CN114727017A - A method, device and computer-readable storage medium for photographing ID photos - Google Patents

Abstract

The invention discloses a method, device and computer-readable storage medium for photographing an ID photo, wherein the method includes: acquiring a preview image including a human face, wherein the preview image includes a depth image and a color corresponding to the depth image image; perform face key point detection according to the color image, and obtain the depth information of the face key point according to the depth image; obtain the preset face pose closest to the current face pose, and based on multiple The viewing angle calculates the adjustment mode of the current face pose. A more user-friendly ID photo shooting solution is realized. By combining the depth image to recognize and adjust the face posture, the effect of ID photo shooting is improved, and the user experience of the ID photo shooting function is enhanced.

Description

A kind of ID photo shooting method, device and computer readable storage medium

technical field

The present invention relates to the field of mobile communications, and in particular, to a method, device and computer-readable storage medium for photographing a certificate.

Background technique

In the prior art, with the continuous development of intelligent terminal devices, the functions of shooting applications based on the device end are becoming more and more abundant. In particular, based on the existing camera solutions, face shooting functions such as ID photo shooting are derived. In the traditional ID photo shooting method, generally only the meeting prompt box is used to prompt the photographer to place the face in the designated position of the screen, for example, the middle position, left and right adjustment, etc. In order to obtain a good shooting effect, when the user places the face at a designated position on the screen, it is often necessary to further adjust the distance between the face and the terminal device, and the posture of the face. However, traditional ID photo shooting cannot accurately realize prompt adjustment based on face posture, resulting in the final shooting effect of ID photos is often unsatisfactory, and the user's shooting experience needs to be improved.

SUMMARY OF THE INVENTION

In order to solve the above-mentioned technical defects in the prior art, the present invention proposes a method for photographing an ID photo, the method comprising:

A preview image containing a human face is acquired, wherein the preview image includes a depth image and a color image corresponding to the depth image.

Performing face key point detection according to the color image, and acquiring depth information of the face key point according to the depth image.

The preset face pose closest to the current face pose is acquired, and the adjustment mode of the current face pose is calculated based on multiple viewing angles.

The prompt information of the adjustment method is generated to assist the user in adjusting the face posture.

Optionally, before the obtaining a preview image including a human face, wherein the preview image includes a depth image and a color image corresponding to the depth image, including:

Preset N types of face pose lists REC_Posture that can be used for photographing ID photos, where N≥1, the face poses include frontal face poses and side face poses, where:

REC_Posture={rec_posture ₁ ,  , rec_posture _N }.

The face pose is a 3D face key point list that contains a predetermined number M and is sorted according to a predetermined rule; wherein, M is a predetermined parameter:

rec_posture _i = {keypoint ₁ , keypoint ₂ , ..., keypoint _M }.

keypoint _i = { _xi , _yi , d _i }.

Among them, x _i , y _i are the abscissa and ordinate of the key point _i in the image, and z _i is the depth information of the key point _i in the image.

Optionally, the obtaining a preview image containing a human face, wherein the preview image includes a depth image and a color image corresponding to the depth image, including:

Get the preview color image pre_image_rgb and the depth image pre_imag_d based on the ToF depth camera.

The depth image pre_imag_d is a depth image registered with the preview color image pre_image_rgb.

Optionally, performing the detection of face key points according to the color image, and obtaining the depth information of the face key points according to the depth image, including:

The face key point detection is performed based on the color image, and the previewed face key point list is obtained:

pre_posture={keypoint ₁ , keypoint ₂ , ..., keypoint _M }.

keypoint _i ={x _i ,y _i }.

Among them, x _i , y _i are the abscissa and ordinate of keypoint _i in the image.

Optionally, performing the detection of face key points according to the color image, and acquiring the depth information of the face key points according to the depth image, further includes:

Based on the depth image of the ToF, obtain the depth information corresponding to the face key points in the current face key point list, including:

Get the resolution of pre_image_rgb and depth image pre_imag_d:

shape_rgb={h_rgb,w_rgb}.

shape_d={h_d,w_d}.

