third operation module is used to perform an alignment operation on multiple second images based on the first image to obtain the aligned multiple second images; the second determination module is used to determine the target image among the aligned multiple second images.

[0155] In the above embodiments of the present application, the third operation module includes: a first processing unit and a first operation unit.

[0156] Among them, the first processing unit is configured to process the first image and multiple second images by using an alignment network to obtain alignment coefficients, where the alignment coefficients include at least one of the following: rotation coefficient, scaling coefficient, and translation coefficient; the first operation unit is configured to perform an alignment operation on the multiple second images based on the alignment coefficients to obtain the aligned multiple second images.

[0157] In the above embodiments of the present application, the first operation module includes: a second processing unit and a third processing unit.

[0158] Among them, the second processing unit is configured to process the target image and the first image by using a matting network to obtain a mask matrix of the target object; the third processing unit is configured to process the first image by using the mask matrix of the target object to obtain an image of the target object.

[0159] In the above embodiments of the present application, the device further includes: a first synthesis module.

[0160] Among them, the first synthesis module is configured to synthesize the image of the target object and the second image to obtain an output image

[0161] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0162] Embodiment 8

[0163] According to an embodiment of the present application, there is also provided an image processing device for implementing the above image processing method, as Figure 10 shown, the device 1000 includes: a first display module 1002 and a first generation module 1004.

[0164] Among them, the first display module is configured to display the first image on the interaction interface; the first generation module is configured to display an image of a generated target object on the interaction interface in response to a matting instruction sensed on the interaction interface, where the matting instruction is used to obtain a second image and perform a matting operation on the first image based on the second image, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen.

[0165] It should be noted here that the above first display module 1002 and first generation module 1004 correspond to steps S402 to S404 in Embodiment 2. The two modules have the same implementation examples and application scenarios as the corresponding steps, but are not limited to the content disclosed in the above Embodiment 1. It should be noted that the above modules, as part of the device, can run in the computer terminal 10 provided in Embodiment 1.

[0166] In the above embodiments of the present application, the first generation module includes: a second determination unit and a second operation unit.

[0167] Among them, the second determination unit is used to determine the target image among the multiple second images obtained, where the similarity between the target image and the first image is greater than the similarity between other images and the first image, and other images are any one of the multiple second images except the target image; the second operation unit is used to perform a matting operation on the first image based on the target image to obtain the image of the target object.

[0168] In the above embodiments of the present application, the device further includes: a fourth operation module and a third determination module.

[0169] Among them, the fourth operation module is used to perform an alignment operation on the multiple second images based on the first image to obtain the aligned multiple second images; the third determination module is used to determine the target image among the aligned multiple second images.

[0170] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0171] Embodiment 9

[0172] According to an embodiment of the present application, there is also provided an image processing device for implementing the above image processing method, as Figure 11 shown, the device 1100 includes: a second acquisition module 1102, a fifth operation module 1104, and a second synthesis module 1106.

[0173] Among them, the second acquisition module is used to acquire a teaching image and a screenshot image, where the teaching image is an image obtained by photographing a teacher and a target screen, and the screenshot image is an image obtained by performing a screenshot operation on the teaching content displayed on the target screen; the fifth operation module is used to perform a matting operation on the teaching image based on the screenshot image to obtain the image of the target object; the second synthesis module is used to synthesize the image of the target object and the screenshot image to obtain an output image.

[0174] It should be noted here that the above second acquisition module 1102, fifth operation module 1104, and second synthesis module 1106 correspond to steps S502 to S506 in Embodiment 3. The instances and application scenarios implemented by the three modules and the corresponding steps are the same, but are not limited to the content disclosed in the above Embodiment 1. It should be noted that the above modules, as part of the device, can run in the computer terminal 10 provided in Embodiment 1.

[0175] In the above embodiments of the present application, the device further includes: a fourth determination module and a sixth operation module.

[0176] Among them, the fourth determination module is used to determine the target image among the multiple captured screenshot images, where the similarity between the target image and the teaching image is greater than the similarity between other images and the teaching image, and the other images are any one of the multiple screenshot images except the target image; the sixth operation module is used to perform a matte operation on the teaching image based on the target image to obtain the image of the target object.

[0177] In the above embodiments of the present application, the device further includes: a seventh operation module and a fifth determination module.

[0178] Among them, the seventh operation module is used to perform an alignment operation on the multiple screenshot images based on the teaching image to obtain the aligned multiple screenshot images; the fifth operation module is used to determine the target image among the aligned multiple screenshot images.

