CN115802144B - Video shooting method and related equipment - Google Patents

Abstract

The application provides a video shooting method and related equipment, wherein the method comprises the following steps: acquiring video frame data shot by a camera, and identifying whether the video frame data contains a human face or not; if the video frame data contains a human face, calculating the proportion of the size of the human face area in the video frame data; judging whether the proportion is larger than or equal to a first preset value; if the ratio is greater than or equal to a first preset value, determining that the recommended video mode is a portrait mode, and performing video shooting based on the portrait mode; if the proportion is smaller than the first preset value, judging whether the proportion is smaller than or equal to a second preset value, wherein the second preset value is smaller than the first preset value; if the ratio is smaller than or equal to a second preset value, determining that the recommended video mode is a principal angle mode, and performing video shooting based on the principal angle mode. According to the embodiment of the application, when the human image video is shot, the video mode recommendation can be performed based on the size proportion of the human face area, so that the display effect of the human image video is effectively optimized.

Description

Video shooting method and related equipment

technical field

The present application relates to the technical field of smart terminals, in particular to a video shooting method and related equipment.

Background technique

With the development of terminal technology, users have higher and higher demands on video shooting functions of electronic devices. Currently, an electronic device can set multiple video modes according to different shooting scenarios for users to choose for video shooting. However, in actual use, the user may not be sure about the current shooting scene, and does not know which video mode to select, or even if the shooting scene is determined, the user does not know how to select the corresponding video mode. For example, when the shooting content contains a portrait, the user may select the portrait mode, so that the electronic device can optimize the effect of the portrait in the video. However, when the portrait is far away from the electronic device, the proportion of the face in the shooting interface The ratio may be small, and even if the portrait is processed, the optimization effect of the portrait may not be good, resulting in poor shooting effect of the portrait video, thereby affecting the user experience.

Contents of the invention

In view of the above, it is necessary to provide a video shooting method and related equipment to solve the technical problem that the shooting effect of the portrait video is not good because the video mode selected by the user does not match the portrait when shooting the portrait video scene.

In a first aspect, the present application provides a video shooting method, the method comprising: acquiring video frame data captured by a camera, identifying whether the video frame data contains a human face; if the video frame data contains a human face, calculating the human face The proportion of the size of the area in the video frame data; judging whether the proportion of the size of the human face area in the video frame data is greater than or equal to a first preset value; if the human face area The proportion of the size of the video frame data in the video frame data is greater than or equal to the first preset value, determine that the recommended video mode is the portrait mode, and perform video shooting based on the portrait mode; if the face area The proportion of the size in the video frame data is smaller than the first preset value, and judging whether the proportion of the size of the face area in the video frame data is less than or equal to a second preset value, The second preset value is smaller than the first preset value; if the size of the face area accounts for a proportion of the video frame data that is less than or equal to the second preset value, the recommended video mode is determined to be A protagonist mode, and video shooting is performed based on the protagonist mode. Through the above technical solution, when shooting a portrait video, the video mode recommendation can be made based on the proportion of the face. When the proportion of the face is large, it is recommended to shoot the video in the portrait mode, which effectively optimizes the display effect of the portrait video. When the proportion of the face is small, it is recommended to use the protagonist mode for video shooting, which effectively optimizes the display effect of the protagonist portrait.

In a possible implementation manner, the calculating the proportion of the size of the human face area in the video frame data includes: using a rectangular frame to identify the human face area identified in the video frame data Determine the size of the human face area based on the rectangular frame; calculate the ratio between the size of the human face area and the size of the video frame image, and obtain the size of the human face area in the video frame data proportion. Through the above technical solution, the size ratio of the face area can be accurately calculated based on the rectangular frame identifying the face area.

In a possible implementation manner, the determining the size of the face area based on the rectangular frame includes: determining the width value of the rectangular frame that identifies the face area in the video frame image as the The width value of the human face area is determined, and the height value of the rectangular frame is determined as the height value of the human face area. Through the above technical solution, the width value and the height value of the size of the face area can be accurately determined based on the size of the rectangular frame identifying the face area.

In a possible implementation manner, the calculating the ratio between the size of the face area and the size of the video frame image includes: calculating the ratio between the width value of the face area and the width value of the video frame image ratio between. Through the above technical solution, the size ratio of the face area can be calculated quickly and accurately.

In a possible implementation manner, the calculating the ratio between the size of the face area and the size of the video frame image includes: calculating the ratio between the height value of the face area and the height value of the video frame image ratio between. Through the above technical solution, the size ratio of the face area can be calculated quickly and accurately.

In a possible implementation manner, the calculating the ratio between the size of the human face area and the size of the video frame image includes: calculating the ratio between the area of the human face area and the area of the video frame image Proportion. Through the above technical solution, the size ratio of the face area can be calculated quickly and accurately.

In a possible implementation manner, the identifying whether the video frame data contains a human face includes: performing format conversion on each video frame image in the video frame data to obtain a video stream; Face recognition is performed on each video frame image, and it is judged whether the video frame data contains a human face; if it is recognized that a continuous preset number of the video frame images contains a human face, it is determined that the video frame data contains a human face. Through the above technical solution, it can be accurately identified whether the video frame data contains a human face.

In a possible implementation manner, the performing video shooting based on the portrait mode includes: performing blur processing on the video frame data captured by the camera. Through the above technical solution, when the size ratio of the face area is relatively large, the portrait can be highlighted, and the display effect of the portrait can be optimized.

In a possible implementation manner, the blurring the video frame data captured by the camera includes: performing portrait matting on the video frame image, extracting the portrait area in the video frame image; The background area of the video frame image is blurred; the extracted portrait area is merged with the blurred background area. Through the above technical solution, it is possible to precisely blur the background of the portrait video, thereby highlighting the portrait and optimizing the display effect of the portrait.

In a possible implementation manner, the performing blur processing on the background area of the video frame image includes: performing Gaussian blur processing on the background area to obtain the blurred background area. Through the above technical solution, the efficiency of background blurring processing can be improved.

In a possible implementation manner, the video shooting based on the protagonist mode includes: shooting a panoramic video and a portrait video of the protagonist; displaying the video frame data of the portrait video of the protagonist in the form of a picture-in-picture in the video frame data of the video. Through the above technical solution, when the size ratio of the face area in the portrait video is relatively small, the portrait video can be enlarged and displayed, and the display effect of the portrait video can be effectively optimized.

In a second aspect, the present application provides an electronic device, which includes a memory and a processor: wherein, the memory is used to store program instructions; the processor is used to read and execute instructions stored in the memory. The program instruction, when the program instruction is executed by the processor, causes the electronic device to execute the above video shooting method.

In a third aspect, the present application provides a chip coupled with a memory in an electronic device, and the chip is used to control the electronic device to execute the above video shooting method.

In a fourth aspect, the present application provides a computer storage medium, the computer storage medium stores program instructions, and when the program instructions are run on an electronic device, the electronic device is made to execute the above video shooting method.

In addition, for the technical effects brought about by the second aspect to the fourth aspect, please refer to the descriptions related to the methods of each design in the above method part, and will not be repeated here.

Description of drawings

FIG. 1 is a schematic diagram of an interface of a camera application program of an electronic device provided by an embodiment of the present application.

FIG. 2 is a schematic diagram of another interface of a camera application program of an electronic device provided by an embodiment of the present application.

Fig. 3 is a software architecture diagram of an electronic device provided by an embodiment of the present application.

Fig. 4 is a flowchart of a video shooting method provided by an embodiment of the present application.

FIG. 5 is a schematic structural diagram of a video shooting system provided by an embodiment of the present application.

Fig. 6 is an effect diagram of using a cascade classifier to recognize a face image provided by an embodiment of the present application.

Fig. 7 is a flow chart of calculating the proportion of the size of the face area in the video frame data provided by an embodiment of the present application.

FIG. 8 is a flow chart of blurring video frame data captured by a camera according to an embodiment of the present application.

FIG. 9 is a schematic structural diagram of a video shooting system provided by another embodiment of the present application.

Fig. 10 is a flowchart of a video shooting method provided by another embodiment of the present application.

Fig. 11 is a flowchart of a video shooting method provided by another embodiment of the present application.

Fig. 12 is a partial flowchart of a video shooting method provided by another embodiment of the present application.

Fig. 13 is a partial flowchart of a video shooting method provided by another embodiment of the present application.

Fig. 14 is a partial flowchart of a video shooting method provided by another embodiment of the present application.

Fig. 15 is a partial flowchart of a video shooting method provided by another embodiment of the present application.

Fig. 16 is a flowchart of a video shooting method provided by another embodiment of the present application.

Fig. 17 is a schematic diagram of decision factors for intelligent scene detection provided by an embodiment of the present application.

FIG. 18 is a schematic diagram of a video specification of a video mode provided by an embodiment of the present application.

Fig. 19 is a flowchart of a video shooting method provided by another embodiment of the present application.

