WO2019233309A1

WO2019233309A1 - Information processing method and apparatus, and electronic device and computer-readable storage medium

Info

Publication number: WO2019233309A1
Application number: PCT/CN2019/088702
Authority: WO
Inventors: 陈岩
Original assignee: Oppo广东移动通信有限公司
Priority date: 2018-06-08
Filing date: 2019-05-28
Publication date: 2019-12-12
Also published as: CN108804656B; CN108804656A

Abstract

An information processing method, comprising: when it is detected that an application program is started, acquiring an object list corresponding to the application program, wherein the object list comprises object category information and an amount corresponding to the object category information; acquiring the time when object category information of the largest amount is recorded in the object list; and if the difference between the time and the current time is not within a pre-set time duration, recommending object category information corresponding to the time.

Description

Information processing method and device, electronic equipment, computer-readable storage medium

Cross-reference to related applications

This application claims the priority of a Chinese patent application filed on June 8, 2018 with the Chinese Patent Office, application number 201810585914.7, and the invention name as "Information Processing Method and Device, Electronic Equipment, Computer-readable Storage Medium" Incorporated by reference in this application.

Technical field

The present application relates to the field of Internet technologies, and in particular, to an information processing method and apparatus, an electronic device, and a computer-readable storage medium.

Background technique

With the development of Internet technology, electronic devices are becoming more and more powerful and can provide a variety of network services. For example, electronic devices can shop through the Internet and listen to music. The electronic device can also process the information through the Internet according to the user's historical behavior and send out some recommended information, thereby improving the convenience of information recommendation.

However, the traditional information processing method has the problem of inaccurate information processing.

Summary of the Invention

The embodiments of the present application provide an information processing method, apparatus, electronic device, and computer-readable storage medium, which can improve the accuracy of information processing.

An information processing method includes:

When it is detected that the application program is opened, obtaining an object list corresponding to the application program, the object list includes object category information and a quantity corresponding to the object category information;

Acquiring the moment when the highest number of object category information is recorded in the object list;

If the time is not within a preset duration from the current time, then object category information corresponding to the time is recommended.

An information processing device includes:

A list obtaining module, configured to obtain a list of objects corresponding to the application when an application program is detected to be opened, where the list of objects includes object category information and a quantity corresponding to the object category information;

A time acquisition module, configured to acquire the time when the highest number of object category information is recorded in the object list;

An information recommendation module is configured to recommend the object category information corresponding to the time if the time is not within a preset duration from the current time.

An electronic device includes a memory and a processor. The memory stores computer-readable instructions. When the instructions are executed by the processor, the processor causes the processor to perform the following steps:

A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the following steps are implemented:

The foregoing information processing method and device, electronic device, and computer-readable storage medium, when detecting that an application program is opened, obtain an object list corresponding to the application program, and the object list includes object category information and a quantity corresponding to the object category information, and obtains The time when the highest amount of object category information is recorded in the object list. If the time is not within a preset time from the current time, then the object category information corresponding to the time is recommended. By obtaining the list of objects and recommending the object information according to the time when the object category information is recorded in the object, the accuracy of the information recommendation is improved, and the accuracy of the information processing is further improved.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions in the embodiments of the present application or the prior art more clearly, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely These are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without paying creative work.

FIG. 1 is a schematic structural diagram of an electronic device according to an embodiment.

FIG. 2 is a flowchart of an information processing method according to an embodiment.

FIG. 3 is a flowchart of a method for obtaining a list of objects in an embodiment.

FIG. 4 is a schematic diagram of an interface of an object list in an embodiment.

FIG. 5 is a schematic diagram of a preview screen captured by a camera in an embodiment.

FIG. 6 is a flowchart of a method for opening or closing an object detection model in an embodiment.

FIG. 7 is a flowchart of a method for training an object detection model in an embodiment.

FIG. 8 is a flowchart of a method for recommending the second-highest number of object category information in an object list according to an embodiment.

FIG. 9 is a structural block diagram of an information processing apparatus in an embodiment.

FIG. 10 is a structural block diagram of an information processing apparatus in another embodiment.

FIG. 11 is a schematic diagram of an image processing circuit in an embodiment.

