WO2022227393A1 - Procédé et appareil de prise d'image photographique, dispositif électronique et support de stockage lisible par ordinateur - Google Patents

Procédé et appareil de prise d'image photographique, dispositif électronique et support de stockage lisible par ordinateur Download PDF

Info

Publication number
WO2022227393A1
WO2022227393A1 PCT/CN2021/120722 CN2021120722W WO2022227393A1 WO 2022227393 A1 WO2022227393 A1 WO 2022227393A1 CN 2021120722 W CN2021120722 W CN 2021120722W WO 2022227393 A1 WO2022227393 A1 WO 2022227393A1
Authority
WO
WIPO (PCT)
Prior art keywords
human body
posture
recommended
scene
image
Prior art date
Application number
PCT/CN2021/120722
Other languages
English (en)
Chinese (zh)
Inventor
徐伟伟
黄岗桂
谢东明
王家园
Original Assignee
上海商汤智能科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 上海商汤智能科技有限公司 filed Critical 上海商汤智能科技有限公司
Priority to KR1020227018878A priority Critical patent/KR20220149503A/ko
Publication of WO2022227393A1 publication Critical patent/WO2022227393A1/fr

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/63—Control of cameras or camera modules by using electronic viewfinders
    • H04N23/631—Graphical user interfaces [GUI] specially adapted for controlling image capture or setting capture parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/61—Control of cameras or camera modules based on recognised objects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/61—Control of cameras or camera modules based on recognised objects
    • H04N23/611—Control of cameras or camera modules based on recognised objects where the recognised objects include parts of the human body
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/62—Control of parameters via user interfaces
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/63—Control of cameras or camera modules by using electronic viewfinders
    • H04N23/631—Graphical user interfaces [GUI] specially adapted for controlling image capture or setting capture parameters
    • H04N23/632—Graphical user interfaces [GUI] specially adapted for controlling image capture or setting capture parameters for displaying or modifying preview images prior to image capturing, e.g. variety of image resolutions or capturing parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/64—Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image

