WO2019137167A1

WO2019137167A1 - Photo album management method and apparatus, storage medium, and electronic device

Info

Publication number: WO2019137167A1
Application number: PCT/CN2018/121981
Authority: WO
Inventors: 陈岩; 刘耀勇
Original assignee: Oppo广东移动通信有限公司
Priority date: 2018-01-10
Filing date: 2018-12-19
Publication date: 2019-07-18
Also published as: CN110109878A

Abstract

A photo album management method and apparatus, a storage medium, and an electronic device, the photo album management method comprising: acquiring an image sample set and a classification label corresponding to each image sample in the image sample set; training a preset learning model according to the image sample set and the classification labels; using the trained learning model to calculate the resolution of at least one image to be processed in a photo album; deleting photos having a resolution lower than a preset threshold so as to manage the photo album.

Description

Album management method, device, storage medium and electronic device

This application claims priority to Chinese Patent Application No. 201810023865.8, entitled "Photo Album Management Method, Apparatus, Storage Media, and Electronic Equipment", which is filed on January 10, 2018, the entire contents of which are incorporated by reference. In this application.

Technical field

The present application relates to the field of computer technologies, and in particular, to a photo album management method, apparatus, storage medium, and electronic device.

Background technique

With the development of terminal technologies, the functions that terminals can support are becoming more and more powerful. For example, the terminal has a camera so as to be able to support a photographing function and the like.

In many scenarios, the user can take a photo using the terminal's shooting function. For example, when the user goes to travel or meets with a friend, the scene can be recorded by the terminal's shooting function. At this time, the terminal stores the captured image in the album, so that when the user wants to recall the good time, the album can be taken from the album. View images in .

However, since the ordinary user's photographing technology is not professional, it is inevitable that there are photos with bad effects such as closed eyes, ghosting, and blurring in the album. These photos usually require manual sorting and deletion by the user, which is a waste of time and effort.

Summary of the invention

The embodiment of the present invention provides a photo album management method, device, storage medium, and electronic device, which can automatically delete a photo with poor shooting effect in an album, and the method is simple.

An embodiment of the present application provides a method for managing a photo album, which is applied to an electronic device, including:

Obtaining an image sample set and a classification mark corresponding to each image sample in the image sample set;

Performing a training on the preset learning model according to the image sample set and the classification mark;

Using the trained learning model to calculate the sharpness of at least one image to be processed in the album;

The image whose resolution is lower than a preset threshold is deleted to manage the album.

The embodiment of the present application further provides an album management device, which is applied to an electronic device, and includes:

An obtaining module, configured to acquire an image sample set, and a classification mark corresponding to each image sample in the image sample set;

a training module, configured to train the preset learning model according to the image sample set and the classification mark;

a calculation module, configured to calculate a sharpness of at least one image to be processed in the album by using the trained learning model;

And a deleting module, configured to delete the image whose resolution is lower than a preset threshold to manage the album.

Further, the album management device further includes a determining module, configured to:

Obtaining a current time and a recorded last management time before the calculating module calculates the sharpness of at least one image to be processed in the album by using the trained learning model;

Determining an image to be processed from the album according to the current time and the last management time recorded;

Trimming the calculation module to perform the operation of calculating the sharpness of at least one image to be processed in the album by using the trained learning model, while saving the current time as a management time record, and returning to perform the acquisition current time, And the last recorded management time operation.

Further, the determining module is configured to:

Determining the number of images in the album in which the shooting time is between the current time and the last management time recorded;

Determining whether the quantity is not less than a preset quantity;

If so, an image of the shooting time between the current time and the last management time recorded is taken as the image to be processed.

Further, the determining module is configured to:

Calculate the interval between the current time and the last recorded management time;

Determining whether the interval duration reaches a preset duration;

If so, an image taken in the album during the interval duration is taken as an image to be processed.

Further, the deleting module is further configured to:

Calculating a similarity between the remaining images in the image to be processed;

Sorting the remaining images with similarity higher than the preset similarity according to the definition from high to low;

The remaining images after the first position are deleted.

