WO2019056938A1 - Image processing method, and computer device, and computer-readable storage medium - Google Patents

Abstract

Provided is an image processing method. The method comprises: acquiring a clustering request in a request queue, wherein the request queue comprises clustering requests arranged in sequence, and the clustering request is used for instructing to carry out clustering on images of a terminal; merging, according to a rule, the clustering requests corresponding to the same account; and carrying out processing on the merged clustering request.

Description

Image processing method and computer device, computer readable storage medium

Cross-reference to related applications

The present application claims priority to Chinese Patent Application No. 200910854675.6, entitled "Image Processing Method and Apparatus, Computer Equipment, Computer Readable Storage Medium", filed on September 20, 2017, the entire contents of which are hereby incorporated by reference. This is incorporated herein by reference.

Technical field

The present application relates to the field of computer technology, and in particular, to an image processing method and a computer device, and a computer readable storage medium.

Background technique

Users can implement various application requirements through smart terminals. However, due to the limited processing power and storage capacity of smart terminals, it is often impossible to process a large number of user operations locally. Therefore, in order to better provide services for the user, after receiving the operation request of the user, the smart terminal sends the operation request to the server for processing, and then returns the processing result to the smart terminal. In this way, the application requirements of the user can be realized without consuming too many resources of the intelligent terminal.

For a single user request, the server can respond quickly and accurately. If you face thousands of users, and a single user may have multiple requests, the server's pressure will multiply. For example, the smart terminal needs to send an album to the server for backup, and at the same time, classify the photos in the album.

Summary of the invention

An embodiment of the present application provides an image processing method, a computer device, and a computer readable storage medium.

An image processing method, the method comprising:

Obtaining a clustering request in a request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered;

Merging clustering requests corresponding to the same account according to rules;

The merged cluster request is processed.

A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following operations:

Obtaining a clustering request in a request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered;

Merging clustering requests corresponding to the same account according to rules;

The merged cluster request is processed.

A computer readable storage medium having stored thereon a computer program, the computer program being executed by the processor as follows:

Obtaining a clustering request in a request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered;

Merging clustering requests corresponding to the same account according to rules;

The merged cluster request is processed.

DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings to be used in the embodiments or the prior art description will be briefly described below. Obviously, the drawings in the following description are only It is a certain embodiment of the present application, and other drawings can be obtained according to the drawings without any creative work for those skilled in the art.

1 is a schematic diagram of an application environment of an image processing method in an embodiment;

2 is a schematic diagram of an application environment of an image processing method in another embodiment;

3 is a schematic diagram of an application environment of an image processing method in still another embodiment;

4 is a hardware interaction timing diagram of an image processing method in an embodiment;

Figure 5 is a flow chart of an image processing method in an embodiment;

6 is a flow chart of an image processing method in another embodiment;

Figure 7 is a flow chart of an image processing method in still another embodiment;

8 is a display diagram of a mobile terminal album classification result in an embodiment;

9 is a server architecture diagram of an image processing method implemented in an embodiment;

FIG. 10 is a schematic structural diagram of an image processing apparatus in an embodiment; FIG.

11 is a schematic structural diagram of an image processing apparatus in another embodiment;

12 is a schematic diagram showing the internal structure of a server in an embodiment;

FIG. 13 is a block diagram showing a part of a structure of a mobile phone related to a computer device according to an embodiment of the present application.

Detailed ways

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting.

It will be understood that the terms "first", "second" and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first client may be referred to as a second client, and similarly, a second client may be referred to as a first client, without departing from the scope of the present application. Both the first client and the second client are clients, but they are not the same client.

FIG. 1 is a schematic diagram of an application environment of an image processing method in an embodiment. As shown in FIG. 1, the application environment includes a first server 12 and a second server cluster 14. The first server 12 is configured to generate a request queue according to the clustering request, and merge the clustering requests corresponding to the same account in the request queue. It can be understood that the clustering request may be initiated by the first server 12 or may be sent by other terminals that are received. The first server 12 is further configured to find the second server 142 in the second server cluster 14 that is in an available state, acquire an image corresponding to the merged cluster request, and send the acquired image to the second server 142. After receiving the image, the second server 142 performs clustering processing on the image. The first server 12 and the second server 142 are devices for providing a computing service in response to a service request, and may be, for example, one or more computers. One or more second servers 142 may be included in the second server cluster 14.

2 is a schematic diagram of an application environment of an image processing method in another embodiment. As shown in FIG. 2, the application environment includes a user terminal 22, a first server 24, and a second server cluster 26. The user terminal 22 is configured to send a clustering request and a corresponding image set to the first server 24. After receiving the clustering request, the first server 24 generates a request queue according to the clustering request, and merges the clustering requests corresponding to the same account in the request queue. The first server 24 can find the second server 262 in the second server cluster 26 in an available state, acquire the image set corresponding to the merged cluster request, and send the acquired image set to the second server 262 in the available state. After receiving the image set, the second server 262 processes the image set. The processing result obtained by the second server 262 in the clustering process may be returned to the user terminal 22 through the first server 24, or may be directly returned to the user terminal 22. The first server 24 and the second server 262 are devices for providing a computing service in response to a service request, and may be, for example, one or more computers. The second server cluster 26 may include one or more second servers 262 for implementing distributed processing on tasks. The user terminal 22 is an electronic device that is mainly used to input user information and output processing results at the outermost periphery of the computer network, and may be, for example, a personal computer, a mobile terminal, a personal digital assistant, a wearable electronic device, or the like. In the embodiment provided by the present application, the user terminal 22 may include one or more.

FIG. 3 is a schematic diagram of an application environment of an image processing method in still another embodiment. As shown in FIG. 3, the application environment includes a user terminal 32, a first server 34, a second server cluster 36, and a third server cluster 38. The user terminal 32 transmits an image set to the third server cluster 38. The third server cluster 38 performs feature recognition processing on the image set, and transmits the obtained cluster feature set to the user terminal 32. The user terminal 32 generates a clustering request according to the clustering feature set, and sends the clustering request to the first server 34. The first server 34 generates a request queue after receiving the clustering request, and clusters the same account in the request queue. Request to merge. The first server 34 may search the second server 362 in the available state of the second server cluster 36, obtain the cluster feature set corresponding to the merged cluster request, and send the acquired cluster feature set to the available state. Two servers 362. After receiving the cluster feature set, the second server 362 performs clustering processing on the cluster feature set. The second server 362 may transmit the clustering processing result to the user terminal 32 through the first server 34, or may directly send the clustering processing result to the user terminal 32. The user terminal 32 classifies the image set based on the clustering processing result. In one embodiment, the third server cluster 38 can send the clustering feature set directly to the second server cluster 36. The first server 34, the second server 362, and the third server 382 are devices for providing a computing service in response to a service request, and may be, for example, one or more computers. One or more second servers 382 may be included in the second server cluster 36, and one or more third server clusters 382 may be included in the third server cluster 38. The user terminal 32 is an electronic device that is mainly used to input user information and output processing results at the outermost periphery of the computer network, and may be, for example, a personal computer, a mobile terminal, a personal digital assistant, a wearable electronic device, or the like. In the embodiment provided by the present application, the user terminal 32 may include one or more.

4 is a hardware interaction timing diagram of an image processing method in one embodiment. As shown in FIG. 4, the hardware interaction process of the image processing method includes operations 402 through 410. among them:

Operation 402, the user terminal sends a clustering request to the first server.