Definitions

  • the present disclosure relates to the field of computer technologies, and in particular, to an image capturing method and apparatus, an electronic device, and a computer-readable storage medium.
  • the embodiments of the present disclosure provide an image capturing technical solution.
  • an image capturing method including: acquiring a scene image corresponding to a shooting interface; recognizing the scene image, determining a scene category corresponding to the scene image and a scene category corresponding to the scene category the recommended human body posture; perform human key point detection on the scene image to determine the object in the scene image and the real human body posture of the object; by displaying the real human body posture and the recommended body posture in the shooting interface Human body posture, guide the object to perform posture adjustment according to the recommended human body posture; and obtain a photographed image of the object when the photographing conditions are met.
  • the image shooting effect is effectively improved, the shooting experience of the user is improved, and the shooting image that satisfies the user can be obtained.
  • the recommended human posture includes a single-person posture
  • the displaying the real human posture and the recommended human posture in the shooting interface includes: according to the object in the scene The position in the image, the real human body posture and the single-person posture are displayed in the shooting interface; or, according to the position of the object in the scene image, the real human body is displayed in the shooting interface.
  • gesture, and the single-person gesture is displayed at the first designated area of the photographing interface.
  • the recommended human body posture includes a multi-person combined posture
  • the method further includes: according to relative positions between multiple objects in the scene image, and each of the multi-person combined postures The relative position between the gestures, and the corresponding gestures of each object are determined from the combined gestures of the multiple people; wherein, displaying the real human body gesture and the recommended human body gesture in the shooting interface includes: for any object , according to the position of the object in the scene image, display the real human body posture of the object and the corresponding posture of the object in the shooting interface; or, according to the position of the object in the scene image , the real human body posture of the object is displayed in the shooting interface, and the corresponding posture of the object is displayed in the second designated area of the shooting interface.
  • the method further includes: determining a key point pair between the first human body key point of the real human body posture and the second human body key point of the recommended human body posture; the first similarity of the key point pair, and from the first human body key points, determine a third human body key point whose first similarity is less than a first preset threshold; wherein, described in the shooting interface Displaying the real human body posture and the recommended human body posture includes: highlighting the region where the third human body key point is located, wherein the highlighting method includes at least one of highlighting, bolding, and changing color. In this way, the parts of the human body with low similarity among the displayed real human body postures can be highlighted, so as to more effectively guide the user to adjust the posture, so as to improve the shooting experience.
  • the method further includes: determining the real human body posture and the recommended human body according to a first similarity of key point pairs between the real human body posture and the recommended human body posture
  • the second similarity between postures, the key point pair is determined according to the first human body key point of the real human body posture and the second human body key point of the recommended human body posture; the second similarity.
  • the method further includes: inputting the scene image to an area recommendation network to obtain a recommended photographing area of the scene image, where the recommended photographing area is used to represent the scene image in the scene image A recommended photographing area, the area recommendation network is a neural network obtained by training the first sample set marking the photographing area; wherein, after obtaining the recommended photographing area of the scene image, the method further includes at least one of the following: In the shooting interface, an identifier is used to indicate the recommended photographing area; the recommended human posture is displayed at the recommended photographing area of the photographing interface, and the recommended photographing area is used to guide the object according to the It is recommended to adjust the position of the photo area. In this way, a photographing area with better visual effect can be recommended to the subject, and the subject can be effectively guided to adjust the position to improve the visual effect of the photographed image, thereby improving the user's photographing experience.
  • recognizing the scene image, determining a scene category corresponding to the scene image and a recommended human pose corresponding to the scene category includes: performing a pose recommendation network on the scene image. Identify and determine the scene category corresponding to the scene image and the recommended human posture corresponding to the scene category, wherein the posture recommendation network is a neural network obtained by training the second sample set marked with the sample scene category and the sample human posture. network, the second sample set includes sample images uploaded by the object. In this way, the pose recommendation network can learn the human pose from the high-quality images, thereby effectively outputting high-quality recommended human poses based on the scene images.
  • the recommended human body postures include multiple ones
  • the method further includes: displaying in the shooting interface a plurality of posture diagrams corresponding to the recommended human body postures; in response to the selection of the posture diagrams Operation, according to the selected posture map, determine and display the recommended human body posture corresponding to the selected posture map.
  • the shooting conditions include: a second degree of similarity between the real human body posture and the recommended human body posture is greater than or equal to a second preset threshold; or, in the shooting interface Shooting controls are triggered.
  • the method further includes: processing the captured image to obtain a processed captured image, wherein the processing includes at least one of the following: performing processing on an object in the captured image Beauty processing, adding a filter to the captured image; the adding a filter to the captured image includes: adjusting the saturation, color temperature, color temperature, etc. of the captured image according to the recommended filter corresponding to the recommended human posture. at least one of brightness.
  • the visual effect of the captured image can be improved, which is conducive to obtaining a captured image that satisfies the user, and improves the image capturing experience.
  • the method is applied to an artificial intelligence education platform, and the artificial intelligence education platform is connected with intelligent hardware; wherein, the artificial intelligence education platform is used to edit the project code for realizing the image capturing method , and send the project code to the intelligent hardware; the intelligent hardware is used to collect scene images and run the project code to obtain the operation result, and send the operation result to the artificial intelligence education platform to The operation result is displayed on the display interface of the artificial intelligence education platform.
  • the artificial intelligence education platform is used to edit the project code for realizing the image capturing method , and send the project code to the intelligent hardware; the intelligent hardware is used to collect scene images and run the project code to obtain the operation result, and send the operation result to the artificial intelligence education platform to The operation result is displayed on the display interface of the artificial intelligence education platform.
  • an image capturing apparatus comprising: an acquisition part configured to acquire a scene image corresponding to a shooting interface; a recognition part configured to recognize the scene image and determine the scene The scene category corresponding to the image and the recommended human body posture corresponding to the scene category; the detection part is configured to perform human key point detection on the scene image, and determine the object in the scene image and the real human body posture of the object
  • the display part is configured to guide the object to adjust the posture according to the recommended human body posture by displaying the real human body posture and the recommended human body posture in the shooting interface; the shooting part is configured to meet the requirements of shooting condition, a captured image of the object is obtained.
  • the recommended human posture includes a single-person posture
  • the display part includes: a first display sub-part, configured to display the position of the object in the scene image according to the position of the object in the scene image.
  • the real human body posture and the single-person posture are displayed in the shooting interface; or, the second display subsection is configured to display the object in the shooting interface according to the position of the object in the scene image.
  • the real human body posture is displayed, and the single-person posture is displayed in the first designated area of the shooting interface.
  • the recommended human body posture includes a multi-person combined posture
  • the apparatus further includes: a position determination part configured to The relative position between each posture in the multi-person combined posture, and the corresponding posture of each object is respectively determined from the multi-person combined posture
  • the display part includes: a third display sub-section, which is configured for any an object, according to the position of the object in the scene image, display the real human body posture of the object and the corresponding posture of the object in the shooting interface; or, the fourth display sub-section is configured to be based on The position of the object in the scene image, the real human body posture of the object is displayed in the shooting interface, and the corresponding posture of the object is displayed in the second designated area of the shooting interface.
  • the apparatus further includes: a first determining part configured to determine the distance between the first human body key point of the real human body posture and the second human body key point of the recommended human body posture The key point pair; the second determination part is configured to determine from the first human body key points that the first similarity is less than the first preset threshold by calculating the first similarity of the key point pair.
  • the third human body key point wherein, the display part, including: a highlight sub-section, is configured to highlight the region where the third human body key point is located, wherein the highlighting method includes highlighting, bolding, changing at least one of the colors.
  • the apparatus further includes: a third determination part, configured to determine the the second similarity between the real human body posture and the recommended human body posture, and the key point pair is determined according to the first human body key point of the real human body posture and the second human body key point of the recommended human body posture;
  • the similarity display part is configured to display the second similarity in the shooting interface.
  • the apparatus further includes: an area determination part, configured to input the scene image into an area recommendation network to obtain a recommended photographing area of the scene image, where the recommended photographing area is used for Representing the area recommended to be photographed in the scene image, and the area recommendation network is a neural network trained by marking the first sample set of the photographing area; wherein, the device further includes: an area display part, configured to In the shooting interface, an identifier is used to indicate the recommended photographing area; or, the recommended human posture is displayed in the recommended photographing area of the photographing interface; the recommended photographing area is used to guide the object according to the recommended photographing area. Adjust the position of the recommended photo area as described above.
  • the area display part is further configured to use an identifier to indicate the recommended photographing area in the photographing interface; and display the recommended photographing area on the photographing interface.
  • the recommended human posture is described.
  • the identifying part is further configured to: identify the scene image through a gesture recommendation network, and determine a scene category corresponding to the scene image and a recommended human body corresponding to the scene category pose, wherein the pose recommendation network is a neural network obtained by training a second sample set marked with sample scene categories and sample human poses, and the second sample set includes sample images uploaded by objects.
  • the pose recommendation network is a neural network obtained by training a second sample set marked with sample scene categories and sample human poses, and the second sample set includes sample images uploaded by objects.
  • the recommended human body postures include a plurality of postures
  • the apparatus further includes: a posture map display part configured to display the posture maps corresponding to the plurality of recommended human body postures in the shooting interface; selecting The part is configured to, in response to a selection operation on the posture map, determine and display a recommended human body posture corresponding to the selected posture map according to the selected posture map.
  • the shooting conditions include: a second degree of similarity between the real human body posture and the recommended human body posture is greater than or equal to a second preset threshold; or, in the shooting interface Shooting controls are triggered.
  • the apparatus further includes: a processing part configured to process the captured image to obtain a processed captured image, wherein the processing includes at least one of the following: Performing beautification processing on the object in the captured image, and adding a filter to the captured image; the adding a filter to the captured image includes: adjusting the captured image according to the recommended filter corresponding to the recommended human posture at least one of saturation, color temperature, and brightness.