For each face keypoint keypoint _i in the previewed face keypoint list pre_posture, calculate its mapping coordinates of pre_imag_d in the depth image:

Obtain the depth information at the position point mappoint _i in the depth image pre_imag_d as the depth information d _{i of the face key point keypoint i :}

keypoint _i = { _xi , _yi , d _i }.

d _i =pre_imag_d(mappoint _i ).

Optionally, obtaining the preset facial posture closest to the current facial posture, and calculating an adjustment method for the current facial posture based on multiple viewing angles, including:

In the preset face pose list REC_Posture, obtain the preset face pose rec_posture _i that is closest to the current face pose, including:

The preset face pose with the smallest average Euclidean distance is acquired as the face pose mindiss_rec_posture closest to the preview face pose.

Optionally, obtaining the preset facial posture closest to the current facial posture, and calculating the adjustment mode of the current facial posture based on multiple viewing angles, further comprising:

Based on multiple perspectives, for the previewed face key point list pre_posture, and the closest recommended face pose mindiss_rec_posture, the face adjustment method is calculated, including:

According to the preset priority order, check the constraints of each view vs _i in the preset multi-view Mul_VS. If the current view meets the constraints, check whether the next view meets the constraints. If the current view does not meet the constraints, Then, the user is prompted to adjust the posture according to the scaling scale scale _i and the rotation angle θ _i .

Optionally, according to the preset priority order, the constraint check is performed on each view vs _i in the preset multi-view Mul_VS, including:

Obtain the current highest priority viewing angle vs _i and its corresponding zoom scale scale _i and rotation angle θ _i .

According to the preset thresholds S_TH and θ_TH, it is judged whether the constraint condition is satisfied, wherein the constraint condition is:

scale _i ≤ S_TH.

θ _i ≤ θ_TH.

The present invention also provides a device for photographing passports, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, when the computer program is executed by the processor Implement the steps of the method for photographing a certificate photo as described in any one of the above.

The present invention also provides a computer-readable storage medium, where a program for photographing a certificate photo is stored on the computer-readable storage medium, and when the program for photographing a certificate photo is executed by a processor, the method for photographing a certificate photo as described in any one of the above is realized. step.

The method, device and computer-readable storage medium for photographing a certificate photo of the present invention are implemented by acquiring a preview image including a human face, wherein the preview image includes a depth image and a color image corresponding to the depth image; according to the color image Performing face key point detection, and obtaining depth information of the face key points according to the depth image; obtaining a preset face pose closest to the current face pose, and calculating the current face based on multiple perspectives attitude adjustment. A more user-friendly ID photo shooting solution is realized. By combining the depth image to identify and adjust the face posture, the effect of ID photo shooting is improved, and the user experience of the ID photo shooting function is enhanced.

Description of drawings

The present invention will be further described below in conjunction with the accompanying drawings and embodiments, in which:

1 is a schematic diagram of a hardware structure of a mobile terminal according to the present invention;

2 is an architectural diagram of a communication network system provided by an embodiment of the present invention;

FIG. 3 is a flow chart of the first embodiment of the method for photographing a certificate photo of the present invention.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

In the following description, suffixes such as 'module', 'component' or 'unit' used to represent elements are used only to facilitate the description of the present invention and have no specific meaning per se. Thus, "module", "component" or "unit" may be used interchangeably.

The terminal may be implemented in various forms. For example, the terminal described in the present invention may include, for example, a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation device, a Mobile terminals such as wearable devices, smart bracelets, and pedometers, as well as stationary terminals such as digital TVs and desktop computers.

In the following description, a mobile terminal will be used as an example, and those skilled in the art will understand that, in addition to elements specially used for mobile purposes, the configurations according to the embodiments of the present invention can also be applied to stationary type terminals.