Further, the album management device further includes an adding module, configured to:

Before the acquiring module acquires the image sample set, detecting whether the electronic device currently has a continuous shooting operation;

If yes, acquiring a plurality of captured images generated by the continuous shooting operation;

Acquiring a captured image deleted by the user from the plurality of captured images as a target image;

A classification mark indicating an unclear is generated for the target image, and the target image is added to the image sample set.

The embodiment of the present application further provides a storage medium, where the storage medium stores a plurality of instructions, and the instructions are adapted to be loaded by a processor to execute any of the foregoing album management methods.

The embodiment of the present application further provides an electronic device, including a processor and a memory, the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used in any one of the foregoing The steps in the album management method described.

DRAWINGS

The technical solutions and other advantageous effects of the present application will be apparent from the detailed description of the embodiments of the present application.

FIG. 1 is a schematic flowchart of a method for managing a photo album according to an embodiment of the present application.

FIG. 2 is another schematic flowchart of a method for managing a photo album according to an embodiment of the present application.

FIG. 3 is a schematic diagram of a scenario of a photo album management process according to an embodiment of the present application.

FIG. 4 is a schematic flowchart of step 204 provided by an embodiment of the present application.

FIG. 5 is another schematic flowchart of step 204 provided in the embodiment of the present application.

FIG. 6 is a schematic structural diagram of an album management apparatus according to an embodiment of the present application.

FIG. 7 is another schematic structural diagram of an album management apparatus according to an embodiment of the present application.

FIG. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application are clearly and completely described in the following with reference to the drawings in the embodiments of the present application. It is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments of the present application without creative efforts are within the scope of the present application.

The embodiment of the present application provides a photo album management method, device, storage medium, and electronic device.

An album management method is applied to an electronic device, comprising: acquiring an image sample set and a classification mark corresponding to each image sample in the image sample set; and training the preset learning model according to the image sample set and the classification mark; The trained learning model calculates the sharpness of at least one image to be processed in the album; and deletes the image whose resolution is lower than the preset threshold to manage the album.

In some embodiments, before calculating the sharpness of at least one image to be processed in the album by using the trained learning model, the method further includes:

Get the current time and the last management time recorded;

Performing the operation of calculating the sharpness of at least one image to be processed in the album by using the trained learning model, while saving the current time as a management time record, and returning to perform the acquisition current time and the recorded upper A management time operation.

In some embodiments, the determining the image to be processed from the album according to the current time and the last management time recorded includes:

Determining whether the quantity is not less than a preset quantity;

Determining whether the interval duration reaches a preset duration;

In some embodiments, after deleting the image whose resolution is lower than a preset threshold, the method further includes:

The remaining images after the first position are deleted.

In some embodiments, the classification mark includes unclearness, and before acquiring the image sample set, the method further includes:

Detecting whether the electronic device currently has a continuous shooting operation;

In some embodiments, the learning model is a deep neural network model, and the training of the preset learning model according to the image sample set and the classification mark includes:

Each image sample in the image sample set and its corresponding classification mark are input into a preset depth neural network model to adjust the weight value and the parameter value of each layer of the neural network.

As shown in FIG. 1 , FIG. 1 is a schematic flowchart of a method for managing a photo album according to an embodiment of the present application, which is applied to an electronic device, and the specific process may be as follows:

101. Acquire an image sample set and a classification mark corresponding to each image sample in the image sample set.

In this embodiment, the classification mark may include clear and unclear, and the image sample set may be set by the user, and mainly includes a positive sample and a negative sample. The positive sample is usually an image with better shooting effect, and the corresponding classification mark can be clear. The negative sample is usually an image with poor shooting effect, and the corresponding classification mark can be unclear. It should be noted that the image in the image sample set may be automatically collected according to the user's previous deletion record, in addition to the user's own setting, mainly involving a negative sample, that is, before the above step 101, The album management method may further include:

In this embodiment, when the user continuously shoots for the same thing multiple times, under normal circumstances, only the best shooting effect is retained in the album, the one that best meets the user's requirements, and the rest are deleted. At this time, the electronic device The deleted photo can be added as a negative sample to the image sample set, so that when the image sample set is subsequently used for training, the trained model can be more in line with the user's behavior habit.

