CN108769513B - Camera photographing method and device - Google Patents
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- CN108769513B CN108769513B CN201810470322.0A CN201810470322A CN108769513B CN 108769513 B CN108769513 B CN 108769513B CN 201810470322 A CN201810470322 A CN 201810470322A CN 108769513 B CN108769513 B CN 108769513B
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
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Abstract
The disclosure relates to a camera photographing method and device, wherein the method comprises the following steps: receiving a photographing instruction of a user, wherein the photographing instruction comprises a designated picture type; determining a photographing parameter corresponding to the specified picture type; and photographing according to the photographing parameters corresponding to the specified picture types to obtain pictures corresponding to the specified picture types. Therefore, the method and the device can meet the photographing requirement of the user and can improve the photographing quality of the camera.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a camera photographing method and apparatus.
Background
With the continuous development of communication technology, smart phones are also more and more widely applied.
In the related art, the types of smart phones are many, and different types of cameras of the smart phones are used for photographing the same object, so that the obtained photographing styles are different, some of the photographing styles are possibly brighter, some of the photographing styles are possibly colder, and the like.
However, the photographing style of the camera of the same type of smart phone is single, and the requirements of the user cannot be met better.
Disclosure of Invention
In order to overcome the problems in the related art, the embodiments of the present disclosure provide a camera photographing method and apparatus.
According to a first aspect of the embodiments of the present disclosure, there is provided a camera photographing method, the method including:
receiving a photographing instruction of a user, wherein the photographing instruction comprises a designated picture type;
determining a photographing parameter corresponding to the specified picture type;
and photographing according to the photographing parameters corresponding to the specified picture types to obtain pictures corresponding to the specified picture types.
Optionally, the specified picture type is one of a plurality of picture types that can be provided by the terminal;
the determining of the photographing parameters corresponding to the specified picture type includes:
acquiring photographing parameters corresponding to various picture types;
and determining the photographing parameters corresponding to the specified picture types from the photographing parameters corresponding to the various picture types.
Optionally, the method further comprises:
obtaining photographing parameters corresponding to various picture types through machine learning;
and storing the photographing parameters corresponding to the various picture types to a specified position.
Optionally, the obtaining of the photographing parameters corresponding to the various picture types through machine learning includes:
establishing a picture type identification model, wherein the picture type identification model is used for identifying the picture type of an input picture according to the input picture;
adjusting camera parameters according to the picture type to be learned, photographing by using the adjusted camera parameters, and inputting a photographed first picture into the picture type identification model;
and when the picture type of the first picture is identified as the picture type to be learned, determining a photographing parameter corresponding to the picture type to be learned according to a camera parameter for photographing the first picture.
Optionally, the establishing the picture type identification model includes:
acquiring training data, wherein the training data comprises pictures of various picture types, and each picture is provided with a label of the picture type;
and performing machine learning on the training data to obtain the picture type identification model.
Optionally, the determining, according to the camera parameter of the first picture, the shooting parameter corresponding to the type of the picture to be learned includes:
and determining the camera parameters for photographing the first picture as the photographing parameters corresponding to the type of the picture to be learned.
Optionally, the determining, according to the camera parameter of the first picture, the shooting parameter corresponding to the type of the picture to be learned includes:
taking the first picture as optimization data, and optimizing the picture type identification model to obtain an optimized picture type identification model;
continuously adjusting the camera parameters, taking pictures by using the continuously adjusted camera parameters, and inputting the second pictures after taking pictures into the optimized picture type identification model;
and when the picture type of the second picture is identified as the picture type to be learned, determining the camera parameter for photographing the second picture as the photographing parameter corresponding to the picture type to be learned.
Optionally, the various picture types include picture types corresponding to different camera photographing styles.
According to a second aspect of the embodiments of the present disclosure, there is provided a camera photographing apparatus, the apparatus including:
the device comprises a receiving module, a processing module and a display module, wherein the receiving module is configured to receive a photographing instruction of a user, and the photographing instruction comprises a specified picture type;
the determining module is configured to determine photographing parameters corresponding to the specified picture types;
and the photographing module is configured to photograph according to the photographing parameters corresponding to the specified picture type to obtain a picture corresponding to the specified picture type.
Optionally, the specified picture type is one of a plurality of picture types that can be provided by the terminal;
the determining module comprises:
the acquisition sub-module is configured to acquire photographing parameters corresponding to various picture types;
the first determining sub-module is configured to determine the photographing parameters corresponding to the specified picture type from the photographing parameters corresponding to the various picture types.
