WO2019096207A1

WO2019096207A1 - Image processing method and computer device, and computer readable storage medium

Info

Publication number: WO2019096207A1
Application number: PCT/CN2018/115653
Authority: WO
Inventors: 柯秀华
Original assignee: Oppo广东移动通信有限公司
Priority date: 2017-11-15
Filing date: 2018-11-15
Publication date: 2019-05-23
Also published as: CN107895020A

Abstract

An image processing method, comprising: counting the number of historical browsing instances for each image in a first image set, wherein the first image set comprises one or more images; acquiring images in the first image set for which the number of historical browsing instances is greater than a number-of-instances threshold, and generating a second image set; and classifying the images in the second image set.

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. 200911132139.1, entitled "Image Processing Method and Apparatus, Computer Equipment, Computer Readable Storage Medium", filed on November 15, 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

With the rapid development of intelligent mobile terminals and Internet technologies, the classification technology for images in smart mobile terminals is becoming more and more mature. Commonly used image classification in smart mobile terminals may include grouping images according to face, location, and time. Grouping the images in the smart mobile terminal enables the user to view the images in the smart mobile terminal, and the manner in which the user views the images in the smart mobile terminal is more convenient and faster.

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:

Counting the number of historical views of each image in the first image set, wherein the first image set includes one or more images;

Obtaining an image of the first image set whose historical browsing times are greater than a threshold of times, and generating a second image set; and

The images in the second set of images are sorted.

A computer device comprising a memory and a processor, wherein the memory stores computer readable instructions, and when the instructions are executed by the processor, the processor implements the following operations:

The images in the second set of images are sorted.

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

The images in the second set of images are sorted.

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.

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

2 is a flow chart of an image processing method in one embodiment.

3 is a flow chart of an image processing method in another embodiment.

FIG. 4 is a diagram showing a result of classification of a mobile terminal album in one embodiment.

Figure 5 is a flow chart of an image processing method in still another embodiment.

Figure 6 is a system architecture diagram of an image processing system in one embodiment.

Fig. 7 is a block diagram showing the structure of an image processing apparatus in an embodiment.

Figure 8 is a block diagram showing the structure of an image processing apparatus in another embodiment.

FIG. 9 is a schematic diagram showing the internal structure of a server in an embodiment.

FIG. 10 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 client 102 and a server 104. The client 102 may be configured to obtain a first image set, and count the historical browsing times of each image in the first image set; obtain an image in the first image set whose historical browsing times are greater than the threshold value, and generate a second image set. Sending a second set of images to the server 104. After receiving the second image set, the server 104 classifies the images in the second image set and returns the classification result to the client 102. It can be understood that the client 102 can also directly send the first image set and the corresponding historical browsing times to the server 102, and the server 102 generates a second image set according to the first image set and the corresponding historical browsing times, and the second The image collection is classified. The client 102 is an electronic device that is mainly used for inputting user information and outputting 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. The server 104 is a device for providing a computing service in response to a service request, and may be, for example, one or more computers. It can be understood that the application environment of the image processing method may include only the client 102, that is, the client 102 is configured to acquire a statistical first image set, generate a second image set according to the first image set, and set the second image set. The images in the category are classified.

2 is a flow chart of an image processing method in one embodiment. As shown in FIG. 2, the image processing method includes operations 202 through 206. among them:

Operation 202: Count the number of historical views of each image in the first image set, wherein the first image set includes one or more images.

In the embodiment provided by the present application, the image is composed of a plurality of pixels, and the pixels are arranged according to a certain rule to form a two-dimensional matrix. When processing an image, each pixel in the image can be processed to change the pixel value of each pixel, and the arrangement of each pixel in the image can also be changed. For example, the processing of the image may be processing such as improving image brightness, improving saturation, feature recognition, classification, and the like. An image collection refers to a collection of several images, and specifically may include one or more images. Generally, the smart terminal stores the picture in the storage space, and the smart terminal can directly obtain the picture from the preset storage address, or traverse all the folders in the smart terminal to obtain the picture. 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 comes with the smart terminal itself and is part of the hardware structure of the client. The external storage refers to an external storage device of the intelligent terminal, and the external storage can perform data transmission with the client through a dedicated interface. For example, the external storage may be an SD card, a USB flash drive or the like. The first image set may include a picture stored in the internal memory and the external memory, or may include only the picture stored in the internal memory, or may include only the picture stored in the external memory, which is not limited herein.