[0179] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0180] Embodiment 10

[0181] According to an embodiment of the present application, there is also provided an image processing device for implementing the above image processing method, as Figure 12 shown, the device 1200 includes: a shooting module 1202, a screenshot module 1204, and an eighth operation module 1206.

[0182] Among them, the shooting module is used to shoot the target object and the target screen to obtain a first image; the screenshot module is used to perform a screenshot operation on the target screen to obtain a second image; the eighth operation module is used to perform a matte operation on the first image based on the second image to obtain the image of the target object.

[0183] It should be noted here that the above shooting module 1202, screenshot module 1204, and eighth operation module 1206 correspond to steps S602 to S606 in Embodiment 4. The instances and application scenarios implemented by the three modules and the corresponding steps are the same, but are not limited to the content disclosed in the above Embodiment 1. It should be noted that the above modules, as part of the device, can run in the computer terminal 10 provided in Embodiment 1.

[0184] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0185] Embodiment 11

[0186] According to an embodiment of the present application, there is also provided an image processing apparatus for implementing the above image processing method, as Figure 13 shown. The apparatus 1300 includes: a first receiving module 1302, a ninth operating module 1304, and a first sending module 1306.

[0187] Among them, the first receiving module is configured to receive a first image and a second image sent by a client. The first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen. The ninth operating module is configured to perform a matte extraction operation on the first image based on the second image to obtain an image of the target object. The first sending module is configured to send an output image to the client.

[0188] It should be noted here that the above first receiving module 1302, ninth operating module 1304, and first sending module 1306 correspond to steps S702 to S706 in Embodiment 5. The examples and application scenarios implemented by the three modules and the corresponding steps are the same, but are not limited to the content disclosed in the above Embodiment 1. It should be noted that the above modules, as part of the apparatus, can run in the computer terminal 10 provided in Embodiment 1.

[0189] It should be noted that the preferred implementation schemes involved in the above embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0190] Embodiment 12

[0191] According to an embodiment of the present application, there is also provided an image processing apparatus for implementing the above image processing method, as Figure 14 shown. The apparatus 1400 includes: a second receiving module 1402, a tenth operating module 1404, and a second sending module 1406.

[0192] Among them, the second receiving module is configured to receive a first image and a second image sent by a client through a first interface. The first interface includes: a first parameter and a second parameter. The parameter value of the first parameter is the first image, and the parameter value of the second parameter is the second image. The first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen. The tenth operating module is configured to perform a matte extraction operation on the first image based on the second image to obtain an image of the target object. The second sending module is configured to send an output image to the client through a second interface. The second interface includes: a second parameter, and the parameter value of the second parameter is the output image.

[0193] It should be noted here that the above-mentioned second receiving module 1402, tenth operating module 1404, and second sending module 1406 correspond to steps S802 to S806 in Embodiment 6. The instances and application scenarios implemented by the three modules and the corresponding steps are the same, but are not limited to the content disclosed in the above-mentioned Embodiment 1. It should be noted that the above-mentioned modules, as part of the device, can run in the computer terminal 10 provided in Embodiment 1.

[0194] It should be noted that the preferred implementation schemes involved in the above-mentioned embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0195] Embodiment 13

[0196] According to an embodiment of the present application, there is also provided a computing device, including:

[0197] A photographing device for photographing a target object and a target screen to obtain a first image;

[0198] A processor that runs a program. When the program runs, the following processing steps are performed on the first image output from the photographing device: taking a screenshot of the target screen to obtain a second image; performing a matte operation on the first image based on the second image to obtain an image of the target object.

[0199] Optionally, the processor is further configured to synthesize the image of the target object and the second image to obtain an output image.

[0200] It should be noted that the preferred implementation schemes involved in the above-mentioned embodiments of the present application are the same as the schemes, application scenarios, and implementation processes provided in Embodiment 1, but are not limited to the schemes provided in Embodiment 1.

[0201] Embodiment 14

[0202] An embodiment of the present application also provides a computer-readable storage medium. Optionally, in this embodiment, the above storage medium can be used to store the program code executed by the image processing method provided in the above embodiment.

[0203] Optionally, in this embodiment, the above storage medium can be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0204] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: obtaining a first image and a second image, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; performing a matting operation on the first image based on the second image to obtain an image of the target object.

[0205] Optionally, the above storage medium is further configured to store program code for performing the following steps: determining a target image among multiple obtained second images, where the similarity between the target image and the first image is greater than the similarity between other images and the first image, and the other images are any one of the multiple second images except the target image; performing a matting operation on the first image based on the target image to obtain an image of the target object.

