CN116366960A

CN116366960A - Shooting method, shooting device, electronic equipment and storage medium

Info

Publication number: CN116366960A
Application number: CN202111582736.0A
Authority: CN
Inventors: 李增亮
Original assignee: Beijing Xiaomi Mobile Software Co Ltd
Current assignee: Beijing Xiaomi Mobile Software Co Ltd
Priority date: 2021-12-22
Filing date: 2021-12-22
Publication date: 2023-06-30

Abstract

The disclosure provides a shooting method, a shooting device, electronic equipment and a storage medium, wherein the shooting method comprises the following steps: responding to shooting operation, and acquiring a plurality of acquired candidate face images; obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image; and determining a photographed target face image according to the photographing effect information of each candidate face image in at least one face partial area. According to the method, the target face image is selected from the multi-frame candidate face images according to the shooting effect information of at least one face area, so that the image with the highest shooting quality can be presented to a user as a final photo according to the shooting effect information of each face area in the multi-frame candidate face images, and user experience is improved.

Description

Shooting method, shooting device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of terminal equipment, and in particular relates to a shooting method, a shooting device, electronic equipment and a storage medium.

Background

With the rapid development of various mobile intelligent terminals, the pixels of cameras on the terminals are higher and higher, so that more and more users tend to use the mobile intelligent terminals to take pictures. Each terminal manufacturer continuously updates terminal hardware to meet the shooting requirements of users so as to improve the shooting quality of photos.

However, the quality of the photographing of the user depends on the photographing level of the user in addition to hardware factors such as the pixel level of the terminal. At the moment when a user takes a picture by using the intelligent terminal, the taken facial expression may be unnatural due to various factors, such as that eyes are not open, smile is stiff, and the like. Therefore, how to capture high quality images has become a problem to be solved.

BRIEF SUMMARY OF THE PRESENT DISCLOSURE

The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.

Therefore, a first object of the present disclosure is to provide a photographing method to realize a target face image selected according to photographing effect information of at least one face region in a multi-frame candidate face image, thereby presenting an image with highest quality of photographing as a final photograph to a user according to photographing effect information of each face region in the multi-frame candidate face image, and improving user experience.

A second object of the present disclosure is to provide a photographing apparatus.

A third object of the present disclosure is to propose an electronic device.

A fourth object of the present disclosure is to propose a computer readable storage medium.

A fifth object of the present disclosure is to propose a computer programme product.

To achieve the above object, an embodiment of a first aspect of the present disclosure provides a photographing method, including: responding to shooting operation, and acquiring a plurality of acquired candidate face images; obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image; and determining a photographed target face image according to the photographing effect information of each candidate face image in the at least one face partial area.

According to the shooting method, the acquired multi-frame candidate face images are acquired through responding to shooting operation; obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image; according to the shooting effect information of each candidate face image in at least one face local area, a shot target face image is determined, and the method selects the target face image in the multi-frame candidate face images according to the shooting effect information of at least one face area, so that according to the shooting effect information of each face area in the multi-frame candidate face images, the image with highest shooting quality can be presented to a user as a final photo, and user experience is improved.

To achieve the above object, an embodiment of a second aspect of the present disclosure provides a photographing apparatus, including: the first acquisition module is used for responding to shooting operation and acquiring a plurality of acquired candidate face images; the second acquisition module is used for acquiring shooting effect information of at least one face local area for each frame in the multi-frame candidate face image; and the determining module is used for determining a shot target face image according to shooting effect information of each candidate face image in the at least one face local area.

To achieve the above object, an embodiment of a third aspect of the present disclosure provides an electronic device, including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the photographing method according to the embodiment of the first aspect of the present disclosure.

In order to achieve the above object, a fourth aspect embodiment of the present disclosure proposes a computer-readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the photographing method according to the first aspect embodiment of the present disclosure.

In order to achieve the above object, a fifth aspect embodiment of the present disclosure proposes a computer program product comprising a computer program which, when executed by a processor, implements a shooting method according to an embodiment of the first aspect of the present disclosure.

Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.