Detailed ways

In order to make the purpose, technical solution, and advantages of the present application clearer, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application.

In one embodiment, as shown in FIG. 1, a schematic diagram of an internal structure of an electronic device is provided. The electronic device includes a processor, a memory, a camera, and a network interface connected through a system bus. The processor is used to provide computing and control capabilities to support the operation of the entire electronic device. The memory is used to store data, programs, and / or instruction codes. The memory stores at least one computer program that can be executed by a processor to implement the information processing method applicable to electronic devices provided in the embodiments of the present application. The memory may include a non-volatile storage medium such as a magnetic disk, an optical disc, a read-only memory (ROM), or a 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 to implement an information processing method provided by various embodiments of the present application. The internal memory provides a cached operating environment for the operating system and computer programs in a non-volatile storage medium. The camera can be used to capture images. The network interface may be an Ethernet card or a wireless network card, and is used to communicate with external electronic devices, for example, it may be used to communicate with a server.

Those skilled in the art can understand that the structure shown in FIG. 1 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 electronic device to which the solution of the present application is applied. The specific electronic device may be Include more or fewer parts than shown in the figure, or combine certain parts, or have a different arrangement of parts.

In one embodiment, an information processing method is provided, which is illustrated by applying to the above electronic device. As shown in FIG. 2, the method includes the following steps:

In step 202, when it is detected that the application program is opened, an object list corresponding to the application program is obtained, and the object list includes object category information and a quantity corresponding to the object category information.

An application is a computer program that performs a specific task or tasks. It can run in user mode, interact with users, and have a visual user interface. There can be multiple types of applications. For example, applications can include shopping, food, video, and music. Electronic devices can categorize applications.

The electronic device can detect the running status of the application program. When the electronic device detects that the application program is open, it can obtain a list of objects corresponding to the opened application program. The object list may include object category information and a quantity corresponding to the object category information. The object category information may be type information of an object related to the application. For example, when the application is a shopping category, the object category information included in the list of objects corresponding to the shopping category application may be a shirt, pants, slippers, skirt, and bag. Information about the types of objects that need to be purchased, such as bags; when the application is gourmet, the object category information included in the list of objects corresponding to the gourmet application can be noodles, stir-fries, barbecues, hot pots, pastries, and types of food such as western food information.

The object list may also include the quantity corresponding to the object category information. For example, when the application is a gourmet, the object category information and the quantity corresponding to the object category information included in the object list corresponding to the gourmet application may be noodles 5, cooking 7. Type information of foods such as barbecue 8, hot pot 6, noodle 2 and western food 1, and the quantity corresponding to the type of food. When the electronic device detects that the application is opened, it can obtain a list of objects corresponding to the opened application.

Step 204: Obtain the moment when the highest amount of object category information is recorded in the object list.

The object list includes object category information and a quantity corresponding to the object category information, and the electronic device can obtain the highest quantity of object category information. At the same time, the electronic device can also obtain the specific moment when the highest object category information is recorded in the object list. The specific time obtained by the electronic device can be accurate to the hour. For example, the highest object category information and the corresponding quantity in the object list obtained by the electronic device are barbecue 8. The time when barbecue 8 is recorded in the object list is April 2018. At 11: 2 on the morning of March 30, the electronic device can obtain that the barbecue 8 was recorded in the list of objects at 11 o'clock on April 30, 2018.

In step 206, if the time is not within a preset duration from the current time, the object category information corresponding to the time is recommended.

The preset duration may be a duration set by a user or a duration set by an electronic device. After the electronic device obtains the time when the highest amount of object category information is recorded in the object list, the electronic device can also obtain the current time and calculate the time between when the highest amount of object category information is recorded in the object list and the current time Time difference. The electronic device can also compare the calculated time period difference with a preset time period. When the calculated time period difference is not within the preset time period, the electronic device can recommend the highest number corresponding to the time recorded in the object list Object category information.