FIG. 20 is a hardware architecture diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

The terms "first" and "second" involved in the embodiments of the present application are used for description purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, a feature defined as "first" and "second" may explicitly or implicitly include one or more of these features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used as examples, illustrations or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application shall not be interpreted as being more preferred or more advantageous than other embodiments or design schemes. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete manner.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of this application. The terms used in the description of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. It should be understood that unless otherwise stated in this application, "/" means or. For example, A/B can mean either A or B. The "and/or" in this application is only an association relationship describing associated objects, indicating that there may be three relationships. For example, A and/or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. "At least one" means one or more. "A plurality" means two or more than two. For example, at least one of a, b or c can represent: a, b, c, a and b, a and c, b and c, a, b and c seven situations.

The user interface (UI) in the embodiment of the present application is a medium interface for interaction and information exchange between an application program or an operating system and a user, and can realize the conversion between the internal form of information and the form acceptable to the user. The user interface of the application program is the source code written in specific computer languages such as JAVA and extensible markup language (XML). Pictures, text, buttons and other controls. Control is the basic element of the user interface. Typical controls include buttons, widgets, toolbars, menu bars, text boxes, scroll bars ( scrollbar), pictures (image) and text (text). The properties and contents of the controls in the interface are defined through labels or nodes. For example, XML specifies the controls contained in the interface through nodes such as <Textview>, <ImgView>, and <VideoView>. A node corresponds to a control or property in the interface, and after the node is parsed and rendered, it is presented as the content visible to the user. In addition, the interfaces of many application programs, such as hybrid applications, usually include web pages. A web page, also called a page, can be understood as a special control embedded in the application program interface. A web page is a source code written in a specific computer language, such as hypertext markup language (hyper text markup language, HTML), cascading style Tables (cascading style sheets, CSS), JAVA scripts (JavaScript, JS), etc., and the source code of the web page can be loaded and displayed as user-recognizable content by the browser or a web page display component similar in function to the browser. The specific content contained in the webpage is also defined by the tags or nodes in the source code of the webpage. For example, HTML defines the elements and attributes of the webpage through <p>, <img>, <video>, and <canvas>.

The commonly used form of user interface is the graphical user interface (graphic user interface, GUI), which refers to the user interface related to computer operation displayed in a graphical way. It may be an icon, window, control and other interface elements displayed on the display screen of the electronic device.

In the case of no conflict, the following embodiments and features in the embodiments can be combined with each other.

With the development of terminal technologies, users have higher and higher demands on video shooting functions in electronic devices. Currently, an electronic device can set multiple video modes according to different shooting scenarios for users to choose for video shooting. However, in actual use, the user may not be sure about the current shooting scene, and does not know which video mode to select, or even if the shooting scene is determined, the user does not know how to select the corresponding video mode. For example, when shooting a portrait, the user may select the portrait mode so that the electronic device can process the portrait in the video. However, when the portrait is far away from the electronic device, the proportion of the human face in the shooting interface may be smaller. Small, even if the portrait is processed, the optimization effect of the portrait may not be good, resulting in poor shooting effect of the portrait video, thereby affecting the user experience.

In order to avoid the poor shooting effect of the portrait video due to the mismatch between the video mode selected by the user and the portrait when shooting the portrait video scene, the embodiment of the present application provides a video shooting method. The ratio of the video mode is recommended, so as to automatically generate a better portrait video, adapt to the user's portrait video shooting needs, and effectively improve the user experience.

In order to better understand the video shooting method provided in the embodiment of the present application, the application scenarios of the video shooting method in the embodiment of the present application are described below with reference to FIG. 1 and FIG. 2 .

Referring to FIG. 1 , when the user starts the camera application of the electronic device and selects video recording, that is, when shooting a video, the electronic device displays preview video frame data on the shooting interface of the camera application. When there are people within the shooting range of the camera, the previewed video frame data will also include portraits, and the proportion of portraits in Figure 1 in the preview interface is relatively small.

The electronic device provides a video mode selection control on the preview interface of the camera application program. When the user triggers the video mode selection control, the electronic device provides controls corresponding to multiple video modes for the user to select, and the user can trigger the control to select the corresponding video mode. In this way, the preview video frame data and the captured video are optimized.

Referring to FIG. 2 , when the user triggers the control corresponding to the portrait mode, the electronic device automatically processes the portrait in the preview video frame data, such as beautification, background blur processing, and the like. However, due to the small proportion of portraits in the preview interface, it is not easy to see the beautification effect, and the blurred background cannot effectively highlight the subject of the portrait, resulting in poor portrait video shooting effect, which affects the user experience.

Referring to FIG. 3 , it is a software architecture diagram of the electronic device provided by the embodiment of the present application. The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Layers communicate through software interfaces. For example, the Android system is divided into four layers, from top to bottom are application program layer 101, framework layer 102, Android runtime (Android runtime) and system library 103, hardware abstraction layer 104, kernel layer 105, and hardware layer 106.

The application layer can consist of a series of application packages. For example, an application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, and device control services.

The framework layer provides application programming interfaces (Application Programming Interface, API) and programming frameworks for applications in the application layer. The application framework layer includes some predefined functions. For example, the application framework layer can include window managers, content providers, view systems, telephony managers, resource managers, notification managers, and more.

Among them, the window manager is used to manage window programs. The window manager can get the size of the display screen, determine whether there is a status bar, lock the screen, capture the screen, etc. Content providers are used to store and retrieve data and make it accessible to applications. Said data may include video, images, audio, calls made and received, browsing history and bookmarks, phonebook, etc. The view system includes visual controls, such as controls for displaying text, controls for displaying pictures, and so on. The view system can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text message notification icon may include a view for displaying text and a view for displaying pictures. The phone manager is used to provide communication functions of electronic devices. For example, the management of call status (including connected, hung up, etc.). The resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, etc. The notification manager enables the application to display notification information in the status bar, which can be used to convey notification-type messages, and can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify the download completion, message reminder, etc. The notification manager can also be a notification that appears on the top status bar of the system in the form of a chart or scroll bar text, such as a notification of an application running in the background, or a notification that appears on the screen in the form of a dialog window. For example, prompting text information in the status bar, issuing a prompt sound, vibrating the electronic device, and flashing the indicator light, etc.

Android Runtime includes core library and virtual machine. The Android runtime is responsible for the scheduling and management of the Android system. The core library consists of two parts: one part is the function function that the java language needs to call, and the other part is the core library of Android.

The application layer and the framework layer run in virtual machines. The virtual machine executes the java files of the application layer and the framework layer as binary files. The virtual machine is used to perform functions such as object life cycle management, stack management, thread management, security and exception management, and garbage collection.

A system library can include multiple function modules. For example, surface manager (surface manager), media library (Media Libraries), 3D graphics processing library (eg: OpenGL ES), 2D graphics engine (eg SGL), etc.

Among them, the surface manager is used to manage the display subsystem, and provides the fusion of 2D and 3D layers for multiple applications. The media library supports playback and recording of various commonly used audio and video formats, as well as still image files, etc. The media library can support a variety of audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc. The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing, etc. 2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is the layer between hardware and software. The kernel layer includes at least a display driver, a camera driver, an audio driver, and a sensor driver.

The kernel layer is the core of the operating system of an electronic device. It is the first layer of software expansion based on hardware. It provides the most basic functions of the operating system and is the basis of the operating system. It is responsible for managing the process, memory, device drivers, files and The network system determines the performance and stability of the system. For example, the kernel can determine when an application operates on certain parts of the hardware.

The kernel layer includes programs closely related to hardware, such as interrupt handlers, device drivers, etc., as well as basic, public, and high-frequency modules, such as clock management modules, process scheduling modules, etc., as well as key data structure. The kernel layer can be set in the processor, or solidified in the internal memory.

The hardware layer includes multiple hardware devices of the electronic device, such as a camera, a display screen, and the like.

Referring to FIG. 4 , it is a flowchart of a video shooting method provided by an embodiment of the present application. The method is applied in an electronic device, and the video shooting method includes:

S101. Acquire video frame data captured by a camera, and identify whether the video frame data includes a human face. If the video frame data contains a human face, the process proceeds to S102; if the video frame data does not contain a human face, the process continues to S101.

As shown in FIG. 3, in an embodiment of the present application, the hardware layer 106 includes an image processor 1061, and the image processor 1061 includes, but not limited to, an image front end (Image Front End, IFE) 1062 and an image processing engine (Image Processing Engine, IPE) 1063. The image processor 1061 communicates with the camera 193 through a mobile industry processor interface (Mobile Industry Processor Interface, MIPI). Camera 193 includes, but is not limited to, a lens and an image sensor. The lens is used to collect light signals within the shooting range of the camera, and the image sensor is used to convert the light signals collected by the lens into electrical signals to obtain image data or video frame data. Wherein, the image data obtained by the image sensor is a RAW image, and the video frame data obtained by the image sensor is a RAW video frame image.

