CN110348419B - Method and device for photographing - Google Patents

Abstract

The embodiment of the disclosure discloses a method and a device for taking a picture. One embodiment of the method comprises: obtaining a photograph including at least one person; carrying out edge detection on the photo to obtain the outline characteristics of at least one person; carrying out face recognition based on the outline characteristics of at least one person to obtain a face area of the at least one person; inputting the outline characteristics and the face area of at least one person into a pre-trained neural network model to obtain position adjustment information; and outputting the position adjustment information. According to the embodiment, when a plurality of persons take a picture, the face area (expression and sight line) of the person is used as a main characteristic, the target characteristics such as standing position and posture are used as auxiliary characteristics, pre-analysis is carried out, and the face area, the standing position and the posture which affect the shooting effect are prompted to be adjusted by the person to be shot in a voice mode according to the analysis result, so that the aims of shortening the shooting time and improving the shooting quality are achieved.

Description

Method and device for taking pictures

technical field

The embodiments of the present disclosure relate to the field of computer technology, and in particular to a method and device for taking pictures.

Background technique

With the development of science and technology, smart devices with cameras have become mainstream terminal devices in the current era, and have become the main tool for taking pictures. When many people are taking pictures at the same time, it is often because some people are blocked, or the facial expressions and sight lines cannot meet the photographer's satisfaction, or the body movements cannot meet the photographer's satisfaction, which will lead to multiple retakes and affect the photography. Efficiency, reducing the user experience.

Contents of the invention

Embodiments of the present disclosure propose methods and devices for taking pictures.

In a first aspect, an embodiment of the present disclosure provides a method for taking pictures, including: acquiring a photo including at least one person; performing edge detection on the photo to obtain at least one person's profile feature; based on the at least one person's profile feature Perform face recognition to obtain at least one person's face area; input the at least one person's contour feature and face area into a pre-trained neural network model to obtain position adjustment information; output the position adjustment information.

In some embodiments, the method also includes: performing key point recognition based on the face area of at least one character to obtain key point features of at least one character; The network model obtains position adjustment information and/or expression adjustment information; outputs position adjustment information and/or expression adjustment information.

In some embodiments, the method also includes: performing key point recognition based on the face area of at least one character to obtain key point features of at least one character; The network model obtains position adjustment information and/or line of sight adjustment information; outputs position adjustment information and/or line of sight adjustment information.

In some embodiments, the line-of-sight adjustment information includes the sequential position of the person to be adjusted in the photo and the direction and angle of head rotation.

In some embodiments, the position adjustment information includes the sequential position of the person to be adjusted in the photo and the direction and distance to be moved.

In a second aspect, an embodiment of the present disclosure provides a device for taking pictures, including: an acquisition unit configured to acquire a photo including at least one person; a first detection unit configured to perform edge detection on the photo to obtain The outline feature of at least one person; the second detection unit is configured to perform face recognition based on the outline feature of at least one person to obtain the face area of at least one person; the adjustment unit is configured to combine the outline feature of at least one person and The face area is input into the pre-trained neural network model to obtain position adjustment information; the output unit is configured to output the position adjustment information.

In some embodiments, the device further includes a third detection unit configured to: perform key point recognition based on the face area of at least one person to obtain key point features of at least one person; and the adjustment unit is further configured to at least A person's contour features, facial regions, and key point features are input into the neural network model to obtain position adjustment information and/or expression adjustment information; and the output unit is further configured to output position adjustment information and/or expression adjustment information.

In some embodiments, the device further includes a fourth detection unit configured to: perform key point recognition based on the face area of at least one person to obtain key point features of at least one person; and the adjustment unit is further configured to at least A character's contour features, face area, and key point features are input into the neural network model to obtain position adjustment information and/or line of sight adjustment information; output position adjustment information and/or line of sight adjustment information.

In some embodiments, the line-of-sight adjustment information includes the sequential position of the person to be adjusted in the photo and the direction and angle of head rotation.