102. Train the preset learning model according to the image sample set and the classification mark.

In this embodiment, the learning model may be a Convolutional Neural Network (CNN) model, which is a deep neural network model for processing large images. During the training process, the user can input the entire image and its corresponding classification mark into the learning model to adjust the weight value and parameter value of each layer of the neural network in the learning model to optimize and realize the learning model. Training.

It should be noted that the training of the learning model may be performed in the electronic device or in the server. When performing in the server, the electronic device only needs to periodically send the image sample set that satisfies the condition to the server, so that The server trains the learning model according to the transmitted image sample set, and periodically updates the trained learning model to the electronic device.

103. Calculate the sharpness of at least one image to be processed in the album by using the trained learning model.

In this embodiment, since the image samples in the image sample set are negative samples and positive samples which are set under the condition that the shooting effect is unclear, the learning model obtained by performing the forward operation on the learning model by using the image sample set (ie, training) The post model can be used for image sharpness recognition, that is, the probability value of each image to meet the user's clear requirements can be calculated. The image to be processed may be all images in the album, or may be a specified partial image.

It should be noted that the acquisition of the image to be processed may be real-time. For example, the user may recognize the image as a to-be-processed image for each time a photo is taken, or may be periodic, for example, the latest photo taken by the user is reached. When the number of times, or the current time reaches the specified time, the electronic device may be triggered to obtain the image to be processed for clarity recognition, that is, before the step 103, the album management method may further include:

Get the current time and the last management time recorded;

Performing the operation of calculating the sharpness of at least one image to be processed in the album by using the learned learning model, and saving the current time as the management time record, and returning to perform the acquisition current time and the recorded last management time Operation.

In this embodiment, the current time can be detected in real time, and whether the image to be processed needs to be acquired according to the number of images captured by the user between the current time and the last management time or according to the interval between the current time and the last management time Perform a sharpness recognition operation.

Further, the foregoing step, "determining an image to be processed from the album according to the current time and the last management time that has been recorded" may specifically include:

Determining the number of images in the album between the current time and the last management time recorded;

Determine whether the quantity is not less than the preset quantity;

In this embodiment, the preset number may be an artificially set 20 or 15, etc., and each time the electronic device performs album management, the electronic device stores the management time at the time, and calculates the subsequent shooting of the user by using the current management time as a starting point. The number of new photos, when the specified number is reached, manages these newly taken photos and repeats the previous operation with the management time as a new starting point.

Alternatively, the above step "determining the image to be processed from the album according to the current time and the last management time recorded" may further include:

Determining whether the interval duration reaches a preset duration;

If so, the image taken in the album during the interval is taken as the image to be processed.

In this embodiment, the management of the album may be automatically started according to the time periodicity, for example, every preset time period, and the preset duration may be manually set by one month or two months, etc., mainly according to The user's own camera frequency depends on the frequency of the camera, and the preset duration can be shorter.

104. Delete an image whose resolution is lower than a preset threshold to manage the album.

In this embodiment, the preset threshold may be an artificially specified 80%, or 70%, etc., and the lower the resolution is, the less the representative image is, and the more the representative image cannot meet the user requirement, the deletion is required.

In addition, since many users often take multiple photos in succession, although the resolution of these photos may be different, they are usually shot for the same subject. If the image to be processed is processed, the resolution will be satisfied. If the photos are all retained, it is inevitable that there will be multiple duplicate photos, which may cause unnecessary waste of the storage space of the electronic device. That is, after the above step 104, the album management method may further include:

The remaining images after the first position are deleted.

In this embodiment, for the image to be processed that the selected shooting effect meets the requirements of the user, the similarity calculation method may be further used to select a photo with repeated shooting content, and for these repeated photos, only the best shooting effect is retained. The one (that is, the highest resolution) can be deleted, and the rest can be deleted to save storage space as much as possible.

It can be seen from the above that the album management method provided in this embodiment is applied to an electronic device, and obtains an image sample set and a classification mark corresponding to each image sample in the image sample set, and presets according to the image sample set and the classification mark pair. The learning model is trained, and then the training model is used to calculate the sharpness of at least one image to be processed in the album, and the image whose resolution is lower than the preset threshold is deleted to manage the album, thereby It can automatically delete photos in the album that have poor shooting results, without the need for manual operation by the user. The method is simple, avoiding waste of storage space and strong practicality.