Specifically, the images stored in the smart terminal can be displayed on the display screen of the smart terminal, and the user can view through the display screen of the smart terminal. The user can select an image to be viewed and input a viewing instruction, and the smart terminal enlarges and displays the image selected by the user for the user to browse. The number of historical views refers to the number of times the user views the image. When the user views the image once, the intelligent terminal increments the number of historical browsing times to count the number of historical views corresponding to each image.

In operation 204, an image in which the number of historical views in the first image set is greater than the threshold of the number of times is obtained, and a second image set is generated.

The image is acquired from the first image set according to the historical browsing number of the image, and a second image set is generated. The traversing each image in the first image set, comparing the obtained historical browsing times with the number of times thresholds, and acquiring an image whose historical browsing times are greater than the number of times threshold, and generating a second image set. The second set of images is a subset of the first set of images, and the second set of images includes all or a portion of the images in the first set of images. For example, the first image set includes three images, namely, image 1, image 2, and image 3, and the corresponding historical browsing times are 1, 20, and 15 times, respectively. Assuming that the number of times threshold is 10 times, an image in which the number of historical views in the first image set is greater than the number of times threshold is acquired, that is, image 2 and image 3, and then a second image set is generated according to image 2 and image 3. It can be understood that the user generally views the image that is more concerned, and the historical browsing times of the image can reflect the degree of attention of the user to the image. The more the user pays more attention to the image, the more the history browsing times. Only the images that the user is more concerned with are processed without having to process all the images, which reduces the consumption of resources.

Further, only the historical browsing times of the images in the first image set in the preset time period may be counted, and then the images in the first image set whose historical browsing times are greater than the number of times threshold are acquired, and the second image set is generated. For example, the number of historical views of an image within one month can be counted, the number of historical views of the image is cleared at the beginning of each month, and the second image set is generated according to the number of historical views counted in the previous month, and then the second image set is The images are sorted. In this way, the generated second image set can be updated in time to update the classification result in time.

Operation 206, classifying images in the second set of images.

Classification is the process of dividing an object collection into multiple object combinations, each of which consists of one or more similar objects. Classifying the images in the second set of images is the process of dividing the images in the second set of images into one or more different types. After the images are classified, they can be marked by the classification label, and the classification label can know which type the image belongs to. Each image has a corresponding image identifier, and the image identifier is used to distinguish different images, and the image identifier can be used to find a corresponding image. Therefore, after the image is classified, the correspondence between the image identifier and the classification label can be established, and the image classification can be viewed through the classification label. For example, the image identification includes "image 1", "image 2", and "image 3", the classification label may include "category 1" and "category 2", and "category 1" corresponds to "image 1" and "image 2", Classification 2" corresponds to "Image 3", and the classification label can be used to view the classification of each image. After classifying the second image set, corresponding image sub-sets may also be generated according to each type of image, and each image sub-set is arranged and displayed, and the user may classify and view the images in the second image set.

In the image processing method provided by the above embodiment, the second image set is generated according to the historical browsing times of each image in the first image set, and the generated second image set is classified. In this way, it is not necessary to process all the images, and only the images that the user pays attention to are processed, thereby realizing the precise processing of the images and reducing the consumption of resources.

3 is a flow chart of an image processing method in another embodiment. As shown in FIG. 3, the image processing method includes operations 302 through 314. among them:

Operation 302, counting the number of historical views of each image in the first image set, wherein the first image set includes one or more images.

In one embodiment, the client may synchronize the image in the first image set to the server, after acquiring the second image set, then the server acquires the second image set according to the first image set, and the second image set is The images are sorted. A communication connection can be established between the client and the server. After the communication connection is successful, the server returns a successful connection information to the client, and then the client sends the first image set to the server according to the connection success information. The server acquires the second image set according to the first image set, and classifies the second image set. After the classification is completed, the classification result is returned to the client. The client may acquire a second image set according to the classification result returned by the server, and classify the second image set. For example, a communication connection can be established between a client and a server through a pair of port numbers. After the connection is established, the first image set is sent through the connected port number.

After receiving the first image set sent by the client, the server may store the first image set, so that the client can obtain the image in the first image set, even if the image in the first image set is deleted. It can be understood that after storing the first image collection, the server can generate a webpage link and return the webpage link to the client. By accessing the webpage link, the client can view the images in the stored first image collection and can download the images in the first image collection. The webpage link corresponds to a webpage document, and the client obtains and reads the webpage document when clicking the webpage link, and the webpage document includes the storage address and the image identifier of the webpage. After obtaining the webpage document, the client parses the webpage document and displays the content in the webpage document, and the user views the image through the displayed content.