Referring to FIG. 5 , it is a schematic structural diagram of a video shooting system provided by an embodiment of the present application. The video capture system 10 includes, but is not limited to, an image front end 1062 , an image processing engine 1063 , a mode switching module 11 , and an out-of-focus processing module 12 .

In an embodiment of the present application, acquiring video frame data captured by a camera, and identifying whether the video frame data contains a human face includes: acquiring video frame data captured by a camera in response to a preset operation by the user, and analyzing the video frame data captured by the camera Perform format conversion to obtain the first video stream, perform scene analysis on the first video stream, and identify whether the video frame data contains a human face. Wherein, the user's preset operation may be an operation of opening a camera application, an operation of opening a camera application and enabling a video function, or an operation of opening a camera application and triggering video shooting.

Specifically, format conversion is performed on the video frame data captured by the camera through the image front end to obtain the first video stream. In an embodiment of the present application, the video frame data captured by the camera includes a plurality of video frame images, the video frame images initially captured by the camera are in RAW format, and each RAW video frame image in the video frame data is processed by the image front end Perform format conversion to obtain a video frame image in BMP (Bitmap, bitmap) format, obtain a first video stream, and transmit the first video stream to an image processing engine. Wherein, the first video stream is a tiny (tiny) stream.

In one embodiment of the present application, a scene recognition module runs in the image processing engine, and after the image processing engine receives the first video stream, the scene recognition module adopts an AI scene detection algorithm to recognize the scene in the first video stream, and judge the video frame Whether the data contains faces. Specifically, the scene recognition module performs face recognition on each video frame image in the first video stream, and judges whether the video frame data contains a human face; The data contains a human face; if it is not recognized that a predetermined number of continuous video frame images contain a human face, it is determined that the video frame data does not contain a human face. Wherein, the preset number is 5. In other embodiments, the preset number can also be set to other values according to requirements.

In an embodiment of the present application, the scene identification module uses an AI scene detection algorithm to identify the scene in the first video stream, and judging whether the video frame data contains a human face includes: the scene identification module uses a cascade classifier to identify the scene in the first video stream In the scene, judge whether the video frame data contains a human face. The cascade classifier is formed by cascading multiple strong classifiers, and each strong classifier is formed by a certain number of weak classifiers through the ADABOOST algorithm (an algorithm that generates the final strong classifier by iterating weak classifiers). Among them, the weak classifier is used to extract the Harr-like rectangular feature of the image, and the rectangular feature refers to a rectangle with a black area and a white area, which can include the original rectangular feature and the extended rectangular feature. Specifically, any rectangle is selected and placed on the video frame image, and then the pixel sum of the black area is subtracted from the pixel sum of the white area, and the obtained value is the feature value of the rectangle feature. If the rectangular feature is placed in the face area and non-face area of the video frame image, the calculated feature values are different, so it can be judged based on the feature value of the rectangular feature whether the area where the rectangular feature is placed in the video frame image is a face area , and then determine whether the video frame image contains a human face.

If the face area can be identified in the video frame image by the cascade classifier, it is determined that the video frame image contains a human face; if the face area is not identified in the video frame image by the cascade classifier, it is determined that the video frame image does not contain human face. If a predetermined number of continuous video frame images contain human faces, it is determined that the video frame data captured by the camera contains human faces. Referring to FIG. 6 , it is an effect diagram of using a cascade classifier to recognize a face image provided by the embodiment of the present application, and the rectangular frame part is the face area.

In other embodiments, face detection methods such as face detection based on template matching, face detection based on appearance shape, face detection based on neural network, face detection based on feature, face detection based on skin color, etc. can also be used. Detect whether a video frame image contains a human face.

S102. Calculate the proportion of the size of the face area in the video frame data.

In an embodiment of the present application, if the video frame data includes a human face, calculate the proportion of the size of the human face region corresponding to the human face in the video frame data; if the video frame data includes multiple human faces, calculate The proportion of the size of the face area corresponding to all faces in the video frame data.

In one embodiment of the present application, the detailed process for calculating the proportion of the size of the face area in the video frame data is shown in Figure 7, specifically including:

S1021. Use a rectangular frame to identify the face area identified in the video frame data.

In an embodiment of the present application, after the scene recognition module recognizes that the video frame data captured by the camera contains a human face, it also uses a rectangular frame to mark the recognized face area in the video frame data. The rectangular frame may be the smallest rectangle enclosing the face area.

Specifically, in the process of face detection, the scene recognition module calculates the coordinates of the face at the same time, and determines the minimum rectangular frame that includes the face area based on the coordinates of the upper, lower, left, and right endpoints of the face area. The lateral edges of the rectangular frame extend horizontally, and the longitudinal edges extend vertically.

S1022. Determine the size of the face area based on the rectangular frame.

In an embodiment of the present application, the size of the human face area may include the width value w, the height value h and the area s of the human face area, the area s is the product of the width value w and the height value h, the width value, the height value and The unit of area is the number of pixels. Determining the size of the human face area based on the rectangular frame includes: determining the width value of the rectangular frame identifying the human face area in the video frame image as the width value of the human face area, and determining the height value of the rectangular frame as the height value of the human face area.

S1023. Calculate the ratio between the size of the face area and the size of the video frame image to obtain the ratio of the size of the face area in the video frame data.

In an embodiment of the present application, the size of the video frame image may include a width value W, a height value H, and an area S of the video frame image, where the area S is the product of the width value W and the height value H, and the width value of the video frame image W, the height value H and the area S are all preset values, the width value W is the number of pixels on the horizontal edge of the video frame image, the height value H is the number of pixels on the vertical edge of the video frame image, and the area S is the video frame image the number of pixels. For example, if the resolution of the video frame image is 640*480, the number of pixels of the horizontal edge of the video frame image is 640, and the number of pixels of the vertical edge of the video frame image is 480, then the number of pixels of the video frame image is 307200.

In an embodiment of the present application, calculating the ratio between the size of the human face area and the size of the video frame image includes: calculating the ratio between the width value of the human face area and the width value of the video frame image. For example, the width value of the face area is 400, and the width value of the video frame image is 640, then the ratio R _w =400/640=0.625 between the width value of the face area and the width value of the video frame image.

In another embodiment of the present application, calculating the ratio between the size of the human face area and the size of the video frame image includes: calculating the ratio between the height value of the human face area and the height value of the video frame image. For example, the height value of the face area is 240, and the width value of the video frame image is 480, then the ratio between the width value of the face area and the width value of the video frame image is R _h =240/480=0.5.

In another embodiment of the present application, calculating the ratio between the size of the human face area and the size of the video frame image includes: calculating the ratio between the area of the human face area and the area of the video frame image. For example, if the area of the face area is 10500 and the area of the video frame image is 307200, then the ratio R s between the width value of the face area and the width value of the video frame image is R _s =10500/307200=0.34.

S103. Determine whether the proportion of the size of the human face area in the video frame data is greater than or equal to a first preset value. If the proportion of the size of the human face area in the video frame data is greater than or equal to the first preset value, the process enters S104; if the proportion of the size of the human face area in the video frame data is smaller than the first preset value, The flow goes to S105.

In an embodiment of the present application, the first preset value is 1/3. In other embodiments, the first preset value can also be set to other values according to requirements.

In another embodiment of the present application, it is determined whether the proportion of the size of the human face area in the video frame data of the continuous preset number of video frame images is greater than or equal to the first preset value, if the continuous preset number of The ratio of the size of the human face area in the video frame image to the video frame data is greater than or equal to the first preset value, and the process enters S104.

In another embodiment of the present application, it is judged whether the number of human faces in the continuous preset number of video frame images is one, and it is judged that the size of the face area in the continuous preset number of video frame images occupies in the video frame data. Whether the proportion of is greater than or equal to the first preset value, if the number of faces in the continuous preset number of video frame images is one, and the size of the face area in the continuous preset number of video frame images occupies in the video frame data The ratio is greater than or equal to the first preset value, and the process goes to S104.

S104. Determine that the recommended video mode is the portrait mode, and perform video shooting based on the portrait mode.

As shown in Figure 5, in one embodiment of the present application, after making a recommendation decision and determining that the recommended video mode is portrait mode, a prompt control is displayed on the camera application interface, and the text "recommended to use portrait mode" is displayed on the prompt control , in response to the user triggering the operation of the prompt control, the mode switching module switches the current video mode to the portrait mode, and performs video shooting based on the portrait mode.

In an embodiment of the present application, the video shooting based on the portrait mode includes: switching to the lens with the largest aperture for video shooting, or increasing the aperture of the lens currently shooting the video to the maximum value.

In another embodiment of the present application, the video shooting based on the portrait mode includes: performing blur processing on the video frame data captured by the camera, and displaying the video frame data after the portrait blur processing on the display screen. Specifically, the camera transmits the captured video frame data to the image front end, converts the format of the video frame data through the image front end, converts the RAW format video frame data into the YUV format video frame data, generates the second video stream, and converts the first The second video stream is transmitted to the image processing engine, and the second video stream is a preview stream. The image processing engine performs blurring processing on the video frame images in the second video stream through the out-of-focus processing module, and blurs the processed video frame data shown on the display.

