CN112488973A - Intelligent image synthesis method and device, computer equipment and storage medium - Google Patents
Intelligent image synthesis method and device, computer equipment and storage medium Download PDFInfo
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- CN112488973A CN112488973A CN202011387223.XA CN202011387223A CN112488973A CN 112488973 A CN112488973 A CN 112488973A CN 202011387223 A CN202011387223 A CN 202011387223A CN 112488973 A CN112488973 A CN 112488973A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
- G06T2207/30201—Face
Abstract
The invention discloses an intelligent image synthesis method, an intelligent image synthesis device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring a plurality of pictures continuously shot in the same scene within a period of time; extracting the face feature data of each person in each picture; selecting the optimal face feature data of the same person from multiple pictures; and synthesizing an optimal picture by the selected optimal face feature data of all people. The invention can ensure that the state of each person in the shot collective image can reach the best, effectively reduces the shooting time of people and greatly reduces the difficulty of the shooter.
Description
Technical Field
The invention relates to the field of image processing, in particular to an intelligent image synthesis method, an intelligent image synthesis device, computer equipment and a storage medium.
Background
With the continuous increase of human activity frequency, a great amount of daily collective activities are developed, the requirement of taking a collective photo together is needed at many times, however, the difficulty of taking an information photo is increased along with the increase of the number of people, and because it is relatively difficult to concentrate the energy of all people on the same time, the states of the individual people always appear in the process of taking the collective photo are not good, although the photographer can take a plurality of photos at one time, the states of the people are not good, and the states of the other people are not good. Finally, only one image which is overall good relative to people can be found as a group photo of people, which not only seriously affects the shooting time, but also hardly achieves the overall image effect.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an intelligent image synthesis method, an intelligent image synthesis device, computer equipment and a storage medium.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, a method for intelligent image synthesis, the method comprising:
acquiring a plurality of pictures continuously shot in the same scene within a period of time;
extracting the face feature data of each person in each picture;
selecting the optimal face feature data of the same person from multiple pictures;
and synthesizing an optimal picture by the selected optimal face feature data of all people.
The further technical scheme is as follows: the step of extracting the face feature data of each person in each picture specifically comprises the following steps:
carrying out face recognition on each picture;
judging whether the state of the face is normal or not according to the face recognition result;
if the state of the face is judged to be normal according to the face recognition result, face state data are extracted and stored;
and if the state of the face is judged to be abnormal according to the face recognition result, marking the abnormal face state data as unqualified data and storing the unqualified data.
The further technical scheme is as follows: in the step of extracting the face state data, the face state data includes eye state information, illumination state information, skin color balance information, face expression information, and face position information.
The further technical scheme is as follows: the step of selecting the optimal face feature data of the same person from the plurality of pictures specifically comprises the following steps:
acquiring the face feature data of all people in a plurality of pictures;
comparing the face feature data of the plurality of pictures to obtain a plurality of pieces of face information of the same person in the plurality of pictures;
and comparing the facial features of a plurality of pieces of face information of the same person to find out the best facial feature.
In a second aspect, an intelligent image synthesis device comprises an acquisition unit, an extraction unit, a selection unit and a synthesis unit;
the acquisition unit is used for acquiring a plurality of pictures which are continuously shot in the same scene within a period of time;
the extraction unit is used for extracting the face feature data of each person in each picture;
the selecting unit is used for selecting the optimal face feature data of the same person from a plurality of pictures;
and the synthesis unit is used for synthesizing an optimal picture through the selected optimal human face feature data of all people.
The further technical scheme is as follows: the extraction unit comprises a face recognition module, a judgment module, an extraction module and a marking module;
the face recognition module is used for carrying out face recognition on each picture;
the judging module is used for judging whether the state of the face is normal or not according to the face recognition result;
the extraction module is used for extracting and storing the face state data;
and the marking module is used for marking the abnormal face state data as unqualified data and storing the unqualified data.
The further technical scheme is as follows: the selection unit comprises an acquisition module, a comparison module and a comparison module;
the acquisition module is used for acquiring the face feature data of all people in the plurality of pictures;
the comparison module is used for comparing the face characteristic data of a plurality of pictures to obtain a plurality of pieces of face information of the same person in the plurality of pictures;
the comparison module is used for comparing the facial features of a plurality of pieces of face information of the same person so as to find out the best facial feature.