Optionally, the apparatus further comprises:
the learning module is configured to obtain photographing parameters corresponding to various picture types through machine learning;
and the storage module is configured to store the photographing parameters corresponding to the various picture types to a specified position.
Optionally, the learning module comprises:
the establishing sub-module is configured to establish a picture type identification model, and the picture type identification model is used for identifying the picture type of the input picture according to the input picture;
the first adjusting submodule is configured to adjust camera parameters according to the picture type to be learned, take pictures by using the adjusted camera parameters and input a first picture after taking pictures into the picture type identification model;
and the second determining sub-module is configured to determine the photographing parameters corresponding to the picture type to be learned according to the camera parameters for photographing the first picture when the picture type of the first picture is identified as the picture type to be learned.
Optionally, the establishing sub-module includes:
the acquisition sub-module is configured to acquire training data, the training data comprises pictures of various picture types, and each picture is provided with a label of the picture type;
and the learning submodule is configured to perform machine learning on the training data to obtain the picture type recognition model.
Optionally, the second determining sub-module includes:
and the third determining submodule is configured to determine the camera parameters for photographing the first picture as the photographing parameters corresponding to the picture type to be learned.
Optionally, the second determining sub-module includes:
the optimization submodule is configured to take the first picture as optimization data and optimize the picture type identification model to obtain an optimized picture type identification model;
the second adjusting submodule is configured to continuously adjust the camera parameters, take a picture by using the continuously adjusted camera parameters, and input a second picture after taking the picture into the optimized picture type identification model;
and the fourth determining sub-module is configured to determine the camera parameters for taking the second picture as the photographing parameters corresponding to the picture type to be learned when the picture type of the second picture is identified as the picture type to be learned.
Optionally, the various picture types include picture types corresponding to different camera photographing styles. According to a third aspect of the embodiments of the present disclosure, there is provided a camera photographing apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
receiving a photographing instruction of a user, wherein the photographing instruction comprises a designated picture type;
determining a photographing parameter corresponding to the specified picture type;
and photographing according to the photographing parameters corresponding to the specified picture types to obtain pictures corresponding to the specified picture types.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
according to the method and the device, when the terminal receives the photographing instruction of the user, the photographing instruction comprises the appointed picture type, the photographing parameter corresponding to the appointed picture type can be determined firstly, then photographing is carried out according to the photographing parameter corresponding to the appointed picture type, and the picture corresponding to the appointed picture type is obtained, so that the photographing requirement of the user is met, and the photographing quality of the camera is improved.
In the method, the terminal can also acquire the photographing parameters corresponding to various picture types, and then determine the photographing parameters corresponding to the specified picture type indicated by the user from the photographing parameters corresponding to various picture types, so that the accuracy of determining the photographing parameters is improved.
In the method, the terminal can also obtain the photographing parameters corresponding to various picture types through machine learning, and then store the photographing parameters corresponding to various picture types to the designated position, so that the photographing parameters corresponding to the picture types can be rapidly acquired from the designated position when photographing is performed, and the photographing speed of the camera is improved.
The terminal can also establish a picture type identification model when shooting parameters corresponding to various picture types are obtained through machine learning, wherein the picture type identification model is used for identifying the picture type of an input picture according to the input picture; and then, aiming at the picture type to be learned, adjusting camera parameters, photographing by using the adjusted camera parameters, inputting the photographed first picture into the picture type identification model, and when the picture type of the first picture is identified to be the picture type to be learned, determining photographing parameters corresponding to the picture type to be learned according to the camera parameters of the photographed first picture, thereby realizing the function of obtaining the photographing parameters corresponding to various picture types through machine learning, and improving the reliability of determining the photographing parameters corresponding to various picture types.