In an embodiment of the present application, the refinement process of performing blurring processing on the video frame data captured by the camera is shown in FIG. 8 , which specifically includes:

S1041. Perform portrait matting on the video frame image, and extract a portrait area in the video frame image.

In an embodiment of the present application, the blurring process is implemented by the out-of-focus processing module based on a bokeh (background blurring) algorithm. Performing portrait matting on the video frame image, and extracting the portrait area in the video frame image includes: inputting the video frame image into a portrait matting model, and extracting the portrait area in the video frame image through the portrait matting model. Among them, the portrait matting model can be FCN ((Fully Convolutional Networks, fully convolutional neural network), semantic segmentation network SegNet, and dense prediction network Unet.

S1042. Perform blurring processing on the background area of the video frame image.

In an embodiment of the present application, the background area of the video frame image is an area in the video frame image other than the portrait area. Performing blur processing on the background area in the video frame image includes: performing Gaussian blur processing on the background area to obtain a blurred background area.

Specifically, performing Gaussian blur processing on the background area includes: presetting the mean and standard deviation of the two-dimensional Gaussian distribution function, dividing the background area into a plurality of n*n preset areas, and dividing the The coordinates of each pixel are input into the two-dimensional Gaussian distribution function to obtain the output value of the two-dimensional Gaussian distribution function, and the output value corresponding to each pixel is divided by the sum of the output values corresponding to all pixels in the preset area to obtain The weight of each pixel in the preset area. Multiply the RGB three-channel pixel value of the pixel by the weight respectively to obtain the pixel value after Gaussian blur processing, and replace the initial pixel value of the pixel point with the pixel value after Gaussian blur processing , to obtain pixels after Gaussian blur processing, and determine an image composed of pixels after Gaussian blur processing in a plurality of n*n preset regions as a video frame image after blurring processing. Among them, n is the blur radius, and its value can be any positive integer. Optionally, the mean value of the two-dimensional Gaussian distribution function is 0, and the standard deviation is 1.5.

S1043, merging the extracted portrait area with the blurred background area.

In an embodiment of the present application, the extracted portrait area is placed at the initial portrait position, and the extracted portrait area and the blurred background area are merged so that the portrait area and the blurred background area are merged.

S105. Determine whether the proportion of the size of the human face area in the video frame data is less than or equal to a second preset value, and the second preset value is smaller than the first preset value. If the proportion of the size of the human face area in the video frame data is less than or equal to the second preset value, the process enters S106; if the proportion of the size of the human face area in the video frame data is greater than the second preset value, The process returns to S101. Wherein, the second preset value is 1/5. In other embodiments, the second preset value can also be set to other values according to requirements.

In another embodiment of the present application, it is judged whether the proportion of the size of the face area in the video frame data of the continuous preset number of video frame images is less than or equal to the second preset value, if the continuous preset number of The ratio of the size of the face area in the video frame image to the video frame data is less than or equal to the second preset value, and the process goes to S106.

In another embodiment of the present application, it is judged whether the number of human faces in the continuous preset number of video frame images is one, and it is judged that the size of the face area in the continuous preset number of video frame images occupies in the video frame data. Whether the ratio is less than or equal to the second preset value, if the number of faces in the continuous preset number of video frame images is multiple, and the size of the face area in the continuous preset number of video frame images is greater than that specified in the video frame data If the proportion is less than or equal to the second preset value, the process goes to S106.

S106. Determine that the recommended video mode is the protagonist mode, and perform video shooting based on the protagonist mode.

In one embodiment of the present application, after a recommendation decision is made and the recommended video mode is determined to be the protagonist mode, a prompt control is displayed on the camera application interface, and the prompt control displays the text "recommended to use the protagonist mode", in response to the user triggering the prompt For the operation of the controls, the mode switching module switches the current video mode to the protagonist mode, and performs video shooting based on the protagonist mode. Wherein, the protagonist is the portrait corresponding to the focus position, and the focus position is determined based on the user's touch selection.

In the protagonist mode, the shooting interface simultaneously displays portrait video frame data and panoramic video frame data, and the portrait video frame data and panoramic video frame data are superimposed and displayed in the form of a picture-in-picture. In an embodiment of the present application, video shooting based on the main character mode includes: controlling one camera to shoot a portrait video of the main character, controlling another camera to shoot a panoramic video, displaying the video frame data of the panoramic video on the shooting interface, and converting the video frame of the portrait video The data is superimposed and displayed in the video frame data of the panoramic video in the form of a picture-in-picture. Among them, the camera that shoots the portrait video of the protagonist is the main camera or the telephoto camera (the camera with the longest focal length), which can track and shoot the protagonist, and the camera that shoots the panoramic video is a wide-angle camera. In other embodiments, the camera that shoots the portrait video and the panoramic video may also be the same camera, and the portrait of the protagonist in the frame data of the panoramic video captured by the camera is intercepted and enlarged, and displayed in a picture-in-picture format.

Specifically, as shown in Figure 9, the camera transmits the captured video frame data to the image front-end, and the image front-end first converts the video frame data into the first video stream (tiny stream), and uses the AI scene detection algorithm for the first video stream. Scene analysis, if the scene analysis result shows that the proportion of the size of the face area in the video frame data is less than or equal to the second preset value, make a recommendation decision for the protagonist mode according to the scene analysis result, and respond to the user operation. Switch to protagonist mode. After the video mode is switched to the protagonist mode, the image front end converts the video frame data captured by the camera into the second video stream (preview stream), and transmits the second video stream to the image processing engine, which optimizes the second video stream Processing, such as anti-shake, noise reduction, color correction, etc. After the image front-end converts the format of the video frame video, it also intercepts the video frame data of the protagonist portrait in the video frame data, and enlarges the video frame data of the protagonist portrait, for example, doubles it to generate a third video stream. The third video stream is Enlarge and track the video stream of the subject, the image front end transmits the third video stream to the image processing engine, and the image processing engine optimizes the third video stream, such as anti-shake, noise reduction, color correction, etc. The image processing engine splices the processed second video stream and the third video stream, and displays the spliced video stream on the display screen, so that the second video stream is completely displayed, and the third video stream is displayed in the form of picture-in-picture .

Referring to FIG. 10 , it is a flowchart of a video shooting method provided by another embodiment of the present application. The method is applied in an electronic device, and the video shooting method includes:

S201. Acquire video frame data captured by a camera, and identify whether the video frame data includes a human face. If the video frame data contains a human face, the process enters S202; if the video frame data does not contain a human face, the process enters S206.

S202. Calculate the proportion of the size of the face area in the video frame data.

S203. Determine whether the proportion of the size of the human face area in the video frame data is greater than or equal to a first preset value. If the proportion of the size of the human face area in the video frame data is greater than or equal to the first preset value, the process enters S204; if the proportion of the size of the human face area in the video frame data is smaller than the first preset value, The flow goes to S205.

S204. Determine that the recommended video mode is the portrait mode, and perform video shooting based on the portrait mode.

S205. Determine whether the proportion of the size of the human face area in the video frame data is less than or equal to a second preset value, and the second preset value is smaller than the first preset value. If the proportion of the size of the human face area in the video frame data is less than or equal to the second preset value, the process enters S206; if the proportion of the size of the human face area in the video frame data is greater than the second preset value, The flow goes to S207.

S206. Determine that the recommended video mode is the protagonist mode, and perform video shooting based on the protagonist mode.

S207. Identify whether the scene information of the video frame data is a night scene. If the scene information of the video frame data is a night scene, the process goes to S208; if the scene information of the video frame data is not a night scene, the process returns to S201.

In an embodiment of the present application, identifying whether the scene information of the video frame data is a night scene scene includes: the scene recognition module acquires a continuous preset number of video frame images, acquires the brightness information luxIndex of each video frame image, and determines the continuous preset Whether the brightness information luxIndex of the number of video frame images is less than or equal to the preset brightness, if the brightness information luxIndex of the continuous preset number of video frame images is less than or equal to the preset brightness, it means that the light brightness of the current shooting scene is relatively dark, determine the video frame The scene information of the data is a night scene; if the brightness information of any video frame image is greater than the preset brightness, it means that the light brightness of the current shooting scene is relatively bright, and it is determined that the scene information of the video frame data is not a night scene. Optionally, the preset number is 5.

S208. Determine that the recommended video mode is a night scene mode, and perform video shooting based on the night scene mode.

In one embodiment of the present application, after making a recommendation decision and determining that the recommended video mode is the night scene mode, a prompt control is displayed on the camera application interface, and the text "Night scene mode is recommended" is displayed on the prompt control, in response to the user triggering the prompt For the operation of the controls, the mode switching module switches the current video mode to the night scene mode, and performs video shooting based on the night scene mode.

Referring to FIG. 11 , it is a flowchart of a video shooting method provided by another embodiment of the present application. The method is applied in an electronic device, and the video shooting method includes:

S301. Acquire video frame data captured by a camera, and identify whether the video frame data includes a human face. If the video frame data contains a human face, the process enters S302; if the video frame data does not contain a human face, the process enters S307.