In this embodiment, the description will be made from the perspective of the album management apparatus, and the integration of the album management apparatus in the electronic device will be specifically described as an example.

Referring to FIG. 2 and FIG. 3, a photo album management method is applied to an electronic device, and the specific process may be as follows:

201. The electronic device detects whether there is a continuous shooting operation currently, and if so, acquires a plurality of captured images generated by the continuous shooting operation as a target image.

202. The electronic device generates a classification mark indicating the unclear for the target image, and adds the target image to the image sample set.

For example, the classification mark may include both clear and unclear. The image sample set mainly includes a positive sample and a negative sample, the classification mark of the positive sample may be clear, and the classification mark of the negative sample may be unclear. The image sample set may include an image automatically collected by the electronic device according to the user's past deletion record, in addition to the image set by the user, for example, when detecting that the user has taken multiple images in a short time (for example, within ten seconds). The electronic device may add the image that is subsequently deleted by the user in the plurality of images as a negative sample in the image sample set.

203. The electronic device acquires the image sample set, and trains the preset learning model according to the image sample set and the classification mark.

For example, the learning model may be a CNN model. During the training process, the user may input the entire image and its corresponding classification mark into the learning model to adjust the weight value and parameter value of each layer of the neural network in the learning model. To optimize it and achieve training on the learning model.

204. The electronic device acquires the current time and the last management time recorded.

205. The electronic device determines an image to be processed from the album according to the current time and the last management time that has been recorded.

For example, the image to be processed may be all images in the album, or may be partial images. The electronic device may acquire the image to be processed in the album in real time for management, or may perform when a certain trigger condition is met, such as reaching a specified time or shooting. A sufficient number of new photos, and so on.

For example, referring to FIG. 4, when the trigger condition is that the specified time is reached, the foregoing step 205 may include:

2051A. Determine a number of images in the album in which the shooting time is between the current time and the last management time recorded;

2052A. Determine whether the quantity is not less than a preset quantity. If yes, perform the following step 2053A, and if no, return to performing the above step 204.

2053A. The image of the shooting time between the current time and the recorded last management time is taken as the image to be processed.

For example, the preset number may be an artificially set 20 or 15, etc., and when the number of new photos taken by the user reaches a preset number after the last management operation, the newly taken part of the photo may be managed as a to-be-processed image. .

For example, referring to FIG. 5, when the trigger condition is to take a sufficient number of new photos, the above step 205 may include:

2051B. Calculate the interval between the current time and the recorded last management time;

2052B. Determine whether the interval duration reaches a preset duration. If yes, perform the following step 2053B. If no, return to step 204 above.

2053B: The image taken in the album during the interval duration is taken as an image to be processed.

For example, the preset duration may be one month or two months set by an artificial one. If the current time reaches the preset duration from the last management operation, the photo in this period may be taken as a to-be-processed image. deal with.

206. The electronic device calculates the sharpness of the image to be processed by using the trained learning model, and at the same time, saves the current time as a management time record, and returns to performing the above step 204.

For example, since the image samples in the image sample set are negative samples and positive samples set with the unclear shooting effect, the CNN model obtained by forward processing the CNN model using the image sample set (ie, the post-training model) is used. Subsequent use can be used for image sharpness recognition, that is, the probability value (ie, sharpness) at which each image meets the user's clear requirements can be calculated.

207. The electronic device deletes the image whose resolution is lower than a preset threshold, and calculates a similarity between the remaining images in the image to be processed.

208. The electronic device sorts the remaining images with the similarity higher than the preset similarity according to the definition from high to low, and deletes the remaining images after the first position to manage the album.

For example, for the selected image to be processed that meets the user's requirements, the similarity calculation method can be used to select a photo with repeated shooting content, and for these repeated photos, only the best shooting effect is retained (ie, The one with the highest resolution can be deleted, and the rest can be deleted to save storage space as much as possible.