According to the method and the terminal, when the photographing parameters corresponding to the type of the picture to be learned are determined according to the camera parameters of the first picture to be photographed, the first picture can be used as optimization data, the picture type recognition model is optimized to obtain the optimized picture type recognition model, the camera parameters are continuously adjusted, the camera parameters which are continuously adjusted are used for photographing, the second picture which is photographed is input into the optimized picture type recognition model, and when the picture type of the second picture is recognized to be the picture type to be learned, the camera parameters of the second picture which is photographed are determined to be the photographing parameters corresponding to the type of the picture to be learned, so that the accuracy of the photographing parameters corresponding to the type of the picture to be learned is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flow chart illustrating a camera photographing method according to an exemplary embodiment of the present disclosure;
fig. 2 is a schematic view of an application scenario of a camera photographing method according to an exemplary embodiment of the present disclosure;
FIG. 3 is a flow chart illustrating another camera photographing method according to an exemplary embodiment of the present disclosure;
FIG. 4 is a flow chart illustrating another camera photographing method according to an exemplary embodiment of the present disclosure;
FIG. 5 is a flow chart illustrating another camera photographing method according to an exemplary embodiment of the present disclosure;
fig. 6 is a flowchart illustrating a camera photographing apparatus according to an exemplary embodiment of the present disclosure;
fig. 7 is a block diagram of a camera photographing apparatus according to an exemplary embodiment of the present disclosure;
FIG. 8 is a block diagram of another camera photographing apparatus according to an exemplary embodiment of the present disclosure;
FIG. 9 is a block diagram of another camera photographing apparatus according to an exemplary embodiment of the present disclosure;
FIG. 10 is a block diagram of another camera photographing apparatus according to an exemplary embodiment of the present disclosure;
FIG. 11 is a block diagram of another camera photographing apparatus according to an exemplary embodiment of the present disclosure;
FIG. 12 is a block diagram of another camera photographing apparatus according to an exemplary embodiment of the present disclosure;
FIG. 13 is a block diagram of another camera photographing apparatus according to an exemplary embodiment of the present disclosure;
fig. 14 is a schematic structural diagram of a camera photographing apparatus according to an exemplary embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
Fig. 1 is a flowchart illustrating a camera photographing method according to an exemplary embodiment, and fig. 2 is a schematic view illustrating an application scenario of the camera photographing method according to an exemplary embodiment; the camera photographing method can be used in a terminal (e.g., a smart phone, a tablet computer, etc.) providing a photographing service, as shown in fig. 1, the camera photographing method includes the following steps 110 and 130:
in step 110, a photographing instruction of the user is received, wherein the photographing instruction includes a specified picture type.
In the embodiment of the disclosure, when a user needs to take a picture, a picture taking instruction is sent to the terminal, and the picture of which picture type needs to be taken is indicated in the picture taking instruction.
In an embodiment, the specified picture type may be one of a plurality of picture types that can be provided by the terminal, and these picture types may be picture types corresponding to different camera photographing styles. Such as: the picture type corresponding to the millet mobile phone photographing style, the picture type corresponding to the Chinese mobile phone photographing style, and the like.
In step 120, the photographing parameters corresponding to the specified picture type are determined.
In the embodiment of the disclosure, before photographing, the terminal needs to determine the photographing parameters of the specified picture type indicated by the user, and then photographs according to the photographing parameters of the specified picture type, so that a picture meeting the user requirements can be photographed.
In one embodiment, the specified picture type indicated by the user may be one of a plurality of picture types that the terminal is capable of providing; when step 120 is executed, photographing parameters corresponding to various picture types may be obtained first; then, the photographing parameters corresponding to the specified picture type indicated by the user are determined from the photographing parameters corresponding to the various picture types, and the specific implementation process of the method can be seen in the embodiment shown in fig. 3.
In one embodiment, the various picture types may include picture types corresponding to different camera photographing styles. Such as: the various picture types can comprise picture types corresponding to the millet mobile phone photographing style, and can also comprise picture types corresponding to the Huawei mobile phone photographing style.
In step 130, a picture is taken according to the photographing parameters corresponding to the specified picture type, so as to obtain a picture corresponding to the specified picture type.
In an exemplary scenario, as shown in fig. 2, a user and a millet terminal. When a user needs to take a picture, a picture taking instruction is sent to the millet terminal, and the user is instructed to take a picture of a specified picture type in the picture taking instruction, for example: the picture type is designated as a picture type corresponding to the mobile phone photographing style; after receiving the photographing instruction of the user, the terminal determines the photographing parameters corresponding to the specified picture type, photographs according to the photographing parameters determining the specified picture type, and finally obtains the picture meeting the user requirement.
It can be seen from the above embodiments that, when a photographing instruction of a user is received, the photographing instruction including a designated picture type, a photographing parameter corresponding to the designated picture type may be determined first, and then photographing is performed according to the photographing parameter corresponding to the designated picture type, so as to obtain a picture corresponding to the designated picture type, thereby meeting the photographing requirement of the user, and further improving the photographing quality of the camera.
The technical solutions provided by the embodiments of the present disclosure are described below with specific embodiments.