In the embodiments provided herein, clustering refers to the process of dividing a collection of objects into a plurality of combinations of objects, each combination of objects being composed of one or more similar objects. The clustering request refers to a command for performing clustering processing on the clustering object set. For example, the clustering object set may be a set corresponding to the image of the terminal. A request queue is a queue formed by one or more clustering requests, and the clustering request is processed according to the request queue. The user terminal may preset a condition for triggering a clustering request to the first server, where the set clustering triggering condition includes at least one of the following methods: the number of newly added pictures in the mobile terminal is greater than a preset number; the current time is a preset time. The time from the last time the clustering request was initiated exceeds the preset time period; the mobile terminal is currently in the charging state.

Operation 404, the first server merges the clustering requests corresponding to the same account in the request queue.

In an embodiment, the clustering request corresponding to the same account in the request queue may be acquired at a preset time interval. The clustering request in the request queue is also sorted according to the request initiation time, and the specified clustering request in the sorted request queue is obtained; and the same clustering request in the request queue corresponding to the account corresponding to the specified clustering request is obtained. Further, the clustering request in the request queue is arranged in a first-to-last order according to the request initiation time, and the first clustering request in the request queue is obtained; and the same account in the request queue corresponding to the first clustering request is acquired. Class request.

Operation 406, the first server sends the merged clustering request to the second server.

In an embodiment, the second server may be a server cluster, and may first obtain status identifiers of the respective second servers, and find a second server that is in an available state according to the status identifier. Further, the obtaining the status identifier of the second server may specifically include at least one of the following methods: the first server receives the status identifier sent by the second server; the first server obtains the status identifier from the status identifier list, where the status identifier list is It is formed according to the status identifier reported by each second server. If there are multiple servers in the available state, the file size of the cluster object set can be obtained, and the cluster object set of the corresponding file size is matched according to the carrying capacity of the plurality of servers in the available state.

Operation 408, the second server receives the merged cluster request and processes the merged cluster request.

In one embodiment, the first server may perform an encryption process on the image before sending the clustering request to the second server. The first server sends the encrypted processed image to the second server in an available state, instructing the second server to perform decryption processing on the clustered object set, and clustering the decrypted clustered object set deal with. The second server may generate label data according to the processing result obtained by the processing, and store the label data in a preset storage space, or may send the label data to the user terminal.

Operation 410, the second server sends the processing result to the user terminal.

In an embodiment, the second server performs clustering processing on the cluster object set corresponding to the clustering request, and obtains a processing result. The clustering object set may be a clustering image set or a clustering feature set corresponding to the clustering image set. A cluster image collection refers to a collection of images that need to be clustered. Each image corresponds to one or more features, and a set of features corresponding to all images in the cluster image set is a cluster feature set. That is to say, the second server may perform clustering processing according to the cluster image set, or may perform clustering processing according to the feature set corresponding to the cluster image set, which is not limited herein. If the second server receives the cluster image set, the cluster image set needs to be subjected to feature recognition processing, and then the cluster feature set obtained by the feature recognition process is clustered. After receiving the processing result, the user terminal classifies the image according to the processing result.

Figure 5 is a flow chart of an image processing method in one embodiment. As shown in FIG. 5, the image processing method includes operations 502 through 506. among them:

Operation 502: Acquire a clustering request in the request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered.

In the embodiments provided herein, clustering refers to the process of dividing a collection of objects into a plurality of combinations of objects, each combination of objects being composed of one or more similar objects. The clustering request refers to a command for performing clustering processing on the clustering object set. For example, the clustering object set may be a set corresponding to the image of the terminal. A request queue is a queue formed by one or more clustering requests, and the clustering request is processed according to the request queue.

It can be understood that the clustering request may be a request sent from one device to another device, or may be a request initiated and processed in the same device. For example, the clustering request initiated by the user terminal may be processed locally at the user terminal or may be sent to the server for processing. Generally, when the sending device sends a clustering request to the receiving device, the clustering request includes information such as a requesting device identifier, a requesting device identifier, a request originating time, and a clustering object set. After receiving the clustering request, the receiving device performs clustering processing according to the clustering object set. If the receiving device receives multiple clustering requests of multiple sending devices, a request queue is formed according to the multiple clustering requests, and the clustering request in the request queue is processed. The request initiation device identifier refers to the unique identifier of the device that initiates the clustering request, and the request receiving device identifier refers to the unique identifier of the device that receives the clustering request, and the cluster initiation time refers to the time when the clustering request is initiated, and the clustering object The set may also be a cluster object identifier, that is, the receiving device may search for an image according to the cluster object identifier and perform clustering processing. For example, the image set is pre-stored on the server, and when the mobile terminal sends the clustering request, only the cluster object identifier needs to be sent, and the server can directly search for the image set according to the cluster object identifier and perform clustering processing.

In an embodiment, multiple application accounts can be logged in the smart terminal. When the application account needs to perform clustering processing, the smart terminal sends a clustering request to the server. The application account refers to an account that is logged in on the smart terminal and is used to distinguish different user operations. Different users can be distinguished by applying the account identifier, and smart terminals with different regions can be identified by the terminal identifier. The application account identifier refers to a unique identifier used to represent the identity of the user, and the terminal identifier refers to a unique identifier that distinguishes different smart terminal devices. The terminal identifier may be, but not limited to, an IP (Internet Protocol) protocol, a MAC (Media Access Control) address, and the like of the smart terminal. For example, the smart terminal may initiate a clustering request to the server, and the server may connect multiple smart terminals and receive clustering requests sent by multiple smart terminals. The user can log in to the smart terminal through the application account, and send a request for clustering the photos in the album to the server through the smart terminal. After receiving the clustering request sent by the smart terminal, the server performs clustering processing on the photos in the album. The result of the clustering process is returned to the smart terminal.

Operation 504, merging clustering requests corresponding to the same account according to rules.

In one embodiment, the clustering requests in the request queue are arranged in a certain order. For example, it is arranged according to the request initiation time, or arranged according to the number of cluster images. The clustering request corresponding to the same account is obtained, and the clustering request corresponding to the same account is merged, and the clustering request corresponding to the same application account identifier is the clustering request corresponding to the same account. The merge clustering request refers to a process of combining multiple clustering requests into one clustering request and processing the merged clustering request, and implementing multiple clustering requests simultaneously. Specifically, the application account identifiers included in each cluster request in the request queue are obtained, and the clustering requests with the same application account identifiers in the request queue are merged.

After obtaining the clustering request corresponding to the same account, you may first count the number of clustering requests corresponding to the same account, and determine whether the number of statistics exceeds the preset number. If the preset number is exceeded, the obtained number will be obtained. The clustering request corresponding to the same account is merged; if the preset number is not exceeded, the waiting state will be entered, and the number of clustering requests corresponding to the same account exceeds the preset number, and then the same account is aggregated. Class requests are merged. For example, the preset number may be two. If only one clustering request corresponding to the same account is obtained, the waiting state is entered, and the number of clustering requests corresponding to the same account exceeds two and then merged. On this basis, in order to prevent the system from waiting to affect the processing of the image, an upper limit value of the waiting time can be set. If the waiting time exceeds the upper limit of the waiting time, the number of clustering requests corresponding to the same account is still If it is less than the preset number, stop waiting and directly perform subsequent processing. Specifically, if the waiting time exceeds the preset waiting time, the waiting is stopped, and the acquired clustering requests corresponding to the same account are directly merged.