Drawings

The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

fig. 1 is a flowchart of a photographing method according to an embodiment of the disclosure;

fig. 2 is a flowchart illustrating another photographing method according to an embodiment of the present disclosure

Fig. 3 is a flowchart illustrating another photographing method according to an embodiment of the disclosure;

fig. 4 is a flowchart illustrating another photographing method according to an embodiment of the disclosure;

fig. 5 is a schematic diagram of a configuration of weights of a facial local area according to an embodiment of the present disclosure;

fig. 6 is a flowchart illustrating another photographing method according to an embodiment of the disclosure;

fig. 7 is a flowchart of another photographing method according to an embodiment of the disclosure;

fig. 8 is a schematic view of capturing effect information of a local area of a face according to an embodiment of the present disclosure;

fig. 9 is a schematic view of shooting effect information of another face local area provided in an embodiment of the present disclosure;

Fig. 10 is a schematic view of shooting effect information of another face local area provided in an embodiment of the present disclosure;

fig. 11 is a flowchart illustrating another photographing method according to an embodiment of the disclosure;

fig. 12 is a schematic structural diagram of a photographing device according to an embodiment of the disclosure;

fig. 13 is a block diagram of an electronic device, according to an example embodiment.

Detailed Description

Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.

At present, as shown in fig. 1, the photographing flow of the electronic device is as follows:

(1) After one frame of original data is processed by a bayer array algorithm and other algorithms in sequence, and then displayed, for example, when a sensor (sensor) outputs n+2th frame of original data, the bayer array algorithm (B2Y algorithm) converts the n+1th frame of original data output before the sensor into a YUV (a color coding method) format, the converted n+1th frame of data is output to other algorithms, the other algorithms process the n (current frame n) of YUV data output before the bayer array, and the processed n frame of data is displayed through a display.

(2) After the YUV data passes through other processing algorithms, buffering is performed.

(3) When the user presses the photographing button, the nth frame in the frame sequence seen by the current display is matched with the nth frame in the buffer, and the nth frame is kept as the final photo and presented to the user, so that the nth frame taken by the user is not necessarily good in photographing effect.

In view of the foregoing, the present disclosure proposes a shooting method, a shooting device, an electronic apparatus, and a storage medium.

Shooting methods, devices, electronic apparatuses, and storage media according to embodiments of the present disclosure are described below with reference to the accompanying drawings.

Fig. 2 is a flowchart illustrating another photographing method according to an embodiment of the disclosure.

As shown in fig. 2, the photographing method includes the steps of:

in step 201, in response to a photographing operation, a plurality of acquired candidate face images are acquired.

In the embodiment of the present disclosure, a plurality of frames of captured candidate face images are acquired in response to a capturing operation of an electronic device having a capturing function. For example, when the user presses a photographing shutter of a camera, a plurality of frames of candidate face images photographed by the camera may be acquired.

Step 202, obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image.

Further, for each frame of candidate face images in the multi-frame candidate face images, at least one face partial region in each frame of candidate face images may be identified, and score calculation may be performed on the at least one face partial region in each frame of candidate face images to determine a shooting score corresponding to the at least one face partial region, and further, the shooting score of the at least one face partial region may be used as shooting effect information of the corresponding face partial region. Wherein, the higher the shooting score, the better the shooting effect of the corresponding facial partial area, and the higher the shooting quality of the corresponding facial partial area, wherein the facial partial area can include but is not limited to eyes, nose, mouth, eyebrows, chin, corners of eyes, face, background, and the like.

In step 203, a photographed target face image is determined according to the photographed effect information of each candidate face image in at least one face partial area.

Further, among the plurality of candidate face images, a target face image having the highest imaging quality is determined based on the imaging effect information of each candidate face image in at least one face partial region.

In summary, the target face image is selected from the multiple frames of candidate face images according to the shooting effect information of at least one face region, so that the image with the highest shooting quality can be presented to the user as a final photo according to the shooting effect information of each face region in the multiple frames of candidate face images, and the user experience is improved.

In order to accurately determine a target face image with highest quality according to the capturing effect information of each candidate face image in at least one face partial area, as shown in fig. 3, fig. 3 is a flowchart of another capturing method provided in an embodiment of the disclosure, as an example, at least one first face image may be selected according to the capturing effect information of each candidate face image in at least one face partial area, and further, according to at least one face image, a target face image may be determined, and the embodiment shown in fig. 3 may include the following steps:

in step 301, in response to a photographing operation, a plurality of acquired candidate face images are acquired.

Step 302, obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image.

Step 303, selecting at least one first face image from a plurality of frames of candidate face images according to the shooting effect information of each candidate face image in at least one face local area.