Fig. 3 is a flow chart illustrating another camera photographing method according to an exemplary embodiment of the present disclosure. The camera photographing method can be used for a terminal (such as a smart phone, a tablet computer and the like) providing photographing service, and is established on the basis of the method shown in FIG. 1, wherein the specified picture type indicated by a user can be one of a plurality of picture types which can be provided by the terminal; in executing step 120, as shown in fig. 3, the following steps 310-320 may be included:
in step 310, photographing parameters corresponding to various picture types are obtained.
In the embodiment of the present disclosure, the photographing parameters corresponding to various image types may be fixed parameters set in advance, or may be optimized parameters obtained through machine learning. And, the photographing parameters corresponding to various picture types may be different.
In one embodiment, the various picture types may include picture types corresponding to different camera photographing styles. Such as: the various picture types can comprise picture types corresponding to the millet mobile phone photographing style, and can also comprise picture types corresponding to the Huawei mobile phone photographing style.
In step 320, the photographing parameters corresponding to the designated picture type indicated by the user are determined from the photographing parameters corresponding to the various picture types.
According to the embodiment, the photographing parameters corresponding to various picture types can be obtained firstly, and then the photographing parameters corresponding to the specified picture types indicated by the user are determined from the photographing parameters corresponding to various picture types, so that the accuracy of determining the photographing parameters is improved.
Fig. 4 is a flow chart illustrating another camera photographing method according to an exemplary embodiment of the present disclosure. The camera photographing method can be used for a terminal (e.g., a smart phone, a tablet computer, etc.) providing photographing service, and is based on the method shown in fig. 3, as shown in fig. 4, the camera photographing method can further include the following steps 410 and 420:
in step 410, photographing parameters corresponding to various picture types are obtained through machine learning. The specific implementation process can be seen in the embodiment shown in fig. 5.
In the embodiment of the present disclosure, the input of Machine Learning (ML) is each picture type, the output is corresponding photographing parameters, and a mapping relationship needs to be learned, and a specific implementation process thereof may refer to the embodiment shown in fig. 5.
In step 420, the photographing parameters corresponding to the various picture types are saved to the designated location.
In the embodiment of the present disclosure, the designated location may be in a local cache, or may be stored in a cloud server.
It can be seen from the above embodiments that the photographing parameters corresponding to various picture types can be obtained through machine learning, and then the photographing parameters corresponding to various picture types are stored to the designated position, so that the photographing parameters corresponding to the picture types can be rapidly obtained from the designated position during photographing, and the photographing speed of the camera is improved.
Fig. 5 is a flow chart illustrating another camera photographing method according to an exemplary embodiment of the present disclosure. The camera photographing method can be used in a terminal (e.g., a smart phone, a tablet computer, etc.) providing photographing service, and is based on the method shown in fig. 4, as shown in fig. 5, when the step 410 is executed, the method can include the following steps 510 and 530:
in step 510, a picture type identification model is established, and the picture type identification model is used for identifying the picture type of the input picture according to the input picture.
In the embodiment of the present disclosure, the picture type identification model may be a fixed model set in advance, or may be a learning model obtained through machine learning. The image type identification model may be a CNN (Convolutional Neural Network) training model.
In an embodiment, when the picture type recognition model is established, the following methods can be adopted, but not limited to:
(1) training data is collected, the training data comprises pictures of various picture types, and each picture is provided with a label of the picture type.
(2) And performing machine learning on the training data to obtain a picture type identification model.
In step 520, the camera parameters are adjusted for the picture type to be learned, the adjusted camera parameters are utilized to take a picture, and the first picture after taking the picture is input into the picture type identification model.
In the embodiment of the present disclosure, the picture type to be learned may refer to any one of a plurality of picture types that can be provided by the terminal, that is, each picture type that can be provided by the terminal may determine the corresponding photographing parameter thereof through machine learning.
In one embodiment, since there are many camera parameters, such as: 1000 pieces. When adjusting the camera parameters, different ways may be used for the adjustment. Such as: the camera parameters are adjusted in a random exhaustive manner.
In step 530, when the picture type of the first picture is identified as the picture type to be learned, the photographing parameter corresponding to the picture type to be learned is determined according to the camera parameter of the first picture.
In the embodiment of the disclosure, after the picture type of the first picture is identified as the picture type to be learned, the terminal may determine whether to determine the camera parameter according to which the first picture is photographed as the photographing parameter corresponding to the picture type to be learned according to an actual situation.
In an embodiment, when the photographing parameter corresponding to the type of the picture to be learned is determined according to the camera parameter for photographing the first picture, the camera parameter for photographing the first picture may be directly determined as the photographing parameter corresponding to the type of the picture to be learned.