For example, the preset duration is 24 hours. The highest amount of object category information obtained by the electronic device and the corresponding quantity is barbecue 8. The time when the barbecue 8 obtained by the electronic device is recorded in the object list is 11 am on April 30, 2018. The current time is 2 pm on May 1, 2018. The time difference between the recorded time and the current time is 27 hours. 27 hours is not within the 24 hour time range. Therefore, the electronic device can recommend the recording of the object list. The object category information at 11 am on April 30, 2018, that is, the electronic device can recommend grilling.

When it is detected that the application is open, the list of objects corresponding to the application is obtained. The list of objects includes the object category information and the quantity corresponding to the object category information. The moment when the highest amount of object category information is recorded in the object list, If the time is not within the preset time from the current time, the object category information corresponding to the time is recommended. By obtaining the list of objects and recommending the object information according to the time when the object category information is recorded in the object, the accuracy of the information recommendation is improved, and the accuracy of the information processing is further improved.

As shown in FIG. 3, in one embodiment, an information processing method provided may further include a process of obtaining a list of objects, and the specific steps include:

In step 302, when it is detected that the camera is turned on, an object detection model is used to detect a preview image collected by the camera to obtain object category information.

The electronic device can also detect the status of the camera. When the electronic device detects that the camera is turned on, the electronic device can control the camera to collect preview images, and the electronic device can also use the object detection model to detect the preview images collected by the camera.

The object detection model may be a model trained to detect object categories based on a neural network, and the object detection model may be a food detection model. For example, when food is included in the preview image captured by the camera acquired by the electronic device, the electronic device may use The object detection model detects the food in the preview screen. Specifically, the types of food may include noodles, stir-fried vegetables, barbecues, hot pots, pastries, and western food. The electronic device can detect the food in the preview screen by using the object detection model, so as to obtain food category information such as noodles, cooking, grilling, hot pot, pasta, and western food.

Step 304: Record the object category information, and update the quantity of the stored object category information to obtain the updated quantity of the object category information.

The stored object category information refers to the object category information that has been stored on the electronic device before the electronic device records the object information, and the stored object category information may include a quantity corresponding to the stored object category information. The electronic device may record the obtained object category information on the basis of having stored the object category information, and update the quantity of the object category information to obtain the updated quantity of the object category information. For example, the stored object category information and the corresponding quantity are noodle 5, barbecue 8, hot pot 6, and the object category information obtained by the electronic device is barbecue. The electronic device can update the quantity of the stored object category information. The object category information and the corresponding quantity may be noodle 5, barbecue 9, hot pot 6.

Step 306: Sort the object category information according to the number of the updated object category information to obtain an object list.

The electronic device may also sort the object category information based on the number of the updated object category information. For example, the updated object category information obtained by the electronic device and the corresponding quantity are noodle 5, barbecue 9, hot pot 6. The electronic device can sort the object category information according to the updated number of object categories, and the electronic device can sort in ascending order. , The number of sorted object category information and the number of object category information are noodles 5, hot pot 6, barbecue 9; electronic devices can also sort in descending order, the number of sorted object category information and object category information is barbecue 9, hot pot 6, Noodles 5. The electronic device may generate an object list according to the sorted object category information and the number of corresponding object category information, and the electronic device may obtain the generated object list.

When the camera is detected to be on, the object detection model is used to detect the preview image collected by the camera to obtain the object category information, record the object category information, and update the number of stored object category information to obtain the updated object category information , Sort the object category information according to the number of updated object category information, and obtain an object list. The electronic device detects the preview image collected by the camera by using the object detection model, and updates the quantity of the object category information according to the detection result, thereby generating an object list and improving the accuracy of the object category information detection result.

In one embodiment, as shown in FIG. 4, the electronic device may obtain the object category information and sort the object category information according to the quantity corresponding to the object category information. For example, the electronic device may sort the object category information in descending order according to the quantity of the object category information. Thereby, an object list 410 is obtained. As shown in FIG. 4, the object list 410 may include the object category information and the quantity corresponding to the object category information. For example, the corresponding quantity is 8 for grilling; the corresponding quantity is 7 for hot pot; the corresponding quantity is 6 for cooking; , The corresponding quantity is 5; pasta, the corresponding quantity is 2; Western food, the corresponding quantity is 1. The electronic device can obtain the generated object list, and can also obtain the moments when these object category information is recorded in the object list.