  • the device is applied to an artificial intelligence education platform, and the artificial intelligence education platform is connected to intelligent hardware; wherein, the artificial intelligence education platform is used to edit and implement the project code of the image capturing device , and send the project code to the intelligent hardware; the intelligent hardware is used to collect scene images and run the project code to obtain the operation result, and send the operation result to the artificial intelligence education platform to The operation result is displayed on the display interface of the artificial intelligence education platform.
  • an electronic device comprising: a processor; a memory configured to store instructions executable by the processor; wherein the processor is configured to invoke the instructions stored in the memory to execute the above method.
  • a computer-readable storage medium having computer program instructions stored thereon, the computer program instructions implementing the above method when executed by a processor.
  • a computer program comprising computer-readable code, which, when the computer-readable code is executed in an electronic device, implements the above method when executed by a processor in the electronic device.
  • the recommended human body pose corresponding to the scene category can be recommended to the user according to the scene category of the scene image, so that the recommended human body pose matches the scene image, and by displaying the real human body pose and The recommended human posture can guide the user to adjust the posture according to the displayed recommended human posture or facilitate the user to guide the subject to adjust the posture according to the displayed recommended human posture. Satisfied taking pictures.
  • FIG. 1 shows a flowchart of an image capturing method according to an embodiment of the present disclosure.
  • FIG. 2 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure.
  • FIG. 3 shows a schematic diagram of a multi-person combined gesture according to an embodiment of the present disclosure.
  • FIG. 4 shows a schematic diagram of a real human body pose according to an embodiment of the present disclosure.
  • FIG. 5 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure.
  • FIG. 6 shows a schematic diagram of a scene image according to an embodiment of the present disclosure.
  • FIG. 7 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure.
  • FIG. 8 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure.
  • FIG. 9 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure.
  • FIG. 10 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure.
  • FIG. 11a and 11b illustrate schematic diagrams of a photographing interface according to an embodiment of the present disclosure.
  • FIG. 12 shows a schematic diagram of an application of an image capturing method according to an embodiment of the present disclosure.
  • FIG. 13 shows a block diagram of an image capturing apparatus according to an embodiment of the present disclosure.
  • FIG. 14 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • FIG. 1 shows a flowchart of an image capturing method according to an embodiment of the present disclosure. As shown in FIG. 1 , the image capturing method includes:
  • step S11 obtain a scene image corresponding to the shooting interface
  • step S12 the scene image is identified, and the scene category corresponding to the scene image and the recommended human posture corresponding to the scene category are determined;
  • step S13 the human body key point detection is performed on the scene image, and the object in the scene image and the real human body posture of the object are determined;
  • step S14 the real human body posture and the recommended human body posture are displayed in the shooting interface, so as to guide the object to adjust the posture according to the recommended human body posture;
  • step S15 when the photographing conditions are satisfied, a photographed image of the object is obtained.
  • the image capturing method may be performed by a terminal device, and the terminal device may be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, or a personal digital assistant (Personal Digital Assistant, PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc., the method can be implemented by the processor calling the computer-readable instructions stored in the memory.
  • UE User Equipment
  • PDA Personal Digital Assistant
  • an application (Application, APP) implementing the image capturing method may be deployed in the terminal device, so that the user can directly use the application to capture images;
  • a shooting mode it is integrated into the shooting function of the terminal device, so that when the user uses the shooting function of the terminal device, the user can select the shooting mode that realizes the image shooting method to shoot the image, which is not made in this embodiment of the present disclosure. limit.
  • an image capturing device such as a camera mounted on the terminal device may be used, or an image capturing device (such as a camera) assembled on the terminal device or a
  • the image acquisition device connected to the device collects the scene image of the actual scene in real time and displays it on the shooting interface of the terminal device.
  • a scene image corresponding to the shooting interface is acquired, that is, a scene image acquired by an image acquisition device is acquired.
  • FIG. 2 shows a schematic diagram of a shooting interface according to an embodiment of the present disclosure.
  • a scene image can be displayed in the shooting interface, and a shooting control 22 for triggering shooting can be provided in the shooting interface, which is used to check the shooting interface.
  • the shooting interface shown in FIG. 2 is an implementation provided by the embodiment of the present disclosure. It should be understood that the present disclosure should not be limited to this, and those skilled in the art can set the functions in the shooting interface according to actual needs. control, which is not limited by this embodiment of the present disclosure.
  • the image capturing method may also be performed by a server, and the server may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud-provided server.
  • the disclosed embodiments do not limit the type of server.
  • step S11 a scene image corresponding to the shooting interface is acquired, that is, the server receives the scene image sent by the terminal, wherein the scene image is collected by the terminal through the image capturing device image.
  • step S12 the recognition of the scene image can be triggered by triggering the trigger control 23 as shown in FIG. After the shooting mode of the image shooting method, the scene image recognition is automatically triggered, which is not limited in this embodiment of the present disclosure.
  • recognizing the scene image, determining the scene category corresponding to the scene image and the recommended human pose corresponding to the scene category may include: recognizing the scene image, and obtaining the scene image in the scene image. at least one of the scene feature and the object feature of the image; according to at least one of the identified scene feature and the object feature, determine the scene type corresponding to the scene image; according to the correspondence between the preset scene type and the recommended human posture , and determine the recommended human pose corresponding to the scene type.
  • the scene features can be used to indicate the scene type of the actual scene where the object is located.
  • the scene features of the grassland can include grass, cattle and sheep, etc.
  • the scene features of the indoor can include walls, windows, etc.
  • the identified scene features include Grassland and cattle and sheep, the scene type corresponding to the obtained scene image is grassland.
  • the object features may include at least one of the following: clothing features, physical features, gender features, and the like.
  • the clothing feature can be used to indicate the clothing of the object
  • the body feature can be used to indicate the body shape of the object (for example, tall and small)
  • the gender feature can be used to indicate the gender of the object.
  • the scene type can be determined in combination with the scene feature and the object feature, so as to obtain a more accurate recommended human pose.
  • the scene category can be "indoor-small-girl-wearing sweater, trousers” , handbag”, and then determine the corresponding recommended human pose according to this scene category.
  • the scene image can be recognized by a pre-trained scene recognition network to obtain scene features and/or object features in the scene image, and the embodiments of the present disclosure do not limit the network type, network result, and training method of the scene recognition network.
  • the identified scene features and object features can also be displayed in the shooting interface, for example, the scene features and object features can be marked by means of tags, so as to improve the interest of image shooting.
  • scene types may correspond to different recommended human postures, and the corresponding relationship between scene types and recommended human postures can be preset.
  • the recommended human pose corresponding to the scene category.
  • a pre-trained gesture recommendation network may also be used to identify the scene image, and determine the scene category corresponding to the scene image and the recommended human pose corresponding to the scene category.
  • the embodiments of the present disclosure do not limit the network type, network structure, training method, etc. of the gesture recommendation network.
  • a self-supervised training method can be used to train the pose recommendation network to learn the recommended human poses under different scene types, or, in other words, to learn the correspondence between the scene types and the recommended human poses, so as to use
  • the pose recommendation network directly obtains the recommended human pose based on the scene image. In this way, compared with the above-mentioned method of determining the recommended human posture according to the corresponding relationship between the preset scene type and the recommended human posture, more abundant scene types and recommended human postures can be obtained intelligently, and more accurate It recommends the recommended human pose corresponding to the scene image to the user.
  • the recommended human body posture may include the human body posture learned from the sample images by the above-mentioned posture recommendation network, for example, may also include the human body posture manually drawn by a user (eg, a technician), to which the embodiments of the present disclosure No restrictions apply.
  • the recommended human posture may be represented in the form of a human skeleton (eg, a skeleton obtained by connecting human joint points), or in the form of a human body outline, which is not limited to this embodiment of the present disclosure.
  • the sample image is a high-quality image with better shooting effect.
  • Learning the human body posture from the high-quality image and recommending it to the user can guide the object to approach the human body posture in the high-quality image, so as to obtain a better shooting effect and improve the user's shooting experience.
  • the recommended human posture may include a single-person posture (for example, a single-person comparison), and may also include a multi-person combined posture (for example, a two-person combination). It should be understood that the recommended human gesture corresponding to the scene category may include at least one single-person gesture and/or at least one multi-person combined gesture.
  • FIG. 3 shows a schematic diagram of a multi-person combined posture according to an embodiment of the present disclosure. As shown in FIG. 3 , the posture n and the posture m together form a posture of “comparing hearts”.
  • the scene types may, for example, at least include: landscape, indoor, outdoor, etc.; wherein, the scenery can be divided into beach, landscape, grassland, lake, etc., and the indoor can be divided into For office, conference room, home, etc., outdoor category can be divided into: street view category, playground category, park category, etc.
  • classification of scene types may not be limited to this.
  • those skilled in the art can preset various scene types according to actual needs, or can also self-learn scene types through the above gesture recommendation network. Examples are not limited.
  • any known human body key point detection method may be used, such as using a human body key point detection network to perform human key point detection on the scene image, which is not made in this embodiment of the present disclosure. limit.
  • the embodiments of the present disclosure do not limit the network type, network structure, and training method of the human body key point detection network.
  • the detection of human body key points on the scene image may include: extracting the key points of human body joint parts of objects in the scene image (for example, the human body key points of 20 joint parts), wherein the key points of the human body joint parts are extracted.
  • the number and position of the points may be determined according to actual requirements, which are not limited in this embodiment of the present disclosure.
  • the object in the scene image and the coordinate values of the key points of multiple human joint parts of the object in the scene image can be determined according to the detected key points of the human body joints; value, and connect the key points of multiple human joint parts according to the structure of the human body to obtain the human skeleton of the object, that is, to obtain the real human body posture of the object.
  • FIG. 4 shows a schematic diagram of a real human body posture according to an embodiment of the present disclosure. As shown in FIG. 4 , 20 key points of joint parts of the human body are connected to obtain the real human body posture of the object.
  • performing human body key point detection on the scene image may also include: extracting contour key points on the human body contour of the object in the scene image, obtaining the human body contour of the object, and then using the human body
  • the contour represents the real human pose of the object.
  • the manner in which the posture of the human body is represented may be determined according to actual needs, which is not limited by the embodiment of the present disclosure.
  • steps S12 and S13 may be performed simultaneously; or step S12 may be performed first, and then step S13 may be performed; or step S13 may be performed first, and then step S12 may be performed. Specifically, it may be set according to factors such as the processing capability of the terminal device, the resource occupancy of the terminal device, and the time delay limit in the application process, which is not limited in this embodiment of the present disclosure.
  • displaying the real human body posture and the recommended human body posture in the shooting interface may include: displaying the human body skeleton corresponding to the recommended human body posture and the human body skeleton corresponding to the real human body posture in the shooting interface ; or, displaying the human body contour corresponding to the recommended human body posture and the human body contour corresponding to the real human body posture in the shooting interface.