In one embodiment, a condition for triggering the classification process may be set, and if the condition is satisfied, the first image set is acquired and the classification process is performed. Specifically, when the preset trigger condition is met, the first image set is acquired. The preset trigger condition refers to a condition of the trigger classification processing set in advance, and when the preset trigger condition is satisfied, the first image set is classified. The preset triggering condition may be, but is not limited to, the number of newly added pictures on the client is greater than the preset number; the current time is the preset time; the time from the last initiation of the classification request exceeds the preset time period; and the mobile terminal is currently in the charging state. For example, when the mobile terminal adds more than 50 pictures, if the current time is between 2 am and 5 am, and the mobile terminal is in a charging state, the mobile terminal initiates a classification request.

It can be understood that multiple application accounts can be logged in the client, and different users correspond to different application accounts, and the corresponding image sets are also different. When the user needs to perform classification processing, the client can send a classification request to the server through the client where the application account is located. That is, when the client initiates a classification request to the server, the requesting device identifier may refer to an application account identifier or a 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 clients. 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 client. For example, the user can log in to the smart terminal through the application account, and send a request for classifying the photo in the album to the server through the smart terminal. After receiving the classification request sent by the smart terminal, the server obtains the historical browsing number in the album is greater than the threshold value. The photo is sorted and the result of the sorting process is returned to the smart terminal. At the same time, for the classification request initiated by the client, the server may record the request status corresponding to the classification request, and is used to indicate the processing procedure of the classification request. For example, the request status may include unprocessed, in-process, re-process, and process completion status, and may be represented by tags such as "R-Result", "De-Handle", "Re-Group", and "End", respectively. The request status tag of the current classification request is read, and the status of the current classification request can be known according to the request status label. If the classification request may be re-processed due to a hardware or software failure, such as a request interruption, the classification request may indicate that the current request status is reprocessed through the "Re-Group" tag.

Since the processing capability of the server is limited, after receiving the classification request, the server may form a request queue, and the classification request in the request queue may be sorted according to the order of the request time, and then the classification request in the request queue is processed in turn. When processing a classification request, the same classification request of the request originating object in the request queue can be merged. Since the request originating objects of the merged classification request are the same, after the merged classification request is processed, the obtained classification processing result may be directly sent to the corresponding request initiation object. For example, the classification request queue includes three classification requests, which are arranged in chronological order: classification request 1, and application account A sends a classification request at 03:30 on August 20, 2017, including image collection 1; Classification request 2, the classification request sent by application account B on August 21, 2017 at 02:41, including image collection 2; classification request 3, application account A, the classification request sent at 04:02 on August 22, 2017, including Image collection 3. Then, the classification request 1 and the classification request 3 are merged, and the image set acquired after the combination is a union of the image collection 1 and the image collection 3.

In operation 304, a threshold number of times corresponding to each image in the first image set is obtained according to an image generation manner.

In one embodiment, the generation mode refers to the manner in which the image is generated. For images generated by different generation methods, the number of times the user browses may be different, so different screening criteria may be adopted for images generated by different generation methods. For example, in a smart terminal, an image may be taken by a camera, or may be acquired by a screen capture, or may be downloaded from a web page or imported from an external device. The corresponding relationship between the image generation mode and the number of times thresholds may be established, and each image of the first image set is traversed, and the corresponding number of times threshold is obtained according to the image generation manner, and the number of times corresponding to the image generated by the different generation modes is different. For example, the threshold value of the number of times corresponding to the captured image is 10 times, the threshold value of the number of times corresponding to the screen capture image is 3 times, and the threshold value of the number of times corresponding to the downloaded image is 5 times. Specifically, traversing each image in the first image set; when the image is generated according to the first generation manner, acquiring a first time threshold corresponding to the first generation manner; when the image is generated according to the second generation manner Obtaining a second number threshold corresponding to the second generation mode. For example, the first generation mode is shooting, and the corresponding first number of thresholds is 8 times; the second generation mode is a screen capture, and the corresponding second number of thresholds is 7 times. In this embodiment, the generation mode and the corresponding number of times thresholds may be arbitrarily set as needed, and the third generation mode, the fourth generation mode, and the like may also be set, and the third time threshold value and the fourth number threshold value are set correspondingly, and Make specific limits.