As shown in FIG. 5, in one embodiment, the electronic device can obtain a preview image collected through a camera. The preview screen obtained by the electronic device may include object category information. As shown in FIG. 5, the preview screen 510 acquired by the electronic device and collected by the camera includes the object category information as hot pot. When the electronic device detects that the camera is turned on, the object detection model can be used to detect the preview image 510 collected by the camera, and the object type information is hot pot. The electronic device can also record the obtained object category information, that is, record hot pots, and update the number of hot pots that have been stored. The electronic device can sort according to the updated quantity to obtain a list of objects.

In an embodiment, as shown in FIG. 6, an information processing method provided may further include a process of opening or closing an object detection model. The specific steps include:

Step 602: Obtain a switch control instruction for the object detection model.

The opening or closing of the object detection model can be controlled by the buttons or controls on the electronic device, or it can be triggered by the electronic device by turning on the camera. The electronic device can obtain a switch control instruction for the object detection model by means of keys, controls, or triggering by turning on a camera.

Step 604: Turn on or off the object detection model according to the switch control instruction.

After the electronic device obtains the switch control instruction for the object detection model, it can detect the state of the current object detection model. If the current object detection model is on, the electronic device can switch the state of the object detection model to the off state according to the obtained switch control instruction; if the current object detection model is off, the electronic device can detect the object according to the obtained switch control instruction. The state of the model is switched to the on state.

By acquiring a switch control instruction for the object detection model, the object detection model is turned on or off according to the switch control instruction. The electronic device can switch the state of the object detection model according to the obtained switch control instruction, which can save resources.

As shown in FIG. 7, in an embodiment, an information processing method provided may further include a process of training an object detection model, and the specific steps include:

Step 702: Obtain a training image that includes object category information.

The electronic device can acquire multiple images containing the object category information and use these images as training images. For example, the electronic device may obtain images containing noodles, stir-fried vegetables, barbecue, hot pot, pasta, and western food, respectively, and use these images as training images.

Step 704: The training image is input to an object detection model, and the object detection model is trained to obtain an object detection model for detecting object category information.

The electronic device can input the training image into the object detection model. Since the training image contains object category information, the electronic device can train the object detection model and obtain an object detection model for detecting the object category information.

By acquiring a training image containing object category information, inputting the training image to an object detection model, and training the object detection model, an object detection model for detecting object category information is obtained. The object detection model is obtained by training based on the image containing the object category information, which can improve the accuracy of the object detection model in detecting the object category information.

In one embodiment, the provided information processing method may further include a process of exiting the object detection model after acquiring the object category information and quantity, and specifically includes: automatically stopping detecting the preview image collected by the camera, and exiting the object detection model.

The electronic device can automatically stop detecting the preview image collected by the camera after acquiring the object category information and quantity. The electronic device can also exit the object detection model after stopping detecting the preview image captured by the camera.

Automatically stop detecting the preview image captured by the camera and exit the object detection model. The electronic device can exit the object detection model after obtaining the object category information and quantity, which can save resources and reduce the power consumption of the electronic device.

In an embodiment, an information processing method provided may further include a process of maintaining the exit state of the object detection model when the camera is turned on again, and specifically includes: maintaining the object detection model in the exit state when it is detected that the camera is turned on again.

After the electronic device stops detecting the preview image collected by the camera and exits the object detection model, it can continuously detect the state of the camera. When the electronic device detects that the camera is turned on again, it can continue to keep the object detection model in an exited state. When the object detection model is continuously in the exiting state, the electronic device can re-obtain the switch control instruction for the object detection model, turn on the object detection model according to the switch control instruction, and re-detect the preview image collected by the camera.

In one embodiment, as shown in FIG. 8, an information processing method provided may further include a process of recommending the second-highest number of object category information in the object list, and the specific steps include:

In step 802, if the time from the current time is within a preset time period, the second highest number of object category information in the object list is acquired.

The electronic device can obtain the moment when the highest amount of object category information is recorded in the object list. The electronic device can also calculate the time period difference between this time and the current time. The electronic device can also compare the calculated time period difference with a preset time period. When the calculated time period difference is within the preset time period, , The electronic device can obtain the second highest number of object category information in the object list.