  • the form of human skeleton or human outline can be used in the shooting interface to display the real human body posture and the recommended human body posture. In this way, the user can be guided to adjust the posture according to the displayed recommended human body posture, so as to achieve a state with a high similarity to the recommended human body posture, thereby obtaining a better shooting effect.
  • FIG. 5 shows a schematic diagram of a shooting interface according to an embodiment of the present disclosure.
  • the real human body posture and the recommended human body posture of the object can be displayed based on the form of the human skeleton; in a possible implementation manner , the real human body posture and the recommended human body posture are displayed in different display forms; for example, different colors can be used to represent the real human body posture and the recommended human body posture of the object, for example, the recommended human body posture is represented by yellow, and the recommended human body posture is represented by green.
  • Real human pose In a possible implementation, as shown in FIG.
  • guide information can also be displayed in the shooting interface, such as displaying the similarity between the real human body posture and the recommended human posture: 30%, and displaying the information that guides the user to adjust the posture.
  • the prompt "Try another action? Try to be consistent with the yellow line”.
  • the real human body posture and the representation of the recommended human body posture shown in FIG. 5 are an implementation manner provided by the embodiments of the present disclosure, and the present disclosure should not be limited to this. It is required to set the display form of the real human body posture and the recommended human body posture, such as color, line thickness, line type, area transparency, etc., as well as setting the content of the guidance information, which is not limited by this embodiment of the present disclosure.
  • step S15 when the image capturing method is executed by the terminal device, the capturing conditions may include that the capturing control in the capturing interface is triggered. It can be understood that the photographer clicks the 2.
  • the shooting controls in the shooting interface shown in FIG. 4 and FIG. 5 trigger the shooting operation, obtain the shooting image of the object and save the local album for viewing.
  • the recommended human body pose corresponding to the scene category can be recommended to the user according to the scene category of the scene image, so that the recommended human body pose matches the scene image, and by displaying the real human body pose and The recommended human posture can guide the user to adjust the posture according to the displayed recommended human posture or facilitate the user to guide the subject to adjust the posture according to the displayed recommended human posture. Satisfied taking pictures.
  • the recommended human posture may include a single-person posture.
  • the real human posture and the recommended human posture are displayed on the shooting interface, including:
  • the real human pose and the single-person pose are displayed in the shooting interface; or,
  • the real human body pose is displayed in the shooting interface, and the single-person pose is displayed at the first designated area of the shooting interface.
  • the above-mentioned human body key point detection on the scene image may include: performing human body detection on the scene image to obtain the object in the scene image and the human body area of the object; and then performing human body key point detection on the object in the human body area. It should be understood that the human body detection can obtain information such as whether an object is included in the scene image, the number of objects included, and the position of the object in the scene image (ie, the position of the human body region in the scene image).
  • the recommended human posture is a single-person posture
  • the real human posture of the object can be displayed directly according to the position of the object;
  • the preset The selection rule is to select an object from multiple objects, and display the real human body posture of the selected object according to the position of the selected object.
  • the preset selection rule may include, for example, selecting an object in the middle of a scene image, or selecting an object closest to the terminal device, etc., which are not limited in this embodiment of the present disclosure.
  • the distance between the object and the terminal device may be obtained by using a technique known in the art, for example, a time of flight (TOF) method may be used, which is not limited in this embodiment of the present disclosure.
  • TOF time of flight
  • the real human pose can be represented and the human pose can be recommended based on the form of human skeleton or human outline.
  • the position of the object in the scene image may include: the position coordinates of the key points of the human body on the human skeleton of the object in the scene image or the human body contour; may also include the human body region of the object in the scene image The position of the object, wherein the human body area of the object can be obtained through the above-mentioned human body detection.
  • the position coordinates of the human body key points can be obtained, and then the real human body posture can be displayed in the shooting interface according to the position coordinates of the human body key points. In this way, the real human body posture in the photographing interface can be displayed following the position of the object.
  • the recommended human posture it can be set to follow the position of the object to display, that is, to display the recommended human posture according to the position of the object in the scene image; it can also be set to the first specified in the shooting interface.
  • the area displays the recommended human body posture, that is, the recommended human body posture is fixedly displayed in a preset area of the shooting interface.
  • displaying the single-person posture in the shooting interface may include: according to the position of the human body region of the object in the scene image, displaying on the human body region of the shooting interface Solo pose.
  • displaying the single-person posture in the shooting interface may include: according to the position of the human body region of the object in the scene image, displaying on the human body region of the shooting interface Solo pose.
  • the single-person posture is displayed in the shooting interface, and may further include: according to the position coordinates of any key point of the human body (such as the key point of the neck) on the real human body posture to display the single-person pose in the shooting interface.
  • This method can be understood as making any key point of the human body in the single-person posture coincide with the key point of the human body corresponding to the real human body posture.
  • the key points of the neck on the two postures can be set to overlap, and the single-person posture can be displayed on this basis. , so as to realize the position display of the single-person posture following the object. In this way, the single-person posture can be displayed more accurately following the position of the object, which is convenient for the user to compare the single-person posture and the real human body posture.
  • the first designated area in the shooting interface can be set according to actual needs, for example, it can be the middle area, the left area, the right area, the four vertex areas, etc. of the shooting interface.
  • the embodiments of the present disclosure are not limited.
  • the size of the range of the first designated area and the display size of the single-person posture in the first designated area may be set according to actual requirements, which are not limited in this embodiment of the present disclosure.
  • the object can be effectively guided to complete the shooting of the single-person posture, and the shooting experience can be improved, and the recommended human posture can be displayed fixedly and can be displayed with the object, which can meet different posture display requirements.
  • the recommended human body posture includes the combined posture of multiple people.
  • the method further includes:
  • the corresponding gestures of each object are respectively determined from the multiple-person combined gestures.
  • the corresponding postures of each object in the scene image in the multi-person combined posture can be effectively determined, so that it is convenient to guide each object to complete a plurality of combined postures.
  • Performing human body key point detection on the scene image may include: performing human body detection on the scene image to obtain the object in the scene image and the human body area of the object; and then performing human key point detection on the object in the human body area. It should be understood that the human body detection can obtain information such as whether an object is included in the scene image, the number of objects included, and the position of the object in the scene image (ie, the position of the human body region in the scene image).
  • the recommended human pose is a multi-person combined pose
  • the number of objects in the scene image is less than the number of objects required to realize the multi-person combined pose, for example, through voice or text, etc. Prompt the user that the number of people is not enough to achieve a multi-person combined posture, thereby guiding the increase of the number of objects to be photographed; or select a human posture that matches the number of objects in the scene image from the multi-person combined posture, and determine the corresponding posture of the object based on the selected human posture .
  • the human body posture that matches the number of objects in the scene image is selected from the combined postures of multiple people.
  • the human body posture that matches the number of objects can be randomly selected, or the human body posture that matches the number of objects can be selected according to a preset selection strategy.
  • the selection strategy may include, for example, selection in order from left to right and from top to bottom, which is not limited to this embodiment of the present disclosure.
  • the selected human body posture is one, the selected human body posture can be directly used as the corresponding posture of the object; if there are multiple selected human body postures, according to the relative positions between the multiple selected human body postures, Determine the corresponding pose of each object.
  • the relative positions of the multiple objects in the scene image and the relative positions of the various gestures in the multi-person combined gesture can be determined.
  • the corresponding poses of each object are determined from the combined poses of multiple people.
  • the relative position may include, for example, a front-rear position, a left-right position, an up-down position, and the like, which is not limited by the embodiment of the present disclosure.
  • FIG. 6 shows a schematic diagram of a scene image according to an embodiment of the present disclosure.
  • the scene image includes object A, object B, and object C
  • the current recommended human pose is, for example, the pose of a group of people shown in FIG. 3 (ie, the "comparison" pose).
  • object A and object B correspond to pose m and pose n respectively, and also That is, it is determined that the corresponding posture of object A is "pose m", and the corresponding posture of object B is "pose n".
  • the relative position between object A and object B is more suitable for completing the multi-person shown in FIG. Combining gestures.
  • step S14 a real human body is displayed in the shooting interface Posture and recommended human posture, including:
  • the real human body posture of the object and the corresponding posture of the object are displayed in the shooting interface; or, according to the position of the object in the scene image, the real human body of the object is displayed in the shooting interface.
  • gesture, and the corresponding gesture of the object is displayed in the second designated area of the shooting interface.
  • the real human body posture of the object is displayed in the shooting interface, and reference may be made to the content disclosed in the above embodiments of the present disclosure.
  • the corresponding posture of the object is displayed in the shooting interface, which can be the same as the method according to the object in the above-mentioned embodiment of the present disclosure.
  • the implementation manner of displaying the single-person posture in the shooting interface at the position in the scene image is the same, which is not limited by the embodiment of the present disclosure.
  • the corresponding posture of each object can be displayed following the position of each object, so as to facilitate guiding each object to compare the corresponding posture with the real human body posture for posture adjustment.
  • the second designated area in the shooting interface can be set according to actual needs, for example, it can be the middle area, left area, right area, four vertex areas, etc. of the shooting interface.
  • the embodiments of the present disclosure are not limited.
  • FIG. 7 shows a schematic diagram of a shooting interface according to an embodiment of the present disclosure. As shown in FIG. 7 , a combined pose of multiple people can be displayed at a second designated area, and according to the position of each object in the scene image, it can be displayed in the shooting interface. The real human pose of each object.
  • the size of the range of the second designated area and the display size of the corresponding posture in the second designated area may be set according to actual needs, which is not limited by the embodiment of the present disclosure.
  • multiple objects can be effectively guided to jointly complete a multi-person combined pose, improving the shooting experience, and the recommended human pose can be displayed in a fixed manner and can be displayed with the object, which can meet different pose display requirements.
  • the object adjusts the posture
  • there may be some human body postures that are close to the recommended human posture while the postures of some human body parts are not close to the recommended human posture, for example, the posture of the arms is close to the recommended human posture (also That is, the similarity is high), and the posture of the legs is not close to the recommended human posture (that is, the similarity is low).
  • the method further includes:
  • step S13 the real human body posture and the recommended human body posture are displayed in the shooting interface, including:
  • the region where the third human body key point is located is highlighted, and the highlighting method includes at least one of highlighting, bolding, and changing color.
  • the first human body key point of the real human body posture and the second human body key point of the recommended human body posture may correspond.
  • the first human body key point of the real human body posture One human body key point includes 20 human body joint key points
  • the second human body key point for the recommended human posture also includes 20 human body joint key points correspondingly.
  • key point pairs can be determined from the first human body key point of the real human body posture and the second human body key point of the recommended human body posture, for example, the left shoulder key point in the first human body key point, and the second human body key point.