In a third aspect, a computer device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the image intelligent synthesis method as described above.
In a fourth aspect, a storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the image intelligent compositing method steps as described above.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of obtaining a plurality of pictures continuously shot in the same scene within a period of time to obtain a plurality of collective images with different expression states, then extracting the face feature data of each person in each picture, finding out the optimal face feature data of the same person from the plurality of pictures, and finally synthesizing the optimal face feature data of all the selected persons into an optimal collective picture. By the method, the shot collective image can be in the best state for everyone, so that the processed image has good effect, the shooting time of everyone is effectively reduced, and the difficulty of the shooter is greatly reduced.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented according to the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more apparent, the following detailed description will be given of preferred embodiments.
Drawings
FIG. 1 is a first flowchart of an embodiment of an intelligent image synthesis method according to the present invention;
FIG. 2 is a second flowchart of an embodiment of an intelligent image synthesis method according to the present invention;
FIG. 3 is a flow chart III of an embodiment of the intelligent image synthesis method of the present invention;
FIG. 4 is a first schematic block diagram of an embodiment of an intelligent image synthesizer according to the present invention;
FIG. 5 is a block diagram illustrating an embodiment of an intelligent image synthesizer according to the present invention;
FIG. 6 is a third schematic block diagram of an embodiment of an intelligent image synthesizer according to the present invention;
FIG. 7 is a schematic block diagram of a computer device of the present invention.
Detailed Description
In order to more fully understand the technical content of the present invention, the technical solution of the present invention will be further described and illustrated with reference to the following specific embodiments, but not limited thereto.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention 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 be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
The invention is suitable for camera shooting and is also suitable for the adjacent fields of image post-processing and the like. The invention greatly reduces the difficulty of group photography but can obtain group photography effect with very high quality because of the poor portrait state caused by different mental concentration of each person when the group photography is performed at present. The method is suitable for camera shooting and is also suitable for the adjacent fields of image post-processing and the like. The invention is described below by means of specific examples.
Referring to fig. 1, the method for intelligently synthesizing images includes the following steps:
and S10, acquiring a plurality of pictures of the same scene which are continuously shot in a period of time, and executing the step S20.
The need to take multiple pictures of the same scene in succession is the fundamental adjustment for the synthesis of subsequent pictures. Taking the example of taking collective graduation photos, 10 graduation photos of a whole class are continuously shot by a camera, and the 10 graduation photos are all finished in the same scene.
S20, extracting the face feature data of each person in each picture, and executing the step S30.
If 50 people are in the whole class, then the facial feature data of 50 people need to be extracted from 10 graduations for each photo.
Specifically, referring to fig. 2, step S20 specifically includes the following steps:
s201, carrying out face recognition on each picture, and executing the step S202 in the next step;
s202, judging whether the state of the face is normal or not according to the face recognition result, if so, executing a step S203, and if not, executing a step S204.
S203, extracting and storing the face state data;
and S204, marking the abnormal face state data as unqualified data and storing the unqualified data.
Specifically, for steps S201 to S204, each shot photo not only includes face information, but also includes other background information, such as trees, flowers, and buildings, and therefore, face recognition needs to be performed on the photo, and the face recognition technology is already a mature technology for the present, and a detailed description of a specific process of face recognition is omitted here. And after the face recognition is finished, judging whether the face state is normal or not according to the face recognition result. The criterion for judging normality is, for example, whether the human eyes are closed, or the face is not right in front but on the side, or something is hidden over the face, etc., and if judged to be normal, the face state data is extracted and stored, and if not, the face state data which is not normal is marked as disqualified data and stored. The face state data comprises: human eye state information, illumination state information, skin color balance information, human face expression information and human face position information.
S30, selecting the best face feature data of the same person from the multiple pictures, and executing the step S40;
since 10 photographs are taken, there are 10 pieces of face feature data for the same person, and it is necessary to select the best face feature data from the 10 pieces.
Specifically, referring to fig. 3, step S30 includes the following steps:
s301, acquiring face feature data of all people in a plurality of pictures;
s302, comparing the face feature data of the multiple pictures to obtain multiple pieces of face information of the same person in the multiple pictures;
s303, comparing the facial features of a plurality of pieces of face information of the same person to find out the best facial feature.