In operation 306, an image in which the number of historical views in the first image set is greater than a corresponding number of times threshold is acquired, and a second image set is generated.

In one embodiment, each image in the first set of images is traversed, and a corresponding number of thresholds is obtained according to the manner in which the images are generated. That is to say, each image has a corresponding history browsing number and number threshold, and the historical browsing times corresponding to each image are compared with the corresponding number threshold. If the historical browsing times are greater than the corresponding number threshold, then This image serves as an image in the second set of images. All images in the first image set that satisfy the condition are filtered to generate a second image set. If the user deletes the image in the first image set, the corresponding image in the second image set may be deleted at the same time to ensure the consistency of the image in the smart terminal.

Operation 308, when the number of images included in the second image set is greater than a quantity threshold, classifying the images in the second image set.

Specifically, after generating the second image set, determining whether the quantity in the second image set is greater than a quantity threshold, and classifying the image in the second image set when the number of images included in the second image set is greater than a quantity threshold . It may be classified according to the scene of the image, or may be classified according to the face in the image, or may be classified according to the generation time, format, size and the like of the image. In this embodiment, the classification standard of the image is not specified. limited. The process of classifying the image may specifically include: extracting classification features of each image in the second image set, and then classifying the images according to the classification features. The classification feature refers to the feature used for classification, for example, according to the generation time, the extracted classification feature is the image generation time; if the classification is performed according to the scene, the extracted classification feature is the scene feature of the image. The classifying the image according to the classification feature may include: inputting the classification feature of the image into the classification model, and then obtaining the classification result output by the classification model. The classification model refers to an algorithm model for classification processing, and the classification model can classify images. For example, commonly used classification models include k-means classification model, hierarchical classification model, SOM (Self-Organizing Maps) classification model, and FCM (Flow Cytometry) classification model. . Specifically, when the image is classified according to the face, the face region in each image in the second image set may be acquired, and the image is classified according to the acquired face region. The face area refers to the area where the face in the image is located, and each face area corresponds to a category. That is to say, if there are multiple face regions in the image, the image can correspond to multiple categories.

It can be understood that after generating the second image set according to the first image set and classifying the image in the second image set, the user can manually add or delete the image in the second image set, or manually change the second image. The classification of images in the collection. For example, when the mobile terminal presents the classification information of the image to be processed, the user can view the information. If the classification information of a certain image to be processed does not meet the personal preference, the classification information of the image to be processed can be manually changed.

Operation 310, generating a corresponding image subset according to each type of image.

In operation 312, the total number of views corresponding to each image subset is counted, wherein the total number of views refers to the sum of historical browsing times corresponding to all images included in the image subset.

After classifying the second set of images, a plurality of categories are formed, each of which corresponds to one or more images. Each type of image can be used as a subset of images, and then each image subset is displayed separately. Specifically, the total number of views corresponding to each image subset is also counted, and then the image subsets are arranged and displayed according to the total number of views. The total number of views refers to the sum of the number of historical views corresponding to all the images included in the image sub-collection. For example, the image sub-collection includes three images, and the historical browsing times corresponding to the three images are 5 times, 3 times, and 8 times, respectively, and the total number of views corresponding to the image sub-collection is 16 times.

Operation 314, displaying each image sub-set according to the total number of views.

In one embodiment, each subset of images may be displayed in an array on the display interface of the smart terminal. First, the total number of views corresponding to each image sub-set is obtained, and then each image sub-set is displayed in descending or ascending order according to the total number of views. It can be understood that, in other embodiments provided by the present application, the display may be arranged according to the number of images included in the image subset, which is not limited herein. FIG. 4 is a diagram showing a result of classification of a mobile terminal album in one embodiment. As shown in FIG. 4, the mobile terminal generates a second image set according to the first image set, and then classifies according to the second image set, and displays the image subset corresponding to each type of image 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 an embodiment, the function of manually moving the group by the user may be set in the smart terminal, that is, the user may manually move the grouping of the image based on the existing classification. You can also hide or edit grouping information, such as deleting or hiding a group, or moving an image from one group to another. The packet can also be encrypted, and the user can only view the corresponding packet after being authenticated, for example, by one or more of information such as a password, a fingerprint, or an iris. Further, it is also possible to display different groups by inputting different authentication information, for example, inputting a fingerprint of a thumb, displaying the group 1 in the first place; inputting the fingerprint of the index finger, and displaying the group 2 in the second position.