Please refer to FIG. 1 , which is a schematic diagram of a hardware structure of a mobile terminal for implementing various embodiments of the present invention. The mobile terminal 100 may include: an RF (Radio Frequency, radio frequency) unit 101 , a WiFi module 102 , an audio output unit 103 , A /V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111 and other components. Those skilled in the art can understand that the structure of the mobile terminal shown in FIG. 1 does not constitute a limitation on the mobile terminal, and the mobile terminal may include more or less components than the one shown, or combine some components, or different components layout.

Below in conjunction with Fig. 1, each component of the mobile terminal is introduced in detail:

The radio frequency unit 101 can be used for receiving and sending signals during transmission and reception of information or during a call. Specifically, after receiving the downlink information of the base station, it is processed by the processor 110; in addition, the uplink data is sent to the base station. Generally, the radio frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 101 can also communicate with the network and other devices through wireless communication. The above-mentioned wireless communication can use any communication standard or protocol, including but not limited to GSM (Global System of Mobile communication, Global System for Mobile Communications), GPRS (General Packet Radio Service, General Packet Radio Service), CDMA2000 (CodeDivision Multiple Access 2000, Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access, Wideband Code Division Multiple Access), TD-SCDMA (Time Division- Synchronous Code Division Multiple Access, Time Division Synchronous Code Division Multiple Access), FDD-LTE (Frequency Division Duplexing-Long Term) Evolution, frequency division duplexing long term evolution) and TDD-LTE (Time Division Duplexing-Long Term Evolution, time division duplexing long term evolution) and so on.

WiFi is a short-distance wireless transmission technology, and the mobile terminal can help users to send and receive emails, browse web pages, access streaming media, etc. through the WiFi module 102, which provides users with wireless broadband Internet access. Although FIG. 1 shows the WiFi module 102, it can be understood that it is not a necessary component of the mobile terminal, and can be completely omitted as required within the scope of not changing the essence of the invention.

When the mobile terminal 100 is in a call signal receiving mode, a talking mode, a recording mode, a voice recognition mode, a broadcast receiving mode, etc., the audio output unit 103 can receive the data received by the radio frequency unit 101 or the WiFi module 102 or store it in the memory 109. The audio data is converted into audio signal and output as sound. Also, the audio output unit 103 may also provide audio output related to a specific function performed by the mobile terminal 100 (eg, call signal reception sound, message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, and the like.

The A/V input unit 104 is used to receive audio or video signals. The A/V input unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, and the graphics processor 1041 can respond to still pictures or images obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The image data of the video is processed. The processed image frames may be displayed on the display unit 106 . The image frames processed by the graphics processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102 . The microphone 1042 can receive sound (audio data) via the microphone 1042 in a phone call mode, a recording mode, a voice recognition mode, etc. operating modes, and can process such sound into audio data. The processed audio (voice) data can be converted into a format that can be transmitted to a mobile communication base station via the radio frequency unit 101 for output in the case of a telephone conversation mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to remove (or suppress) noise or interference generated in the process of receiving and transmitting audio signals.

The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 1061 according to the brightness of the ambient light, and the proximity sensor can close the display panel 1061 and the display panel 1061 when the mobile terminal 100 is moved to the ear. / or backlight. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (usually three axes), and can detect the magnitude and direction of gravity when it is stationary. games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for the fingerprint sensor, pressure sensor, iris sensor, molecular sensor, gyroscope, barometer, hygrometer, etc. Other sensors such as thermometers, infrared sensors, etc. will not be repeated here.

The display unit 106 is used to display information input by the user or information provided to the user. The display unit 106 may include a display panel 1061, and the display panel 1061 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

The user input unit 107 may be used to receive input numerical or character information, and generate key signal input related to user settings and function control of the mobile terminal. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072 . The touch panel 1071, also referred to as a touch screen, can collect the user's touch operations on or near it (such as the user's finger, stylus, etc., any suitable object or attachment on or near the touch panel 1071). operation), and drive the corresponding connection device according to the preset program. The touch panel 1071 may include two parts, a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the touch controller. To the processor 110, and can receive the command sent by the processor 110 and execute it. In addition, the touch panel 1071 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 1071 , the user input unit 107 may also include other input devices 1072 . Specifically, other input devices 1072 may include, but are not limited to, one or more of physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, operation sticks, etc., which are not specifically limited here. .