S302. Calculate the proportion of the size of the face area in the video frame data.

S303. Determine whether the proportion of the size of the human face area in the video frame data is greater than or equal to a first preset value. If the proportion of the size of the human face area in the video frame data is greater than or equal to the first preset value, the process enters S304; if the proportion of the size of the human face area in the video frame data is smaller than the first preset value, The flow goes to S305.

S304. Determine that the recommended video mode is the portrait mode, and perform video shooting based on the portrait mode.

S305. Determine whether the proportion of the size of the human face area in the video frame data is less than or equal to a second preset value, and the second preset value is smaller than the first preset value. If the proportion of the size of the human face area in the video frame data is less than or equal to the second preset value, the process enters S306; if the proportion of the size of the human face area in the video frame data is greater than the second preset value, The flow goes to S307.

S306. Determine that the recommended video mode is the protagonist mode, and perform video shooting based on the protagonist mode.

S307. Identify whether the scene information of the video frame data is a night scene. If the scene information of the video frame data is a night scene, the process goes to S308; if the scene information of the video frame data is not a night scene, the process goes to S309.

S308. Determine that the recommended video mode is a night scene mode, and perform video shooting based on the night scene mode.

S309. Identify whether the scene information of the video frame data is a high dynamic range (High Dynamic Range, HDR) scene. If the scene information of the video frame data is a high dynamic range scene, the process enters S310; if the scene information of the video frame data is not a high dynamic range scene, the process returns to S301.

In an embodiment of the present application, identifying whether the scene information of the video frame data is a high dynamic range scene includes: the scene recognition module acquires a preset number of video frame images in the video frame data, and acquires a preset number of video frame images in a row The dynamic range value drValue of the continuous preset number of video frame images determines whether the dynamic range value drValue is greater than or equal to the preset dynamic range value, if the dynamic range value drValue of the continuous preset number of video frame images is greater than or equal to the preset dynamic range value The range value determines that the scene information of the video frame data is a high dynamic range scene; if the dynamic range value drValue of any video frame image is less than the preset dynamic range value, it is determined that the scene information of the video frame data is not a high dynamic range scene. In an embodiment of the present application, the dynamic range value is a ratio between the highest brightness and the lowest brightness in the image. Optionally, the preset dynamic range value is 50.

S310. Determine that the recommended video mode is a high dynamic range mode, and perform video shooting based on the high dynamic range mode.

In one embodiment of the present application, after making a recommendation decision and determining that the recommended video mode is the high dynamic range mode, a prompt control is displayed on the camera application interface, and the text "recommended to use HDR mode" is displayed on the prompt control, in response to the user The operation of the prompt control is triggered, and the mode switching module switches the current video mode to the high dynamic range mode, and performs video shooting based on the high dynamic range mode.

Refer to FIGS. 12-13 , which are flowcharts of a video shooting method provided by another embodiment of the present application. The method is applied in an electronic device, and the video shooting method includes:

S401. Acquire video frame data captured by a camera, and identify whether the video frame data contains a human face. If the video frame data contains a human face, the process enters S402; if the video frame data does not contain a human face, the process enters S407.

S402. Calculate the proportion of the size of the face area in the video frame data.

S403. Determine whether the proportion of the size of the human face area in the video frame data is greater than or equal to a first preset value. If the proportion of the size of the human face area in the video frame data is greater than or equal to the first preset value, the process enters S404; if the proportion of the size of the human face area in the video frame data is smaller than the first preset value, The flow goes to S405.

S404. Determine that the recommended video mode is the portrait mode, and perform video shooting based on the portrait mode.

S405. Determine whether the proportion of the size of the human face area in the video frame data is less than or equal to a second preset value, and the second preset value is smaller than the first preset value. If the proportion of the size of the human face area in the video frame data is less than or equal to the second preset value, the process enters S406; if the proportion of the size of the human face area in the video frame data is greater than the second preset value, The flow goes to S407.

S406. Determine that the recommended video mode is the protagonist mode, and perform video shooting based on the protagonist mode.

S407. Identify whether the scene information of the video frame data is a night scene. If the scene information of the video frame data is a night scene, the process goes to S408; if the scene information of the video frame data is not a night scene, the process goes to S409.

S408. Determine that the recommended video mode is a night scene mode, and perform video shooting based on the night scene mode.

S409. Identify whether the scene information of the video frame data is a high dynamic range scene. If the scene information of the video frame data is a high dynamic range scene, the process enters S410; if the scene information of the video frame data is not a high dynamic range scene, the process enters S411.

S410. Determine that the recommended video mode is a high dynamic range mode, and perform video shooting based on the high dynamic range mode.

S411. Identify whether the scene information of the video frame data is a macro scene. If the scene information of the preview stream is a macro scene, the process enters S412; if the scene information of the video frame data is not a macro scene, the process returns to S401.

In an embodiment of the present application, identifying whether the scene information of the video frame data is a macro scene includes: the scene identification module judges the video frame based on the movement data vcmCode of the voice coil motor of the camera, the focus status of the camera focusStatus and the calibration data calibData of the camera Whether the scene information of the data is a macro scene. Specifically, the scene recognition module obtains the moving data vcmCode of the camera voice coil motor, the focus status of the camera focusStatus, and the calibration data calibData of the camera, and judges whether the focus status of the camera is successful. Whether the movement data vcmCode of the voice coil motor is greater than or equal to the preset movement data. If the movement data vcmCode of the voice coil motor of the camera is greater than or equal to the preset movement data, determine whether the calibration data calibData of the camera is normal. If the calibration data calibData of the camera is normal, If the camera fails to focus, the moving distance of the camera is large enough, and the calibration data of the camera is normal, indicating that the camera may not be able to focus due to the size of the focusing distance. It is determined that the scene information of the video frame data is a macro scene; if the camera’s focus If the status focusStatus is successful, or the movement data vcmCode of the voice coil motor of the camera is less than the preset movement data, or the calibration data calibData of the camera is abnormal, it is determined that the scene information of the video frame data is not a macro scene. Optionally, the preset movement data is 10 movement steps of the voice coil motor.

S412. Determine that the recommended video mode is a macro mode, and perform video shooting based on the macro mode.

In one embodiment of the present application, after making a recommendation decision and determining that the recommended video mode is the macro mode, a prompt control is displayed on the camera application interface, and the text "recommended to use macro mode" is displayed on the prompt control, in response to the user The operation of the prompt control is triggered, and the mode switching module switches the current video mode to the macro mode, and video shooting is performed based on the macro mode.

Refer to FIGS. 14-15 , which are flowcharts of a video shooting method provided by another embodiment of the present application. The method is applied in an electronic device, and the video shooting method includes:

S501. Acquire video frame data captured by a camera, and identify whether the video frame data includes a human face. If the video frame data contains a human face, the process enters S502; if the video frame data does not contain a human face, the process enters S506.

S502. Calculate the proportion of the size of the face area in the video frame data.

S503. Determine whether the proportion of the size of the human face area in the video frame data is greater than or equal to a first preset value. If the proportion of the size of the human face area in the video frame data is greater than or equal to the first preset value, the process enters S504; if the proportion of the size of the human face area in the video frame data is smaller than the first preset value, The flow goes to S505.

S504. Determine that the recommended video mode is the portrait mode, and perform video shooting based on the portrait mode.

S505. Determine whether the proportion of the size of the face area in the video frame data is less than or equal to a second preset value, and the second preset value is smaller than the first preset value. If the proportion of the size of the human face area in the video frame data is less than or equal to the second preset value, the process enters S506; if the proportion of the size of the human face area in the video frame data is greater than the second preset value, The flow goes to S507.

S506. Determine that the recommended video mode is the protagonist mode, and perform video shooting based on the protagonist mode.

S507. Identify whether the scene information of the video frame data is a night scene. If the scene information of the video frame data is a night scene, the process goes to S508; if the scene information of the video frame data is not a night scene, the process goes to S509.

S508. Determine that the recommended video mode is a night scene mode, and perform video shooting based on the night scene mode.

S509. Identify whether the scene information of the video frame data is a high dynamic range scene. If the scene information of the video frame data is a high dynamic range scene, the process enters S510; if the scene information of the preview stream is not a high dynamic range scene, the process enters S511.

S510. Determine that the recommended video mode is a high dynamic range mode, and perform video shooting based on the high dynamic range mode.

S511. Identify whether the scene information of the video frame data is a macro scene. If the scene information of the video frame data is a macro scene, the process goes to S512; if the scene information of the video frame data is not a macro scene, the process goes to S513.