The image processing method provided by the foregoing embodiment generates a second image set according to the historical browsing times of each image in the first image set, and when the number of images in the second image set is greater than the quantity threshold, the generated second image set The classification process is performed, and the image sub-sets are generated according to the classified images to be arranged and displayed. In this way, it is not necessary to process all the images, and only the images that the user pays attention to are processed, thereby realizing the precise processing of the images and reducing the consumption of resources. At the same time, the user can classify and view the images that are more concerned, adapt to the needs of different users, and improve the user's viscosity.

Figure 5 is a flow chart of an image processing method in still another embodiment. As shown in FIG. 5, operations 502 through 508 are image processing methods corresponding to the first classification of the first image set, and operations 512 through 518 are image processing corresponding to the classification of the newly added images in the first image set. method. The process of first classifying the first image set includes:

Operation 502, recording the number of historical views of each image in the first image set.

Operation 504: Acquire an image whose historical browsing times are greater than a threshold of times, and generate a second image set.

Operation 506, when the number of images in the second image set is greater than the number threshold, classify the images in the second image set.

Operation 508 results in a classified image.

The process of classifying the newly added images in the first image set may specifically include:

Operation 512, obtaining a new image.

Operation 514 records the number of historical views of the newly added image.

In operation 516, when the historical browsing times are greater than the number of times threshold, the image is identified and classified.

Operation 518 results in image classification.

The image processing method provided by the foregoing embodiment generates a second image set according to the historical browsing times of each image in the first image set, and when the number of images in the second image set is greater than the quantity threshold, the generated second image set The classification process is performed, and the image sub-sets are generated according to the classified images to be arranged and displayed. In this way, it is not necessary to process all the images, and only the images that the user pays attention to are processed, thereby realizing the precise processing of the images and reducing the consumption of resources. At the same time, the user can classify and view the images that are more concerned, and adapt to the needs of different users. When an image is added to the first image collection, the newly added image can be classified and processed in time to improve the user's viscosity.

Figure 6 is a system architecture diagram of an image processing system in one embodiment. As shown in FIG. 6, the image processing system includes a client 62, a local server 64, and a cloud server 66. The client 62 can initiate the classification request by using the trigger 622, and then obtain the first image set and the corresponding historical browsing times from the image database 620 through the data obtaining module 624, and then store the first image set in the backup database 626. The first set of images and the corresponding number of historical views are sent to the local server 64. After receiving the first image set and the corresponding historical browsing times, the local server 64 packages the first image set and the corresponding historical browsing times through the package packaging module 640, and browses the packaged first image set and the corresponding history browsing. The number of times is sent to the cloud server 66 through the communication module 642 for classification processing. The cloud server 66 delivers the obtained server classification result to the local server 64. The local server 64 receives the server classification result through the communication module 642, and parses the server classification result through the package parsing module 644, and then transmits the parsed classification result to the client 62. After the client 62 receives the classified result, the second image set is generated from the first image set stored in the backup database 626 according to the classification result, and the second image set is classified and processed, and then the classified second. The image collection is displayed.

Fig. 7 is a block diagram showing the structure of an image processing apparatus in an embodiment. As shown in FIG. 7, the image processing apparatus 700 includes a count statistic module 702, a set generation module 704, and an image classification module 706. among them:

The number statistics module 702 is configured to count the number of historical views of each image in the first image set, wherein the first image set includes one or more images.

The set generation module 704 is configured to acquire an image in which the historical browsing times in the first image set are greater than a threshold value, and generate a second image set.

The image classification module 706 is configured to classify the images in the second image set.

The image processing apparatus provided by the above embodiment generates a second image set according to the historical browsing times of each image in the first image set, and classifies the generated second image set. In this way, it is not necessary to process all the images, and only the images that the user pays attention to are processed, thereby realizing the precise processing of the images and reducing the consumption of resources.

Figure 8 is a block diagram showing the structure of an image processing apparatus in another embodiment. As shown in FIG. 8, the image processing apparatus 800 includes a count statistic module 802, a set generation module 804, an image classification module 806, and a classification display module 808. among them:

The number statistics module 802 is configured to count historical browsing times of each image in the first image set, wherein the first image set includes one or more images.

The set generation module 804 is configured to acquire an image in the first image set whose historical browsing times are greater than a threshold value, and generate a second image set.