[0206] Optionally, the above storage medium is further configured to store program code for performing the following steps: obtaining the similarity between each second image and the first image; determining the second image corresponding to the maximum similarity as the target image.

[0207] Optionally, the above storage medium is further configured to store program code for performing the following steps: using a similarity estimation network to process multiple second images and the first image to obtain a target image.

[0208] Optionally, the above storage medium is further configured to store program code for performing the following steps: performing an alignment operation on multiple second images based on the first image to obtain the aligned multiple second images; determining the target image among the aligned multiple second images.

[0209] Optionally, the above storage medium is further configured to store program code for performing the following steps: using an alignment network to process the first image and multiple second images to obtain alignment coefficients, where the alignment coefficients include at least one of the following: rotation coefficient, scaling coefficient, and translation coefficient; performing an alignment operation on multiple second images based on the alignment coefficients to obtain the aligned multiple second images.

[0210] Optionally, the above storage medium is further configured to store program code for performing the following steps: after performing a matting operation on the first image based on the second image to obtain an image of the target object, synthesizing the image of the target object and the second image to obtain an output image.

[0211] Optionally, the above storage medium is further configured to store program code for performing the following steps: using a matting network to process the target image and the first image to obtain a mask matrix of the target object; using the mask matrix of the target object to process the first image to obtain an image of the target object.

[0212] Example 15

[0213] The embodiment of the present application further provides a computing device, which can be any one of the computing devices in a computing device cluster. Optionally, in this embodiment, the above computing device can also be replaced with a terminal device such as a mobile device.

[0214] Optionally, in this embodiment, the above computing device can be located in at least one of multiple network devices in a computer network.

[0215] In this embodiment, the above computing device can execute the program code of the following steps in the image processing method: obtaining a first image and a second image, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; performing a matting operation on the first image based on the second image to obtain an image of the target object.

[0216] Optionally, Figure 15 is a structural block diagram of a computing device according to Embodiment 15 of the present application, as Figure 15 shown, the computing device may include: one or more (only one is shown in the figure) processors 1502, a memory 1504.

[0217] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the image processing method and device in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the above image processing method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, a flash memory, or other non-volatile solid-state memories. In some instances, the memory may further include a memory remotely set relative to the processor, and these remote memories can be connected to the terminal A through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0218] The processor can call the information and application programs stored in the memory through a transmission device to execute the following steps: obtaining a first image and a second image, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; performing a matting operation on the first image based on the second image to obtain an image of the target object.

[0219] Optionally, the above-mentioned processor may also execute program code for the following steps: determining a target image among multiple acquired second images, where the similarity between the target image and the first image is greater than the similarity between other images and the first image, and the other images are any second image other than the target image among the multiple second images; performing a matting operation on the first image based on the target image to obtain an image of the target object.

[0220] Optionally, the above-mentioned processor may also execute program code for the following steps: obtaining the similarity between each second image and the first image; determining the second image corresponding to the maximum similarity as the target image.

[0221] Optionally, the above-mentioned processor may also execute program code for the following steps: processing multiple second images and the first image by using a similarity estimation network to obtain the target image.

[0222] Optionally, the above-mentioned processor may also execute program code for the following steps: performing an alignment operation on multiple second images based on the first image to obtain the aligned multiple second images; determining the target image among the aligned multiple second images.

[0223] Optionally, the above-mentioned processor may also execute program code for the following steps: processing the first image and multiple second images by using an alignment network to obtain alignment coefficients, where the alignment coefficients include at least one of the following: rotation coefficient, scaling coefficient, and translation coefficient; performing an alignment operation on multiple second images based on the alignment coefficients to obtain the aligned multiple second images.

[0224] Optionally, the above-mentioned processor may also execute program code for the following steps: processing the target image and the first image by using a matting network to obtain a mask matrix of the target object; processing the first image by using the mask matrix of the target object to obtain an image of the target object.

[0225] Optionally, the above-mentioned processor may also execute program code for the following steps: after performing a matting operation on the first image based on the second image to obtain an image of the target object, synthesizing the image of the target object and the second image to obtain an output image.

[0226] Those of ordinary skill in the art can understand that Figure 15 the structure shown is only schematic, and the computing device may also be a terminal device such as a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a personal digital assistant, and a Mobile Internet Device (MID), a PAD, etc. Figure 15 It does not limit the structure of the above-mentioned electronic device. For example, computing device A may also include more Figure 15more or fewer components (such as network interfaces, display devices, etc.) shown, or having a configuration different from that Figure 15 shown.