Similarly, the electronic device can also obtain the time when the next-highest number of object category information is recorded in the object list and calculate the time period difference between the time and the current time. The electronic device can also calculate the calculated time period difference Compared with the preset time period, when the calculated time period difference is not within the preset time period, the electronic device can perform step 804; when the calculated time period difference is within the preset time period, the electronic device can obtain the object The third highest number of object category information in the list, and so on.

Step 804: Recommend the next highest number of object category information in the object list.

When the recording time of the highest number of object category information in the object list is within a preset time from the current time, and the recording time of the second highest number of object category information in the object list is not within the preset time, the electronic device The second-highest number of object category information in the object list can be recommended. For example, the object category information and the corresponding quantity recorded in the object list are barbecue 8, hot pot 6, noodle 5, respectively. When the time difference between the time when barbecue 8 is recorded in the object list and the current time is within a preset time The electronic device can record the time difference between the time of the hot pot 6 and the current time in the object list. If the time difference is not within a preset time period, the electronic device can recommend the hot pot 6 in the object list.

If the time from the current time is within a preset time period, the second highest number of object category information in the object list is obtained, and the second highest number of object category information in the object list is recommended. When the time is within a preset time from the current time, recommending the second-highest number of object category information can improve the accuracy of the information recommendation, thereby improving the accuracy of information processing.

In one embodiment, an information processing method is provided. The specific steps for implementing the method are as follows:

First, the electronic device can acquire a training image that contains object category information. The electronic device can acquire multiple images containing the object category information and use these images as training images. For example, the electronic device may obtain images containing noodles, stir-fried vegetables, barbecue, hot pot, pasta, and western food, respectively, and use these images as training images. The training image is input to an object detection model, and the object detection model is trained to obtain an object detection model for detecting object category information. The electronic device can input the training image into the object detection model. Since the training image contains object category information, the electronic device can train the object detection model and obtain an object detection model for detecting the object category information.

Then, when it is detected that the camera is turned on, the electronic device can use the object detection model to detect the preview image collected by the camera to obtain object category information. The electronic device can also detect the status of the camera. When the electronic device detects that the camera is turned on, the electronic device can control the camera to collect preview images, and the electronic device can also use the object detection model to detect the preview images collected by the camera. The electronic device may also record the object category information, and update the quantity of the stored object category information to obtain the updated quantity of the object category information.

Furthermore, the electronic device can automatically stop detecting the preview image collected by the camera after acquiring the object category information and quantity. The electronic device can also exit the object detection model after stopping detecting the preview image captured by the camera. After the electronic device stops detecting the preview image collected by the camera and exits the object detection model, it can continuously detect the state of the camera. When the electronic device detects that the camera is turned on again, it can continue to keep the object detection model in an exited state. When the object detection model is continuously in the exiting state, the electronic device can re-obtain the switch control instruction for the object detection model, turn on the object detection model according to the switch control instruction, and re-detect the preview image collected by the camera.

Then, the electronic device may sort the object category information according to the updated quantity of the object category information to obtain an object list. The electronic device may also sort the object category information based on the number of the updated object category information. The electronic device can sort the object category information according to the number of updated object categories, the electronic device can sort in ascending order, and the electronic device can sort in descending order. The electronic device may generate an object list according to the sorted object category information and the number of corresponding object category information, and the electronic device may obtain the generated object list.

When it is detected that the application is open, the electronic device may obtain a list of objects corresponding to the application, and the list of objects includes object category information and a quantity corresponding to the object category information. The electronic device can detect the running status of the application program. When the electronic device detects that the application program is open, it can obtain a list of objects corresponding to the opened application program. The object list may include object category information and a quantity corresponding to the object category information. The object category information may be type information of an object related to the application. The object list may further include a quantity corresponding to the object category information. When the electronic device detects that the application is opened, it may obtain an object list corresponding to the opened application.

Then, the electronic device can obtain the moment when the highest amount of object category information is recorded in the object list. The object list includes object category information and a quantity corresponding to the object category information, and the electronic device can obtain the highest quantity of object category information. At the same time, the electronic device can also obtain the specific moment when the highest object category information is recorded in the object list.