In the embodiment of the disclosure, according to the shooting effect information of each candidate face image in at least one face local area, one or more first face images with better shooting effect are selected from multiple frames of candidate face images.

Step 304, determining a target facial image according to at least one first facial image.

As an example, in the case where the first face image is one frame, the first face image may be regarded as the target face image.

As another example, in the case where the first face image is a plurality of frames (at least two frames), the plurality of frames of face images may be image-synthesized, and the synthesized image may be the target face image.

It should be noted that, the execution process of steps 301 to 302 may refer to the execution process of the above embodiment, and will not be described herein.

In summary, by selecting at least one first face image from a plurality of frames of candidate face images according to the capturing effect information of each candidate face image in at least one face partial area, and determining a target face image according to the at least one first face image, at least one first face image from the plurality of frames of candidate face images can be selected, and further, the target face image can be accurately determined according to the at least one face image.

In order to accurately select a first face image with a better capturing effect from multiple frames of candidate face images, as shown in fig. 4, fig. 4 is a flowchart of another capturing method according to an embodiment of the present disclosure, in the embodiment of the present disclosure, an effect evaluation value of each candidate face image may be determined according to capturing effect information of each candidate face image in at least one face local area and a weight of each face local area, and further, according to the effect evaluation value of each candidate face image, a captured target face image may be selected from multiple candidate face images, the embodiment shown in fig. 4 may include the following steps:

In step 401, in response to a photographing operation, a plurality of acquired candidate face images are acquired.

Step 402, obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image.

Step 403, determining an effect evaluation value of each candidate face image according to the shooting effect information of each candidate face image in at least one face local area and the weight of each face local area.

In the embodiment of the disclosure, according to the shooting effect information of each frame face image in at least one face local area and the weight of each face local area, weighting and summing are performed on at least one face local area in each frame face image, and according to the weighting and summing result, the effect evaluation value of each candidate face image is determined.

For example, taking the facial local area as eyes, eyebrows, mouth, chin, corners of eyes, face and background as examples, for the w-1 frame of facial image, the shooting effect information of each facial local area in the w-1 frame of facial image may be eye 8, eyebrow 8, mouth 8, chin 8, corners of eyes 8, sides 8 and background 8; for the w frame face part image, the shooting effect information of each face part area in the w frame face part image can be 9 minutes of eyes, 8 minutes of eyebrows, 10 minutes of mouth, 8 minutes of chin, 8 minutes of corners of eyes, 8 minutes of side faces and 8 minutes of background; for the w+1 frame face image, the shooting effect information of each face partial area in the w+1 frame face image may be 9 minutes for eyes, 8 minutes for eyebrows, 8 minutes for mouth, 8 minutes for chin, 8 minutes for corners of eyes, 8 minutes for side faces, and 8 minutes for background. Further, the face partial areas of the frame face images are weighted and summed according to the weights corresponding to the face partial areas, and the weighted and summed result is used as the effect evaluation value of the candidate face images. For example, the effect evaluation value of the w-1 frame face image is that the weight of eyes is 30%, the weight of eyebrows is 10%, the weight of mouth is 20%, the weight of chin is 10%, the weight of corners of eyes is 10%, the weight of side faces is 10% and the weight of background is 10%, and is: 8.3+8.0.1+8.0.2+8.0.1+8.0.1+8.0.1+8.0.1+8.0.1 =8 points; the effect evaluation value of the w frame face image is: 9+8.0.1+10.0.2+8.0.1+8.0.1+8.0.1+8.0.1+8.0.1 =8.7 min; the effect evaluation value of the w+1 frame face image is: 9+8.0.1+8.0.2+8.0.1+8.0.1+8.0.1+8.0.1+8.0.1 =8.3 minutes.

The weight of each face partial region may be a set weight, or may be configured by the user according to personal preference on the photographing interface. For example, as shown in fig. 5, the user can configure the weight of the eyes, the weight of the eyebrows, and the weight of the mouth according to personal preference.

Step 404, selecting at least one first face image from the plurality of face images based on the effect evaluation value of each face image candidate.