In an embodiment, when the photographing parameter corresponding to the type of the picture to be learned is determined according to the camera parameter of the first picture to be photographed, machine learning may be continued, and the photographing parameter corresponding to the type of the picture to be learned is determined according to the optimized picture type recognition model, and a specific implementation process thereof may refer to the embodiment shown in fig. 6.
As can be seen from the above embodiments, when the photographing parameters corresponding to various picture types are obtained through machine learning, a picture type identification model may be established, where the picture type identification model is used to identify the picture type of an input picture according to the input picture; and then, aiming at the picture type to be learned, adjusting camera parameters, photographing by using the adjusted camera parameters, inputting the photographed first picture into the picture type identification model, and when the picture type of the first picture is identified to be the picture type to be learned, determining photographing parameters corresponding to the picture type to be learned according to the camera parameters of the photographed first picture, thereby realizing the function of obtaining the photographing parameters corresponding to various picture types through machine learning, and improving the reliability of determining the photographing parameters corresponding to various picture types.
Fig. 6 is a flow chart illustrating another camera photographing method according to an exemplary embodiment of the present disclosure. The camera photographing method can be used in a terminal (e.g., a smart phone, a tablet computer, etc.) providing photographing service, and is based on the method shown in fig. 5, as shown in fig. 6, when executing step 530, the method can include the following steps 610 and 630:
in step 610, the first picture is used as optimization data, and the picture type recognition model is optimized to obtain an optimized picture type recognition model.
In step 620, the camera parameters are continuously adjusted, the camera parameters after continuous adjustment are utilized to take a picture, and the second picture after taking a picture is input into the optimized picture type recognition model.
In step 630, when the picture type of the second picture is identified as the picture type to be learned, the camera parameters for photographing the second picture are determined as the photographing parameters corresponding to the picture type to be learned.
As can be seen from the above embodiments, when determining the photographing parameters corresponding to the type of the picture to be learned according to the camera parameters for photographing the first picture, the first picture may be taken as the optimized data, the picture type recognition model is optimized to obtain the optimized picture type recognition model, the camera parameters are continuously adjusted, the camera parameters which are continuously adjusted are used for photographing, the photographed second picture is input into the optimized picture type recognition model, and when the picture type of the second picture is recognized as the type of the picture to be learned, the camera parameters for photographing the second picture are determined as the photographing parameters corresponding to the type of the picture to be learned, so that the accuracy of the photographing parameters corresponding to the type of the picture to be learned is improved.
Corresponding to the embodiment of the camera photographing method, the disclosure also provides an embodiment of a camera photographing device.
As shown in fig. 7, fig. 7 is a block diagram of a camera photographing apparatus according to an exemplary embodiment, which may be used on a terminal (e.g., a smart phone, a tablet computer, etc.) providing a photographing service and used for executing the camera photographing method shown in fig. 1, and as shown in fig. 7, the camera photographing apparatus may include:
a receiving module 71, configured to receive a photographing instruction of a user, where the photographing instruction includes a specified picture type;
a determining module 72 configured to determine a photographing parameter corresponding to the specified picture type;
and the photographing module 73 is configured to photograph according to the photographing parameters corresponding to the specified picture type to obtain a picture corresponding to the specified picture type.
It can be seen from the above embodiments that, when a photographing instruction of a user is received, the photographing instruction including a designated picture type, a photographing parameter corresponding to the designated picture type may be determined first, and then photographing is performed according to the photographing parameter corresponding to the designated picture type, so as to obtain a picture corresponding to the designated picture type, thereby improving the photographing efficiency of the camera and better meeting the photographing requirement of the user.
As shown in fig. 8, fig. 8 is a block diagram of another camera photographing device according to an exemplary embodiment of the present disclosure, which is based on the foregoing embodiment shown in fig. 7, wherein the specified picture type is one of multiple picture types that can be provided by the terminal; as shown in fig. 8, the determining module 72 may include:
an obtaining sub-module 81 configured to obtain photographing parameters corresponding to various picture types;
the first determining sub-module 82 is configured to determine the photographing parameters corresponding to the specified picture type from the photographing parameters corresponding to the various picture types.
According to the embodiment, the photographing parameters corresponding to various picture types can be obtained firstly, and then the photographing parameters corresponding to the specified picture types indicated by the user are determined from the photographing parameters corresponding to various picture types, so that the accuracy of determining the photographing parameters is improved.