It can be understood that when the clustering request in the request queue is processed, the processing speed can be controlled, and each time the processing is performed, the clustering request in a certain time period is acquired for processing. Then, the operations 502 to 504 may specifically include: acquiring the clustering request in the request queue according to the preset time period, and combining the clustering requests in the preset time period corresponding to the same account according to the rule. Specifically, it may be that the clustering request initiated in the time period is obtained for processing at a certain interval, or may be performed after the last time the clustering request processing is completed, and the next clustering request is performed at a certain interval. For example, it may be that when the whole point is obtained, the clustering request of the previous time period is acquired for merging, then at 11:00, the clustering request initiated between 10:00 and 11:00 is acquired for merging. It is also possible to wait for half an hour for the next processing after the last clustering request processing is completed.

Operation 506, processing the merged cluster request.

In an embodiment, the merged clustering request may be processed by directly clustering the image corresponding to the clustering request directly, or sending the clustering request to the server to indicate the server pairing. The corresponding image is requested to be clustered, which is not limited herein. The operation 506 may specifically include: sending a clustering request, and/or performing clustering processing on the image of the terminal according to the clustering request. After the clustering request is merged, the clustering object set in each clustering request is merged. Since the application accounts of the merged clustering request are the same, after the merged clustering request is processed, the obtained clustering processing result may be Send directly to the smart terminal where the application account is logged in.

In the embodiment provided by the present application, the clustering request may be processed by a server cluster containing one or more servers, which may be used for clustering the clustering object set. Generally, when a server provides a clustering service, the working state can be divided into an available state and a non-available state. In the available state, the server can receive the clustering request and cluster the clustering request; in the non-available state, the server cannot receive the clustering request and clusters the clustering request. For example, when a server is performing a task or a hardware failure occurs, it is marked as unavailable, and the server cannot perform clustering in the non-available state.

Specifically, each server can indicate the current working status by using the status identifier, and the status identifier can be obtained to determine whether the server is available. That is, the server that is in the available state may include: obtaining the status identifier of each server, and searching for the server in the available state according to the status identifier. Further, the status identifier of the obtaining server may include at least one of the following methods: receiving a status identifier sent by the server, and obtaining a status identifier from the status identifier list, where the status identifier list is formed according to the status identifier reported by each server. .

After the server in the available state is found, the clustering request is sent to the server in the available state for clustering. The cluster object set corresponding to the merged cluster request includes all cluster object sets in the plurality of clustering requests before the merge, and if there are repeated cluster objects, only one clustering process is needed. The clustering object set refers to a set of objects used for performing clustering processing. For example, the clustering object set may be a set of images of the terminal, or may be a set of features corresponding to the image of the terminal.

The image processing method provided by the foregoing embodiment combines the clustering requests corresponding to the same account in the request queue, and performs clustering processing on the merged clustering request. In this way, the same object in multiple clustering requests only needs to perform clustering processing once, without multiple clustering processing, which improves the efficiency of image processing and saves resources.

Figure 6 is a flow chart of an image processing method in another embodiment. As shown in FIG. 6, the image processing method includes operations 602 through 608. among them:

Operation 602: Acquire a clustering request in the request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered.

In the embodiment provided by the present application, the clustering request may include information such as a request originating device identifier, a request receiving device identifier, a request initiation time, a clustering object set, and an attribute parameter of the clustering object. The attribute parameter of the clustering object refers to a parameter indicating an attribute of the clustering object set. For example, the attribute parameter of the clustering object may refer to a file size, a size, a file format, and the like of a picture used for clustering.

Operation 604, sorting the clustering requests in the request queue according to the request initiation time, and obtaining the specified clustering request in the sorted request queue.

The clustering requests in the request queue are sorted according to the request initiation time, and the specified clustering request is obtained according to the sorted request queue. For example, the clustering request is sorted in ascending order according to the request initiation time, or the clustering request is sorted in descending order according to the request initiation time, and the specified clustering request is obtained according to the sorted request queue. Generally, after receiving a plurality of clustering requests, the request receiving device cannot process all clustering requests simultaneously due to limited processing capability. Then, the request receiving device may form a request queue according to the order in which the request is initiated, and process the clustering request with the request initiation time first, and request the clustering request post-processing after the initiation time. The specified clustering request refers to the clustering request in the request queue that meets the specified conditions, and the obtained specified clustering request is used as the clustering request currently processed. After the clustering request of the request queue is sorted according to the request initiation time, the specified clustering request may be acquired according to the request initiation time. For example, a clustering request for sorting the first bit in the request queue, sorting the clustering request for the last bit. The operation 604 may specifically include: arranging the clustering requests in the request queue according to the request initiation time in a first-to-last order, and acquiring the first clustering request in the request queue. The first clustering request obtained is the first clustering request in the request queue, that is, the clustering request with the earliest request initiation time in the request queue.

In other embodiments provided by the present application, the operation 604 may further be: sorting the clustering requests in the request queue according to the priority of the requesting initiating device, and acquiring the specified clustering request in the sorted request queue. The priority of the requesting device refers to the priority of processing the task corresponding to the requesting device, for example, the clustering request priority processing initiated by the mobile terminal, and the clustering request initiated by the PC (Personal Computer) . It is also possible to sort the clustering requests in the request queue according to the size of the clustering object set corresponding to the clustering request. For example, a clustering request that takes up a large space in an image collection is preferentially processed, and a clustering request with a small space is post-processing.

Operation 606: Acquire a clustering request in the request queue that is the same as the account corresponding to the specified clustering request, and merge the acquired clustering requests.

In one embodiment, after obtaining the specified clustering request, comparing the specified clustering request with each clustering request in the request queue, obtaining the same clustering request as the application account identifier of the specified clustering request, and The acquired clustering requests are merged. After receiving the clustering request, the clustering task list may be established according to the application account identifier in the clustering request, and the clustering task list records the application account identifier of each clustering request, the requesting device identifier, the request receiving device identifier, and the request. Initiate time and other information. The operation 606 may specifically include: acquiring an application account identifier of the specified clustering request, traversing the clustering task list, and acquiring a clustering request that is the same as the application account identifier of the specified clustering request.

Further, if the number of clustering requests in the request queue corresponding to the account corresponding to the specified clustering request is the same, the number of clustering requests in the request queue corresponding to the account corresponding to the specified clustering request exceeds a preset number. And obtain the same clustering request in the request queue as the account corresponding to the specified clustering request. If the number of clustering requests in the request queue that are the same as the account corresponding to the specified clustering request is less than the preset number, the waiting state is entered; if the waiting time exceeds the preset time, the waiting is stopped, and the request is obtained. The same clustering request in the queue as the account corresponding to the specified clustering request.