Further, the effect evaluation values of the candidate face images are compared, and at least one first face image is selected from the plurality of candidate face images based on the comparison result. For example, a candidate face image having the highest effect evaluation value is selected from among the plurality of candidate face images as the first face image. Wherein the effect evaluation value may indicate a photographing quality of the candidate face image. For example, the effect evaluation value of the w-1 frame image is 8 minutes, the effect evaluation value of the w frame image is 8.7 minutes, the effect evaluation value of the w+1 frame image is 8.3 minutes, the effect evaluation value of the w frame image is highest, and the w frame image can be the selected first face image. The candidate face image with the highest effect evaluation value can be one or more, and the corresponding selected first face image is at least one.

Step 405, determining a target facial image according to at least one first facial image.

It should be noted that, the execution process of steps 401 to 402 and step 405 may refer to the execution process of the above embodiment, which is not described herein.

In summary, the effect evaluation value of each candidate face image is determined according to the shooting effect information of each candidate face image in at least one face local area and the weight of each face local area; at least one first face image is selected from the plurality of face images based on the effect evaluation value of each face image candidate, whereby at least one face partial region in each face image is weighted and summed, and the effect evaluation value of each face image candidate can be accurately determined based on the weighted and summed result, and the first face image can be accurately selected from the plurality of face images based on the effect evaluation value of each face image candidate.

In order to accurately determine a captured target face image according to capturing effect information of each candidate face image in at least one face partial area, as shown in fig. 6, fig. 6 is a flowchart of another capturing method according to an embodiment of the present disclosure. As another example, a second face image whose capturing effect information of each face partial area satisfies a set condition may be selected from among a plurality of frames of face partial images based on capturing effect information of each face partial area in at least one face partial area, and further, a target face image may be obtained based on the face partial area corresponding to each second face image. The embodiment shown in fig. 6 may include the following steps:

In step 601, in response to a photographing operation, a plurality of acquired candidate face images are acquired.

Step 602, obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image.

Step 603, selecting a second face image with the shooting effect information of each face partial area meeting the set condition from the multi-frame candidate face images according to the shooting effect information of each candidate face image in at least one face partial area.

In the embodiment of the disclosure, according to the shooting effect information of each face local area in each frame of candidate face image, the shooting effect of each corresponding face local area in each candidate face image is compared, and according to the comparison result, a second face image that the shooting effect information of each face local area meets the set condition is selected from multiple frames of candidate face images. The setting condition may be that the photographing effect is optimal or the photographing quality is highest, and the photographing effect information of each face partial area satisfies the setting condition may be that the photographing effect of each face partial area is highest, for example, taking the face partial area as the mouth, the photographing effect score of the mouth of the W-1 frame candidate face image is 1, the photographing effect score of the mouth of the W frame candidate face image is 8, the photographing effect score of the mouth of the w+1 frame candidate face image is 10, and the w+1 frame candidate face image may be regarded as the second face image corresponding to the mouth. For another example, taking the face partial region as the eyebrow, the capturing effect score of the eyebrow of the W-1 frame candidate face image is 10 points, the capturing effect score of the eyebrow of the W frame candidate face image is 2 points, the capturing effect score of the eyebrow of the w+1 frame candidate face image is 5 points, and the W-1 frame candidate face image can be regarded as the second face image corresponding to the eyebrow.

Step 604, synthesizing according to the facial local areas corresponding to the second facial images to obtain the target facial image.

As an example, the corresponding face partial region in each second face image is truncated, and the face partial regions truncated in each second face image are synthesized to obtain the target face image.

That is, the corresponding face partial region may be extracted from the second face image, and the face partial regions extracted from the face images may be synthesized, and the synthesized face image may be used as the target face image.

For example, the second face image corresponding to the mouth is a w+1 frame candidate face image, the second face image corresponding to the eyebrow is a w-1 frame candidate face image, the second face image corresponding to the nose is a w frame candidate face image, the second face image corresponding to the eyes is a w+2 frame candidate face image, and the like, further, the eyebrow may be cut out from the w+1 frame candidate face image, the nose may be cut out from the w-1 frame candidate face image, the eyes may be cut out from the w+2 frame candidate face image, further, the cut-out eyebrow, the nose, the eyes, and the like may be synthesized, and the synthesized image may be a target face image, wherein it is to be explained that the face partial areas cut out from the respective second face images correspond to the same target object.

It should be noted that, the execution of steps 601-602 may refer to the execution of the above embodiment, and will not be described herein.