In some embodiments, the position adjustment information includes the sequential position of the person to be adjusted in the photo and the direction and distance to be moved.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device, on which one or more programs are stored, when one or more programs are processed by one or more executed by a processor, so that one or more processors implement the method according to any one of the first aspect.

In a fourth aspect, the embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, wherein, when the program is executed by a processor, the method according to any one of the first aspect is implemented.

In the method and device for taking pictures provided by the embodiments of the present disclosure, according to the trained neural network model, when multiple people take pictures, the facial area (expression, line of sight) of the person is the main feature, and the standing position, posture and other targets The features are auxiliary features, pre-analyzed, and according to the analysis results, the subject will be prompted to adjust the facial area, standing position, posture, etc. that affect the shooting effect, so as to shorten the shooting time and improve the shooting quality.

Description of drawings

Other characteristics, objects and advantages of the present disclosure will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present disclosure can be applied;

FIG. 2 is a flowchart of one embodiment of a method for taking pictures according to the present disclosure;

3a-3e are schematic diagrams of an application scenario of the method for taking pictures according to the present disclosure;

FIG. 4 is a flowchart of another embodiment of a method for taking pictures according to the present disclosure;

Fig. 5 is a schematic structural diagram of an embodiment of a device for taking pictures according to the present disclosure;

FIG. 6 is a structural schematic diagram of a computer system suitable for implementing an electronic device according to an embodiment of the present disclosure.

Detailed ways

The present disclosure will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, rather than to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

It should be noted that, in the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and embodiments.

FIG. 1 shows an exemplary system architecture 100 to which embodiments of the method for taking pictures or the apparatus for taking pictures of the present disclosure can be applied.

As shown in FIG. 1 , a system architecture 100 may include terminal devices 101 , 102 , 103 , a network 104 and a server 105 . The network 104 is used as a medium for providing communication links between the terminal devices 101 , 102 , 103 and the server 105 . Network 104 may include various connection types, such as wires, wireless communication links, or fiber optic cables, among others.

Users can use terminal devices 101 , 102 , 103 to interact with server 105 via network 104 to receive or send messages and the like. Various communication client applications can be installed on the terminal devices 101, 102, 103, such as camera applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

The terminal devices 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they can be various electronic devices with cameras, including but not limited to cameras, smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III , Moving Picture Experts Compression Standard Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Compression Standard Audio Layer 4) Players, Laptop Portable Computers and Desktop Computers, etc. When the terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. It can be implemented as a plurality of software or software modules (for example, to provide distributed services), or as a single software or software module. No specific limitation is made here.

The server 105 may be a server that provides various services, for example, a background adjustment server that supports photos taken on the terminal devices 101 , 102 , 103 . The background adjustment server can analyze and process the received photos and other data, and feed back the processing results (such as information reminding to adjust the body position or expression) to the terminal device, and then the terminal device outputs it in the form of voice or text.

It should be noted that the server may be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, multiple software or software modules for providing distributed services), or can be implemented as a single software or software module. No specific limitation is made here.

It should be noted that the method for taking pictures provided by the embodiments of the present disclosure may be executed by the terminal devices 101 , 102 , 103 or by the server 105 . Correspondingly, the device for taking pictures can be set in the terminal devices 101 , 102 , 103 or in the server 105 . No specific limitation is made here.

It should be understood that the numbers of terminal devices, networks and servers in Fig. 1 are only illustrative. According to the implementation needs, there can be any number of terminal devices, networks and servers.

Continuing to refer to FIG. 2 , a flow 200 of an embodiment of the method for taking pictures according to the present disclosure is shown. The method for taking pictures includes the following steps:

Step 201, acquire a photo including at least one person.

In this embodiment, the executing subject of the method for taking pictures (for example, the server or terminal device shown in FIG. 1 ) may receive a picture including at least one character from the terminal that the user uses to take pictures through a wired connection or a wireless connection. . A character may also appear to be obscured by other objects. Group photos of multiple people are more likely to be blocked by other people.