In the embodiment provided by the present application, the acquired clustering requests are merged, specifically, the clustering object sets corresponding to the respective clustering requests are merged. The obtaining the cluster object set may include: acquiring a cluster object set corresponding to the clustering request of the same account, and performing clustering processing on the acquired union of the cluster object set. That is, for multiple clustering requests initiated by the same application account, the clustering object set of the clustering request may be merged and processed together, and each clustering object is processed only once without repeating clustering in the clustering request. The object is processed repeatedly multiple times. For example, the clustering request queue includes three clustering requests, which are arranged in chronological order: clustering request 1, and application account A sends a clustering request at 03:30 on August 20, 2017, including Clustering object set 1; clustering request 2, clustering request sent by application account B at 02:41 on August 21, 2017, including cluster object set 2; clustering request 3, application account A in August 2017 The clustering request sent at 04:02 on the 22nd contains the clustering object set 3. Then, the clustering request 1 and the clustering request 3 are merged, and the clustering object set obtained after the combination is the union of the clustering object set 1 and the clustering object set 3.

Operation 608, processing the merged cluster request.

In one of the embodiments, the clustering process can be implemented by the server cluster, and each server can indicate the current working state through the status identifier, and the status identifier can be used to obtain the server in the available state. If there are multiple servers in the available state, the file size of the cluster object set can be obtained, and the cluster object set of the corresponding file size is matched according to the carrying capacity of the plurality of servers in the available state. For example, a cluster object collection that occupies a large space is sent to a server with a large carrying capacity for clustering processing.

The sending the clustering object set to the server in the available state for performing the clustering process may include: if there are two or more servers in the available state, acquiring the clustering object corresponding to the merged clustering request The attribute parameters of the collection, and the load parameters of the server; the server is searched according to the attribute parameter and the load parameter, and the cluster object set is sent to the server for clustering processing. The attribute parameter may be a file size of the cluster object collection, and the load parameter refers to a parameter indicating a maximum load processing capability of the server. The larger the general load parameter, the stronger the processing power of the server; the smaller the load parameter, the weaker the processing capability of the server.

The image processing method provided by the foregoing embodiment first sorts the request queue according to the request initiation time, and acquires the specified cluster request in the request queue. The clustering request in the request queue for the same account as the specified clustering request is then merged, and the merged clustering request is processed. In this way, when the same clustering object is used in multiple clustering requests, only one processing is needed, and multiple clustering processing is not required, which improves the efficiency of image processing and saves resources.

Figure 7 is a flow chart of an image processing method in still another embodiment. As shown in FIG. 7, the image processing method includes operations 702 through 712. among them:

Operation 702: Acquire a clustering request in the request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered.

In one embodiment, the clustering request includes a clustering object set, and the clustering object set may be a clustering image set and/or a clustering feature set. The cluster image set refers to a set of images used for clustering processing, and the cluster feature set refers to a set of features corresponding to images used for clustering. The operation 702 may further include: acquiring a cluster object set, wherein the cluster object set may be a cluster image set, or may be a set of features extracted according to the cluster image set. For example, if the images in the album are classified according to the face, then the cluster feature set is a set of face regions in the image. The clustering feature can represent the features used for classification in the cluster image collection, and only upload the cluster feature set for clustering processing, which improves the efficiency of clustering. It can be understood that the clustering feature set may be uploaded by the smart terminal, or may be uploaded by the feature server, where the feature server refers to a server that performs feature recognition processing on the cluster image set, and the feature server sends the aggregate according to the smart terminal. The clustering features extracted by the class of image collection form a clustering feature set.

In an embodiment, when the smart terminal requests to cluster the pictures in the album, the condition for triggering the clustering request to the first server may be set in advance, and the set clustering triggering condition includes at least one of the following methods: The number of new pictures in the smart terminal is greater than the preset number; the current time is the preset time; the time from the last time the clustering request is initiated exceeds the preset time period; the smart terminal is currently in the charging state. For example, when the new terminal adds more than 50 pictures, if the current time is between 2 am and 5 am, and the smart terminal is in the charging state, the smart terminal initiates a clustering request.

The smart terminal stores the image in the storage space, and the smart terminal can directly obtain the image from the preset storage address, or traverse all the folders in the smart terminal to obtain the image. Generally, the storage space of the smart terminal is divided into an internal memory and an external memory. The internal memory refers to the memory that the mobile terminal itself has, and is a part of the hardware structure of the intelligent terminal. The external storage refers to a storage device external to the mobile terminal, and the external storage can perform data transmission with the smart client through a dedicated interface. For example, the external storage may be an SD card, a USB flash drive or the like. The album sent by the smart terminal may include all the pictures stored by the smart terminal, or may only include a part of the pictures stored by the smart terminal. For example, the album sent by the smart terminal may include all the pictures in the internal memory and the external memory, or may only include the pictures in the internal memory.

Operation 704, the clustering request in the request queue is arranged according to the request initiation time in a first-to-last order, obtaining the first clustering request in the request queue, and acquiring the same aggregation in the request queue corresponding to the first clustering request. Class request.

In an embodiment, the smart terminal may initiate a clustering request to the first server, and after receiving the clustering request sent by the smart terminal, the first server generates a request queue according to the received clustering request, and allocates to each by request queue control. The clustering task of the second server. Generally, the request queues generated by the first server are arranged in the order of the request initiation time, that is, the clustering request with the top requesting time is prioritized, and the clustering request located first in the request queue is preferentially processed each time. The first clustering request is the first clustering request in the request queue.

In an embodiment, after obtaining the first clustering request, the application account identifier corresponding to each clustering request in the request queue may be traversed, and the clustering request with the same application account identifier corresponding to the first clustering request is obtained, that is, the same The clustering request corresponding to the account. Generally, clustering cluster image collections has a classification criterion, then it is necessary to first extract features of each image in the cluster image collection, and then classify the images according to the extracted features. That is to say, the received cluster object set may be a cluster feature set or a cluster image set. For example, according to the face classification of the pictures in the album, it is necessary to know which category the picture belongs to according to the extracted face area, then the cluster image set is the entire album, and the cluster feature set is extracted from each picture in the album. A collection of face regions. If the clustering object set is a feature set, the clustering process may be directly performed according to a preset clustering algorithm; if the clustering object set is a to-be-classified object set, the feature forming feature set in the to-be-classified object set is first extracted, and The feature set is clustered according to a preset clustering algorithm, and the classified object set can be classified according to the clustering processing result.

In one embodiment, the cluster object set corresponding to the cluster request of the same account is obtained, and the acquired union of the cluster object set is used as the final processed cluster object set. For example, the same account initiates two clustering requests, the first clustering request contains 50 images, and the second clustering request contains 60 images. The first and second clustering requests are included. 10 repeated pictures, then after the first and second clustering requests are merged, the 10 repeated pictures are processed only once, and then the combined clustering request contains a total of 100 pictures.

Operation 706, the obtained clustering request is classified according to the terminal identifier.

In one embodiment, the same application account can log in to multiple smart terminals, and one smart terminal can only log in to one application account at the same time. Therefore, the clustering requests corresponding to the same account can be classified according to the terminal identifier. Each clustering request includes an application account identifier and a corresponding terminal identifier. After obtaining a clustering request corresponding to the same account, the clustering request is classified according to different terminal identifiers. Generally, the clustering object collections in different intelligent terminals are different. When the application accounts are logged into different intelligent terminals, different clustering object sets can be initiated to the server.

Operation 708: Acquire a target clustering request in each type of clustering request, and merge the obtained target clustering requests, where the target clustering request is in each type of clustering request, and the interval between the request time and the current time is the smallest. Clustering request.