In summary, a second face image is selected from a plurality of frames of candidate face images by using the shooting effect information of each candidate face image in at least one face partial area, wherein the shooting effect information of each face partial area meets a set condition; the second face images corresponding to the face partial areas can be accurately selected from the plurality of candidate face images by synthesizing the face partial areas corresponding to the second face images to obtain the target face image, and further, the photographed target face image can be accurately obtained from the second face images.

In order to accurately acquire the capturing effect information of at least one facial partial area, as shown in fig. 7, fig. 7 is a flowchart of another capturing method according to an embodiment of the present disclosure. In an embodiment of the present disclosure, image features of each frame of candidate face images may be input to a trained recognition model, which may output capturing effect information of at least one face partial area, and the embodiment shown in fig. 7 may include the steps of:

In step 701, in response to a photographing operation, a plurality of acquired candidate face images are acquired.

Step 702, extracting image features for each frame of candidate face images.

In the embodiment of the disclosure, after a plurality of frames of candidate face images are acquired, feature extraction may be performed on each frame of candidate face images to obtain corresponding image features of each frame of candidate face images.

As an example, each frame of face candidate image may be input into an image feature extraction model for image feature extraction, and the image feature extraction model may output image features corresponding to each frame of face candidate image. The image feature extraction model may be a trained model, and the image feature extraction model has been learned to obtain a correspondence between each frame of candidate face image and a corresponding image feature.

In step 703, the extracted image features are input into the trained recognition model to obtain the capturing effect information of at least one facial partial area output by the recognition model.

Further, the extracted image features may be input to a trained recognition model, which may output photographic effect information of at least one facial partial region. The recognition model learns to obtain the corresponding relation between the image characteristics and the shooting effect information of at least one facial local area.

Alternatively, the extracted image features are input into a trained recognition model that can output a photographic score for at least one facial partial region.

For example, as shown in fig. 8, the extracted image features are input into a trained recognition model, and the recognition model outputs a mouth shooting score (e.g., a mouth without smile has a shooting score of 1 score and a mouth with smile has a shooting score of 10 score); for another example, as shown in fig. 9, the extracted image features are input into a trained recognition model, and the recognition model outputs the shooting scores of the face and the chin (for example, the shooting score of the front face is 2 points, the shooting score of the side face is 10 points, the shooting score of the flat chin is 2 points, and the shooting score of the sharp chin is 10 points); for another example, as shown in fig. 10, the extracted image features are input into a trained recognition model, and the recognition model outputs a score of the shot of the background (e.g., a score of 10 for a clean shot of the background and a score of 1 for other backgrounds).

It should be noted that, in the embodiment of the present disclosure, training of the recognition model may be performed through training data, where the training data is labeled with classification and scoring of the facial local area, so that the recognition model with expression extraction capability and shooting scoring of the corresponding facial local area may be trained through the training data.

Step 704, determining a photographed target face image according to the photographed effect information of each candidate face image in at least one face partial area.

It should be noted that, the execution of step 701 and step 704 may refer to the execution of the above embodiment, which is not described herein.

In summary, image features are extracted for each frame of candidate face images; the extracted image features are input into the trained recognition model to obtain the shooting effect information of at least one face partial area output by the recognition model, so that the shooting effect information of at least one face partial area can be accurately obtained.

In order to more clearly illustrate the above embodiments, an example will now be described.

For example, as shown in fig. 11, the steps of the photographing method according to the embodiment of the disclosure may be as follows:

(1) After one frame of original data is processed by the Bayer array algorithm and other algorithms in sequence, display is performed on display, for example, when the sensor outputs the n+2th frame of original data, the Bayer array algorithm converts the n+1th frame of original data output before the sensor into YUV format, the converted n+1th frame of data is output to other algorithms, the other algorithms process the n frame of YUV data output before the Bayer array algorithm, and the processed n frame of data is displayed through a display. The method comprises the steps of carrying out a first treatment on the surface of the

(2) After the YUV data passes through other processing algorithms, caching is carried out;

(3) Identifying the local area (local feature) of the face of each cached frame of data (each frame of candidate face image) through an AI algorithm, scoring the local area of the face according to an AI model (a trained identification model), and storing the scored data result in real time, wherein the data result is paired with each frame of data during storage;

(4) The user presses a photographing button, a frame selection algorithm performs weight weighting calculation on scoring results of the facial partial areas in each frame of data, a frame w (best frame w) with the highest scoring of the weighting results is selected, and the frame w is presented to the user as a final photo. The frame w is the data frame with the highest shooting quality.