Further, the touch panel 1071 can cover the display panel 1061, and when the touch panel 1071 detects a touch operation on or near it, it transmits it to the processor 110 to determine the type of the touch event, and then the processor 110 determines the type of the touch event according to the touch event. Type provides corresponding visual output on display panel 1061 . Although in FIG. 1 , the touch panel 1071 and the display panel 1061 are used as two independent components to realize the input and output functions of the mobile terminal, but in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated The input and output functions of the mobile terminal are implemented, which is not specifically limited here.

The interface unit 108 serves as an interface through which at least one external device can be connected to the mobile terminal 100 . For example, external devices may include wired or wireless headset ports, external power (or battery charger) ports, wired or wireless data ports, memory card ports, ports for connecting devices with identification modules, audio input/output (I/O) ports, video I/O ports, headphone ports, and more. The interface unit 108 may be used to receive input (eg, data information, power, etc.) from an external device and transmit the received input to one or more elements within the mobile terminal 100 or may be used to communicate between the mobile terminal 100 and the external Transfer data between devices.

The memory 109 may be used to store software programs as well as various data. The memory 109 may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program (such as a sound playback function, an image playback function, etc.) required for at least one function, and the like; Data created by the use of the mobile phone (such as audio data, phone book, etc.), etc. Additionally, memory 109 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The processor 110 is the control center of the mobile terminal, uses various interfaces and lines to connect various parts of the entire mobile terminal, runs or executes the software programs and/or modules stored in the memory 109, and calls the data stored in the memory 109. , perform various functions of the mobile terminal and process data, so as to monitor the mobile terminal as a whole. The processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem The processor mainly handles wireless communication. It can be understood that, the above-mentioned modulation and demodulation processor may not be integrated into the processor 110 .

The mobile terminal 100 may also include a power supply 111 (such as a battery) for supplying power to various components. Preferably, the power supply 111 may be logically connected to the processor 110 through a power management system, so as to manage charging, discharging, and power consumption management through the power management system and other functions.

Although not shown in FIG. 1 , the mobile terminal 100 may also include a Bluetooth module, etc., which will not be described herein again.

To facilitate understanding of the embodiments of the present invention, the following describes a communication network system on which the mobile terminal of the present invention is based.

Please refer to FIG. 2. FIG. 2 is an architecture diagram of a communication network system according to an embodiment of the present invention. The communication network system is an LTE system of universal mobile communication technology. ) 201 , E-UTRAN (Evolved UMTS Terrestrial Radio Access Network, Evolved UMTS Terrestrial Radio Access Network) 202 , EPC (Evolved Packet Core, Evolved Packet Core) 203 and operator's IP service 204 .

Specifically, the UE 201 may be the above-mentioned terminal 100, which will not be repeated here.

E-UTRAN 202 includes eNodeB 2021 and other eNodeB 2022 and the like. The eNodeB 2021 may be connected to other eNodeBs 2022 through a backhaul (eg, X2 interface), the eNodeB 2021 is connected to the EPC 203 , and the eNodeB 2021 may provide access from the UE 201 to the EPC 203 .

EPC 203 may include MME (Mobility Management Entity, Mobility Management Entity) 2031, HSS (Home Subscriber Server, Home Subscriber Server) 2032, other MME 2033, SGW (Serving Gate Way, Serving Gateway) 2034, PGW (PDN Gate Way, Packet Data Network) gateway) 2035 and PCRF (Policy and Charging Rules Function, policy and charging function entity) 2036 and so on. Among them, MME2031 is a control node that processes signaling between UE201 and EPC203, and provides bearer and connection management. The HSS2032 is used to provide some registers to manage functions such as the home location register (not shown in the figure), and to store some user-specific information about service characteristics, data rates, etc. All user data can be sent through SGW2034, PGW2035 can provide IP address allocation and other functions of UE 201, PCRF2036 is the policy and charging control policy decision point of service data flow and IP bearer resources, it is the policy and charging execution function A unit (not shown) selects and provides available policy and charging control decisions.