In an embodiment of the present application, identifying whether the scene information of the video frame data is a macro scene includes: the scene identification module judges the video frame based on the movement data vcmCode of the voice coil motor of the camera, the focus status of the camera focusStatus and the calibration data calibData of the camera Whether the scene information of the data is a macro scene. Specifically, the scene recognition module obtains the moving data vcmCode of the camera voice coil motor, the focus status of the camera focusStatus, and the calibration data calibData of the camera, and judges whether the focus status of the camera is successful. Whether the movement data vcmCode of the voice coil motor is greater than or equal to the preset movement data. If the movement data vcmCode of the voice coil motor of the camera is greater than or equal to the preset movement data, determine whether the calibration data calibData of the camera is normal. If the calibration data calibData of the camera is normal, If the camera fails to focus, the moving distance of the camera is large enough, and the calibration data of the camera is normal, indicating that the camera may not be able to focus due to the size of the focusing distance. It is determined that the scene information of the video frame data is a macro scene; if the camera’s focus If the status focusStatus is successful, or the movement data vcmCode of the voice coil motor of the camera is less than the preset movement data, or the calibration data calibData of the camera is abnormal, it is determined that the scene information of the video frame data is not a macro scene. Optionally, the preset movement data is 10 movement steps of the voice coil motor.

S512. Determine that the recommended video mode is a macro mode, and perform video shooting based on the macro mode.

S513. Identify whether the scene information of the video frame data is a multi-camera scene. If the scene information of the video frame data is a multi-camera scene, the process proceeds to S514; if the scene information of the video frame data is not a multi-camera scene, the process returns to S501.

In an embodiment of the present application, identifying whether the scene information of the video frame data is a macro scene includes: the scene recognition module acquires a preset number of video frame images in the video frame data, and identifies the video frame images in the preset number of video frame images Whether pets are included, if pets are included in a predetermined number of consecutive video frame images, it is determined that the scene information of the video frame data is a multi-lens scene; if any video frame image does not contain pets, it is determined that the scene information of the video frame data is not a multi-lens scene Scenes. That is, the scene recognition module determines that the scene information of the video frame data is a multi-lens scene based on the scene analysis result aiScencDetResult that a predetermined number of consecutive video frame images contain pets.

S514. Determine that the recommended video mode is a multi-camera mode, and perform video shooting based on the multi-camera mode.

In one embodiment of the present application, after making a recommendation decision and determining that the recommended video mode is the multi-camera mode, a prompt control is displayed on the camera application interface, and the text "recommended to use multi-camera mode" is displayed on the prompt control, in response to user The operation of the prompt control is triggered, and the mode switching module switches the current video mode to the multi-lens mode, and video shooting is performed based on the multi-lens mode.

The embodiment of the present application provides an overall solution for supporting intelligent scene detection and video mode recommendation in normal recording mode, which can identify typical user scenes according to currently supported video modes, that is, open typical scenes in normal recording mode Detection logic, after detecting a typical scene, recommend the corresponding best video mode to the user.

The currently supported video modes are HDR mode, portrait mode, protagonist mode, night scene mode, macro mode, and multi-lens mode. The specific solutions include: enter the normal video preview interface, enable Master AI (intelligent shooting assistant) by default, and perform intelligent scene detection. According to the scene detection results, a recommendation dialog box will pop up. After the user clicks to select the mode, it will enter the corresponding video mode. After entering the selected recording mode, scene detection is no longer supported. The method of exiting the current mode: Manually cross off the icon of the current mode or click the recording. After the recording is completed, it will automatically return to the normal recording mode and start a new round of scene detection.

Referring to FIG. 16 , it is a flowchart of a video shooting method provided by another embodiment of the present application.

S601, enter the normal video recording mode.

S602. Determine whether the Master AI is enabled. If the Master AI is not enabled, the process enters S603; if the Master AI is enabled, the process enters S604.

S603. Keep the video mode as a normal video recording mode.

S604, Master AI recognizes the shooting scene.

S605. Determine whether there is a matching scene. If there is a matching scene, the process goes to S606; if there is no matching scene, the process goes to S603.

S606. Perform video mode decision recommendation based on the identified matching scene. Wherein, the decision-making recommendation process specifically includes: S607, detecting an HDR scene and stabilizing a certain number of frames. S608, switch the video mode to the HDR mode. S609, detecting a night scene, and stabilizing for a certain number of frames. S610, switch the video mode to night scene mode. S611, a single person is detected, the face ratio is greater than or equal to 1/3, and the number of frames is stable. S612, switch the video mode to the portrait mode, and enable the blur function. S613, multiple people are detected, the maximum face ratio is less than or equal to 1/5, and a certain number of frames is stabilized. S614. Switch the video mode to the protagonist mode. S615. A pet is detected and stabilized for a certain number of frames. S616, switch the video mode to a multi-mirror mode. S617, detecting a macro scene, and stabilizing for a certain number of frames. S618, switching the video mode to a macro mode. When manually closing the current video mode, the S619 enters the normal video mode.

Refer to Figure 17 for the decision factors for intelligent scene detection for Master AI, and see Figure 18 for the video specifications of each video mode.

Referring to FIG. 19 , it is a flowchart of a video shooting method provided by another embodiment of the present application.

S701. Input video frame data.

S702. Output a video mode to be recommended according to the decision factor.

S703, judging whether the current camera meets the zoom capability required by the video mode to be recommended. If the current camera meets the zoom capability required by the video mode to be recommended, the process enters S704; if the current camera does not meet the zoom capability required by the video mode to be recommended, the process returns to S701.

In an embodiment of the present application, if the zoom ratio range of the current camera meets the zoom specification required by the video mode to be recommended, it is determined that the current camera meets the zoom capability required by the video mode to be recommended; The zoom specification required by the video mode to be recommended determines that the current camera does not meet the zoom capability required by the video mode to be recommended. For example, as shown in Figure 18, the zoom specification of the portrait mode is 1x-2x (1x-2x), and the zoom ratio range of the current camera is 1x-4x, then it is determined that the zoom ratio range of the current camera meets the zoom ratio required by the portrait mode Specification.

S704, decide a recommended video mode. That is, the output video mode is decided as the recommended video mode.

The main difference between this solution and intelligent scene recognition for taking photos: 1) As long as the face is recognized by MasterAI for taking photos and the priority conditions of the scene are met, it will enter the portrait blur; in MasterAI for video, it will enter different modes according to the size of the face, More than 1/3 of the face scenes will enter the portrait blur mode, and less than 1/5 of the face scenes will enter the protagonist mode; 2) There are differences in the implementation methods: MasterAI enters the portrait blur when taking pictures, and only adds blur to the normal camera link And beautification algorithm; Video MasterAI will perform a mode jump, jumping into portrait mode (video blur) and protagonist mode.

The embodiment of the present application also provides an electronic device 100, as shown in FIG. Ultra-mobile Personal Computer, UMPC), netbook, and cell phone, personal digital assistant (Personal Digital Assistant, PDA), augmented reality (Augmented Reality, AR) device, virtual reality (Virtual Reality, VR) device, artificial intelligence (Artificial Intelligence, AI) devices, wearable devices, vehicle-mounted devices, smart home devices and/or smart city devices, the embodiment of the present application does not specifically limit the specific type of the electronic device 100 .

The electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (Universal Serial Bus, USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, and an antenna 2 , mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone jack 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193, display screen 194, and A subscriber identification module (Subscriber Identification Module, SIM) card interface 195 and the like. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, bone conduction sensor 180M, etc.

It can be understood that, the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device 100 . In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown in the figure, or combine certain components, or separate certain components, or arrange different components. The illustrated components can be realized in hardware, software or a combination of software and hardware.

The processor 110 may include one or more processing units, for example: the processor 110 may include an application processor (Application Processor, AP), a modem processor, a graphics processor (Graphics Processing Unit, GPU), an image signal processor ( Image Signal Processor, ISP), controller, video codec, digital signal processor (Digital Signal Processor, DSP), baseband processor, and/or neural network processor (Neural-network Processing Unit, NPU), etc. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.

The controller can generate an operation control signal according to the instruction opcode and timing signal, and complete the control of fetching and executing the instruction.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in processor 110 is a cache memory. The memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to use the instruction or data again, it can be called directly from the memory. Repeated access is avoided, and the waiting time of the processor 110 is reduced, thereby improving the efficiency of the system.

In some embodiments, processor 110 may include one or more interfaces. The interface may include an integrated circuit (Inter-integrated Circuit, I2C) interface, an integrated circuit built-in audio (Inter-integrated CircuitSound, I2S) interface, a pulse code modulation (Pulse Code Modulation, PCM) interface, a universal asynchronous receiver (universal asynchronous receiver) /transmitter, UART) interface, Mobile Industry Processor Interface (MIPI), General-Purpose Input/Output (GPIO) interface, Subscriber Identity Module (Subscriber Identity Module, SIM) interface, and/or Universal Serial Bus (Universal Serial Bus, USB) interface, etc.

The I2C interface is a bidirectional synchronous serial bus, including a serial data line (Serial Data Line, SDA) and a serial clock line (Derail Clock Line, SCL). In some embodiments, processor 110 may include multiple sets of I2C buses. The processor 110 can be respectively coupled to the touch sensor 180K, the charger, the flashlight, the camera 193 and the like through different I2C bus interfaces. For example, the processor 110 may be coupled to the touch sensor 180K through the I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to realize the touch function of the electronic device 100 .