According to the shooting method, the acquired multi-frame candidate face images are acquired through responding to shooting operation; obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image; according to the shooting effect information of each candidate face image in at least one face local area, a shot target face image is determined, and the method selects the target face image in a plurality of frames of candidate face images according to the shooting effect information of at least one face area, so that the image with highest shooting quality can be presented to a user as a final photo (namely the target face image) according to the shooting effect information of each face area in the plurality of frames of candidate face images, and user experience is improved.

In order to achieve the above embodiments, the present disclosure proposes a photographing apparatus.

Fig. 12 is a schematic structural diagram of a photographing device according to an embodiment of the disclosure.

As shown in fig. 12, the photographing apparatus 1200 includes: a first acquisition module 1210, a second acquisition module 1220, and a determination module 1230.

Wherein, the first obtaining module 1210 is configured to obtain, in response to a capturing operation, a plurality of frames of collected candidate face images; a second obtaining module 1220, configured to obtain, for each frame in the multi-frame candidate face image, capturing effect information of at least one local area of the face; a determining module 1230 is configured to determine a captured target face image according to capturing effect information of each candidate face image in at least one face partial area.

As one possible implementation of an embodiment of the present disclosure, the determining module 1230 is configured to: selecting at least one first face image from a plurality of frames of candidate face images according to shooting effect information of each candidate face image in at least one face local area; a target facial image is determined from the at least one first facial image.

As one possible implementation of an embodiment of the present disclosure, the determining module 1230 is further configured to: determining an effect evaluation value of each candidate face image according to shooting effect information of each candidate face image in at least one face local area and the weight of each face local area; at least one first face image is selected from the plurality of face images based on the effect evaluation value of each face image candidate.

As one possible implementation of an embodiment of the present disclosure, the determining module 1230 is further configured to: when the first face image is one frame, the first face image is set as a target face image; and if the first facial image is at least two frames, performing multi-frame synthesis on the at least two frames of the first facial image to obtain a target facial image.

As one possible implementation of an embodiment of the present disclosure, the determining module 1230 is further configured to: selecting a second face image of which the shooting effect information of each face local area meets a set condition from a plurality of frames of candidate face images according to the shooting effect information of each candidate face image in at least one face local area; and synthesizing according to the facial local areas corresponding to the second facial images to obtain the target facial image.

As one possible implementation of an embodiment of the present disclosure, the determining module 1230 is further configured to: intercepting corresponding facial local areas in each second facial image; and synthesizing the facial local areas cut out from the second facial images to obtain a target facial image.

As one possible implementation manner of the embodiment of the present disclosure, the first obtaining module 1210 is configured to: extracting image features from each frame of candidate face image; the extracted image features are input into a trained recognition model to obtain shooting effect information of at least one facial local area output by the recognition model.

According to the shooting device, the acquired multi-frame candidate face images are acquired by responding to shooting operation; obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image; according to the shooting effect information of each candidate face image in at least one face local area, a shot target face image is determined, and the method selects the target face image in a plurality of frames of candidate face images according to the shooting effect information of at least one face area, so that according to the shooting effect information of each face area in the plurality of frames of candidate face images, the image with highest shooting quality can be presented to a user as a final photo, and user experience is improved.

In order to implement the above embodiments, the present disclosure also proposes an electronic device. The electronic device includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the photographing method described in the above embodiment.

In order to implement the above-described embodiments, the present disclosure also proposes a computer-readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the photographing method described in the above-described embodiments.

In order to implement the above embodiments, the present disclosure also proposes a computer program product comprising a computer program which, when executed by a processor, implements the shooting method described in the above embodiments.

Fig. 13 is a block diagram of an electronic device 1300, according to an example embodiment. As shown in fig. 13, the electronic apparatus 1300 includes:

a memory 1310 and a processor 1320, a bus 1330 connecting different components (including the memory 1310 and the processor 1320), the memory 1310 storing a computer program, the processor 1320 implementing the photographing method according to the embodiments of the present disclosure when executing the program.

Bus 1330 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 1300 typically includes a variety of electronic device readable media. Such media can be any available media that is accessible by the electronic device 1300 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 1310 may also include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 1340 and/or cache memory 1350. The electronic device 1300 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system 1360 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 13, commonly referred to as a "hard disk drive"). Although not shown in fig. 13, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 1330 through one or more data medium interfaces. Memory 1310 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present disclosure.