Step 202, performing edge detection on the photo to obtain at least one person's contour feature.

In this embodiment, an edge detection algorithm is used to separate the characters from the background, and feature information such as the position and posture of the characters are extracted to obtain the contour features of each character. An edge detection algorithm in the prior art may be used, for example, a multi-scale edge detection algorithm based on wavelet transform and the like. As shown in Figure 3a, after performing edge detection, the outline of each person in the photo can be determined.

Step 203, performing face recognition based on the contour features of at least one person to obtain the face area of at least one person.

In this embodiment, face recognition is further performed based on the contour features determined in step 202 to obtain the face area of each person. First determine the outline, you can narrow the recognized area, thereby improving the speed of face recognition. The existing face recognition technology can be used to identify the face area. No identity verification is required, so a high-speed but coarse-grained neural network model can be used for face recognition.

Step 204, input the contour feature and face area of at least one person into the pre-trained neural network model to obtain position adjustment information.

In this embodiment, the specific implementation is shown in Fig. 3b. If the detected target is only unoccluded, it is not necessary to extract key point features, only contour features and face areas are required. The neural network model can be trained in a supervised manner by using photos including at least one person marked with outline features, face regions, and position adjustment information as training samples. The input of the neural network model is the contour features and face area of the person in the photo, and the expected output is position adjustment information. By continuously adjusting the parameters of the neural network model, the output of the neural network model can achieve the expected output effect. The basic principle is to identify which person's face area is incomplete, that is, it is blocked, and then calculate how much to move in which direction to not be blocked. The output of the neural network model can be to move the occluded person, or to move the person blocking others, and finally realize that all people are not occluded. Optionally, the moving ranges of the two moving modes can be calculated, and the person with the smaller moving range is selected to move. For example, if A is blocked by B, if you want A to move, you need to move 10cm so that it will neither be blocked by B or others nor others, but if you let B move, you need to move 5cm so that you can neither block A or others, and will not be obscured by others. In the end, the scheme of moving B can be regarded as the optimal adjustment scheme. Optionally, both schemes can also be informed to the person being photographed, and the person being photographed can choose who to move. Optionally, it is also possible to only output who is blocked and which part is blocked, and the moving distance is not calculated, and the person being photographed is notified and then the person being photographed moves by himself.

Step 205, outputting position adjustment information.

In this embodiment, the position adjustment information can be output by voice or text. As shown in Figure 3c, it is fed back to the photographed city in the form of voice. If the subject of execution of the present disclosure is a terminal device, the position adjustment information may be output directly through voice or text. If the subject of execution of the present disclosure is a server, it is necessary to send the position adjustment information to the terminal device, and the terminal device outputs the position adjustment information.

Continuing to refer to FIG. 3d, FIG. 3d is a schematic diagram of an application scenario of the method for taking pictures according to this embodiment. In the application scenario shown in Figure 3d, when multiple people take pictures, the faces of the people being photographed are partially blocked. The specific process is as follows:

Step S01: Start the camera and enter the camera mode

Step S02: Using an edge detection algorithm, separate the person from the background, and extract feature information such as the person's position and posture.

Step S03: Recognize the face area of each subject through the facial recognition algorithm.

Step S04: Input the trained neural network for the position, posture, and face area of the person

Step S05: The neural network analyzes the feature information, as shown in Figure 3d, through the face area and key feature point information, it can be known that the face area of the subject is blocked, and combined with the character position information obtained in step S02, we can get Knowing that the face of the third person to be photographed from the right is covered, and combined with the person and background information obtained in step S02, the adjustment information can be obtained, such as "the face of the third person to be photographed from the right is blocked Obscuration, please move a little to the right".

Step S06: The adjustment information is fed back to the person being photographed in the form of voice.

Step S07: Steps S02 to S06 are repeated, and when all adjustments meet the requirements, the photographing is completed.