  • the left shoulder key point in the points is a pair of key point pairs, 20 key points of human body joints, and 20 key point pairs can be determined.
  • the first human body key point of the real human body posture may carry a mark
  • the second human body key point of the recommended human body posture may also carry a mark.
  • the marks can be used to indicate key points on different joint parts or different contour positions of the human body, and through the same/similar marks on the two postures, it is easy to determine the first human key point of the real human body posture, which is the same as that of the human body. Keypoint pairs between the second human keypoints for the recommended human pose.
  • the above method of determining key point pairs through identification is an implementation method disclosed in the embodiments of the present disclosure.
  • those skilled in the art can use any known method to determine the first human body key point and the first human body key point.
  • the key point pair between two human body key points is not limited in this embodiment of the present disclosure.
  • the first similarity of a keypoint pair can be represented by the distance (such as Euclidean distance, cosine distance, etc.) between two keypoints in the keypoint pair, or by the deviation of the distance, This embodiment of the present disclosure is not limited.
  • the first similarity may be set to be negatively correlated with the distance or the deviation from the distance, that is, the smaller the distance or the smaller the deviation of the distance, the higher the first similarity. It should be understood that the number of key point pairs is consistent with the number of first degrees of similarity, that is, each key point pair corresponds to its respective first degree of similarity.
  • the deviation of the distance can be understood as the difference between the distance of any key point pair and the average value of the distances of all key point pairs.
  • the average value of the distances of 20 key point pairs is X
  • the key point The distance to a is x
  • the deviation of the distance of the key point to a is "x-X", that is, "x-X” can be used to represent the first similarity of the key point to a.
  • the display manner of the recommended human body posture following the object display or fixed display, choose to use the distance or the deviation of the distance to represent the first similarity.
  • the distance can be used to represent the first similarity
  • the deviation of the distance can be used to represent the first similarity
  • the position of the object in the scene image will change, and the distance may not be able to accurately characterize the first similarity of the key point pair. This is because The long distance of any key point pair does not mean that the key point pair is not similar, it may be that the overall distance between the real human body posture and the recommended human body posture is far away. first similarity.
  • the first preset threshold may be determined according to an actual requirement, a calculation method of the first similarity, and the like, which is not limited in this embodiment of the present disclosure.
  • the first similarity is less than the first preset threshold, which means that the similarity of the key point pair corresponding to the first similarity is low, that is, the similarity between the partial posture of the human body part represented by the key point pair and the recommended human posture
  • the first similarity degree is greater than or equal to the first preset threshold, it may mean that the similarity degree of the key point pair corresponding to the first similarity degree is high, that is, the partial posture of the human body part represented by the key point pair is the same as It is recommended that the human pose has a high similarity.
  • determining a third human body key point whose first similarity is less than a first preset threshold from the first human body key points by calculating the first similarity of the key point pair may include: by Calculate the first similarity of the key point pair, determine the target key point pair whose first similarity is less than the first preset threshold from the key point pair, and use the first human body key point in the target key point pair as the third human body key point , that is, the third human body key point whose first similarity is less than the first preset threshold is determined from the first human body key point.
  • highlighting the area where the third human body key point is located may be understood as highlighting the area occupied by the third human body key point in the shooting interface.
  • a solid circle is used to represent the joint parts of the human body, then the area occupied by the solid circle may be the area where the third human body key point is located.
  • the highlighting manner includes at least one of highlighting, bolding, and changing color, which are some implementation manners provided by the embodiments of the present disclosure. In fact, those skilled in the art can design different highlighting according to actual needs.
  • the manner of display is not limited to this embodiment of the present disclosure.
  • the color is changed, for example, the third human body key point in the real human body posture displayed in green is changed to red, which is not limited in this embodiment of the present disclosure.
  • FIG. 8 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure.
  • the key points 80 of the knee joint parts with lower similarity may be represented in a bold manner.
  • the connection lines 81 and 82 corresponding to the key points 80 of the joint parts are bolded, which is not limited by the embodiment of the present disclosure.
  • the parts of the human body with low similarity among the displayed real human body postures can be highlighted, so as to more effectively guide the user to adjust the posture, so as to improve the shooting experience.
  • the similarity between the real human body posture and the recommended human body posture can be displayed in the shooting interface to guide the subject to adjust the posture.
  • the method further includes:
  • the second similarity is displayed in the shooting interface.
  • the first similarity of the keypoint pair can be characterized by the distance of the keypoint pair or the deviation of the distance.
  • determining the second similarity between the real human body posture and the recommended human posture according to the first similarity of the key point pairs may include: according to the accumulated value of the distances of all key point pairs, the average value, variance, or standard deviation to determine the second similarity, which is not limited in this embodiment of the present disclosure.
  • the accumulated value, average value, variance or standard deviation of the distance may be negatively correlated with the second similarity, that is, the smaller the accumulated value, mean, variance or standard deviation of the distance, etc., the higher the second similarity is. On the contrary, the larger the cumulative value, mean, variance or standard deviation of the distance, the lower the second similarity. It should be understood that the second similarity may reflect the overall similarity between the real human body posture and the recommended human body posture.
  • the second similarity in order for the object to understand the level of the second similarity, or to understand the level of the similarity, the second similarity may be displayed in the shooting interface in the form of a percentage. Based on this, the cumulative value, average value, variance or standard deviation of the distance can be set, and there is a mapping relationship with the second similarity, so that according to the mapping relationship, the cumulative value, average value, variance or standard deviation of the distance can be mapped to The second similarity in percentage form.
  • mapping relationship between the accumulated value, average value, variance or standard deviation of the distance and the second similarity may be used, which is not limited in this embodiment of the present disclosure.
  • the second degree of similarity is displayed in the photographing interface, for example, the second degree of similarity may be displayed in the manner shown in FIG. 5 and FIG. 8 . It should be understood that a person skilled in the art can design a display manner of the second similarity according to actual requirements, which is not limited by this embodiment of the present disclosure.
  • the second similarity between the real human body posture and the recommended human body posture can also be obtained through a neural network, that is, the first human body key point corresponding to the real human body posture and the recommended human body posture can be obtained.
  • the second human body key point is input into the neural network, and the second similarity is output.
  • the first human body key point and the second human body key point can be input into the neural network in the form of a vector or a matrix.
  • the embodiments of the present disclosure do not limit the network type, network structure, and training method of the neural network.
  • the overall similarity between the real human body posture and the recommended human posture can be displayed, so as to effectively guide the user to adjust the posture according to the overall similarity, and improve the shooting experience.
  • the shooting conditions may include that the second similarity between the real human body posture and the recommended human body posture is greater than or equal to a second preset threshold. In this way, the shooting operation can be automatically triggered when the real human body posture is highly similar to the recommended human body posture, and the shooting image can be obtained and saved in a local album for the object to view.
  • the server may determine that the second similarity between the real human body posture and the recommended human body posture is greater than or equal to a second preset threshold.
  • a shooting instruction is sent to the terminal device, and the terminal device is triggered to perform a shooting operation through the shooting instruction to obtain a shot image.
  • the terminal device may automatically trigger a capturing operation when the second similarity between the real human body posture and the recommended human body posture is greater than or equal to a second preset threshold , to get the captured image.
  • the second preset threshold may be set according to actual requirements, for example, may be set to 80%, which is not limited in this embodiment of the present disclosure. It should be understood that the shooting conditions may also include that the shooting controls in the shooting interface are triggered, that is, the photographer manually performs the shooting operation.
  • the method further includes:
  • the area recommendation network is a neural network obtained by training the first sample set of marking the photographing area.
  • the method further includes at least one of the following:
  • an identifier is used to indicate the recommended shooting area
  • the recommended human posture is displayed in the recommended photographing area of the photographing interface, and the recommended photographing area is used to guide the object to adjust the position according to the recommended photographing area.
  • the region recommendation network may adopt a known neural network, such as a convolutional neural network, etc.
  • the embodiment of the present disclosure does not limit the network type, network structure, and training method of the region recommendation network.
  • the sample images of the first sample set may be images with better visual effects.
  • the object can be guided to adjust the position to improve the visual effect of the photographed image, thereby improving the user's photographing experience.
  • the region where the human body is located in the sample images of the first sample set may be the marked photographing region. It should be understood that, a known labeling technology may be used to realize labeling of the human body region in the sample image of the first sample set, which is not limited in this embodiment of the present disclosure.
  • the identifier used to indicate the recommended photographing area may be displayed in the photographing interface in any form such as characters and/or graphics, so as to guide the user object to adjust the position according to the recommended photographing area.
  • the embodiments of the present disclosure are not limited.
  • FIG. 9 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure. As shown in FIG. 9 , a recommended photographing area may be indicated in the form of graphics, prompt words "recommended station", and the like.
  • the recommended human posture may be displayed in a fixed area (the first designated area or the second designated area) of the photographing interface.
  • the recommended human body posture may be displayed in the recommended photographing area of the photographing interface, in other words, the recommended photographing area may be indicated by the recommended human body posture.
  • the above-mentioned first designated area or second designated area may include a recommended photographing area. In this way, the object can be effectively guided to adjust the position according to the recommended photographing area, and it is also convenient for the object to adjust the posture according to the recommended human body posture.
  • the recommended photographing area may be indicated by both the identifier and the recommended human posture, or the recommended photographing area may be indicated by only the identifier or the recommended human posture, which is not limited by the embodiment of the present disclosure.
  • determining whether the object is in the recommended photographing area may be determined according to the position of the object's human body area in the scene image, or may be determined according to the position coordinates of the key points of the human body, which is not limited in this embodiment of the present disclosure.
  • a photographing area with better visual effect can be recommended to the object, and the object can be effectively guided to adjust the position to improve the visual effect of the photographed image, thereby improving the user's photographing experience.
  • the scene image can be recognized by the gesture recommendation network, and the scene category corresponding to the scene image and the recommended human pose corresponding to the scene category can be determined.
  • the scene image is identified, and the scene category corresponding to the scene image and the recommended human posture corresponding to the scene category are determined, including:
  • the scene image is recognized by the gesture recommendation network, and the scene category corresponding to the scene image and the recommended human pose corresponding to the scene category are determined.
  • the gesture recommendation network is obtained by training the second sample set marked with the sample scene category and the sample human pose.
  • the neural network of the second sample set includes sample images uploaded by the object.
  • the pose recommendation network may adopt a known neural network, for example, a convolutional neural network, a residual neural network, and the like.