As shown in fig. 9, fig. 9 is a block diagram of another camera photographing apparatus according to an exemplary embodiment shown in the present disclosure, which is based on the foregoing embodiment shown in fig. 8, and as shown in fig. 9, the apparatus may further include:
the learning module 91 is configured to obtain photographing parameters corresponding to various picture types through machine learning;
the saving module 92 is configured to save the photographing parameters corresponding to the various picture types to a specified position.
It can be seen from the above embodiments that the photographing parameters corresponding to various picture types can be obtained through machine learning, and then the photographing parameters corresponding to various picture types are stored to the designated position, so that the photographing parameters corresponding to the picture types can be rapidly obtained from the designated position during photographing, and the photographing speed of the camera is improved.
As shown in fig. 10, fig. 10 is a block diagram of another camera photographing device according to an exemplary embodiment of the present disclosure, which is based on the foregoing embodiment shown in fig. 9, and as shown in fig. 10, the learning module 91 may include:
the establishing sub-module 101 is configured to establish a picture type identification model, where the picture type identification model is used for identifying a picture type of an input picture according to the input picture;
the first adjusting submodule 102 is configured to adjust camera parameters for a picture type to be learned, take a picture by using the adjusted camera parameters, and input a first picture after taking the picture into the picture type identification model;
the second determining submodule 103 is configured to determine, when it is identified that the picture type of the first picture is the picture type to be learned, a photographing parameter corresponding to the picture type to be learned according to a camera parameter of photographing the first picture.
As can be seen from the above embodiments, when the photographing parameters corresponding to various picture types are obtained through machine learning, a picture type identification model may be established, where the picture type identification model is used to identify the picture type of an input picture according to the input picture; and then, aiming at the picture type to be learned, adjusting camera parameters, photographing by using the adjusted camera parameters, inputting the photographed first picture into the picture type identification model, and when the picture type of the first picture is identified to be the picture type to be learned, determining photographing parameters corresponding to the picture type to be learned according to the camera parameters of the photographed first picture, thereby realizing the function of obtaining the photographing parameters corresponding to various picture types through machine learning, and improving the reliability of determining the photographing parameters corresponding to various picture types.
As shown in fig. 11, fig. 11 is a block diagram of another camera photographing device according to an exemplary embodiment of the present disclosure, which is based on the foregoing embodiment shown in fig. 10, and as shown in fig. 11, the establishing sub-module 101 may include:
the acquisition sub-module 111 is configured to acquire training data, wherein the training data comprises pictures of various picture types, and each picture is provided with a label of the picture type;
a learning sub-module 112 configured to perform machine learning on the training data to obtain the picture type identification model.
As shown in fig. 12, fig. 12 is a block diagram of another camera photographing device according to an exemplary embodiment of the present disclosure, which is based on the foregoing embodiment shown in fig. 10, and as shown in fig. 12, the second determining sub-module 103 may include:
a third determining submodule 121, configured to determine the camera parameter for taking the first picture as the photographing parameter corresponding to the picture type to be learned.
As shown in fig. 13, fig. 13 is a block diagram of another camera photographing device according to an exemplary embodiment of the present disclosure, which is based on the foregoing embodiment shown in fig. 10, and as shown in fig. 13, the second determining sub-module 103 may include:
the optimization submodule 131 is configured to take the first picture as optimization data and optimize the picture type identification model to obtain an optimized picture type identification model;
a second adjusting submodule 132 configured to continue adjusting the camera parameters, perform photographing by using the camera parameters after continuing adjustment, and input a second photographed picture into the optimized picture type recognition model;
a fourth determining sub-module 132, configured to determine, when it is identified that the picture type of the second picture is the picture type to be learned, the camera parameter for taking the second picture as the photographing parameter corresponding to the picture type to be learned.
As can be seen from the above embodiments, when determining the photographing parameters corresponding to the type of the picture to be learned according to the camera parameters for photographing the first picture, the first picture may be taken as the optimized data, the picture type recognition model is optimized to obtain the optimized picture type recognition model, the camera parameters are continuously adjusted, the camera parameters which are continuously adjusted are used for photographing, the photographed second picture is input into the optimized picture type recognition model, and when the picture type of the second picture is recognized as the type of the picture to be learned, the camera parameters for photographing the second picture are determined as the photographing parameters corresponding to the type of the picture to be learned, so that the accuracy of the photographing parameters corresponding to the type of the picture to be learned is improved.
In an embodiment, based on the aforementioned embodiment shown in any one of fig. 8 to fig. 13, the various picture types include picture types corresponding to different camera photographing styles.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. One of ordinary skill in the art can understand and implement it without inventive effort.