In one embodiment, the target clustering request refers to a clustering request for merge processing in the acquired clustering request. An intelligent terminal can initiate multiple clustering requests. At this time, it can be judged according to the initiation time. It is not necessary to process each request, and only needs to process the latest request. Firstly, the clustering requests corresponding to the same account are classified according to the terminal identifier, and the clustering request recently initiated in each type of clustering request is obtained, that is, the clustering request with the smallest interval from the current time is requested. For example, the clustering request corresponding to the same account includes: clustering request 1, the clustering request sent by the terminal identifier A at 03:30 on August 20, 2017, including the clustering object set 1; the clustering request 2, the terminal identifier B The clustering request sent at 02:41 on August 21, 2017, including the clustering object set 2; the clustering request 3, the clustering request sent by the terminal identifier B at 04:02 on August 22, 2017, including clustering The object set 4; the clustering request 3, the clustering request sent by the terminal identifier B at 05:02 on August 25, 2017, includes the clustering object set 4. Then, the acquired target clustering request includes a clustering request 2 and a clustering request 4, and the clustering request 2 and the clustering request 4 are combined.

Operation 710, processing the merged cluster request.

In an embodiment, the server may report the working status in real time or periodically, and generate a status label list according to the reported working status, where the server identifier and the corresponding status identifier are recorded in the status label list. The server identifier is a unique identifier that distinguishes different servers, and the state identifier is an identifier that marks the working status of the server. By reading the status label list, you can obtain the status labels of each server, and obtain the working status of each server according to the status labels. It can be understood that the status label list may include only the server identifier corresponding to the server in the available state, that is, the server corresponding to the server identifier recorded in the status label list is in an available state, and the status label list is empty, indicating that the status label list is empty. All servers are unavailable. After finding the server in the available state, the target server can also be searched by the preset routing algorithm, and the cluster object collection is sent to the target server for clustering. For example, the preset routing algorithm may be a load balancing algorithm, and the load balancing algorithm may be a random algorithm, a polling algorithm, a source address hash algorithm, etc., and is not limited herein.

In the embodiment provided by the present application, before the operation 710, the method further includes: performing a cryptographic process on the cluster object set. Encryption processing refers to the process of changing the original information by a special algorithm so that unauthorized users cannot know the original information. The clustering object set can be encrypted by an encryption algorithm such as 3DES (Triple Data Encryption Algorithm) or RC5. It can be understood that the encryption processing on the clustering object set may be before the operation 702, that is, after the first server receives the clustering request, the clustering object set after the encryption processing is cached into the clustering request queue.

If the clustering object set is encrypted, the clustering object set needs to be decrypted before the clustering object set is clustered. The operation 710 specifically includes: sending the merged clustering request and the corresponding encrypted clustering object set to the server, and instructing the server to perform decryption processing on the clustering object set, and decrypting the processed The clustering object set is clustered. The decryption process refers to a process of restoring the encrypted information to the original information, and the encryption process and the decryption process are opposite processes.

The clustering process can divide the cluster object set into a plurality of categories according to one or more features. For example, people can be divided into men and women according to their gender. They can be divided into teenagers, youth, middle-aged and so on according to their age. There are more combinations according to gender and age. Generally, clustering object sets can be classified according to the clustering model. Commonly used clustering models include k-means clustering model, hierarchical clustering model, SOM clustering model and FCM clustering model. Further, the clustering model can be trained according to the clustering sample set. The cluster sample set refers to a sample set used for training to obtain a model, and the cluster sample set may be a sample set generated according to the cluster object set, or may be a sample set specifically used for training the cluster model. The result of the clustering process is used to classify the image set. The operation 710 may further include: sending, by the server, the clustering process result to the smart terminal, where the clustering process result is used to indicate the smart terminal to the terminal. The images are classified.

Operation 712, generating tag data according to the processing result obtained by processing the clustering request.

In one embodiment, the tag data refers to an identifier for marking the classification attribute of the cluster object in the cluster object set. For example, if the cluster object 1 belongs to the category 1, the formed tag data may be “category 1”. It can be understood that each cluster object in the cluster object set has a corresponding object identifier, which is used to identify the uniqueness of the cluster object. According to the tag data generated by the clustering processing result, a one-to-one correspondence can be established with the object identifier. The generated tag data may be stored in a preset storage space or may be sent to the user terminal, which is not limited herein.

It can be understood that if there is a clustering object that has been clustered before in the cluster object set corresponding to the currently initiated clustering request, the processed clustering objects can be extracted without clustering. Currently, only clustering processing is performed on unprocessed cluster objects. The operation 710 may specifically include: sending the cluster object set to the server in an available state, and instructing the server to perform clustering processing on the cluster object other than the historical cluster object in the cluster object set. Among them, the historical clustering object refers to the clustering object processed by history. The object identifier of the cluster object in the cluster object collection may be compared with the object identifier corresponding to the label data stored in the preset storage space, and the cluster object matching the object identifier is a historical cluster object. In addition, the request initiation object identifier of the cluster object collection and the request initiation object identifier corresponding to the label data may be matched first. If there is a matching request initiation object identifier, the historical cluster object is searched according to the object identifier.

In an embodiment, the clustering model stored on the server is updated, and each version of the clustering model has a corresponding model identifier, and the label data stored in the preset storage space also has a corresponding model version for marking. The version of the clustering model that generates the tag data. If there is a historical clustering object in the clustering object set, the model identifier corresponding to the historical clustering object may be compared with the model identifier of the current clustering model. If they are the same, the historical clustering object is not re-clustering; if different , the historical clustering object is re-clustered. After re-clustering, the re-clustered processing result of the historical clustering object generates label data to cover the original label data.

FIG. 8 is a diagram showing a result of classification of a mobile terminal album in one embodiment. As shown in FIG. 8, the mobile terminal classifies the pictures in the album according to the clustering processing result returned by the server, and displays the classified result on the interface of the mobile terminal. Six classification results are displayed on the interface in this embodiment, including "Class 1", "Class 2", "Class 3", "Class 4", "Class 5" and "Class 6", respectively. Contains several common images, click on the corresponding category to view the images in the category.

In the image processing method provided by the foregoing embodiment, the clustering request queue is first sorted according to the request initiation time, and the first clustering request in the clustering request queue is obtained. Then, the clustering request corresponding to the same account of the first clustering request in the clustering request queue is merged, and the merged clustering request is clustered. In this way, the same clustering object in multiple clustering requests only needs to perform clustering processing once, without multiple clustering processing, which improves the efficiency of image processing and saves resources.

FIG. 9 is a server architecture diagram of an image processing method implemented in an embodiment. The server architecture diagram includes a first server and a second server cluster, and the second server cluster includes a plurality of second servers. The first server is for providing a cluster access service 902 and a queue service 904 for providing a clustering service 906 and a tag data service 908. The cluster access service 902 is configured to receive a clustering request, and the queue service 904 is configured to generate a request queue according to the clustering request received by the clustering access service, and send the clustering object set to the second server to perform clustering processing. The clustering service 906 is configured to perform clustering processing according to the clustering object set sent by the first server, and the label data service 908 is configured to generate label data according to the clustering processing result, and store the label data. The second server may provide a state detection interface, and the first server periodically detects the state detection interface of each second server, and obtains the state identifier through the state detection interface. The second server may report the status identifier to the first server, that is, when the working status of the second server changes, the second server sends a status identifier to the first server, and reports the current working status. For example, when the current clustering task processing of the second server is completed, the current working state is reported to the first server as an available state. The working status of each second server may be recorded in the form of a status list in the first server. When the second server is required to perform clustering processing, the status label of each second server may be obtained by reading the status list, and according to The status tag finds the second server that is available.