For steps S301-S303, after the face recognition in each of the 10 graduation photos is completed, the face feature data of all people in the 10 pictures are taken out; then, the facial feature data comparison is performed on the 10 pictures, the purpose of the facial feature data comparison is to find out the facial information of the same person in the 10 pictures, and after the facial feature data of the same person in the 10 pictures are found out, the facial feature data of the same person in the 10 pictures are selected.
And S40, synthesizing an optimal picture through the selected optimal face feature data of all people.
Since the best facial feature data for each of the 50 people in 10 pictures has been found, the best facial feature data for these 50 people is combined with the photographed background to create a best picture.
To sum up: the method comprises the steps of obtaining a plurality of pictures continuously shot in the same scene within a period of time to obtain a plurality of collective images with different expression states, then extracting the face feature data of each person in each picture, finding out the optimal face feature data of the same person from the plurality of pictures, and finally synthesizing the optimal face feature data of all the selected persons into an optimal collective picture. By the method, the shot collective image can be in the best state for everyone, so that the processed image has good effect, the shooting time of everyone is effectively reduced, and the difficulty of the shooter is greatly reduced.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Corresponding to the above image intelligent synthesis method, the specific embodiment of the invention further provides an image intelligent synthesis device. Referring to fig. 4, the apparatus includes an obtaining unit 1, an extracting unit 2, a selecting unit 3 and a synthesizing unit 4;
the system comprises an acquisition unit 1, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a plurality of pictures which are continuously shot in the same scene within a period of time;
the extraction unit 2 is used for extracting the face feature data of each person in each picture;
the selecting unit 3 is used for selecting the optimal human face feature data of the same person from a plurality of pictures;
and the synthesis unit 4 is used for synthesizing an optimal picture through the selected optimal face feature data of all people.
Further, referring to fig. 5, the extracting unit 2 includes a face recognizing module 21, a determining module 22, an extracting module 23, and a marking module 24;
a face recognition module 21, configured to perform face recognition on each picture;
the judging module 22 is used for judging whether the state of the face is normal according to the face recognition result;
the extraction module 23 is used for extracting and storing the face state data;
and the marking module 24 is used for marking the abnormal face state data as unqualified data and storing the unqualified data.
Further, referring to fig. 6, the selecting unit 3 includes an obtaining module 31, a comparing module 32 and a comparing module 33;
the acquiring module 31 is configured to acquire face feature data of all people in multiple pictures;
the comparison module 32 is used for comparing the face feature data of the multiple pictures to obtain multiple pieces of face information of the same person in the multiple pictures;
the comparison module 33 is configured to compare facial features of multiple pieces of face information of the same person to find out the best facial feature.
As shown in fig. 7, the embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the image intelligent synthesis method described above are implemented.
The computer device 700 may be a terminal or a server. The computer device 700 includes a processor 720, memory, and a network interface 750, which are connected by a system bus 710, where the memory may include non-volatile storage media 730 and internal memory 740.
The non-volatile storage medium 730 may store an operating system 731 and computer programs 732. The computer program 732, when executed, may cause the processor 720 to perform any of a variety of image intelligent compositing methods.
The processor 720 is used to provide computing and control capabilities, supporting the operation of the overall computer device 700.
The internal memory 740 provides an environment for the execution of the computer program 732 in the non-volatile storage medium 730, and when the computer program 732 is executed by the processor 720, the processor 720 can be caused to execute any one of the image intelligent synthesis methods.
The network interface 750 is used for network communication such as sending assigned tasks and the like. Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing device 700 to which the disclosed aspects apply, as a particular computing device 700 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components. Wherein the fig. 7 processor 720 is configured to execute the program code stored in the memory to implement the following steps:
the image intelligent synthesis method, the method of figure 7 includes:
acquiring a plurality of pictures continuously shot in the same scene within a period of time;
extracting the face feature data of each person in each picture;
selecting the optimal face feature data of the same person from multiple pictures;
and synthesizing an optimal picture by the selected optimal face feature data of all people.
The further technical scheme is as follows: fig. 7 is a step of extracting the face feature data of each person in each picture, which specifically includes:
carrying out face recognition on each picture;
judging whether the state of the face is normal or not according to the face recognition result;
if the state of the face is judged to be normal according to the face recognition result, face state data are extracted and stored;
and if the state of the face is judged to be abnormal according to the face recognition result, marking the abnormal face state data as unqualified data and storing the unqualified data.