It can be seen from the above that the album management method provided in this embodiment is applied to an electronic device, wherein the electronic device can detect whether there is a continuous shooting operation currently, and if so, acquire a plurality of captured images generated by the continuous shooting operation as a target image. Next, generating a classification mark indicating the unclearness for the target image, and adding the target image to the image sample set, and then acquiring the image sample set, and training the preset learning model according to the image sample set and the classification mark Then, obtaining the current time and the last management time recorded, and determining the image to be processed from the album according to the current time and the last management time recorded, and then calculating the to-be-processed by using the trained learning model The sharpness of the image is then deleted, and the image with the lower resolution than the preset threshold is deleted, and the similarity between the remaining images in the image to be processed is calculated, and then the remaining images with the similarity higher than the preset similarity are followed. The sharpness is sorted from high to low, and the remaining images after the first position are made. In addition, the album is managed, and at the same time, the current time is saved as a management time record, and the operation of acquiring the current time and the recorded last management time is returned, so that the user can select according to the previous selection criteria of the user. Automatically filter out photos with poor shooting results and delete them without manual operation by the user. The method is simple, avoiding waste of storage space and being practical.

According to the method described in the foregoing embodiment, the embodiment is further described from the perspective of the album management device, and the album management device may be implemented as an independent entity or integrated in an electronic device, such as a terminal. The terminal can include a mobile phone, a tablet computer, a personal computer, and the like.

An embodiment of the present application provides a photo album management apparatus, which is applied to an electronic device, and includes:

In some embodiments, the album management device further includes a determining module for:

In some embodiments, the determining module is configured to:

Determining whether the quantity is not less than a preset quantity;

In some embodiments, the determining module is configured to:

Determining whether the interval duration reaches a preset duration;

In some embodiments, after deleting the image whose resolution is lower than a preset threshold, the deleting module is further configured to:

The remaining images after the first position are deleted.

In some embodiments, the classification mark includes unclear, and the album management apparatus further includes an adding module for:

In some embodiments, the learning model is a deep neural network model, and the training module is specifically configured to:

Referring to FIG. 6 , FIG. 6 specifically describes an album management apparatus provided by an embodiment of the present application, which is applied to an electronic device. The album management apparatus may include: an obtaining module 10, a training module 20, a computing module 30, and a deleting module 40, where:

(1) Acquisition module 10

The obtaining module 10 is configured to acquire an image sample set and a classification mark corresponding to each image sample in the image sample set.

In this embodiment, the classification mark may include clear and unclear, and the image sample set may be set by the user, and mainly includes a positive sample and a negative sample. The positive sample is usually an image with better shooting effect, and the corresponding classification mark can be clear. The negative sample is usually an image with poor shooting effect, and the corresponding classification mark can be unclear. It should be noted that the image in the image sample set may be automatically collected according to the user's past deletion record, in addition to the user's own setting, mainly involving a negative sample, that is, please refer to FIG. The management device can also include an add module 50 for:

Before the acquiring module 10 acquires the image sample set, detecting whether the electronic device currently has a continuous shooting operation;

(2) Training module 20

The training module 20 is configured to train the preset learning model according to the image sample set and the classification mark.

(3) Calculation module 30

The calculating module 30 is configured to calculate the sharpness of at least one image to be processed in the album by using the trained learning model.

It should be noted that the acquisition of the image to be processed may be real-time. For example, the user may recognize the image as a to-be-processed image for each time a photo is taken, or may be periodic, for example, the latest photo taken by the user is reached. A certain number, or when the current time reaches the specified time, the electronic device may be triggered to acquire the image to be processed for clarity recognition, that is, the album management device may further include a determining module 60, configured to:

Before the calculating module 30 calculates the sharpness of at least one image to be processed in the album by using the trained learning model, acquiring the current time and the last management time recorded;

Trimming the calculation module to perform the operation of calculating the sharpness of at least one image to be processed in the album by using the trained learning model, while saving the current time as a management time record, and returning to perform the acquisition current time and the recorded The last administrative time operation.

In this embodiment, the determining module 60 can detect the current time in real time, and determine whether the need is obtained according to the number of images captured by the user between the current time and the last management time, or according to the interval between the current time and the last management time. The image to be processed is subjected to a sharpness recognition operation.