The IP service 204 may include Internet, intranet, IMS (IP Multimedia Subsystem, IP Multimedia Subsystem) or other IP services.

Although the LTE system is described above as an example, those skilled in the art should know that the present invention is not only applicable to the LTE system, but also applicable to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA and future new wireless communication systems. The network system, etc., is not limited here.

Based on the above-mentioned mobile terminal hardware structure and communication network system, various embodiments of the method of the present invention are proposed.

Example 1

FIG. 3 is a flow chart of the first embodiment of the method for photographing a certificate photo of the present invention. A method for photographing a certificate, the method comprising:

S1. Acquire a preview image including a human face, where the preview image includes a depth image and a color image corresponding to the depth image.

S2. Perform face key point detection according to the color image, and acquire depth information of the face key point according to the depth image.

S3. Acquire a preset face pose that is closest to the current face pose, and calculate the adjustment mode of the current face pose based on multiple viewing angles.

S4. Generate prompt information of the adjustment mode to assist the user in adjusting the face posture.

In this embodiment, a variety of face poses are preset, and the face pose estimation based on the depth image of the ToF depth camera is used to remind the subject to adjust the face pose when taking pictures. For example, tuck the chin or turn the face to the left, so that the user can get a professional photographer-like guidance for better shooting results.

In this embodiment, specifically, first, a preview image containing a human face is acquired, wherein the preview image includes a depth image and a color image corresponding to the depth image; Detecting, and obtaining the depth information of the key points of the face according to the depth image; finally, obtaining the preset face pose closest to the current face pose, and calculating the adjustment of the current face pose based on multiple perspectives method, and generating prompt information of the adjustment method to assist the user in adjusting the face posture.

The beneficial effect of this embodiment is that, by acquiring a preview image containing a human face, the preview image includes a depth image and a color image corresponding to the depth image; performing face key point detection according to the color image, and, Obtain the depth information of the face key points according to the depth image; obtain the preset face pose closest to the current face pose, and calculate the adjustment mode of the current face pose based on multiple perspectives. A more user-friendly ID photo shooting solution is realized. By combining the depth image to identify and adjust the face posture, the effect of ID photo shooting is improved, and the user experience of the ID photo shooting function is enhanced.

Embodiment 2

Based on the above-mentioned embodiment, in this embodiment, firstly, N kinds of face pose lists REC_Posture that can be used for taking ID photos are preset, N≥1; the face poses include but are not limited to frontal faces, side faces, etc., Among them, the face pose list is expressed as:

REC_Posture={rec_posture ₁ ,  , rec_posture _N }.

In this embodiment, the face pose is a 3D face key point list that includes a predetermined number M and is sorted according to a predetermined rule. Among them, M is a predetermined parameter, for example, M=128. The above face pose is expressed as:

rec_posture _i = {keypoint ₁ , keypoint ₂ , ..., keypoint _M }.

keypoint _i = { _xi , _yi , d _i }.

Among them, x _i , y _i are the abscissa and ordinate of the key point _i in the image, and z _i is the depth information of the key point _i in the image.

In this embodiment, the preview color image pre_image_rgb and the ToF-based depth image pre_imag_d are obtained; the depth image pre_imag_d must be the depth image registered with the color image pre_image_rgb; optionally, the depth image pre_imag_d may be the depth image pre_imag_d Depth images for resolution calculations, etc.

In this embodiment, face key point detection is performed based on a color image, and a previewed face key point list is obtained, which is expressed as:

pre_posture={keypoint ₁ , keypoint ₂ , ..., keypoint _M }.

keypoint _i ={x _i ,y _i }.