Figure 10 is a block diagram showing the structure of an image processing apparatus in an embodiment. As shown in FIG. 10, the image processing apparatus 1000 includes a request acquisition module 1002, a request merge module 1004, and a request processing module 1006. among them:

The request obtaining module 1002 is configured to acquire a clustering request in a request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered.

The request merge module 1004 is configured to merge the clustering requests corresponding to the same account according to rules.

The request processing module 1006 is configured to process the merged cluster request.

The image processing apparatus provided in the foregoing embodiment combines the clustering requests corresponding to the same account in the request queue, and performs clustering processing on the merged clustering request. In this way, the same object in multiple clustering requests only needs to perform clustering processing once, without multiple clustering processing, which improves the efficiency of image processing and saves resources.

Figure 11 is a block diagram showing the structure of an image processing apparatus in another embodiment. As shown in FIG. 11, the image processing apparatus 1100 includes a request acquisition module 1102, a request merging module 1104, a request processing module 1106, and a label generation module 1108. among them:

The request obtaining module 1002 is configured to acquire a clustering request in a request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered.

The request merge module 1004 is configured to merge the clustering requests corresponding to the same account according to rules.

The request processing module 1006 is configured to process the merged cluster request.

The tag generation module 1108 is configured to generate tag data according to the processing result obtained by processing the clustering request.

The image processing apparatus provided in the foregoing embodiment combines the clustering requests corresponding to the same account in the request queue, and performs clustering processing on the merged clustering request. In this way, the same object in multiple clustering requests only needs to perform clustering processing once, without multiple clustering processing, which improves the efficiency of image processing and saves resources.

In one embodiment, the request merge module 1004 is further configured to sort the clustering request in the request queue according to the request initiation time, and obtain a specified clustering request in the sorted request queue; and obtain the request queue in the request queue The same clustering request for the account corresponding to the specified clustering request is specified, and the acquired clustering requests are merged.

In one embodiment, the request merging module 1004 is further configured to arrange the clustering request in the request queue according to the request initiation time in a first-to-last order, and obtain a first clustering request in the request queue; The same clustering request in the queue corresponding to the first clustering request is requested, and the acquired clustering requests are merged.

In the embodiment of the present application, the request merging module 1004 is further configured to acquire the clustering request in the request queue according to the preset time period, and merge the clustering requests in the preset time period corresponding to the same account according to the rule.

In the embodiment of the present application, the request merging module 1004 is further configured to classify the clustering request corresponding to the same account according to the terminal identifier; acquire a target clustering request in each type of clustering request, and aggregate the acquired target The class request is merged, wherein the target clustering request is a clustering request in which the request time is the smallest interval from the current time in each type of clustering request.

In the embodiment provided by the present application, the request processing module 1006 is further configured to send the clustering request, and/or perform clustering processing on the image of the terminal according to the clustering request.

The division of each module in the above image processing apparatus is for illustrative purposes only. In other embodiments, the image processing apparatus may be divided into different modules as needed to complete all or part of the functions of the image processing apparatus.

The embodiment of the present application also provides a computer readable storage medium. One or more non-transitory computer readable storage media containing computer executable instructions that, when executed by one or more processors, cause the processor to:

Obtaining a clustering request in a request queue, wherein the request queue includes a clustering request sequentially arranged, the clustering request is used to indicate that the image of the terminal is clustered;

Merging clustering requests corresponding to the same account according to rules;

The merged cluster request is processed.

In an embodiment, the performing, by the processor, the merging the clustering request corresponding to the same account according to the rule comprises:

Sorting the clustering requests in the request queue according to the request initiation time, and obtaining the specified clustering request in the sorted request queue;

Obtaining the same clustering request in the request queue as the account corresponding to the specified clustering request, and merging the acquired clustering requests.

In the embodiment provided by the application, the performing, by the processor, the merging the clustering request corresponding to the same account according to the rule includes:

Arranging the clustering requests in the request queue according to the request initiation time in a first-to-last order, and acquiring the first clustering request in the request queue;

Obtaining the same clustering request in the request queue as the account corresponding to the first clustering request, and merging the acquired clustering requests.

In one embodiment, the clustering request in the acquisition request queue executed by the processor, and the clustering request corresponding to the same account according to the rule is merged, including:

Acquiring the clustering request in the request queue according to the preset time period, and combining the clustering requests in the preset time period corresponding to the same account according to the rule.

In still another embodiment, the performing, by the processor, combining the clustering requests corresponding to the same account according to the rules includes:

And classifying the clustering request corresponding to the same account by the terminal identifier;

Obtaining a target clustering request in each type of clustering request, and merging the acquired target clustering requests, wherein the target clustering request is the smallest interval between the request time and the current time in each type of clustering request Clustering request.

In the embodiment provided by the application, the processing, by the processor, the processing of the merged clustering request includes:

Sending the clustering request, and/or performing clustering processing on the image of the terminal according to the clustering request.

In one embodiment, the method performed by the processor further includes:

The tag data is generated based on the processing result obtained by processing the clustering request.

Figure 12 is a schematic diagram showing the internal structure of a server in an embodiment. As shown in FIG. 12, the server includes a processor, a non-volatile storage medium, an internal memory, and a network interface connected by a system bus. The non-volatile storage medium of the server stores an operating system and a computer program. The computer program is executed by the processor to implement a clustering processing method. The server's processor is used to provide computing and control capabilities that support the operation of the entire server. The network interface of the server is used to communicate with an external terminal through a network connection, such as receiving a clustering request sent by the terminal and returning a clustering processing result to the terminal. The server can be implemented with a stand-alone server or a server cluster consisting of multiple servers. It will be understood by those skilled in the art that the structure shown in FIG. 12 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the server to which the solution of the present application is applied. The specific server may include More or fewer components are shown in the figures, or some components are combined, or have different component arrangements.

The embodiment of the present application also provides a computer device. As shown in FIG. 13 , for the convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, refer to the method part of the embodiment of the present application. The computer device may be any terminal device including a mobile phone, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), a vehicle-mounted computer, a wearable device, and the like, taking a computer device as a mobile phone as an example. :

FIG. 13 is a block diagram showing a part of a structure of a mobile phone related to a computer device according to an embodiment of the present application. Referring to FIG. 13 , the mobile phone includes: a radio frequency (RF) circuit 1310 , a memory 1320 , an input unit 1330 , a display unit 1340 , a sensor 1350 , an audio circuit 1360 , a wireless fidelity (WiFi) module 1370 , and a processor 1380 . And power supply 1390 and other components. It will be understood by those skilled in the art that the structure of the handset shown in FIG. 13 does not constitute a limitation to the handset, and may include more or less components than those illustrated, or some components may be combined, or different components may be arranged.

The RF circuit 1310 can be used for receiving and transmitting information during the transmission and reception of information or during the call. The downlink information of the base station can be received and processed by the processor 1380. The uplink data can also be sent to the base station. Generally, RF circuits include, but are not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, RF circuitry 1310 can also communicate with the network and other devices via wireless communication. The above wireless communication may use any communication standard or protocol, including but not limited to Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (Code Division). Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), e-mail, Short Messaging Service (SMS), and the like.