The I2S interface can be used for audio communication. In some embodiments, processor 110 may include multiple sets of I2S buses. The processor 110 may be coupled to the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170 . In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 through the I2S interface, so as to realize the function of answering calls through the Bluetooth headset.

The PCM interface can also be used for audio communication, sampling, quantizing and encoding the analog signal. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 can also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communication. The bus can be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is generally used to connect the processor 110 and the wireless communication module 160 . For example: the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to realize the Bluetooth function. In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 through the UART interface, so as to realize the function of playing music through the Bluetooth headset.

The MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193 . The MIPI interface includes a camera serial interface (Camera Serial Interface, CSI), a display serial interface (DisplaySerial Interface, DSI), etc. In some embodiments, the processor 110 communicates with the camera 193 through the CSI interface to realize the shooting function of the electronic device 100 . The processor 110 communicates with the display screen 194 through the DSI interface to realize the display function of the electronic device 100 .

The GPIO interface can be configured by software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can be used to connect the processor 110 with the camera 193 , the display screen 194 , the wireless communication module 160 , the audio module 170 , the sensor module 180 and so on. The GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.

The USB interface 130 is an interface conforming to the USB standard specification, specifically, it may be a Mini USB interface, a Micro USB interface, a USB Type C interface, and the like. The USB interface 130 can be used to connect a charger to charge the electronic device 100 , and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones and play audio through them. This interface can also be used to connect other electronic devices 100, such as AR devices.

It can be understood that the interface connection relationship between the modules shown in the embodiment of the present invention is only a schematic illustration, and does not constitute a structural limitation of the electronic device 100 . In other embodiments of the present application, the electronic device 100 may also adopt different interface connection manners in the foregoing embodiments, or a combination of multiple interface connection manners.

The charging management module 140 is configured to receive a charging input from a charger. Wherein, the charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 can receive charging input from the wired charger through the USB interface 130 . In some wireless charging embodiments, the charging management module 140 may receive a wireless charging input through a wireless charging coil of the electronic device 100 . While the charging management module 140 is charging the battery 142 , it can also supply power to the electronic device 100 through the power management module 141 .

The power management module 141 is used for connecting the battery 142 , the charging management module 140 and the processor 110 . The power management module 141 receives the input from the battery 142 and/or the charging management module 140 to provide power for the processor 110 , the internal memory 121 , the display screen 194 , the camera 193 , and the wireless communication module 160 . The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, and battery health status (leakage, impedance). In some other embodiments, the power management module 141 may also be disposed in the processor 110 . In some other embodiments, the power management module 141 and the charging management module 140 may also be set in the same device.

The wireless communication function of the electronic device 100 can be realized by the antenna 1 , the antenna 2 , the mobile communication module 150 , the wireless communication module 160 , a modem processor, a baseband processor, and the like.

Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 may be used to cover single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization of the antennas. For example: Antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 can provide wireless communication solutions including 2G/3G/4G/5G applied on the electronic device 100 . The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (Low Noise Amplifier, LNA) and the like. The mobile communication module 150 can receive electromagnetic waves through the antenna 1, filter and amplify the received electromagnetic waves, and send them to the modem processor for demodulation. The mobile communication module 150 can also amplify the signals modulated by the modem processor, and convert them into electromagnetic waves and radiate them through the antenna 1 . In some embodiments, at least part of the functional modules of the mobile communication module 150 may be set in the processor 110 . In some embodiments, at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be set in the same device.

A modem processor may include a modulator and a demodulator. Wherein, the modulator is used for modulating the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low frequency baseband signal. Then the demodulator sends the demodulated low-frequency baseband signal to the baseband processor for processing. The low-frequency baseband signal is passed to the application processor after being processed by the baseband processor. The application processor outputs sound signals through audio equipment (not limited to speaker 170A, receiver 170B, etc.), or displays images or videos through display screen 194 . In some embodiments, the modem processor may be a stand-alone device. In some other embodiments, the modem processor may be independent from the processor 110, and be set in the same device as the mobile communication module 150 or other functional modules.

The wireless communication module 160 can provide wireless local area network (WirelessLocal Area Networks, WLAN) (such as wireless fidelity (Wireless Fidelity, Wi-Fi) network), bluetooth (Bluetooth, BT), global navigation satellite system, etc. applied on the electronic device 100. (Global Navigation Satellite System, GNSS), frequency modulation (Frequency Modulation, FM), near field communication technology (Near Field Communication, NFC), infrared technology (Infrared, IR) and other wireless communication solutions. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2 , frequency-modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110 . The wireless communication module 160 can also receive the signal to be sent from the processor 110 , frequency-modulate it, amplify it, and convert it into electromagnetic waves through the antenna 2 for radiation.

In some embodiments, the antenna 1 of the electronic device 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through wireless communication technology. Described wireless communication technology can comprise Global System For Mobile Communications (Global System For Mobile Communications, GSM), General Packet Radio Service (General Packet Radio Service, GPRS), Code Division Multiple Access (CodeDivision Multiple Access, CDMA), broadband code Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM , and/or IR technology, etc. The GNSS can include Global Positioning System (Global Positioning System, GPS), Global Navigation Satellite System (Global Navigation Satellite System, GLONASS), Beidou Navigation Satellite System (Beidou Navigation Satellite System, BDS), Quasi-Zenith Satellite System (Quasi- Zenith Satellite System, QZSS) and/or Satellite Based Augmentation Systems (Satellite Based Augmentation Systems, SBAS).

The electronic device 100 realizes the display function through the GPU, the display screen 194 , and the application processor. The GPU is a microprocessor for image processing, and is connected to the display screen 194 and the application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos and the like. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (Liquid Crystal Display, LCD), an organic light-emitting diode (Organic Light-Emitting Diode, OLED), an active matrix organic light-emitting diode or an active matrix organic light-emitting diode (Active-MatrixOrganic Light-Emitting Diode). , AMOLED), flexible light-emitting diodes (Flex Light-EmittingDiode, FLED), Miniled, Microled, Micro-OLED, quantum dot light-emitting diodes (Quantum Dot LightEmitting Diodes, QLED), etc. In some embodiments, the electronic device 100 may include 1 or N display screens 194 , where N is a positive integer greater than 1.

The electronic device 100 can realize the shooting function through the ISP, the camera 193 , the video codec, the GPU, the display screen 194 and the application processor.

The ISP is used for processing the data fed back by the camera 193 . For example, when taking a picture, open the shutter, the light is transmitted to the photosensitive element of the camera through the lens, and the light signal is converted into an electrical signal, and the photosensitive element of the camera transmits the electrical signal to the ISP for processing, and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on image noise, brightness, and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be located in the camera 193 .

Camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects it to the photosensitive element. The photosensitive element may be a Charge Coupled Device (Charge Coupled Device, CCD) or a Complementary Metal-Oxide-Semiconductor (Complementary Metal-Oxide-Semiconductor, CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, and then transmits the electrical signal to the ISP to convert it into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. DSP converts digital image signals into standard RGB, YUV and other image signals. In some embodiments, the electronic device 100 may include 1 or N cameras 193 , where N is a positive integer greater than 1.

Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the electronic device 100 selects a frequency point, the digital signal processor is used to perform Fourier transform on the energy of the frequency point.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 can play or record videos in various encoding formats, such as: Moving Picture Experts Group (Moving Picture Experts Group, MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

NPU is a neural network (Neural-Network, NN) computing processor. By referring to the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and continuously learn by itself. Applications such as intelligent cognition of the electronic device 100 can be realized through the NPU, such as image recognition, face recognition, speech recognition, text understanding, and the like.

The internal memory 121 may include one or more random access memories (Random Access Memory, RAM) and one or more non-volatile memories (Non-Volatile Memory, NVM).

Random access memory can include Static Random-Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (Synchronous Dynamic Random Access Memory, SDRAM), double data rate synchronous Dynamic random access memory (Double Data Rate Synchronous Dynamic Random Access Memory, DDR SDRAM, such as the fifth generation DDR SDRAM is generally called DDR5 SDRAM), etc.;

Non-volatile memory may include magnetic disk storage devices, flash memory (flash memory).

According to the operating principle, flash memory can include NOR FLASH, NAND FLASH, 3D NAND FLASH, etc. According to the potential order of storage cells, it can include single-level storage cells (Single-Level Cell, SLC), multi-level storage cells (Multi-Level Cell, MLC), triple-level storage unit (Triple-Level Cell, TLC), fourth-level storage unit (Quad-Level Cell, QLC), etc., which can include Universal Flash Storage (UFS), embedded Multimedia memory card (embedded Multi Media Card, eMMC), etc.

The random access memory can be directly read and written by the processor 110 , can be used to store executable programs (such as machine instructions) of an operating system or other running programs, and can also be used to store data of users and application programs.