Further, the determining module 60 is specifically configured to:

Determine whether the quantity is not less than the preset quantity;

Alternatively, the determining module 60 can further be used to:

Determining whether the interval duration reaches a preset duration;

(4) Delete module 40

The deleting module 40 is configured to delete the image with the lower resolution than the preset threshold to manage the album.

In addition, since many users often take multiple photos in succession, although the resolution of these photos may be different, they are usually shot for the same subject. If the image to be processed is processed, the resolution will be satisfied. The photos are all retained, and it is inevitable that there are multiple duplicate photos, which causes unnecessary waste of the storage space of the electronic device, that is, after deleting the image whose resolution is lower than the preset threshold, the deleting module 40 can also Used for:

The remaining images after the first position are deleted.

In the specific implementation, the foregoing units may be implemented as a separate entity, or may be implemented in any combination, and may be implemented as the same or a plurality of entities. For the specific implementation of the foregoing, refer to the foregoing method embodiments, and details are not described herein.

It can be seen from the above that the album management apparatus provided in this embodiment is applied to an electronic device, and the acquisition module 10 acquires an image sample set and a classification mark corresponding to each image sample in the image sample set, and the training module 20 according to the image sample set and The classification mark trains the preset learning model, and then the calculation module 30 calculates the sharpness of at least one image to be processed in the album by using the trained learning model, and the deleting module 40 performs the image with the lower definition than the preset threshold. Delete to manage the album, so that the photo with bad shooting effect can be automatically deleted in the album, without manual operation by the user, the method is simple, the waste of storage space is avoided, and the utility is strong.

In addition, the embodiment of the present application further provides an electronic device, which may be a device such as a smart phone or a tablet computer. As shown in FIG. 8, the electronic device 900 includes a processor 901, a memory 902, a display screen 903, and a control circuit 904. The processor 901 is electrically connected to the memory 902, the display screen 903, and the control circuit 904, respectively.

The processor 901 is a control center of the electronic device 900, and connects various parts of the entire electronic device using various interfaces and lines, executes the electronic by running or loading an application stored in the memory 902, and calling data stored in the memory 902. The various functions and processing data of the device enable overall monitoring of the electronic device.

In this embodiment, the processor 901 in the electronic device 900 loads the instructions corresponding to the process of one or more applications into the memory 902 according to the following steps, and is stored in the memory 902 by the processor 901. In the application, thus implementing various functions:

Training the preset learning model according to the image sample set and the classification mark;

In some embodiments, the processor is further configured to perform: before calculating the sharpness of the at least one image to be processed in the album using the trained learning model:

Get the current time and the last management time recorded;

Determining whether the quantity is not less than a preset quantity;

Determining whether the interval duration reaches a preset duration;

In some embodiments, after deleting the image whose resolution is lower than a preset threshold, the processor is further configured to:

The remaining images after the first position are deleted.

In some embodiments, the classification mark includes unclearness, and the processor is further configured to perform: before acquiring the image sample set:

Memory 902 can be used to store applications and data. The application stored in the memory 902 contains instructions executable in the processor. Applications can form various functional modules. The processor 901 executes various functional applications and data processing by running an application stored in the memory 902.

The display screen 903 can be used to display information entered by the user or information provided to the user as well as various graphical user interfaces of the terminal, which can be composed of images, text, icons, video, and any combination thereof.

The control circuit 904 is electrically connected to the display screen 903 for controlling the display screen 903 to display information.

In some embodiments, as shown in FIG. 8, the electronic device 900 further includes a radio frequency circuit 905, an input unit 906, an audio circuit 907, a sensor 908, and a power source 909. The processor 901 is electrically connected to the radio frequency circuit 905, the input unit 906, the audio circuit 907, the sensor 908, and the power source 909, respectively.

The radio frequency circuit 905 is used for transmitting and receiving radio frequency signals to establish wireless communication with network devices or other electronic devices through wireless communication, and to transmit and receive signals with network devices or other electronic devices.

The input unit 906 can be configured to receive input digits, character information, or user characteristic information (eg, fingerprints), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function controls. The input unit 906 can include a fingerprint identification module.