  • a convolutional neural network for example, a convolutional neural network, a residual neural network, and the like.
  • the embodiments of the present disclosure do not limit the network type, network structure, training method, etc. of the gesture recommendation network.
  • the self-supervised training method can be used to make the posture recommendation network learn the recommended human posture under different scene types, or, in other words, learn the correspondence between the scene type and the recommended human posture, so that the posture recommendation network can be used based on Scene image output recommends human pose.
  • first sample set and the second sample set may be the same sample set or different sample sets, and the source of the sample images in the first sample set and the second sample set is not limited in the embodiment of the present disclosure. .
  • a known labeling technology can be used to realize labeling of the sample scene category and the sample human body posture in the sample images of the second sample set, which is not limited by the embodiment of the present disclosure.
  • an application program for realizing the image capturing method or a capturing mode for realizing the image capturing method may also provide an upload function for uploading a sample image, and the sample image may be an image that has been captured by the user with a relatively low effect. good image.
  • the second sample set used for training the pose recommendation network can be enriched, so that the trained pose recommendation network can output the recommended human pose based on the scene image more accurately.
  • the sample image uploaded by the user is the image that the user authorizes to enter the background training posture recommendation network, so the sample image uploaded by the user is only visible to the background, and what other users see are the processed recommended human posture and the corresponding posture. image (for example, the human face is hidden from the gesture image, or the human face is replaced or the character is replaced, etc.), thereby ensuring the privacy and security of the user, wherein the gesture image can be used to select different recommended human gestures.
  • the image uploading function may be implemented in an application program and/or a shooting mode by using a known technology in the art, which is not limited by the embodiment of the present disclosure.
  • the training process of the gesture recommendation network may be performed on the server.
  • the sample images can be uploaded to the server to expand the second sample set, and the posture recommendation network can be periodically retrained based on the second sample set to obtain a new version of the posture recommendation network, and then update the deployed posture recommendation network in the terminal device.
  • the pose recommendation network In this way, the posture recommendation network can learn rich human body postures, so that the recommended human postures output by the trained posture recommendation network are more comprehensive and richer.
  • the retraining process of the posture recommendation network deployed in the terminal device can also be performed on the terminal device, that is, the sample image uploaded by the object can be directly used to retrain the deployed gesture recommendation network on the terminal device.
  • the pose recommendation network is incrementally trained. In this way, the recommended human posture output by the deployed posture recommendation network can be closer to the object's preference and targeted.
  • the second sample set may be high-quality images with better shooting effects.
  • the posture recommendation network can learn the human body posture from the high-quality images, thereby effectively outputting high-quality recommended human postures based on scene images. , to guide the subject to approach the high-quality recommended human posture, so as to obtain a better shooting effect and improve the user's shooting experience.
  • the method further includes:
  • a recommended human body posture corresponding to the selected posture map is determined and displayed.
  • FIG. 10 shows a schematic diagram of a photographing interface according to an embodiment of the present disclosure.
  • the photographing interface can display a plurality of posture diagrams, and can also display the recognized scene features “window frame, wall” and objects Features "Sweater, Pants, Hand Raise” for more fun.
  • the identified scene features and object features can be obtained by referring to the method of identifying the scene image in the above-mentioned embodiments of the present disclosure.
  • a sliding control for viewing multiple posture diagrams may also be provided in the photographing interface, so as to view other posture graphs that are not currently displayed in the photographing interface through a sliding operation.
  • the displayed posture map may be a thumbnail, which can be understood as a preview map, a schematic diagram, etc. corresponding to the recommended human body posture. It should be understood that the posture map corresponds to the recommended human body posture, and according to any selected posture map, the corresponding recommended human body posture can be obtained.
  • the selection operation for the posture map may include, for example, operations such as swiping to view the posture map, clicking to select any posture map, and the like. Among them, the selected posture map can be highlighted to achieve friendly human-computer interaction.
  • the displayed posture map can be a thumbnail image.
  • the zoomed-in corresponding to the posture map can be displayed in the shooting interface. map, so that the object can view the pose map more clearly.
  • FIG. 11a and 11b illustrate schematic diagrams of a photographing interface according to an embodiment of the present disclosure.
  • the enlarged image corresponding to the selected posture image can be displayed in the middle area, wherein, in response to operations such as long-pressing the enlarged image, double-clicking the enlarged image, etc., the image can be previewed in the enlarged image as shown in Fig. 11b.
  • the recommended human pose corresponding to the pose map can be displayed in the middle area, wherein, in response to operations such as long-pressing the enlarged image, double-clicking the enlarged image, etc.
  • the recommended human pose corresponding to the pose map.
  • the selected posture diagram and the corresponding recommended human posture can be determined by clicking the "shoot the same style" button shown in Figure 11a and Figure 11b, and shown in Figure 5, Figure 7,
  • the recommended human posture corresponding to the selected posture map is displayed in the photographing interface shown in FIG. 8 .
  • the manner of displaying the recommended human body posture reference may be made to the above-mentioned embodiments of the present disclosure.
  • the manner of displaying the attitude diagram and selecting the attitude diagram in FIG. 10 , FIG. 11 a , and FIG. 11 b above is an implementation method provided by the embodiment of the present disclosure.
  • those skilled in the art can design the The display manner of the attitude map in the shooting interface, the selection method for the attitude map, etc., are not limited in this embodiment of the present disclosure.
  • the method further includes:
  • the captured image is processed to obtain a processed captured image, wherein the processing includes at least one of the following: performing beautification processing on an object in the captured image, and adding a filter to the captured image;
  • the adding a filter to the captured image includes: adjusting at least one of saturation, color temperature, and brightness of the captured image according to a recommended filter corresponding to the recommended human posture.
  • any known image beautifying technology can be used to realize the beautifying processing of the object in the captured image, which is not limited by the embodiment of the present disclosure.
  • the process of beauty processing may include: determining the position of the face in the captured image, then determining the position of the facial defect, and performing processing such as filling, repairing or filtering according to the location of the facial defect.
  • the recommended human pose can be learned from the sample image.
  • the recommended filter can be obtained by analyzing the filter of the sample image. Based on this, the recommended human pose can correspond to the recommended filter. .
  • the recommended filter may include recommended filter parameters such as saturation, color temperature, and brightness.
  • adjusting at least one of the saturation, color temperature, and brightness of the captured image may include: adjusting at least one of the saturation, color temperature, and brightness of the captured image to match the recommended filter The filter parameters are the same.
  • the visual effect of the captured image can be improved, which is beneficial to obtain the captured image that satisfies the user, and improves the image capturing experience.
  • the image capturing method of the embodiment of the present disclosure can be applied to artificial intelligence education platforms, social sharing platforms, video image capturing software, etc., and can intelligently recommend recommended human bodies corresponding to the scene images according to the scene images.
  • the posture is used for self-portrait and camera composition guidance for users, which improves the fun of image shooting, improves the shooting experience, and is conducive to obtaining a shooting image that satisfies the user.
  • intelligent hardware connected to the artificial intelligence education platform can be used, such as Jetson Nano (a microcomputer developed by NVIDIA), Raspberry Pi (a microcomputer) ) etc. to implement the above image capturing method, through this method, based on the form of visualization, it is easier for the user to inspire learning interest.
  • Jetson Nano a microcomputer developed by NVIDIA
  • Raspberry Pi a microcomputer
  • FIG. 12 shows a schematic diagram of an application of an image capturing method according to an embodiment of the present disclosure.
  • the artificial intelligence education platform is connected to the intelligent hardware, wherein the artificial intelligence education platform is used to edit the project code to realize the image capturing method, and send the project code to the intelligent hardware, and the intelligent hardware is used to collect scene images And run the project code, get the running results, and send the running results to the artificial intelligence education platform to display the running results in the display interface of the artificial intelligence education platform.
  • the scene image, the captured image and the operation result of the image capturing method can be displayed on the display interface of the web page of the artificial intelligence education platform.
  • the running results may include recommended human body postures, real human body postures, captured images after adding filters to the captured images, captured images after beautifying the objects in the captured images, and the like.
  • the artificial intelligence education platform can support students to edit and implement the project code of the above-mentioned image shooting method on the platform, such as training code such as posture recommendation network, and connect with intelligent hardware, which will be used to realize the above-mentioned image shooting.
  • the project code of the method is sent to the intelligent hardware for execution, so that the scene image can be collected in real time by the intelligent hardware, and the above-mentioned image capturing method can be realized based on the collected scene image.
  • students can also edit the project code on the artificial intelligence education platform to update and optimize the above-mentioned image shooting methods, such as optimizing each neural network.
  • the present disclosure also provides image capturing devices, electronic devices, computer-readable storage media, and programs, all of which can be used to implement any image capturing method provided by the present disclosure.
  • image capturing devices electronic devices, computer-readable storage media, and programs, all of which can be used to implement any image capturing method provided by the present disclosure.
  • FIG. 13 shows a block diagram of an image capturing apparatus according to an embodiment of the present disclosure. As shown in FIG. 13 , the apparatus includes:
  • the acquiring part 101 is configured to acquire the scene image corresponding to the shooting interface
  • the identification part 102 is configured to identify the scene image, and determine the scene category corresponding to the scene image and the recommended human posture corresponding to the scene category;
  • the detection part 103 is configured to perform human body key point detection on the scene image, and determine the object in the scene image and the real human body posture of the object;
  • the display part 104 is configured to guide the object to perform posture adjustment according to the recommended human body posture by displaying the real human body posture and the recommended human body posture in the shooting interface;
  • the photographing part 105 is configured to obtain a photographed image of the object when photographing conditions are satisfied.
  • the recommended human posture includes a single-person posture
  • the display part 104 includes: a first display sub-part, configured to, according to the position of the object in the scene image, display The real human body posture and the single-person posture are displayed in the shooting interface; or, the second display sub-section is configured to display all the objects in the shooting interface according to the position of the object in the scene image.
  • the real human body posture is displayed, and the single-person posture is displayed in the first designated area of the shooting interface.
  • the recommended human body posture includes a multi-person combined posture
  • the apparatus further includes: a position determination part configured to The relative position between each gesture in the multi-person combined gesture, and the corresponding gesture of each object is respectively determined from the multi-person combined gesture
  • the display part 104 includes: a third display sub-section, which is configured for For any object, according to the position of the object in the scene image, the real human body posture of the object and the corresponding posture of the object are displayed in the shooting interface; or, the fourth display subsection is configured as According to the position of the object in the scene image, the real human body posture of the object is displayed in the shooting interface, and the corresponding posture of the object is displayed in the second designated area of the shooting interface.
  • the apparatus further includes: a first determining part configured to determine the distance between the first human body key point of the real human body posture and the second human body key point of the recommended human body posture The key point pair; the second determination part is configured to determine from the first human body key points that the first similarity is less than the first preset threshold by calculating the first similarity of the key point pair.