The memory 1320 can be used to store software programs and modules, and the processor 1380 executes various functional applications and data processing of the mobile phone by running software programs and modules stored in the memory 1320. The memory 1320 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as an application of a sound playing function, an application of an image playing function, etc.); The data storage area can store data (such as audio data, address book, etc.) created according to the use of the mobile phone. Moreover, memory 1320 can include high speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The input unit 1330 can be configured to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the handset 1300. Specifically, the input unit 1330 may include a touch panel 1331 and other input devices 1332. The touch panel 1331, which may also be referred to as a touch screen, can collect touch operations on or near the user (such as a user using a finger, a stylus, or the like on the touch panel 1331 or near the touch panel 1331. Operation) and drive the corresponding connection device according to a preset program. In one embodiment, the touch panel 1331 can include two portions of a touch detection device and a touch controller. Wherein, the touch detection device detects the touch orientation of the user, and detects a signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts the touch information into contact coordinates, and sends the touch information. The processor 1380 is provided and can receive commands from the processor 1380 and execute them. In addition, the touch panel 1331 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 1331, the input unit 1330 may further include other input devices 1332. Specifically, other input devices 1332 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control buttons, switch buttons, etc.).

The display unit 1340 can be used to display information input by the user or information provided to the user as well as various menus of the mobile phone. The display unit 1340 can include a display panel 1341. In one embodiment, the display panel 1341 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like. In one embodiment, the touch panel 1331 may cover the display panel 1341. When the touch panel 1331 detects a touch operation thereon or nearby, the touch panel 1331 transmits to the processor 1380 to determine the type of the touch event, and then the processor 1380 is The type of touch event provides a corresponding visual output on display panel 1341. Although in FIG. 13 , the touch panel 1331 and the display panel 1341 are used as two independent components to implement the input and input functions of the mobile phone, in some embodiments, the touch panel 1331 and the display panel 1341 may be integrated. Realize the input and output functions of the phone.

The handset 1300 can also include at least one type of sensor 1350, 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 1341 according to the brightness of the ambient light, and the proximity sensor may close the display panel 1341 and/or when the mobile phone moves to the ear. Or backlight. The motion sensor may include an acceleration sensor, and the acceleration sensor can detect the magnitude of the acceleration in each direction, and the magnitude and direction of the gravity can be detected at rest, and can be used to identify the gesture of the mobile phone (such as horizontal and vertical screen switching), and vibration recognition related functions (such as Pedometer, tapping, etc.; in addition, the phone can also be equipped with gyroscopes, barometers, hygrometers, thermometers, infrared sensors and other sensors.

Audio circuitry 1360, speaker 1361, and microphone 1362 can provide an audio interface between the user and the handset. The audio circuit 1360 can transmit the converted electrical data of the received audio data to the speaker 1361, and convert it into a sound signal output by the speaker 1361; on the other hand, the microphone 1362 converts the collected sound signal into an electrical signal, by the audio circuit 1360. After receiving, it is converted into audio data, and then processed by the audio data output processor 1380, transmitted to another mobile phone via the RF circuit 1310, or outputted to the memory 1320 for subsequent processing.

WiFi is a short-range wireless transmission technology. The mobile phone can help users to send and receive emails, browse web pages and access streaming media through the WiFi module 1370. It provides users with wireless broadband Internet access. Although FIG. 13 shows the WiFi module 1370, it will be understood that it does not belong to the essential configuration of the handset 1300 and may be omitted as needed.

The processor 1380 is a control center for the handset that connects various portions of the entire handset using various interfaces and lines, by executing or executing software programs and/or modules stored in the memory 1320, and invoking data stored in the memory 1320, The phone's various functions and processing data, so that the overall monitoring of the phone. In one embodiment, processor 1380 can include one or more processing units. In one embodiment, processor 1380 can integrate an application processor and a modem processor, where the application processor primarily processes an operating system, user interface, and applications, etc.; the modem processor primarily processes wireless communications. It will be appreciated that the above described modem processor may also not be integrated into the processor 1380.

The handset 1300 also includes a power source 1390 (such as a battery) that powers the various components. Preferably, the power source can be logically coupled to the processor 1380 via a power management system to enable management of charging, discharging, and power management functions through the power management system.

In one embodiment, the handset 1300 can also include a camera, a Bluetooth module, and the like.

In the embodiment of the present application, the processor 1380 included in the mobile phone 1300 implements the above image processing method when executing a computer program stored in a memory.

One of ordinary skill in the art can understand that all or part of the process of implementing the above embodiments can be completed by a computer program to instruct related hardware, and the program can be stored in a non-volatile computer readable storage medium. Wherein, the program, when executed, may include the flow of an embodiment of the methods as described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or the like.

Any reference to a memory, storage, database or other medium as used herein may include non-volatile and/or volatile memory. Suitable non-volatile memories can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM), which acts as an external cache. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronization. Link (Synchlink) DRAM (SLDRAM), Memory Bus (Rambus) Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM).

The above-mentioned embodiments are merely illustrative of several embodiments of the present application, and the description thereof is more specific and detailed, but is not to be construed as limiting the scope of the claims. It should be noted that a number of variations and modifications may be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, the scope of the invention should be determined by the appended claims.

Claims (27)

1. An image processing method, the method comprising:

   Obtaining a clustering request in a request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered;

   Merging clustering requests corresponding to the same account according to rules;

   The merged cluster request is processed.
2. The image processing method according to claim 1, wherein the merging the clustering requests corresponding to the same account according to the rules comprises:

   Arranging the clustering requests in the request queue according to the request initiation time in a first-to-last order, and acquiring the first clustering request in the request queue;

   Obtaining the same clustering request in the request queue as the account corresponding to the first clustering request, and merging the acquired clustering requests.
3. The image processing method according to claim 1, wherein the merging the clustering requests corresponding to the same account according to the rules comprises:

   Sorting the clustering requests in the request queue according to the request initiation time, and obtaining the specified clustering request in the sorted request queue;

   Obtaining the same clustering request in the request queue as the account corresponding to the specified clustering request, and merging the acquired clustering requests.
4. The image processing method according to claim 1, wherein the merging the clustering requests corresponding to the same account according to the rules comprises:

   Sorting the clustering requests in the request queue according to the priority of the requesting device, and obtaining the specified clustering request in the sorted request queue;

   Obtaining the same clustering request in the request queue as the account corresponding to the specified clustering request, and merging the acquired clustering requests.
5. The image processing method according to claim 3 or 4, wherein the acquiring the clustering request in the request queue that is the same as the account corresponding to the specified clustering request comprises:

   Counting, in the request queue, the number of clustering requests that are the same as the account corresponding to the specified clustering request;

   If the number of clustering requests in the request queue that are the same as the account corresponding to the specified clustering request exceeds a preset number, acquiring the same number of accounts in the request queue corresponding to the specified clustering request Class request

   If the number of clustering requests in the request queue that are the same as the account corresponding to the specified clustering request is less than the preset number, the waiting state is entered; when the waiting time exceeds the preset time, the waiting is stopped. And acquiring a clustering request in the request queue that is the same as the account corresponding to the specified clustering request.
6. The image processing method according to claim 1, wherein the acquiring the clustering request in the request queue and merging the clustering requests corresponding to the same account according to the rules comprises:

   Acquiring the clustering request in the request queue according to the preset time period, and combining the clustering requests in the preset time period corresponding to the same account according to the rule.
7. The image processing method according to claim 1, wherein the merging the clustering requests corresponding to the same account according to the rules comprises:

   The clustering request corresponding to the same account is classified according to the terminal identifier;

   Obtaining a target clustering request in each type of clustering request, and merging the acquired target clustering requests, wherein the target clustering request is the smallest interval between the request time and the current time in each type of clustering request Clustering request.
8. The image processing method according to any one of claims 1 to 7, wherein the processing of the merged clustering request comprises:

   Sending the clustering request, and/or performing clustering processing on the image of the terminal according to the clustering request.
9. The image processing method according to claim 1, wherein the method further comprises:

   The tag data is generated based on the processing result obtained by processing the clustering request.
10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following operations:

    Obtaining a clustering request in a request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered;

    Merging clustering requests corresponding to the same account according to rules;

    The merged cluster request is processed.
11. The computer device according to claim 10, wherein when the processor performs the merging of clustering requests corresponding to the same account according to rules, the processor further performs the following operations:

    Arranging the clustering requests in the request queue according to the request initiation time in a first-to-last order, and acquiring the first clustering request in the request queue;

    Obtaining the same clustering request in the request queue as the account corresponding to the first clustering request, and merging the acquired clustering requests.
12. The computer device according to claim 10, wherein when the processor performs the merging of clustering requests corresponding to the same account according to rules, the processor further performs the following operations:

    Sorting the clustering requests in the request queue according to the request initiation time, and obtaining the specified clustering request in the sorted request queue;

    Obtaining the same clustering request in the request queue as the account corresponding to the specified clustering request, and merging the acquired clustering requests.
13. The computer device according to claim 10, wherein when the processor performs the merging of clustering requests corresponding to the same account according to rules, the processor further performs the following operations:

    Sorting the clustering requests in the request queue according to the priority of the requesting device, and obtaining the specified clustering request in the sorted request queue;

    Obtaining the same clustering request in the request queue as the account corresponding to the specified clustering request, and merging the acquired clustering requests.
14. The computer device according to claim 12 or 13, wherein the processor performs the following operation when performing the clustering request that acquires the same account in the request queue corresponding to the specified clustering request :

    Counting, in the request queue, the number of clustering requests that are the same as the account corresponding to the specified clustering request;

    If the number of clustering requests in the request queue that are the same as the account corresponding to the specified clustering request exceeds a preset number, acquiring the same number of accounts in the request queue corresponding to the specified clustering request Class request

    If the number of clustering requests in the request queue that are the same as the account corresponding to the specified clustering request is less than the preset number, the waiting state is entered; when the waiting time exceeds the preset time, the waiting is stopped. And acquiring a clustering request in the request queue that is the same as the account corresponding to the specified clustering request.
15. The computer device according to claim 10, wherein the processor performs a clustering request in the acquisition request queue, and when the clustering request corresponding to the same account is combined according to a rule, the following operations are also performed:

    Acquiring the clustering request in the request queue according to the preset time period, and combining the clustering requests in the preset time period corresponding to the same account according to the rule.
16. The computer device according to claim 10, wherein when the processor performs the merging of clustering requests corresponding to the same account according to rules, the processor further performs the following operations:

    The clustering request corresponding to the same account is classified according to the terminal identifier;

    Obtaining a target clustering request in each type of clustering request, and merging the acquired target clustering requests, wherein the target clustering request is the smallest interval between the request time and the current time in each type of clustering request Clustering request.
17. The computer device according to any one of claims 10-16, wherein the processor performs the processing of the merged clustering request, and further performs the following operations:

    Sending the clustering request, and/or performing clustering processing on the image of the terminal according to the clustering request.
18. The computer device of claim 10, wherein the processor further performs the following operations:

    The tag data is generated based on the processing result obtained by processing the clustering request.
19. A computer readable storage medium having stored thereon a computer program, wherein the computer program is executed by the processor as follows:

    Obtaining a clustering request in a request queue, where the request queue includes a clustering request that is sequentially arranged, and the clustering request is used to indicate that the image of the terminal is clustered;

    Merging clustering requests corresponding to the same account according to rules;

    The merged cluster request is processed.
20. The computer readable storage medium according to claim 19, wherein when the computer program is executed by the processor to merge the clustering requests corresponding to the same account according to rules, the computer program further performs the following operations:

    Arranging the clustering requests in the request queue according to the request initiation time in a first-to-last order, and acquiring the first clustering request in the request queue;

    Obtaining the same clustering request in the request queue as the account corresponding to the first clustering request, and merging the acquired clustering requests.
21. The computer readable storage medium according to claim 19, wherein when the computer program is executed by the processor to merge the clustering requests corresponding to the same account according to rules, the computer program further performs the following operations:

    Sorting the clustering requests in the request queue according to the request initiation time, and obtaining the specified clustering request in the sorted request queue;

    Obtaining the same clustering request in the request queue as the account corresponding to the specified clustering request, and merging the acquired clustering requests.
22. The computer readable storage medium according to claim 19, wherein when the computer program is executed by the processor to merge the clustering requests corresponding to the same account according to rules, the computer program further performs the following operations:

    Sorting the clustering requests in the request queue according to the priority of the requesting device, and obtaining the specified clustering request in the sorted request queue;

    Obtaining the same clustering request in the request queue as the account corresponding to the specified clustering request, and merging the acquired clustering requests.
23. A computer readable storage medium according to claim 21 or 22, wherein said computer program is executed by said processor to acquire said same number of accounts in said request queue corresponding to said specified clustering request When the class requests, it also performs the following operations:

    Counting, in the request queue, the number of clustering requests that are the same as the account corresponding to the specified clustering request;

    If the number of clustering requests in the request queue that are the same as the account corresponding to the specified clustering request exceeds a preset number, acquiring the same number of accounts in the request queue corresponding to the specified clustering request Class request

    If the number of clustering requests in the request queue that are the same as the account corresponding to the specified clustering request is less than the preset number, the waiting state is entered; when the waiting time exceeds the preset time, the waiting is stopped. And acquiring a clustering request in the request queue that is the same as the account corresponding to the specified clustering request.
24. The computer readable storage medium according to claim 19, wherein said computer program is executed by said processor to perform a clustering request in said acquisition request queue, and merges clustering requests corresponding to the same account according to rules At the same time, the following operations are also performed:

    Acquiring the clustering request in the request queue according to the preset time period, and combining the clustering requests in the preset time period corresponding to the same account according to the rule.
25. The computer readable storage medium according to claim 19, wherein when the computer program is executed by the processor to merge the clustering requests corresponding to the same account according to rules, the computer program further performs the following operations:

    The clustering request corresponding to the same account is classified according to the terminal identifier;

    Obtaining a target clustering request in each type of clustering request, and merging the acquired target clustering requests, wherein the target clustering request is the smallest interval between the request time and the current time in each type of clustering request Clustering request.
26. The computer readable storage medium according to any one of claims 19-25, wherein the computer program is executed by the processor to perform the processing on the merged clustering request, and further, the following operations are performed:

    Sending the clustering request, and/or performing clustering processing on the image of the terminal according to the clustering request.
27. A computer readable storage medium according to claim 19, wherein said computer program is executed by said processor such that said processor further performs the following operations:

    The tag data is generated based on the processing result obtained by processing the clustering request.

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