Among them, x _i , y _i are the abscissa and ordinate of keypoint _i in the image.

In this embodiment, based on the ToF depth image, the depth information corresponding to the face key points in the current face key point list is obtained, specifically, including the following three steps:

First, get the resolution of pre_image_rgb and depth image pre_imag_d:

shape_rgb={h_rgb,w_rgb}.

shape_d={h_d,w_d}.

Second, for each face keypoint keypoint _i in the previewed face keypoint list pre_posture, calculate the mapping coordinates of pre_imag_d in the depth image:

Third, obtain the depth information at the position point mappoint _i in the depth image pre_imag_d as the depth information d _{i of the face key point keypoint i :}

keypoint _i = { _xi , _yi , d _i }.

d _i =pre_imag_d(mappoint _i ).

In this embodiment, in the preset face pose list REC_Posture, obtaining the preset face pose rec_posture _i that is closest to the current face pose specifically includes the following six steps:

First, when N==1 (only one recommended face pose is preset), the currently preset face pose is directly used as the preset face pose closest to the preview face pose;

Second, when N==2, for the current preview face key point list pre_posture, calculate the single difference between it and the face key point list rec_posture _i of each preset face pose in the preset face pose list REC_Posture The adaptive matrix H _i :

H _i =finHomo(pre_posture,rec_posture _i ).

Third, select M ₂ predetermined image calibration key points (such as image origin, image center point, image lower left point, image lower right point, image upper right point, etc.), where M ₂ ≥2.

Fourth, for each calibration point keypoint _i in cb_keypoint, according to the single face key point list rec_posture _i of each preset face pose list in the preview face key point list pre_posture and the preset face pose list REC_Posture Responsive matrix H _i , find its transformed coordinate position:

in:

keypoint _i,j ^′ =keypoint _j *H _i .

Fifth, for each cb_keypoint _i ^′ , find the average Euclidean distance between cb_keypoint and cb_keypoint _i ^′ :

in:

x1=keypoint _i,j ^′ .x.

x2=keypoint _j .x.

y1=keypoint _i, ^j′.y .

y2=keypoint _j.y.

d1=keypoint _i,j ^′ .d.

d2=keypoint _j.d.

Sixth, obtain the preset face pose with the smallest average Euclidean distance as the face pose mindiss_rec_posture that is closest to the preview face pose.

In this embodiment, for the currently previewed face pose pre_posture, and the recommended face pose mindiss_rec_posture closest to it, the face adjustment method is calculated based on multiple viewing angles, which specifically includes the following four steps:

First, obtain a preset multi-view Mul_VS; the multi-view includes multiple feature point pairs, such as (left eye outer corner, right eye outer corner), (forehead midpoint, chin midpoint), etc.; The feature point pair can describe the current face pose feature, and each perspective in the multi-view angle is arranged according to the established priority:

Mul_VS={vs ₁ ,vs ₂ ,...}.

vs _i ={kp ₁ ,kp ₂ }.

Second, for each view vs _i in the multi-view Mul_VS, obtain the key points corresponding to the preview face pose pre_posture and the key points corresponding to the recommended mindiss_rec_posture face pose:

pre_vs _i = {keypoint ^kp1 , keypoint ^kp2 }.

rec_vs _i = {keypoint ^kp1 , keypoint ^kp2 }.

Among them, the three-dimensional vector (keypoint ^kp1 →keypoint ^kp2 ) composed of keypoint pairs in pre_vs _i is expressed as:

pre_vec _i ={x1,y1,z1}.

x1=pre_vs _i .keypoint ^kp2 .x-pre_vs _i .keypoint ^kp1 .x.

y1=pre_vs _i .keypoint ^kp2 .y-pre_vs _i .keypoint ^kp1 .y.

z1=pre_vs _i .keypoint ^kp2 .d-pre_vs _i .keypoint ^kp1 .d.