The method provided by the above-mentioned embodiments of the present disclosure determines whether the face is blocked by detecting the outline and face area of the person being photographed, and reminds the person being photographed to change the position if it is blocked. So as to achieve the purpose of shortening the shooting time and improving the shooting quality.

Further referring to FIG. 4 , it shows a flow 400 of still another embodiment of the method for taking pictures. The flow 400 of the method for taking pictures includes the following steps:

Step 401, acquiring a photo including at least one person;.

Step 402, performing edge detection on the photo to obtain at least one profile feature of a person.

Step 403, performing face recognition based on the contour features of at least one person, and obtaining the face area of at least one person.

Steps 401-403 are basically the same as steps 201-203, so they are not repeated here.

Step 404: Perform key point recognition based on the face area of at least one person to obtain key point features of at least one person.

In this embodiment, through the facial feature point detection technology, the face area is recognized according to the previous part, and the key feature points of the face are automatically located, such as the outline points of the parts of the face such as eyes, nose tip, and mouth corners.

Step 405: Input the contour features, face area, and key point features of at least one person into the neural network model to obtain position adjustment information and/or expression adjustment information and/or gaze adjustment information.

In this embodiment, the input of the model includes key point features in addition to contour features and face regions. In this way, the model can not only judge whether the face is blocked, but also judge whether the expression is not in place (for example, judge whether the person being photographed is not smiling through key points such as the corner of the mouth), whether the person being photographed is looking at the camera, etc. Thus, adjustment information is generated, and the adjustment information includes at least one of the following: position adjustment information, expression adjustment information, and line of sight adjustment information. The adjustment information includes the position of the person to be adjusted, for example, the third person on the left in the second row, and the content to be adjusted. The position adjustment information includes the sequential position of the person to be adjusted in the photo and the direction and distance to be moved. The line-of-sight adjustment information includes the sequential position of the person to be adjusted in the photo and the direction and angle of head rotation. The expression adjustment information includes the sequential position of the person to be adjusted in the photo and the adjustment target, such as smiling or not frowning.

Step 406, outputting position adjustment information and/or expression adjustment information and/or line of sight adjustment information.

In this embodiment, if adjustment is necessary, at least one of the following adjustment information is output: position adjustment information, expression adjustment information, and line of sight adjustment information.

Figure 3e is an application scenario where the line of sight of the subject needs to be adjusted when multiple people are taking pictures. The specific process is as follows:

Step S01: The camera starts up and enters into a photographing mode.

Step S02: Using an edge detection algorithm, separate the person from the background, and extract feature information such as the person's position and posture.

Step S03: Recognize the face area of each subject through the facial recognition algorithm.

Step S04: Through the facial feature point detection technology, according to the previous part to identify the face area, automatically locate the key feature points of the face, such as the contour points of the parts of the face such as eyes, nose tip, mouth corner points, etc., through the position of the feature points of the eyes Distribute information and make line-of-sight judgments.

Step S05: Input the trained neural network for the position, posture, facial features and key feature point information of the person.

Step S06: The neural network analyzes the feature information, as shown in Figure 3e, through the facial features and key feature point information, it can be known that the subject's line of sight needs to be adjusted, and combined with the character position information obtained in step 2, it is known that from the right If the line of sight of the third subject from the right needs to be adjusted, the adjustment information can be obtained, such as "the third subject from the right, please look here".

Step S07: The adjustment information is fed back to the person being photographed in the form of voice

Step S08: Repeat steps S02 to S07, and when all adjustments meet the requirements, the photographing is completed.

It can be seen from FIG. 4 that, compared with the embodiment corresponding to FIG. 2 , the process 400 of the method for taking pictures in this embodiment embodies the steps of detecting performance and line of sight. Therefore, the solution described in this embodiment can further help to take high-quality photos.

Further referring to FIG. 5 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for taking pictures. This device embodiment corresponds to the method embodiment shown in FIG. 2 , and the device Specifically, it can be applied to various electronic devices.