If the time is not within a preset duration from the current time, the electronic device may recommend the object category information corresponding to the time. After the electronic device obtains the time when the highest amount of object category information is recorded in the object list, the electronic device can also obtain the current time and calculate the time between the time when the highest amount of object category information is recorded in the object list and the current time Time difference. The electronic device can also compare the calculated time period difference with a preset time period. When the calculated time period difference is not within the preset time period, the electronic device can recommend the highest number corresponding to the time recorded in the object list Object category information.

If the time is within a preset time from the current time, the electronic device can obtain the second-highest number of object category information in the object list. The electronic device can obtain the moment when the highest amount of object category information is recorded in the object list. The electronic device can also calculate the time period difference between this time and the current time. The electronic device can also compare the calculated time period difference with a preset time period. When the calculated time period difference is within the preset time period, , The electronic device can obtain the second highest number of object category information in the object list. When the recording time of the highest number of object category information in the object list is within a preset time from the current time, and the recording time of the second highest number of object category information in the object list is not within the preset time, the electronic device The second-highest number of object category information in the object list can be recommended.

The electronic device can also obtain switch control instructions for the object detection model. The opening or closing of the object detection model can be controlled by the buttons or controls on the electronic device, or it can be triggered by the electronic device by turning on the camera. The electronic device can obtain a switch control instruction for the object detection model by means of keys, controls, or triggering by turning on a camera. After the electronic device obtains the switch control instruction for the object detection model, it can detect the state of the current object detection model. If the current object detection model is on, the electronic device can switch the state of the object detection model to the off state according to the obtained switch control instruction; if the current object detection model is off, the electronic device can detect the object according to the obtained switch control instruction. The state of the model is switched to the on state.

It should be understood that although the steps in the above flowcharts are sequentially displayed in accordance with the directions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless explicitly stated in this document, the execution of these steps is not strictly limited, and these steps can be performed in other orders. Moreover, at least a part of the steps in the above flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

FIG. 9 is a structural block diagram of an information processing apparatus according to an embodiment. As shown in FIG. 9, the apparatus includes a list obtaining module 910, a time obtaining module 920, and an information recommendation module 930, among which:

The list obtaining module 910 is configured to obtain an object list corresponding to the application when it is detected that the application is open, and the object list includes object category information and a quantity corresponding to the object category information.

The time acquisition module 920 is configured to acquire the time when the highest amount of object category information is recorded in the object list.

The information recommendation module 930 is configured to recommend the object category information corresponding to the time if the time is not within a preset duration from the current time.

In one embodiment, as shown in FIG. 10, an information processing apparatus provided further includes: a category information acquisition module 940, a quantity update module 950, and a quantity ranking module 960, where:

A category information acquisition module 940 is configured to detect the preview image collected by the camera using an object detection model when it is detected that the camera is turned on, to obtain object category information.

The quantity update module 950 is configured to record the object category information and update the quantity of the stored object category information to obtain the updated quantity of the object category information.

The quantity sorting module 960 is configured to sort the object category information according to the updated quantity of the object category information to obtain an object list.

In one embodiment, the category information obtaining module 940 may be further configured to obtain a switch control instruction for the object detection model, and turn the object detection model on or off according to the switch control instruction.

In one embodiment, the category information acquisition module 940 may be further configured to obtain a training image containing object category information, input the training image to an object detection model, and train the object detection model to obtain an object for detecting the object category information. Detection model.

In one embodiment, the quantity update module 950 may also be used to automatically stop detecting the preview image collected by the camera and exit the object detection model.

In one embodiment, the quantity update module 950 may be further configured to keep the object detection model in an exit state when it is detected that the camera is turned on again.

In one embodiment, the information recommendation module 930 may be further configured to obtain the next highest number of object category information in the object list and recommend the second highest number of object category information in the object list if the time is within a preset time from the current time.

The division of each module in the above-mentioned information processing apparatus is only for illustration. In other embodiments, the information processing apparatus may be divided into different modules according to requirements to complete all or part of the functions of the above-mentioned information processing apparatus.