A program/utility 1380 having a set (at least one) of program modules 1370 may be stored, for example, in memory 1310, such program modules 1370 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 1370 generally perform the functions and/or methods in the embodiments described in this disclosure.

The electronic device 1300 may also communicate with one or more external devices 1390 (e.g., keyboard, pointing device, display 1391, etc.), one or more devices that enable a user to interact with the electronic device 1300, and/or any device (e.g., network card, modem, etc.) that enables the electronic device 1300 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1392. Also, the electronic device 1300 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, e.g., the internet, through a network adapter 1393. As shown in fig. 13, network adapter 1393 communicates with other modules of electronic device 1300 via bus 1330. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 1300, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

Processor 1320 executes programs stored in memory 1310, thereby performing various functional applications and data processing.

In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.

Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.

Furthermore, each functional unit in the embodiments of the present disclosure may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.

The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the present disclosure, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the present disclosure.

Claims

1. A photographing method, comprising the steps of:

responding to shooting operation, and acquiring a plurality of acquired candidate face images;

obtaining shooting effect information of at least one face local area for each frame in the multi-frame candidate face image;

and determining a photographed target face image according to the photographing effect information of each candidate face image in the at least one face partial area.

2. The method according to claim 1, wherein the determining a photographed target face image based on photographing effect information of each of the candidate face images in the at least one face partial region includes:

selecting at least one first face image from the plurality of frames of candidate face images according to shooting effect information of each candidate face image in the at least one face local area;

And determining the target face image according to the at least one first face image.

3. The method according to claim 2, wherein selecting at least one first face image from the plurality of frames of candidate face images based on the capturing effect information of each of the candidate face images in the at least one face partial area includes:

determining an effect evaluation value of each candidate face image according to shooting effect information of each candidate face image in the at least one face local area and the weight of each face local area;

at least one first face image is selected from the plurality of face images based on the effect evaluation value of each of the face images.

4. The method of claim 2, wherein said determining said target facial image from said at least one first facial image comprises:

in a case where the first face image is one frame, the first face image is taken as the target face image;

and under the condition that the first facial image is at least two frames, performing multi-frame synthesis on the at least two frames of the first facial image to obtain the target facial image.

5. The method according to claim 1, wherein the determining a photographed target face image based on photographing effect information of each of the candidate face images in the at least one face partial region includes:

selecting a second face image of which the shooting effect information of each face local area meets a set condition from a plurality of frames of the candidate face images according to the shooting effect information of each candidate face image in the at least one face local area;

and synthesizing according to the facial local areas corresponding to the second facial images to obtain the target facial image.

6. The method of claim 5, wherein the synthesizing according to the face local area corresponding to each of the second face images to obtain the target face image includes:

intercepting the corresponding facial local area in each second facial image;

and synthesizing the facial local areas cut out from the second facial images to obtain the target facial image.

7. The method according to any one of claims 1 to 6, wherein the acquiring, for each frame in the multi-frame candidate face image, capturing effect information of at least one face partial area includes:

Extracting image features from the candidate face images of each frame;

and inputting the extracted image features into a trained recognition model to obtain shooting effect information of the at least one facial local area output by the recognition model.

8. A photographing apparatus, comprising:

the first acquisition module is used for responding to shooting operation and acquiring a plurality of acquired candidate face images;

the second acquisition module is used for acquiring shooting effect information of at least one face local area for each frame in the multi-frame candidate face image;

and the determining module is used for determining a shot target face image according to shooting effect information of each candidate face image in the at least one face local area.

9. The apparatus of claim 8, wherein the means for determining is configured to:

10. The apparatus of claim 9, wherein the determining module is further configured to:

11. The apparatus of claim 9, wherein the determining module is further configured to:

12. The apparatus of claim 8, wherein the determining module is further configured to:

13. The apparatus of claim 12, wherein the determining module is further configured to:

intercepting the corresponding facial local area in each second facial image;

14. The apparatus of any one of claims 8-13, wherein the first acquisition module is configured to:

extracting image features from the candidate face images of each frame;

15. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the shooting method of any one of claims 1-7.

16. A computer readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform the shooting method of any of claims 1-7.

17. A computer program product comprising a computer program which, when executed by a processor, implements the shooting method according to any one of claims 1-7.