  • the apparatus further includes: a third determination part, configured to determine the the second similarity between the real human body posture and the recommended human body posture, and the key point pair is determined according to the first human body key point of the real human body posture and the second human body key point of the recommended human body posture;
  • the similarity display part is configured to display the second similarity in the shooting interface.
  • the apparatus further includes: an area determination part, configured to input the scene image into an area recommendation network to obtain a recommended photographing area of the scene image, where the recommended photographing area is used for Representing the area recommended to be photographed in the scene image, and the area recommendation network is a neural network trained by marking the first sample set of the photographing area; wherein, the device further includes: an area display part, configured to In the shooting interface, an identifier is used to indicate the recommended photographing area; or, the recommended human posture is displayed in the recommended photographing area of the photographing interface; the recommended photographing area is used to guide the object according to the recommended photographing area. Adjust the position of the recommended photo area as described above.
  • the area display part is further configured to use an identifier to indicate the recommended photographing area in the photographing interface; and display the recommended photographing area on the photographing interface.
  • the recommended human posture is described.
  • the identifying part 102 is further configured to identify the scene image through a gesture recommendation network, and determine a scene category corresponding to the scene image and a recommended human body corresponding to the scene category pose, wherein the pose recommendation network is a neural network obtained by training a second sample set marked with sample scene categories and sample human poses, and the second sample set includes sample images uploaded by objects.
  • the pose recommendation network is a neural network obtained by training a second sample set marked with sample scene categories and sample human poses, and the second sample set includes sample images uploaded by objects.
  • the recommended human body postures include a plurality of postures
  • the apparatus further includes: a posture map display part configured to display the posture maps corresponding to the plurality of recommended human body postures in the shooting interface; selecting The part is configured to, in response to a selection operation on the posture map, determine and display a recommended human body posture corresponding to the selected posture map according to the selected posture map.
  • the shooting conditions include: a second degree of similarity between the real human body posture and the recommended human body posture is greater than or equal to a second preset threshold; or, in the shooting interface Shooting controls are triggered.
  • the apparatus further includes: a processing part configured to process the captured image to obtain a processed captured image, wherein the processing includes at least one of the following: Performing beautification processing on the object in the captured image, and adding a filter to the captured image; the adding a filter to the captured image includes: adjusting the captured image according to the recommended filter corresponding to the recommended human posture at least one of saturation, color temperature, and brightness.
  • the device is applied to an artificial intelligence education platform, and the artificial intelligence education platform is connected to intelligent hardware; wherein, the artificial intelligence education platform is used to edit and implement the project code of the image capturing device , and send the project code to the intelligent hardware; the intelligent hardware is used to collect scene images and run the project code to obtain the operation result, and send the operation result to the artificial intelligence education platform to The operation result is displayed on the display interface of the artificial intelligence education platform.
  • the recommended human body pose corresponding to the scene category can be recommended to the user according to the scene category of the scene image, so that the recommended human body pose matches the scene image, and by displaying the real human body pose and The recommended human posture can guide the user to adjust the posture according to the displayed recommended human posture or facilitate the user to guide the subject to adjust the posture according to the displayed recommended human posture. Satisfied taking pictures.
  • the functions or included parts of the apparatus provided in the embodiments of the present disclosure may be configured to execute the methods described in the above method embodiments, and the specific implementation may refer to the above method embodiments. For brevity, I won't go into details here.
  • Embodiments of the present disclosure further provide a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the foregoing method is implemented.
  • Computer-readable storage media can be volatile or non-volatile computer-readable storage media.
  • An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory configured to store instructions executable by the processor; wherein the processor is configured to invoke the instructions stored in the memory to execute the above method.
  • Embodiments of the present disclosure also provide a computer program product, including computer-readable codes.
  • a processor in the device executes the image capturing method for implementing the image capturing method provided by any of the above embodiments. instruction.
  • Embodiments of the present disclosure further provide another computer program product for storing computer-readable instructions, which, when executed, cause the computer to perform the operations of the image capturing method provided by any of the foregoing embodiments.
  • the electronic device in the embodiment of the present disclosure may be provided as a terminal device or a server.
  • FIG. 14 shows a block diagram of an electronic device 800 according to an embodiment of the present disclosure.
  • electronic device 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc. terminal.
  • an electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814 , and the communication component 816 .
  • the processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations.
  • the processing component 802 can include one or more processors 820 to execute instructions to perform all or some of the steps of the methods described above.
  • processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components.
  • processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.
  • Memory 804 is configured to store various types of data to support operation at electronic device 800 . Examples of such data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, and the like. Memory 804 may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read only memory
  • EPROM erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic or Optical Disk Magnetic Disk
  • Power supply assembly 806 provides power to various components of electronic device 800 .
  • Power supply components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800 .
  • Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user.
  • the touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or swipe action, but also detect the duration and pressure associated with the touch or swipe action.
  • the multimedia component 808 includes a front-facing camera and/or a rear-facing camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each of the front and rear cameras can be a fixed optical lens system or have focal length and optical zoom capability.
  • Audio component 810 is configured to output and/or input audio signals.
  • audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when electronic device 800 is in operating modes, such as calling mode, recording mode, and voice recognition mode.
  • the received audio signal may be further stored in memory 804 or transmitted via communication component 816 .
  • audio component 810 also includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, or the like. These buttons may include, but are not limited to: home button, volume buttons, start button, and lock button.
  • Sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of electronic device 800 .
  • the sensor assembly 814 can detect the on/off state of the electronic device 800, the relative positioning of the components, such as the display and the keypad of the electronic device 800, the sensor assembly 814 can also detect the electronic device 800 or one of the electronic device 800 Changes in the position of components, presence or absence of user contact with the electronic device 800 , orientation or acceleration/deceleration of the electronic device 800 and changes in the temperature of the electronic device 800 .
  • Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact.
  • Sensor assembly 814 may also include a light sensor, such as a complementary metal oxide semiconductor (CMOS) or charge coupled device (CCD) image sensor, for use in imaging applications.
  • CMOS complementary metal oxide semiconductor
  • CCD charge coupled device
  • the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices.
  • the electronic device 800 may access a wireless network based on a communication standard, such as wireless network (WiFi), second generation mobile communication technology (2G) or third generation mobile communication technology (3G), or a combination thereof.
  • the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module may be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • electronic device 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A programmed gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation is used to perform the above method.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field programmable A programmed gate array
  • controller microcontroller, microprocessor or other electronic component implementation is used to perform the above method.
  • a non-volatile computer-readable storage medium such as a memory 804 comprising computer program instructions executable by the processor 820 of the electronic device 800 to perform the above method is also provided.
  • the present disclosure may be a system, method and/or computer program product.
  • the computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present disclosure.
  • a computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disk read only memory
  • DVD digital versatile disk
  • memory sticks floppy disks
  • mechanically coded devices such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
  • Computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.
  • the computer readable program instructions described herein may be downloaded to various computing/processing devices from a computer readable storage medium, or to an external computer or external storage device over a network such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or network interface in each computing/processing device receives computer-readable program instructions from a network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
  • Computer program instructions for carrying out operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages.
  • Source or object code written in any combination, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as the "C" language or similar programming languages.
  • the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through the Internet connect).
  • LAN local area network
  • WAN wide area network
  • custom electronic circuits such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) can be personalized by utilizing state information of computer readable program instructions.
  • Computer readable program instructions are executed to implement various aspects of the present disclosure.
  • These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer or other programmable data processing apparatus to produce a machine that causes the instructions when executed by the processor of the computer or other programmable data processing apparatus , resulting in means for implementing the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
  • These computer readable program instructions can also be stored in a computer readable storage medium, these instructions cause a computer, programmable data processing apparatus and/or other equipment to operate in a specific manner, so that the computer readable medium storing the instructions includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
  • Computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executing on a computer, other programmable data processing apparatus, or other device to implement the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more functions for implementing the specified logical function(s) executable instructions.
  • the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions.
  • the computer program product can be specifically implemented by hardware, software or a combination thereof.
  • the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), etc. Wait.
  • a software development kit Software Development Kit, SDK
  • the recommended human body pose corresponding to the scene category can be recommended to the user according to the scene category of the scene image, so that the recommended human body pose matches the scene image, and by displaying the real human body pose and The recommended human posture can guide the user to adjust the posture according to the displayed recommended human posture or facilitate the user to guide the subject to adjust the posture according to the displayed recommended human posture. Satisfied taking pictures. And, by displaying the single-person posture on the shooting interface, the subject can be effectively guided to complete the shooting of the single-person posture, improving the shooting experience, and it is recommended that the human body posture can be displayed fixedly and can be displayed with the object, which can meet different posture display needs.
  • the human body pose can be displayed fixedly and can be displayed with the object, which can meet different pose display needs.
  • the parts of the human body with low similarity among the displayed real human body postures can be highlighted, so as to more effectively guide the user to adjust the posture, so as to improve the shooting experience.
  • the overall similarity between the real human body posture and the recommended human posture can be displayed, so as to effectively guide the user to adjust the posture according to the overall similarity, and improve the shooting experience.
  • the pose recommendation network can be used to learn human poses from high-quality images, so as to effectively output high-quality recommended human poses based on scene images. And it is convenient for the user to select different recommended human body postures, to satisfy different posture preferences, to improve the shooting experience, and to be beneficial to obtain a shooting image that satisfies the user. And, applying the methods in the embodiments of the present disclosure in an artificial intelligence education platform enables users to learn through interesting cases, and learn artificial intelligence algorithms such as human body gesture recognition and human body key point detection.