The non-volatile memory can also store executable programs and data of users and application programs, etc., and can be loaded into the random access memory in advance for the processor 110 to directly read and write.

The external memory interface 120 can be used to connect an external non-volatile memory, so as to expand the storage capacity of the electronic device 100 . The external non-volatile memory communicates with the processor 110 through the external memory interface 120 to implement a data storage function. For example, files such as music and video are stored in an external non-volatile memory.

The internal memory 121 or the external memory interface 120 is used to store one or more computer programs. One or more computer programs are configured to be executed by the processor 110 . The one or more computer programs include a plurality of instructions, and when the plurality of instructions are executed by the processor 110, the screen display detection method executed on the electronic device 100 in the above embodiment can be implemented, so as to realize the screen display detection function of the electronic device 100 .

The electronic device 100 can implement audio functions through the audio module 170 , the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. Such as music playback, recording, etc.

The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signal. The audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be set in the processor 110 , or some functional modules of the audio module 170 may be set in the processor 110 .

Speaker 170A, also referred to as a "horn", is used to convert audio electrical signals into sound signals. Electronic device 100 can listen to music through speaker 170A, or listen to hands-free calls.

Receiver 170B, also called "earpiece", is used to convert audio electrical signals into sound signals. When the electronic device 100 receives a call or a voice message, the receiver 170B can be placed close to the human ear to receive the voice.

The microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a phone call or sending a voice message, the user can put his mouth close to the microphone 170C to make a sound, and input the sound signal to the microphone 170C. The electronic device 100 may be provided with at least one microphone 170C. In some other embodiments, the electronic device 100 may be provided with two microphones 170C, which may also implement a noise reduction function in addition to collecting sound signals. In some other embodiments, the electronic device 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and realize directional recording functions, etc.

The earphone interface 170D is used for connecting wired earphones. The earphone interface 170D may be a USB interface 130, or a 3.5mm Open Mobile Terminal Platform (OMTP) standard interface, or a Cellular Telecommunications Industry Association of the USA (CTIA) standard interface.

The keys 190 include a power key, a volume key and the like. The key 190 may be a mechanical key. It can also be a touch button. The electronic device 100 may receive key input and generate key signal input related to user settings and function control of the electronic device 100 .

The motor 191 can generate a vibrating reminder. The motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback. For example, touch operations applied to different applications (such as taking pictures, playing audio, etc.) may correspond to different vibration feedback effects. The motor 191 may also correspond to different vibration feedback effects for touch operations acting on different areas of the display screen 194 . Different application scenarios (for example: time reminder, receiving information, alarm clock, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

The indicator 192 can be an indicator light, and can be used to indicate charging status, power change, and can also be used to indicate messages, missed calls, notifications, and the like.

The SIM card interface 195 is used for connecting a SIM card. The SIM card can be connected and separated from the electronic device 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195 . The electronic device 100 may support 1 or N SIM card interfaces, where N is a positive integer greater than 1. SIM card interface 195 can support Nano SIM card, Micro SIM card, SIM card and so on. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards may be the same or different. The SIM card interface 195 is also compatible with different types of SIM cards. The SIM card interface 195 is also compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as calling and data communication. In some embodiments, the electronic device 100 adopts an eSIM, that is, an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100 . The embodiment of the present application also provides a computer storage medium, the computer storage medium stores computer instructions, and when the computer instructions are run on the electronic device 100, the electronic device 100 executes the above-mentioned related method steps to realize the video in the above-mentioned embodiment. Shooting method.

An embodiment of the present application further provides a computer program product, which, when running on a computer, causes the computer to execute the above-mentioned related steps, so as to implement the video shooting method in the above-mentioned embodiment.

In addition, an embodiment of the present application also provides a device, which may specifically be a chip, a component or a module, and the device may include a connected processor and a memory; wherein the memory is used to store computer-executable instructions, and when the device is running, The processor can execute the computer-executable instructions stored in the memory, so that the chip executes the video shooting method in the above method embodiments.

Wherein, the electronic device, computer storage medium, computer program product or chip provided in this embodiment is all used to execute the corresponding method provided above, therefore, the beneficial effects it can achieve can refer to the corresponding method provided above The beneficial effects in the method will not be repeated here.

Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and brevity of the description, only the division of the above-mentioned functional modules is used as an example for illustration. In practical applications, the above-mentioned functions can be allocated according to needs It is completed by different functional modules, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

In the several embodiments provided in this application, it should be understood that the disclosed devices and methods may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules or 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 it can be integrated into another device, or some features can be omitted, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The unit described as a separate component may or may not be physically separated, and a component displayed as a unit may be one physical unit or multiple physical units, that is, it may be located in one place, or may be distributed to multiple different places. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the software product is stored in a storage medium Among them, several instructions are included to make a device (which may be a single-chip microcomputer, a chip, etc.) or a processor (processor) execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes. .

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application without limitation. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solutions of the present application.

Claims (13)

1. A video shooting method, characterized in that the method comprises: Obtain the video frame data captured by the camera, and identify whether the video frame data contains a human face; If the video frame data includes a human face, calculate the proportion of the size of the human face area in the video frame data; judging whether the proportion of the size of the face area in the video frame data is greater than or equal to a first preset value; If the proportion of the size of the face area in the video frame data is greater than or equal to the first preset value, determine that the recommended video mode is portrait mode, and perform video shooting based on the portrait mode; If the proportion of the size of the human face area in the video frame data is less than the first preset value, determine whether the proportion of the size of the human face area in the video frame data is less than or equal to a second preset value, the second preset value being smaller than the first preset value; If the proportion of the size of the human face area in the video frame data is less than or equal to the second preset value, determine that the recommended video mode is the protagonist mode, and perform video shooting based on the protagonist mode, including : Switch the video mode to the protagonist mode, convert the video frame data captured by the camera into a second video stream, the video frame data includes panoramic video frame data and protagonist portrait video frame data, and intercept the The video frame data of the portrait of the protagonist in the video frame data, the video frame data of the portrait of the protagonist is amplified to generate a third video stream, the second video stream and the third video stream are spliced, and the spliced The video stream is displayed on the display screen, so that the second video stream of the panoramic video frame data is completely displayed, and the third video stream of the protagonist's portrait video frame data is displayed in the form of a picture-in-picture. 2. video shooting method as claimed in claim 1, is characterized in that, the ratio that the size of described calculating people's face area occupies in described video frame data comprises: Using a rectangular frame to identify the face area identified in the video frame data; determining the size of the face area based on the rectangular frame; Calculate the ratio between the size of the human face area and the size of the video frame image to obtain the proportion of the size of the human face area in the video frame data. 3. The video shooting method according to claim 2, wherein the determining the size of the human face region based on the rectangular frame comprises: Determining the width value of the rectangular frame identifying the human face area in the video frame image as the width value of the human face area, and determining the height value of the rectangular frame as the height value of the human face area . 4. The video shooting method according to claim 3, wherein the calculation of the ratio between the size of the human face area and the size of the video frame image comprises: Calculate the ratio between the width value of the face area and the width value of the video frame image. 5. The video shooting method according to claim 3, wherein the calculation of the ratio between the size of the human face area and the size of the video frame image comprises: Calculate the ratio between the height value of the face area and the height value of the video frame image. 6. The video shooting method according to claim 3, wherein the calculation of the ratio between the size of the human face area and the size of the video frame image comprises: Calculate the ratio between the area of the face area and the area of the video frame image. 7. The video shooting method according to claim 1, wherein said identifying whether said video frame data comprises a human face comprises: performing format conversion on each video frame image in the video frame data to obtain a video stream; Perform face recognition on each video frame image in the video stream, and determine whether the video frame data contains a human face; If it is identified that a predetermined number of consecutive video frame images contain a human face, it is determined that the video frame data contains a human face. 8. The video shooting method according to claim 1, wherein said taking video based on the portrait mode comprises: Perform blurring processing on the video frame data captured by the camera. 9. The video shooting method according to claim 8, wherein said performing blur processing on the video frame data captured by the camera comprises: Carrying out portrait matting to the video frame image, extracting the portrait area in the video frame image; Perform blurring processing on the background area of the video frame image; The extracted portrait area is fused with the blurred background area. 10. The video shooting method according to claim 9, wherein said blurring the background area of said video frame image comprises: Gaussian blur processing is performed on the background area to obtain a blurred background area. 11. An electronic device, characterized in that the electronic device comprises a memory and a processor, wherein: The memory is used to store program instructions; The processor is configured to read and execute the program instructions stored in the memory, and when the program instructions are executed by the processor, the electronic device executes any one of claims 1 to 10. The video recording method described in the item. 12. A chip coupled with a memory in an electronic device, wherein the chip is used to control the electronic device to execute the video shooting method according to any one of claims 1 to 10. 13. A computer storage medium, characterized in that the computer storage medium stores program instructions, and when the program instructions are run on the electronic equipment, the electronic equipment executes any one of claims 1 to 10. The video shooting method described.