The image classification module 806 is configured to classify images in the second image set.

The classification display module 808 is configured to generate a corresponding image subset according to each type of image, and display each image subset.

In an embodiment, the set generation module 804 is further configured to acquire, according to a method for generating an image, a threshold value of a number of times corresponding to each image in the first image set; and obtain a historical browsing number in the first image set that is greater than a corresponding number of times An image of the threshold, generating a second set of images.

In an embodiment, the set generation module 804 is further configured to traverse each image in the first image set; when the image is generated according to the first generation manner, acquiring the first corresponding to the first generation manner a threshold of a number of times; when the image is generated according to the second generation manner, acquiring a second threshold value corresponding to the second generation mode.

In one embodiment, the image classification module 806 is further configured to classify the images in the second image set when the number of images included in the second image set is greater than a quantity threshold.

In an embodiment, the image classification module 806 is further configured to acquire a face region in each of the second image sets, and classify the image according to the acquired face region.

In an embodiment, the classification display module 808 is further configured to count the total number of browsing times corresponding to each image subset, wherein the total number of browsing times refers to a sum of historical browsing times corresponding to all images included in the image subset; The total number of views displays the respective image subsets in an array.

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.

FIG. 9 is a schematic diagram showing the internal structure of a server in an embodiment. As shown in FIG. 9, the server includes a processor, a non-volatile storage medium, an internal memory, and a network interface connected by a system bus. The processor is used to provide computing and control capabilities to support the operation of the entire computer device. The memory is used to store data, programs, etc., and at least one computer program is stored on the memory, and the computer program can be executed by the processor to implement the wireless network communication method suitable for the computer device provided in the embodiments of the present application. The memory may include a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random storage memory (Random-Access-Memory, RAM). For example, in one embodiment, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by a processor for implementing an image processing method provided by the above embodiments. The internal memory provides a cached operating environment for operating system computer programs in a non-volatile storage medium. The network interface may be an Ethernet card or a wireless network card or the like for communicating with an external computer device. 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. 9 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 a ratio. 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 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 an image in which the number of historical browsing times in the first image set is greater than a threshold of times, and generating a second image set;

The images in the second set of images are sorted.

In an embodiment, the acquiring, by the processor, the acquiring the image that the historical browsing times in the first image set is greater than the threshold value includes:

Obtaining a threshold number of times corresponding to each image in the first image set according to an image generation manner;

Acquiring an image in the first image set that has a historical browsing number greater than a corresponding number of times threshold, and generating a second image set.

In one embodiment, the obtaining, by the processor, the threshold number of times corresponding to each image in the first image set according to the manner of generating the image includes:

Traversing each of the first set of images;

Obtaining a first time threshold corresponding to the first generation manner, when the image is generated according to the first generation manner;

And acquiring, when the image is generated according to the second generation manner, a second threshold value corresponding to the second generation manner.

In one embodiment, the performing, by the processor, the classifying the images in the second set of images comprises:

The images in the second set of images are classified when the number of images included in the second set of images is greater than a number threshold.

Obtaining a face region in each of the images in the second image set, and classifying the image according to the acquired face region.

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

A corresponding image subset is generated according to each type of image, and each image subset is arranged and displayed.

In one embodiment, the performing, by the processor, performing the permutation display of each image subset includes:

Counting the total number of views corresponding to each image sub-set, wherein the total number of views refers to the sum of historical browsing times corresponding to all images included in the image sub-set;

Each image sub-set is arranged and displayed according to the total number of views.

A computer program product comprising instructions which, when run on a computer, cause the computer to perform the image processing method provided by the above embodiments.