The further technical scheme is as follows: in the step of extracting the face state data in fig. 7, the face state data in fig. 7 includes eye state information, illumination state information, skin color balance information, facial expression information, and face position information.
The further technical scheme is as follows: fig. 7 is a step of selecting the optimal face feature data of the same person from multiple pictures, which specifically includes:
acquiring the face feature data of all people in a plurality of pictures;
comparing the face feature data of the plurality of pictures to obtain a plurality of pieces of face information of the same person in the plurality of pictures;
and comparing the facial features of a plurality of pieces of face information of the same person to find out the best facial feature.
It should be understood that, in the embodiment of the present Application, the Processor 720 may be a Central Processing Unit (CPU), and the Processor 720 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Those skilled in the art will appreciate that the configuration of computer device 700 depicted in FIG. 7 is not intended to be limiting of computer device 700 and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be implemented in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical functional division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another device, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and 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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The technical contents of the present invention are further illustrated by the examples only for the convenience of the reader, but the embodiments of the present invention are not limited thereto, and any technical extension or re-creation based on the present invention is protected by the present invention. The protection scope of the invention is subject to the claims.
Claims (9)
1. An intelligent image synthesis method, characterized in that the method comprises:
acquiring a plurality of pictures continuously shot in the same scene within a period of time;
extracting the face feature data of each person in each picture;
selecting the optimal face feature data of the same person from multiple pictures;
and synthesizing an optimal picture by the selected optimal face feature data of all people.
2. The intelligent image synthesis method according to claim 1, wherein the step of extracting the face feature data of each person in each picture specifically comprises:
carrying out face recognition on each picture;
judging whether the state of the face is normal or not according to the face recognition result;
if the state of the face is judged to be normal according to the face recognition result, face state data are extracted and stored;
and if the state of the face is judged to be abnormal according to the face recognition result, marking the abnormal face state data as unqualified data and storing the unqualified data.
3. The intelligent image synthesis method according to claim 2, wherein in the step of extracting the face state data, the face state data includes eye state information, illumination state information, skin color balance information, facial expression information, and face position information.
4. The intelligent image synthesis method according to claim 1, wherein the step of selecting the optimal face feature data of the same person from the plurality of pictures specifically comprises:
acquiring the face feature data of all people in a plurality of pictures;
comparing the face feature data of the plurality of pictures to obtain a plurality of pieces of face information of the same person in the plurality of pictures;
and comparing the facial features of a plurality of pieces of face information of the same person to find out the best facial feature.
5. The intelligent image synthesis device is characterized by comprising an acquisition unit, an extraction unit, a selection unit and a synthesis unit;
the acquisition unit is used for acquiring a plurality of pictures which are continuously shot in the same scene within a period of time;
the extraction unit is used for extracting the face feature data of each person in each picture;
the selecting unit is used for selecting the optimal face feature data of the same person from a plurality of pictures;
and the synthesis unit is used for synthesizing an optimal picture through the selected optimal human face feature data of all people.
6. The intelligent image synthesis device according to claim 5, wherein the extraction unit comprises a face recognition module, a judgment module, an extraction module and a marking module;
the face recognition module is used for carrying out face recognition on each picture;
the judging module is used for judging whether the state of the face is normal or not according to the face recognition result;
the extraction module is used for extracting and storing the face state data;
and the marking module is used for marking the abnormal face state data as unqualified data and storing the unqualified data.
7. The intelligent image synthesis device according to claim 6, wherein the selection unit comprises an acquisition module, a comparison module and a comparison module;
the acquisition module is used for acquiring the face feature data of all people in the plurality of pictures;
the comparison module is used for comparing the face characteristic data of a plurality of pictures to obtain a plurality of pieces of face information of the same person in the plurality of pictures;
the comparison module is used for comparing the facial features of a plurality of pieces of face information of the same person so as to find out the best facial feature.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the image intelligent synthesis method steps according to any one of claims 1 to 4 when executing the computer program.
9. A storage medium, characterized in that the storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to carry out the image intelligent composition method steps according to any one of claims 1 to 4.
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| CN113630552A (en) * | 2021-07-16 | 2021-11-09 | 深圳全王科技有限公司 | Collective photographing system |
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| CN113630552A (en) * | 2021-07-16 | 2021-11-09 | 深圳全王科技有限公司 | Collective photographing system |
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