[0227] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware of the terminal device through a program, and the program can be stored in a computer-readable storage medium. The storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, etc.

[0228] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments.

[0229] In the above embodiments of the present application, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0230] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the units or modules can be in an electrical or other form.

[0231] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0232] In addition, the functional units in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0233] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0234] The above are only the preferred embodiments of this application. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of this application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this application.

Claims

1. An image processing method, characterized in that, Including: Obtain a first image and a second image, where the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; Perform a matting operation on the first image based on the second image to obtain an image of the target object; Composite the image of the target object and the second image to obtain an output image, where the background clarity of the output image is greater than that of the first image.

2. The method according to claim 1, wherein In the case of continuously obtaining the second image multiple times, the method includes: Determine a target image among the multiple second images obtained, where the similarity between the target image and the first image is greater than the similarity between other images and the first image, and the other images are any one of the multiple second images except the target image; Perform a matting operation on the first image based on the target image to obtain an image of the target object.

3. The method according to claim 2, wherein Use a similarity estimation network to process the multiple second images and the first image to obtain the target image.

4. The method according to claim 2, wherein Before determining the target image among the multiple second images obtained, the method further includes: Perform an alignment operation on the multiple second images based on the first image to obtain the aligned multiple second images; Determine the target image among the aligned multiple second images.

5. The method according to claim 4, characterized in that, Performing an alignment operation on the multiple second images based on the first image to obtain the aligned multiple second images includes: Use an alignment network to process the first image and the multiple second images to obtain alignment coefficients, where the alignment coefficients include at least one of the following: rotation coefficient, scaling coefficient, and translation coefficient; Perform an alignment operation on the multiple second images based on the alignment coefficients to obtain the aligned multiple second images.

6. The method according to any one of claims 2 to 5, characterized in that Performing a matting operation on the first image based on the target image to obtain an image of the target object includes: Use a matting network to process the target image and the first image to obtain a mask matrix of the target object; Use the mask matrix of the target object to process the first image to obtain an image of the target object.

7. An image processing method, characterized in that, Including: Display the first image on the interaction interface; In response to a matting instruction sensed on the interaction interface, display an image of the target object on the interaction interface, where the matting instruction is used to obtain a second image and perform a matting operation on the first image based on the second image, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; In response to a composite instruction sensed on the interaction interface, display an output image on the interaction interface, where the output image is obtained by compositing the image of the target object and the second image, and the background clarity of the output image is greater than that of the first image.

8. An image processing method, characterized in that, Including: Obtain a teaching image and a screenshot image, where the teaching image is an image obtained by photographing a teacher and a target screen, and the screenshot image is an image obtained by performing a screenshot operation on the teaching content displayed on the target screen; Perform a matte extraction operation on the teaching image based on the captured screenshot image to obtain the image of the teacher; Composite the image of the teacher and the captured screenshot image to obtain an output image, wherein the background clarity of the output image is greater than that of the teaching image.

9. An image processing method, characterized in that, Including: The server receives a first image and a second image sent by the client, wherein the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; The server performs a matte extraction operation on the first image based on the second image to obtain the image of the target object; The server composites the image of the target object and the second image to obtain an output image, wherein the background clarity of the output image is greater than that of the first image; The server sends the output image to the client.

10. An image processing method, characterized in that, Including: The server receives a first image and a second image sent by the client through a first interface, wherein the first interface includes: a first parameter and a second parameter, the parameter value of the first parameter is the first image, the parameter value of the second parameter is the second image, the first image is an image obtained by photographing a target object and a target screen, and the second image is an image obtained by performing a screenshot operation on the content displayed on the target screen; The server performs a matte extraction operation on the first image based on the second image to obtain the image of the target object; The server composites the image of the target object and the second image to obtain an output image, wherein the background clarity of the output image is greater than that of the first image; The server sends the output image to the client through a second interface, wherein the second interface includes: a second parameter, and the parameter value of the second parameter is the output image.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the image processing method according to any one of claims 1 to 10.

12. A computing device, characterized in that, The computing device includes: a processor and a memory, the processor is used to run the program stored in the memory, wherein when the program runs, it executes the image processing method according to any one of claims 1 to 10.

13. A computing device, characterized in that, Including: A photographing device for photographing a target object and a target screen to obtain a first image; A processor that runs a program, wherein when the program runs, it performs the following processing steps on the first image output from the photographing device: perform a screenshot operation on the target screen to obtain a second image; perform a matte extraction operation on the first image based on the second image to obtain the image of the target object; composite the image of the target object and the second image to obtain an output image, wherein the background clarity of the output image is greater than that of the first image.

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