Similarly, the three-dimensional vector (keypoint ^kp1 →keypoint ^kp2 ) composed of keypoint pairs in rec_vs _i is expressed as:

rec_vec _i ={x2,y2,z2}.

x2=rec_vs _i .keypoint ^kp2 .x-rec_vs _i .keypoint ^kp1 .x.

y2=rec_vs _i .keypoint ^kp2 .y-rec_vs _i .keypoint ^kp1 .y.

z2=rec_vs _i .keypoint ^kp2 .d-rec_vs _i .keypoint ^kp1 .d.

Third, calculate the scaling scale scale _i :

Fourth, calculate the rotation angle θ _i :

θ _i =acos(pre_vec _i ·rec_vec _i /||pre_vec _i ||||rec_vec _i ||).

In this embodiment, a predetermined scaling scale threshold S_TH and a rotation angle threshold θ_TH are obtained.

In this embodiment, a constraint check is performed on each view vs _i in the multi-view Mul_VS according to a predetermined priority order, which specifically includes the following two steps:

First, obtain the current highest priority viewing angle vs _i and its corresponding scaling scale _i and rotation angle θ _i .

Second, based on the established thresholds S_TH and θ_TH, determine whether the constraints are met; the constraints are:

scale _i ≤ S_TH.

θ _i ≤ θ_TH.

In this embodiment, if the current viewing angle satisfies the constraints, check whether the next viewing angle satisfies the constraints; if the current viewing angle does not meet the constraints, the user is prompted to adjust the posture according to the scaling scale scale _i and the rotation angle θ _i .

The beneficial effect of this embodiment is that, by acquiring a preview image containing a human face, the preview image includes a depth image and a color image corresponding to the depth image; performing face key point detection according to the color image, and, Obtain the depth information of the face key points according to the depth image; obtain the preset face pose closest to the current face pose, and calculate the adjustment mode of the current face pose based on multiple perspectives. A more user-friendly ID photo shooting solution is realized. By combining the depth image to identify and adjust the face posture, the effect of ID photo shooting is improved, and the user experience of the ID photo shooting function is enhanced.

Embodiment 3

Based on the above embodiments, the present invention also provides a device for photographing a certificate, the device includes a memory, a processor, and a computer program stored on the memory and running on the processor, the computer program being When the processor is executed, the steps of the method for photographing a certificate photo as described in any one of the above are realized.

It should be noted that the above device embodiments and method embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, and the technical features in the method embodiments are correspondingly applicable in the device embodiments, which will not be repeated here.

Embodiment 4

Based on the above embodiments, the present invention also proposes a computer-readable storage medium, where an ID photo shooting program is stored on the computer-readable storage medium. The steps of how to take ID photos.

It should be noted that the above-mentioned medium embodiments and method embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, and the technical features in the method embodiments are all applicable in the medium embodiments, which will not be repeated here.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages or disadvantages of the embodiments.

From the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk, CD), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present invention.

The embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments, which are merely illustrative rather than restrictive. Under the inspiration of the present invention, without departing from the scope of protection of the present invention and the claims, many forms can be made, which all belong to the protection of the present invention.

Claims (10)