The embodiment of the present application also provides a computer device. As shown in FIG. 10, 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. 10 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. 10, the mobile phone includes: a radio frequency (RF) circuit 1010, a memory 1020, an input unit 1030, a display unit 1040, a sensor 1050, an audio circuit 1060, a wireless fidelity (WiFi) module 1070, and a processor 1080. And power supply 1090 and other components. It will be understood by those skilled in the art that the structure of the handset shown in FIG. 10 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 1010 can be used for receiving and transmitting signals during the transmission and reception of information or during a call. The downlink information of the base station can be received and processed by the processor 1080. 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 circuit 1010 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 1020 can be used to store software programs and modules, and the processor 1080 executes various functional applications and data processing of the mobile phone by running software programs and modules stored in the memory 1020. The memory 1020 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 1020 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 1030 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 1000. Specifically, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, which may also be referred to as a touch screen, can collect touch operations on or near the user (such as the user using a finger, a stylus, or the like on the touch panel 1031 or near the touch panel 1031. Operation) and drive the corresponding connection device according to a preset program. In one embodiment, the touch panel 1031 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 1080 is provided and can receive commands from the processor 1080 and execute them. In addition, the touch panel 1031 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 1031, the input unit 1030 may also include other input devices 1032. Specifically, other input devices 1032 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 1040 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 1040 may include a display panel 1041. In one embodiment, the display panel 1041 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 1031 can cover the display panel 1041. When the touch panel 1031 detects a touch operation thereon or nearby, the touch panel 1031 transmits to the processor 1080 to determine the type of the touch event, and then the processor 1080 is The type of touch event provides a corresponding visual output on display panel 1041. Although the touch panel 1031 and the display panel 1041 are used as two independent components to implement the input and input functions of the mobile phone in FIG. 10, in some embodiments, the touch panel 1031 may be integrated with the display panel 1041. Realize the input and output functions of the phone.

The handset 1000 can also include at least one type of sensor 1050, 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 1041 according to the brightness of the ambient light, and the proximity sensor may close the display panel 1041 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 1060, speaker 1061, and microphone 1062 can provide an audio interface between the user and the handset. The audio circuit 1060 can transmit the converted electrical data of the received audio data to the speaker 1061, and convert it into a sound signal output by the speaker 1061; on the other hand, the microphone 1062 converts the collected sound signal into an electrical signal, by the audio circuit 1060. After receiving, it is converted into audio data, and then processed by the audio data output processor 1080, transmitted to another mobile phone via the RF circuit 1010, or outputted to the memory 1020 for subsequent processing.

WiFi is a short-range wireless transmission technology. The mobile phone through the WiFi module 1070 can help users to send and receive e-mail, browse the web and access streaming media, etc. It provides users with wireless broadband Internet access. Although FIG. 10 shows the WiFi module 1070, it can be understood that it does not belong to the essential configuration of the mobile phone 1000 and can be omitted as needed.

The processor 1080 is the control center of the handset, which 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 1020, and invoking data stored in the memory 1020, The phone's various functions and processing data, so that the overall monitoring of the phone. In one embodiment, processor 1080 can include one or more processing units. In one embodiment, the processor 1080 can integrate an application processor and a modem processor, wherein the application processor primarily processes an operating system, a user interface, an application, and the like; 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 1080.

The mobile phone 1000 also includes a power source 1090 (such as a battery) for powering various components. Preferably, the power source can be logically coupled to the processor 1080 through a power management system to manage functions such as charging, discharging, and power management through the power management system.

In one embodiment, the handset 1000 may also include a camera, a Bluetooth module, and the like.

In the embodiment of the present application, the processor 1080 included in the mobile terminal implements the image processing method provided by the foregoing embodiment when executing the computer program stored in the memory.

Any reference to a memory, storage, database or other medium 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 present application. Therefore, the scope of the invention should be determined by the appended claims.

Claims

An image processing method, the method comprising:

Counting the number of historical views of each image in the first image set, wherein the first image set includes one or more images;

Obtaining an image of the first image set whose historical browsing times are greater than a threshold of times, and generating a second image set; and

The images in the second set of images are sorted.
The image processing method according to claim 1, wherein the acquiring an image in which the number of historical browsing times in the first image set is greater than a threshold value includes:

Obtaining a threshold number of times corresponding to each image in the first image set according to an image generation manner; and

Acquiring an image in the first image set that has a historical browsing number greater than a corresponding number of times threshold, and generating a second image set.
The image processing method according to claim 2, wherein the obtaining the threshold number of times corresponding to each image in the first image set according to the image generation manner comprises:

Traversing each of the first set of images;

Obtaining a first time threshold corresponding to the first generation manner when the image is generated according to the first generation manner; and

And acquiring, when the image is generated according to the second generation manner, a second threshold value corresponding to the second generation manner.
The image processing method according to claim 1, wherein the classifying the images in the second image set comprises:

The images in the second set of images are classified when the number of images included in the second set of images is greater than a number threshold.
The image processing method according to claim 4, wherein the classifying the images in the second image set comprises:

Obtaining a face region in each of the images in the second image set, and classifying the image according to the acquired face region.
The image processing method according to any one of claims 1 to 5, further comprising:

A corresponding image subset is generated according to each type of image, and each image subset is arranged and displayed.
The image processing method according to claim 6, wherein the displaying and displaying the respective image subsets comprises:

Counting the total number of views corresponding to each image sub-set, wherein the total number of views refers to the sum of historical browsing times corresponding to all images included in the image sub-collection;

Each image sub-set is arranged and displayed according to the total number of views.
A computer device comprising a memory and a processor, wherein the memory stores computer readable instructions, and when the instructions are executed by the processor, the processor implements the following operations:

Counting the number of historical views of each image in the first image set, wherein the first image set includes one or more images;

Obtaining an image of the first image set whose historical browsing times are greater than a threshold of times, and generating a second image set; and

The images in the second set of images are sorted.
The computer device according to claim 8, wherein when the processor performs the acquiring an image in which the number of historical browsing times in the first image set is greater than a threshold number of times, the processor further performs the following operations:

Obtaining a threshold number of times corresponding to each image in the first image set according to an image generation manner; and

Acquiring an image in the first image set that has a historical browsing number greater than a corresponding number of times threshold, and generating a second image set.
The computer device according to claim 9, wherein the processor further performs the following operations when the threshold value of the number of times corresponding to each image in the first image set is obtained according to the image generation manner:

Traversing each of the first set of images;

Obtaining a first time threshold corresponding to the first generation manner when the image is generated according to the first generation manner; and

And acquiring, when the image is generated according to the second generation manner, a second threshold value corresponding to the second generation manner.
The computer device according to claim 8, wherein when the processor performs the classifying the images in the second image set, the processor further performs the following operations:

The images in the second set of images are classified when the number of images included in the second set of images is greater than a number threshold.
The computer device according to claim 11, wherein when the processor performs the classifying the images in the second image set, the processor further performs the following operations:

Obtaining a face region in each of the images in the second image set, and classifying the image according to the acquired face region.
The computer device according to any one of claims 8 to 12, wherein the processor further performs the following operations:

A corresponding image subset is generated according to each type of image, and each image subset is arranged and displayed.
The computer device according to claim 13, wherein when the processor performs the arrangement of displaying the respective image subsets, the processor further performs the following operations:

Counting the total number of views corresponding to each image sub-set, wherein the total number of views refers to the sum of historical browsing times corresponding to all images included in the image sub-collection;

Each image sub-set is arranged and displayed according to the total number of views.
A computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements the following operations:

Counting the number of historical views of each image in the first image set, wherein the first image set includes one or more images;

Obtaining an image of the first image set whose historical browsing times are greater than a threshold of times, and generating a second image set; and

The images in the second set of images are sorted.
The computer readable storage medium according to claim 15, wherein when the computer program is executed by the processor to acquire an image in which the number of historical browsing times in the first image set is greater than a threshold number of times, the computer program further performs the following operations:

Obtaining a threshold number of times corresponding to each image in the first image set according to an image generation manner; and

Acquiring an image in the first image set that has a historical browsing number greater than a corresponding number of times threshold, and generating a second image set.
The computer readable storage medium according to claim 16, wherein the computer program is executed by the processor when the threshold of the number of times corresponding to each image in the first image set is acquired according to a manner of generating an image, Also do the following:

Traversing each of the first set of images;

Obtaining a first time threshold corresponding to the first generation manner when the image is generated according to the first generation manner; and

And acquiring, when the image is generated according to the second generation manner, a second threshold value corresponding to the second generation manner.
The computer readable storage medium according to claim 15, wherein when said computer program is executed by said processor to classify images in said second image set, said computer program further performs the following operations:

The images in the second set of images are classified when the number of images included in the second set of images is greater than a number threshold.
The computer readable storage medium according to claim 18, wherein when said computer program is executed by said processor to classify images in said second set of images, further performing the following operations:

Obtaining a face region in each of the images in the second image set, and classifying the image according to the acquired face region.
The computer readable storage medium according to any one of claims 15 to 19, wherein when the computer program is executed by the processor, the following operations are further performed:

A corresponding image subset is generated according to each type of image, and each image subset is arranged and displayed.
The computer readable storage medium according to claim 20, wherein when the computer program is executed by the processor to display the respective image subsets, the following operations are further performed:

Counting the total number of views corresponding to each image sub-set, wherein the total number of views refers to the sum of historical browsing times corresponding to all images included in the image sub-collection;

Each image sub-set is arranged and displayed according to the total number of views.