As shown in fig. 14, fig. 14 is a schematic structural diagram of a camera photographing device 1400 according to an exemplary embodiment of the present disclosure. For example, the apparatus 1400 may be a mobile phone with routing capability, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and the like.
Referring to fig. 14, apparatus 1400 may include one or more of the following components: a processing component 1402, a memory 1404, a power component 1406, a multimedia component 1408, an audio component 1410, an input/output (I/O) interface 1412, a sensor component 1414, and a communication component 1416.
The processing component 1402 generally controls the overall operation of the device 1400, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. Processing component 1402 may include one or more processors 1420 to execute instructions to perform all or a portion of the steps of the methods described above. Further, processing component 1402 can include one or more modules that facilitate interaction between processing component 1402 and other components. For example, the processing component 1402 can include a multimedia module to facilitate interaction between the multimedia component 1408 and the processing component 1402.
The memory 1404 is configured to store various types of data to support operations at the apparatus 1400. Examples of such data include instructions for any application or method operating on device 1400, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 1404 may be implemented by any type of volatile or non-volatile storage device or combination of devices, 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 disks.
The power supply component 1406 provides power to the various components of the device 1400. The power components 1406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 1400.
The multimedia component 1408 includes a screen that provides an output interface between the device 1400 and the user. In some embodiments, 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 an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1408 includes a front-facing camera and/or a rear-facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 1400 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 1410 is configured to output and/or input audio signals. For example, the audio component 1410 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 1400 is in operating modes, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 1404 or transmitted via the communication component 1416. In some embodiments, audio component 1410 further includes a speaker for outputting audio signals.
I/O interface 1412 provides an interface between processing component 1402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor component 1414 includes one or more sensors for providing various aspects of state assessment for the apparatus 1400. For example, the sensor component 1414 may detect an open/closed state of the apparatus 1400, a relative positioning of components, such as a display and keypad of the apparatus 1400, a change in position of the apparatus 1400 or a component of the apparatus 1400, the presence or absence of user contact with the apparatus 1400, an orientation or acceleration/deceleration of the apparatus 1400, and a change in temperature of the apparatus 1400. The sensor assembly 1414 may include a proximity sensor configured to detect the presence of a nearby object in the absence of any physical contact. The sensor assembly 1414 may also include a photosensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1414 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, a microwave sensor, or a temperature sensor.
The communication component 1416 is configured to facilitate wired or wireless communication between the apparatus 1400 and other devices. The device 1400 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 1416 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1416 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, 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.
In an exemplary embodiment, the apparatus 1400 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 Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described camera photographing method.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as the memory 1404 that includes instructions executable by the processor 1420 of the apparatus 1400 to perform the above-described method. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (13)
1. A camera photographing method, the method comprising:
receiving a photographing instruction of a user, wherein the photographing instruction comprises a designated picture type;
determining a photographing parameter corresponding to the specified picture type;
photographing according to the photographing parameters corresponding to the specified picture types to obtain pictures corresponding to the specified picture types;
determining the photographing parameters corresponding to the specified picture types, including:
establishing a picture type identification model, wherein the picture type identification model is used for identifying the picture type of an input picture according to the input picture;
adjusting camera parameters according to the picture type to be learned, photographing by using the adjusted camera parameters, and inputting a photographed first picture into the picture type identification model;
when the picture type of the first picture is identified as the picture type to be learned, determining a photographing parameter corresponding to the picture type to be learned according to a camera parameter for photographing the first picture;
the determining of the photographing parameters corresponding to the type of the picture to be learned according to the camera parameters for photographing the first picture comprises:
taking the first picture as optimization data, and optimizing the picture type identification model to obtain an optimized picture type identification model;
continuously adjusting the camera parameters, taking pictures by using the continuously adjusted camera parameters, and inputting the second pictures after taking pictures into the optimized picture type identification model;
and when the picture type of the second picture is identified as the picture type to be learned, determining the camera parameter for photographing the second picture as the photographing parameter corresponding to the picture type to be learned.
2. The method according to claim 1, wherein the specified picture type is one of a plurality of picture types that the terminal can provide;
the determining of the photographing parameters corresponding to the specified picture type includes:
acquiring photographing parameters corresponding to various picture types;
and determining the photographing parameters corresponding to the specified picture types from the photographing parameters corresponding to the various picture types.
3. The method of claim 2, further comprising:
obtaining photographing parameters corresponding to various picture types through machine learning;
and storing the photographing parameters corresponding to the various picture types to a specified position.