As shown in FIG. 5 , the device 500 for taking pictures in this embodiment includes: an acquisition unit 501 , a first detection unit 502 , a second detection unit 503 and an output unit 505 . The acquisition unit 501 is configured to acquire a photo including at least one person; the first detection unit 502 is configured to perform edge detection on the photo to obtain the outline feature of at least one person; the second detection unit 503 is configured to be based on at least one Perform face recognition on the contour features of the person to obtain at least one face area of the person; the adjustment unit 504 is configured to input the contour feature and face area of at least one person into the pre-trained neural network model to obtain position adjustment information; output A unit 505 configured to output position adjustment information.

In this embodiment, the specific processing of the acquisition unit 501, the first detection unit 502, the second detection unit 503, and the output unit 505 of the device 500 for photographing can refer to steps 201, 202, Step 203 , step 204 and step 205 .

In some optional implementations of this embodiment, the device 500 further includes a third detection unit (not shown in the drawings), configured to: perform key point recognition based on the facial area of at least one person, and obtain at least one The key point feature of the character; and the adjustment unit is further configured to input the contour feature, face area, and key point feature of at least one character into the neural network model to obtain position adjustment information and/or expression adjustment information; and the output unit is further configured to output position adjustment information and/or expression adjustment information.

In some optional implementations of this embodiment, the device 500 further includes a fourth detection unit (not shown in the drawings), configured to: perform key point recognition based on the face area of at least one person, and obtain at least one The key point features of the character; and the adjustment unit is further configured to input the contour feature, face area, and key point feature of at least one character into the neural network model to obtain position adjustment information and/or line of sight adjustment information; output position adjustment information and/or or sight adjustment information.

In some optional implementation manners of this embodiment, the line-of-sight adjustment information includes the sequential position of the person to be adjusted in the photo and the direction and angle of head rotation.

In some optional implementation manners of this embodiment, the position adjustment information includes the sequential position of the person to be adjusted in the photo and the direction and distance to be moved.

Referring now to FIG. 6 , it shows a schematic structural diagram of an electronic device (such as the server or terminal device in FIG. 1 ) 600 suitable for implementing the embodiments of the present disclosure. The terminal equipment in the embodiments of the present disclosure may include but not limited to cameras, mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), car mobile terminals such as terminals (for example, car navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, and the like. The terminal device/server shown in FIG. 6 is only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure.

As shown in FIG. 6, an electronic device 600 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which may be randomly accessed according to a program stored in a read-only memory (ROM) 602 or loaded from a storage device 608. Various appropriate actions and processes are executed by programs in the memory (RAM) 603 . In the RAM 603, various programs and data necessary for the operation of the electronic device 600 are also stored. The processing device 601 , ROM 602 and RAM 603 are connected to each other through a bus 604 . An input/output (I/O) interface 605 is also connected to the bus 604 .

Typically, the following devices can be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speaker, vibration an output device 607 such as a computer; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While FIG. 6 shows electronic device 600 having various means, it should be understood that implementing or having all of the means shown is not a requirement. More or fewer means may alternatively be implemented or provided. Each block shown in FIG. 6 may represent one device, or may represent multiple devices as required.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, where the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609 , or from storage means 608 , or from ROM 602 . When the computer program is executed by the processing device 601, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed. It should be noted that the computer-readable medium described in the embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the embodiments of the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable signal medium may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: acquires a photo including at least one person; performs edge detection on the photo to obtain at least one person Contour features of at least one character; face recognition based on the contour features of at least one character to obtain the face area of at least one character; input the contour features and face area of at least one character into the pre-trained neural network model to obtain position adjustment information; output Position adjustment information.

Computer program code for carrying out operations of embodiments of the present disclosure may be written in one or more programming languages, or combinations thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, Also included are conventional procedural programming languages - such as the "C" 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 cases involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider). Internet connection).