1. A method for photographing an ID photo, wherein the method comprises: obtaining a preview image containing a human face, wherein the preview image includes a depth image and a color image corresponding to the depth image; Performing face key point detection according to the color image, and acquiring depth information of the face key point according to the depth image; Obtaining the preset facial posture closest to the current facial posture, and calculating the adjustment mode of the current facial posture based on multiple viewing angles; The prompt information of the adjustment method is generated to assist the user in adjusting the face posture. 2. The method for photographing an ID photo according to claim 1, wherein before the obtaining a preview image including a human face, the preview image includes a depth image and a color image corresponding to the depth image, including: Preset N types of face pose lists REC_Posture that can be used for photographing ID photos, where N≥1, the face poses include frontal face poses and side face poses, where: REC_Posture={rec_posture ₁ ,...,rec_posture _N }; The face pose is a 3D face key point list that contains a predetermined number M and is sorted according to a predetermined rule; wherein, M is a predetermined parameter: rec_posture _i = {keypoint ₁ , keypoint ₂ , ..., keypoint _M }; keypoint _i = {x _i , y _i , d _i }; Among them, x _i , y _i are the abscissa and ordinate of the key point _i in the image, and z _i is the depth information of the key point _i in the image. 3. The method for photographing a certificate photo according to claim 2, wherein the acquisition includes a preview image of a human face, wherein the preview image includes a depth image and a color image corresponding to the depth image, including: Get the preview color image pre_image_rgb and the depth image pre_imag_d based on the ToF depth camera; The depth image pre_imag_d is a depth image registered with the preview color image pre_image_rgb. 4. The method for photographing an ID photo according to claim 3, wherein the detection of the key points of the human face is performed according to the color image, and the depth information of the key points of the human face is obtained according to the depth image, include: The face key point detection is performed based on the color image, and the previewed face key point list is obtained: pre_posture={keypoint ₁ ,keypoint ₂ ,...,keypoint _M }; keypoint _i ={x _i ,y _i }; Among them, x _i , y _i are the abscissa and ordinate of keypoint _i in the image. 5. The method for photographing an ID photo according to claim 4, wherein the detection of a face key point is performed according to the color image, and the depth information of the face key point is obtained according to the depth image, Also includes: Based on the depth image of the ToF, obtain the depth information corresponding to the face key points in the current face key point list, including: Get the resolution of pre_image_rgb and depth image pre_imag_d: shape_rgb={h_rgb,w_rgb}; shape_d = {h_d, w_d}; For each face keypoint keypoint _i in the previewed face keypoint list pre_posture, calculate its mapping coordinates of pre_imag_d in the depth image:

Obtain the depth information at the position point mappoint _i in the depth image pre_imag_d as the depth information d _{i of the face key point keypoint i :} keypoint _i = {x _i , y _i , d _i }; d _i =pre_imag_d(mappoint _i ). 6. The method for photographing a certificate photo according to claim 5, wherein the acquisition of the preset facial posture closest to the current facial posture, and the adjustment mode of the current facial posture is calculated based on multiple viewing angles, include: In the preset face pose list REC_Posture, obtain the preset face pose rec_posture _i that is closest to the current face pose, including: The preset face pose with the smallest average Euclidean distance is acquired as the face pose mindiss_rec_posture closest to the preview face pose. 7. The method for photographing a certificate photo according to claim 6, wherein the acquisition of the preset facial posture closest to the current facial posture is performed, and the adjustment mode of the current facial posture is calculated based on multiple viewing angles, Also includes: Based on multiple perspectives, for the previewed face key point list pre_posture, and the closest recommended face pose mindiss_rec_posture, the face adjustment method is calculated, including: According to the preset priority order, check the constraints of each view vs _i in the preset multi-view Mul_VS. If the current view meets the constraints, check whether the next view meets the constraints. If the current view does not meet the constraints, Then, the user is prompted to adjust the posture according to the scaling scale scale _i and the rotation angle θ _i . 8. The method for photographing an ID photo according to claim 7, wherein, according to a preset priority order, constraint checking is performed on each viewing angle vs _i in the preset multi-viewing angle Mul_VS, comprising: Obtain the current highest priority viewing angle vs _i and its corresponding zoom scale scale _i and rotation angle θ _i ; According to the preset thresholds S_TH and θ_TH, it is judged whether the constraint condition is satisfied, wherein the constraint condition is: scale _i ≤ S_TH; θ _i ≤ θ_TH. 9. A device for photographing passports, characterized in that the device comprises a memory, a processor, and a computer program stored on the memory and running on the processor, the computer program being executed by the processor When executed, the steps of the method for photographing a certificate photo according to any one of claims 1 to 8 are realized. 10. A computer-readable storage medium, wherein the computer-readable storage medium stores an ID photo shooting program, and when the ID photo capturing program is executed by a processor, any one of claims 1 to 8 is implemented. The steps of the ID photo shooting method described in the item.

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