4. The method of claim 1, wherein the establishing the picture type recognition model comprises:
acquiring training data, wherein the training data comprises pictures of various picture types, and each picture is provided with a label of the picture type;
and performing machine learning on the training data to obtain the picture type identification model.
5. The method according to claim 1, wherein the determining the photographing parameters corresponding to the type of the picture to be learned according to the camera parameters for photographing the first picture comprises:
and determining the camera parameters for photographing the first picture as the photographing parameters corresponding to the type of the picture to be learned.
6. The method according to any one of claims 2 to 4, wherein the various picture types include picture types corresponding to different camera photographing styles.
7. A camera photographing device, the device comprising:
the device comprises a receiving module, a processing module and a display module, wherein the receiving module is configured to receive a photographing instruction of a user, and the photographing instruction comprises a specified picture type;
the determining module is configured to determine photographing parameters corresponding to the specified picture types;
the photographing module is configured to photograph according to the photographing parameters corresponding to the specified picture type to obtain a picture corresponding to the specified picture type;
the determination model is specifically configured to:
establishing a picture type identification model, wherein the picture type identification model is used for identifying the picture type of an input picture according to the input picture;
adjusting camera parameters according to the picture type to be learned, photographing by using the adjusted camera parameters, and inputting a photographed first picture into the picture type identification model;
when the picture type of the first picture is identified as the picture type to be learned, determining a photographing parameter corresponding to the picture type to be learned according to a camera parameter for photographing the first picture;
the determining of the photographing parameters corresponding to the type of the picture to be learned according to the camera parameters for photographing the first picture comprises:
taking the first picture as optimization data, and optimizing the picture type identification model to obtain an optimized picture type identification model;
continuously adjusting the camera parameters, taking pictures by using the continuously adjusted camera parameters, and inputting the second pictures after taking pictures into the optimized picture type identification model;
and when the picture type of the second picture is identified as the picture type to be learned, determining the camera parameter for photographing the second picture as the photographing parameter corresponding to the picture type to be learned.
8. The apparatus according to claim 7, wherein the specified picture type is one of a plurality of picture types that the terminal can provide;
the determining module comprises:
the acquisition sub-module is configured to acquire photographing parameters corresponding to various picture types;
the first determining sub-module is configured to determine the photographing parameters corresponding to the specified picture type from the photographing parameters corresponding to the various picture types.
9. The apparatus of claim 8, further comprising:
the learning module is configured to obtain photographing parameters corresponding to various picture types through machine learning;
and the storage module is configured to store the photographing parameters corresponding to the various picture types to a specified position.
10. The apparatus of claim 7, wherein the establishing sub-module comprises:
the acquisition sub-module is configured to acquire training data, the training data comprises pictures of various picture types, and each picture is provided with a label of the picture type;
and the learning submodule is configured to perform machine learning on the training data to obtain the picture type recognition model.
11. The apparatus of claim 7, further comprising: a second determination submodule; the second determination submodule includes:
and the third determining submodule is configured to determine the camera parameters for photographing the first picture as the photographing parameters corresponding to the picture type to be learned.
12. The apparatus according to any one of claims 8 to 10, wherein the various picture types include picture types corresponding to different camera photographing styles.
13. A camera photographing device, the device comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
receiving a photographing instruction of a user, wherein the photographing instruction comprises a designated picture type;
determining a photographing parameter corresponding to the specified picture type;
photographing according to the photographing parameters corresponding to the specified picture types to obtain pictures corresponding to the specified picture types;
determining the photographing parameters corresponding to the specified picture types, including:
establishing a picture type identification model, wherein the picture type identification model is used for identifying the picture type of an input picture according to the input picture;
adjusting camera parameters according to the picture type to be learned, photographing by using the adjusted camera parameters, and inputting a photographed first picture into the picture type identification model;
when the picture type of the first picture is identified as the picture type to be learned, determining a photographing parameter corresponding to the picture type to be learned according to a camera parameter for photographing the first picture;
the determining of the photographing parameters corresponding to the type of the picture to be learned according to the camera parameters for photographing the first picture comprises:
taking the first picture as optimization data, and optimizing the picture type identification model to obtain an optimized picture type identification model;
continuously adjusting the camera parameters, taking pictures by using the continuously adjusted camera parameters, and inputting the second pictures after taking pictures into the optimized picture type identification model;
and when the picture type of the second picture is identified as the picture type to be learned, determining the camera parameter for photographing the second picture as the photographing parameter corresponding to the picture type to be learned.
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