The audio circuit 907 can provide an audio interface between the user and the terminal through a speaker and a microphone.

Electronic device 900 may also include at least one type of sensor 908, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel according to the brightness of the ambient light, and the proximity sensor may close the display panel and/or the backlight when the terminal moves to the ear. . As a kind of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in all directions (usually three axes). When it is stationary, it can detect the magnitude and direction of gravity. It can be used to identify the gesture of the mobile phone (such as horizontal and vertical screen switching, related Game, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for the terminal can also be configured with gyroscopes, barometers, hygrometers, thermometers, infrared sensors and other sensors, no longer Narration.

Power source 909 is used to power various components of electronic device 900. In some embodiments, the power supply 909 can be logically coupled to the processor 901 through a power management system to enable functions such as managing charging, discharging, and power management through the power management system.

Although not shown in FIG. 8, the electronic device 900 may further include a camera, a Bluetooth module, and the like, and details are not described herein again.

It will be understood by those skilled in the art that all or part of the steps of the various methods in the above embodiments may be completed by instructions or controlled by related hardware, which may be stored in a computer readable storage medium. And loaded and executed by the processor. To this end, an embodiment of the present invention provides a storage medium in which a plurality of instructions are stored, which can be loaded by a processor to perform the steps in any of the album management methods provided by the embodiments of the present invention.

The storage medium may include: a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

The steps in the album management method provided in the embodiment of the present invention can be implemented by using the instructions stored in the storage medium. Therefore, any album management method provided by the embodiment of the present invention can be implemented. For the beneficial effects, see the previous embodiments in detail, and details are not described herein again.

For the specific implementation of the foregoing operations, refer to the foregoing embodiments, and details are not described herein again.

In the above, although the present application has been disclosed in the above preferred embodiments, the above-described preferred embodiments are not intended to limit the application, and those skilled in the art can make various kinds without departing from the spirit and scope of the present application. The scope of protection of the present application is subject to the scope defined by the claims.

Claims

A photo album management method is applied to an electronic device, which includes:

Obtaining an image sample set and a classification mark corresponding to each image sample in the image sample set;

Performing a training on the preset learning model according to the image sample set and the classification mark;

Using the trained learning model to calculate the sharpness of at least one image to be processed in the album;

The image whose resolution is lower than a preset threshold is deleted to manage the album.
The album management method according to claim 1, wherein before calculating the sharpness of at least one image to be processed in the album by using the trained learning model, the method further comprises:

Get the current time and the last management time recorded;

Determining an image to be processed from the album according to the current time and the last management time recorded;

Performing the operation of calculating the sharpness of at least one image to be processed in the album by using the trained learning model, while saving the current time as a management time record, and returning to perform the acquisition current time and the recorded upper A management time operation.
The album management method according to claim 2, wherein the determining the image to be processed from the album according to the current time and the recorded last management time comprises:

Determining the number of images in the album in which the shooting time is between the current time and the last management time recorded;

Determining whether the quantity is not less than a preset quantity;

If so, an image of the shooting time between the current time and the last management time recorded is taken as the image to be processed.
The album management method according to claim 2, wherein the determining the image to be processed from the album according to the current time and the recorded last management time comprises:

Calculate the interval between the current time and the last recorded management time;

Determining whether the interval duration reaches a preset duration;

If so, an image taken in the album during the interval duration is taken as an image to be processed.
The album management method according to claim 1, wherein after deleting the image whose resolution is lower than a preset threshold, the method further includes:

Calculating a similarity between the remaining images in the image to be processed;

Sorting the remaining images with similarity higher than the preset similarity according to the definition from high to low;

The remaining images after the first position are deleted.
The album management method according to claim 1, wherein the classification mark includes unclearness, and before acquiring the image sample set, the method further includes:

Detecting whether the electronic device currently has a continuous shooting operation;

If yes, acquiring a plurality of captured images generated by the continuous shooting operation;

Acquiring a captured image deleted by the user from the plurality of captured images as a target image;

A classification mark indicating an unclear is generated for the target image, and the target image is added to the image sample set.
The album management method according to claim 1, wherein the learning model is a deep neural network model, and the training of the preset learning model according to the image sample set and the classification mark comprises:

Each image sample in the image sample set and its corresponding classification mark are input into a preset depth neural network model to adjust the weight value and the parameter value of each layer of the neural network.
An album management device is applied to an electronic device, including:

An obtaining module, configured to acquire an image sample set, and a classification mark corresponding to each image sample in the image sample set;

a training module, configured to train the preset learning model according to the image sample set and the classification mark;

a calculation module, configured to calculate a sharpness of at least one image to be processed in the album by using the trained learning model;

And a deleting module, configured to delete the image whose resolution is lower than a preset threshold to manage the album.
The album management device according to claim 8, wherein the album management device further comprises a determining module, configured to:

Obtaining a current time and a recorded last management time before the calculating module calculates the sharpness of at least one image to be processed in the album by using the trained learning model;

Determining an image to be processed from the album according to the current time and the last management time recorded;

Trimming the calculation module to perform the operation of calculating the sharpness of at least one image to be processed in the album by using the trained learning model, while saving the current time as a management time record, and returning to perform the acquisition current time, And the last recorded management time operation.
The album management device according to claim 9, wherein said determining module is configured to:

Determining the number of images in the album in which the shooting time is between the current time and the last management time recorded;

Determining whether the quantity is not less than a preset quantity;

If so, an image of the shooting time between the current time and the last management time recorded is taken as the image to be processed.
The album management device according to claim 9, wherein said determining module is configured to:

Calculate the interval between the current time and the last recorded management time;

Determining whether the interval duration reaches a preset duration;

If so, an image taken in the album during the interval duration is taken as an image to be processed.
The album management device according to claim 8, wherein the deleting module is further configured to: after deleting the image whose resolution is lower than a preset threshold;

Calculating a similarity between the remaining images in the image to be processed;

Sorting the remaining images with similarity higher than the preset similarity according to the definition from high to low;

The remaining images after the first position are deleted.
The album management device according to claim 8, wherein the classification mark includes unclearness, and the album management device further includes an adding module, configured to:

Before the acquiring module acquires the image sample set, detecting whether the electronic device currently has a continuous shooting operation;

If yes, acquiring a plurality of captured images generated by the continuous shooting operation;

Acquiring a captured image deleted by the user from the plurality of captured images as a target image;

A classification mark indicating an unclear is generated for the target image, and the target image is added to the image sample set.
The album management device according to claim 8, wherein the learning model is a deep neural network model, and the training module is specifically configured to:

Each image sample in the image sample set and its corresponding classification mark are input into a preset depth neural network model to adjust the weight value and the parameter value of each layer of the neural network.
A storage medium, wherein the storage medium stores a plurality of instructions adapted to be loaded by a processor to perform the album management method of claim 1.
An electronic device comprising a processor and a memory, the processor being electrically connected to the memory, the memory for storing instructions and data, the processor for performing the album management method according to claim 1. A step of.
The electronic device of claim 16, wherein the processor is further configured to perform: before calculating the sharpness of the at least one image to be processed in the album using the trained learning model:

Get the current time and the last management time recorded;

Determining an image to be processed from the album according to the current time and the last management time recorded;

Performing the operation of calculating the sharpness of at least one image to be processed in the album by using the trained learning model, while saving the current time as a management time record, and returning to perform the acquisition current time and the recorded upper A management time operation.
The electronic device of claim 17, wherein the processor is specifically configured to perform:

Determining the number of images in the album in which the shooting time is between the current time and the last management time recorded;

Determining whether the quantity is not less than a preset quantity;

If so, an image of the shooting time between the current time and the last management time recorded is taken as the image to be processed.
The electronic device of claim 17, wherein the processor is specifically configured to perform:

Calculate the interval between the current time and the last recorded management time;

Determining whether the interval duration reaches a preset duration;

If so, an image taken in the album during the interval duration is taken as an image to be processed.
The electronic device according to claim 16, wherein after deleting the image whose resolution is lower than a preset threshold, the processor is further configured to:

Calculating a similarity between the remaining images in the image to be processed;

Sorting the remaining images with similarity higher than the preset similarity according to the definition from high to low;

The remaining images after the first position are deleted.