Abstract

La présente divulgation concerne un procédé et un appareil de prise d'image photographique, un dispositif électronique et un support de stockage lisible par ordinateur. Le procédé comprend : l'obtention d'une image de scène correspondant à une interface de prise de photographie ; la reconnaissance de l'image de scène et la détermination d'une catégorie de scène correspondant à l'image de scène et d'une posture de corps humain recommandée correspondant à la catégorie de scène ; la réalisation d'une détection de point clé de corps humain sur l'image de scène et la détermination d'un objet dans l'image de scène et d'une posture de corps humain réelle de l'objet ; l'affichage de la posture de corps humain réelle et de la posture de corps humain recommandée dans l'interface de photographie pour guider l'objet à réaliser un ajustement de posture selon la posture de corps humain recommandée ; et lorsqu'une condition de prise de photographie est satisfaite, l'obtention d'une image photographique de l'objet. Selon les modes de réalisation de la présente divulgation, l'effet de prise d'image photographique peut être efficacement amélioré de sorte qu'une image photographique, dont l'utilisateur est satisfait, peut être obtenue.
PCT/CN2021/120722 2021-04-28 2021-09-26 Procédé et appareil de prise d'image photographique, dispositif électronique et support de stockage lisible par ordinateur WO2022227393A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
KR1020227018878A KR20220149503A (ko) 2021-04-28 2021-09-26 이미지 촬영 방법 및 장치, 전자 기기 및 컴퓨터 판독 가능한 저장 매체

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110467998.6A CN113194254A (zh) 2021-04-28 2021-04-28 图像拍摄方法及装置、电子设备和存储介质
CN202110467998.6 2021-04-28

Publications (1)

Publication Number Publication Date
WO2022227393A1 true WO2022227393A1 (fr) 2022-11-03

Family

ID=76979970

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/120722 WO2022227393A1 (fr) 2021-04-28 2021-09-26 Procédé et appareil de prise d'image photographique, dispositif électronique et support de stockage lisible par ordinateur

Country Status (3)

Country Link
KR (1) KR20220149503A (fr)
CN (1) CN113194254A (fr)
WO (1) WO2022227393A1 (fr)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111627115A (zh) * 2020-05-26 2020-09-04 浙江商汤科技开发有限公司 互动合影方法及装置、互动装置以及计算机存储介质
CN113194254A (zh) * 2021-04-28 2021-07-30 上海商汤智能科技有限公司 图像拍摄方法及装置、电子设备和存储介质
CN113923355A (zh) * 2021-09-30 2022-01-11 上海商汤临港智能科技有限公司 一种车辆及图像拍摄方法、装置、设备、存储介质
CN114285988B (zh) * 2021-12-03 2024-04-09 维沃移动通信有限公司 显示方法、装置、电子设备及存储介质
CN114598817A (zh) * 2022-03-04 2022-06-07 无锡乐骐科技股份有限公司 一种基于多人交互动作判断的人机交互判断方法及装置
CN116823869A (zh) * 2022-03-18 2023-09-29 华为技术有限公司 背景替换的方法和电子设备

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107734251A (zh) * 2017-09-29 2018-02-23 维沃移动通信有限公司 一种拍照方法和移动终端
CN110543578A (zh) * 2019-08-09 2019-12-06 华为技术有限公司 物体识别方法及装置
CN110868538A (zh) * 2019-11-11 2020-03-06 三星电子（中国）研发中心 推荐拍摄姿态的方法和电子设备
US20200106955A1 (en) * 2018-09-29 2020-04-02 Apple Inc. Devices, Methods, and Graphical User Interfaces for Assisted Photo-Taking
CN111277759A (zh) * 2020-02-27 2020-06-12 Oppo广东移动通信有限公司 构图提示方法、装置、存储介质及电子设备
CN112351185A (zh) * 2019-08-07 2021-02-09 华为技术有限公司 拍照方法和移动终端
CN113194254A (zh) * 2021-04-28 2021-07-30 上海商汤智能科技有限公司 图像拍摄方法及装置、电子设备和存储介质

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107239728B (zh) * 2017-01-04 2021-02-02 赛灵思电子科技(北京)有限公司 基于深度学习姿态估计的无人机交互装置与方法
CN109727208A (zh) * 2018-12-10 2019-05-07 北京达佳互联信息技术有限公司 滤镜推荐方法、装置、电子设备及存储介质
CN111062276A (zh) * 2019-12-03 2020-04-24 广州极泽科技有限公司 基于人机交互的人体姿态推荐方法、装置、机器可读介质及设备

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107734251A (zh) * 2017-09-29 2018-02-23 维沃移动通信有限公司 一种拍照方法和移动终端
US20200106955A1 (en) * 2018-09-29 2020-04-02 Apple Inc. Devices, Methods, and Graphical User Interfaces for Assisted Photo-Taking
CN112351185A (zh) * 2019-08-07 2021-02-09 华为技术有限公司 拍照方法和移动终端
CN110543578A (zh) * 2019-08-09 2019-12-06 华为技术有限公司 物体识别方法及装置
CN110868538A (zh) * 2019-11-11 2020-03-06 三星电子（中国）研发中心 推荐拍摄姿态的方法和电子设备
CN111277759A (zh) * 2020-02-27 2020-06-12 Oppo广东移动通信有限公司 构图提示方法、装置、存储介质及电子设备
CN113194254A (zh) * 2021-04-28 2021-07-30 上海商汤智能科技有限公司 图像拍摄方法及装置、电子设备和存储介质

Also Published As

Publication number Publication date
CN113194254A (zh) 2021-07-30
KR20220149503A (ko) 2022-11-08

Similar Documents

Publication Publication Date Title
WO2022227393A1 (fr) Procédé et appareil de prise d'image photographique, dispositif électronique et support de stockage lisible par ordinateur
US10880495B2 (en) Video recording method and apparatus, electronic device and readable storage medium
WO2022179026A1 (fr) Procédé et appareil de traitement d'images, dispositif électronique et support de stockage
WO2021135601A1 (fr) Procédé et appareil de photographie auxiliaire, équipement terminal, et support d'enregistrement
WO2021023059A1 (fr) Procédé de photographie et terminal mobile
US10488208B2 (en) Communication system, control method, and storage medium
KR20220124127A (ko) 사진 촬영과 관련된 추천 정보를 제공하는 방법 및 시스템
CN107635095A (zh) 拍摄照片的方法、装置、存储介质及拍摄设备
CN111541907B (zh) 物品显示方法、装置、设备及存储介质
CN111726536A (zh) 视频生成方法、装置、存储介质及计算机设备
US11030733B2 (en) Method, electronic device and storage medium for processing image
WO2022042776A1 (fr) Procédé de photographie et terminal
WO2018120662A1 (fr) Procédé de photographie, appareil de photographie et terminal
WO2018233254A1 (fr) Procédé, dispositif et équipement électronique de reconnaissance d'objets basée sur un terminal
CN106200917B (zh) 一种增强现实的内容显示方法、装置及移动终端
WO2021047069A1 (fr) Procédé de reconnaissance faciale et dispositif terminal électronique
WO2020093798A1 (fr) Procédé et appareil d'affichage d'image cible, terminal, et support de stockage
CN112702521A (zh) 图像拍摄方法及装置、电子设备、计算机可读存储介质
CN114387445A (zh) 对象关键点识别方法及装置、电子设备和存储介质
CN112099639A (zh) 展示属性的调整方法、装置、展示设备和存储介质
CN115529378A (zh) 一种视频处理方法及相关装置
CN113781408A (zh) 一种图像拍摄智能指导系统及方法
CN108986803B (zh) 场景控制方法及装置、电子设备、可读存储介质
CN116320721A (zh) 一种拍摄方法、装置、终端及存储介质
JP7293362B2 (ja) 撮影方法、装置、電子機器及び記憶媒体

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 2022527185

Country of ref document: JP

Kind code of ref document: A

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21938857

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21938857

Country of ref document: EP

Kind code of ref document: A1