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 a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions. 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 they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. The described units may also be set in a processor. For example, it may be described as: a processor includes an acquisition unit, a first detection unit, a second detection unit, an adjustment unit, and an output unit. Wherein, the names of these units do not constitute a limitation on the unit itself under certain circumstances, for example, the acquisition unit may also be described as "a unit that acquires a photo including at least one person".

The above description is only a preferred embodiment of the present disclosure and an illustration of the applied technical principle. It should be understood by those skilled in the art that the scope of the invention involved in this disclosure is not limited to the technical solution formed by the specific combination of the above technical features, but also covers the technical solutions made by the above technical features without departing from the inventive concept. Other technical solutions formed by any combination of or equivalent features thereof. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

Claims (12)

1. A method for taking a photograph, comprising:

obtaining a photograph including at least one person;

carrying out edge detection on the photo to obtain the outline characteristics of the at least one person;

performing face recognition based on the outline characteristics of the at least one person to obtain a face area of the at least one person;

inputting the contour feature and the face area of the at least one person into a pre-trained neural network model to obtain position adjustment information, wherein the position adjustment information comprises: the person being blocked and the location being blocked;

and outputting the position adjustment information.

2. The method of claim 1, wherein the method further comprises:

performing key point identification on the basis of the face area of the at least one person to obtain key point characteristics of the at least one person;

inputting the contour feature, the face area and the key point feature of the at least one figure into the neural network model to obtain position adjustment information and/or expression adjustment information;

and outputting the position adjustment information and/or the expression adjustment information.

3. The method of claim 1, wherein the method further comprises:

performing key point identification on the basis of the face area of the at least one person to obtain key point characteristics of the at least one person;

inputting the contour feature, the face area and the key point feature of the at least one person into the neural network model to obtain position adjustment information and/or sight line adjustment information;

and outputting the position adjustment information and/or the sight line adjustment information.

4. The method according to claim 3, wherein the sight line adjustment information includes a sequential position of the person to be adjusted in the photograph and a direction and angle of head rotation.

5. The method according to one of claims 1 to 4, wherein the position adjustment information comprises the sequential position of the person to be adjusted in the photograph and the direction and distance to be moved.

6. An apparatus for taking a picture, comprising:

an acquisition unit configured to acquire a photograph including at least one person;

a first detection unit configured to perform edge detection on the photo to obtain an outline feature of the at least one person;

a second detection unit configured to perform face recognition based on the contour feature of the at least one person to obtain a face region of the at least one person;

an adjusting unit configured to input the contour feature and the face region of the at least one person into a pre-trained neural network model to obtain position adjustment information, wherein the position adjustment information includes: the person being blocked and the location being blocked;

an output unit configured to output the position adjustment information.

7. The apparatus of claim 6, wherein the apparatus further comprises a third detection unit configured to:

performing key point identification on the basis of the face area of the at least one person to obtain key point characteristics of the at least one person; and

the adjusting unit is further configured to input the contour feature, the face area and the key point feature of the at least one person into the neural network model, so as to obtain position adjusting information and/or expression adjusting information; and

the output unit is further configured to output the position adjustment information and/or the expression adjustment information.

8. The apparatus of claim 6, wherein the apparatus further comprises a fourth detection unit configured to:

performing key point identification on the basis of the face area of the at least one person to obtain key point characteristics of the at least one person; and

the adjusting unit is further configured to input the contour feature, the face area and the key point feature of the at least one person into the neural network model, so as to obtain position adjusting information and/or sight line adjusting information;

and outputting the position adjustment information and/or the sight line adjustment information.

9. The apparatus according to claim 8, wherein the sight line adjustment information includes a sequential position of the person to be adjusted in the photograph and a direction and angle of head rotation.

10. The apparatus according to one of claims 6 to 9, wherein the position adjustment information comprises the sequential position of the person to be adjusted in the photograph and the direction and distance to be moved.

11. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.

12. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-5.

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