For the specific limitation of the information processing device, refer to the limitation on the information processing method described above, which is not repeated here. Each module in the above-mentioned information processing apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the hardware in or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

The implementation of each module in the information processing apparatus provided in the embodiments of the present application may be in the form of a computer program. The computer program can be run on a terminal or a server. The program module constituted by the computer program can be stored in the memory of the terminal or server. When the computer program is executed by a processor, the steps of the method described in the embodiments of the present application are implemented.

An embodiment of the present application further provides a computer-readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions, which when executed by one or more processors, cause the processors to perform the steps of the information processing method.

A computer program product containing instructions that, when run on a computer, causes the computer to perform an information processing method.

An embodiment of the present application further provides an electronic device. The electronic device includes an image processing circuit, and the image processing circuit may be implemented by using hardware and / or software components, and may include various processing units that define an ISP (Image Signal Processing) pipeline. FIG. 11 is a schematic diagram of an image processing circuit in an embodiment. As shown in FIG. 11, for ease of description, only aspects of the image processing technology related to the embodiments of the present application are shown.

As shown in FIG. 11, the image processing circuit includes an ISP processor 1140 and a control logic 1150. The image data captured by the imaging device 1110 is first processed by the ISP processor 1140. The ISP processor 1140 analyzes the image data to capture image statistical information that can be used to determine and / or one or more control parameters of the imaging device 1110. The imaging device 1110 may include a camera having one or more lenses 1112 and an image sensor 1114. The image sensor 1114 may include a color filter array (such as a Bayer filter). The image sensor 1114 may obtain light intensity and wavelength information captured by each imaging pixel of the image sensor 1114, and provide a set of raw data that may be processed by the ISP processor 1140. Image data. The sensor 1120 (such as a gyroscope) may provide parameters (such as image stabilization parameters) of the acquired image processing to the ISP processor 1140 based on the interface type of the sensor 1120. The sensor 1120 interface may use a SMIA (Standard Mobile Imaging Architecture) interface, other serial or parallel camera interfaces, or a combination of the foregoing interfaces.

In addition, the image sensor 1114 may also send the original image data to the sensor 1120. The sensor 1120 may provide the original image data to the ISP processor 1140 based on the interface type of the sensor 1120, or the sensor 1120 stores the original image data in the image memory 1130.

The ISP processor 1140 processes the original image data pixel by pixel in a variety of formats. For example, each image pixel may have a bit depth of 8, 10, 12, or 14 bits, and the ISP processor 1140 may perform one or more image processing operations on the original image data and collect statistical information about the image data. The image processing operations may be performed with the same or different bit depth accuracy.

The ISP processor 1140 may also receive image data from the image memory 1130. For example, the sensor 1120 interface sends the original image data to the image memory 1130, and the original image data in the image memory 1130 is then provided to the ISP processor 1140 for processing. The image memory 1130 may be a part of a memory device, a storage device, or a separate dedicated memory in an electronic device, and may include a DMA (Direct Memory Access) feature.

When receiving raw image data from the image sensor 1114 interface or from the sensor 1120 interface or from the image memory 1130, the ISP processor 1140 may perform one or more image processing operations, such as time-domain filtering. The processed image data may be sent to the image memory 1130 for further processing before being displayed. The ISP processor 1140 receives processed data from the image memory 1130, and performs image data processing on the processed data in the original domain and in the RGB and YCbCr color spaces. The image data processed by the ISP processor 1140 may be output to a display 1170 for viewing by a user and / or further processed by a graphics engine or a GPU (Graphics Processing Unit). In addition, the output of the ISP processor 1140 can also be sent to the image memory 1130, and the display 1170 can read image data from the image memory 1130. In one embodiment, the image memory 1130 may be configured to implement one or more frame buffers. In addition, the output of the ISP processor 1140 may be sent to an encoder / decoder 1160 to encode / decode image data. The encoded image data can be saved and decompressed before being displayed on the display 1170 device. The encoder / decoder 1160 may be implemented by a CPU or a GPU or a coprocessor.

The statistical data determined by the ISP processor 1140 may be sent to the control logic 1150 unit. For example, the statistical data may include statistical information of the image sensor 1114 such as auto exposure, auto white balance, auto focus, flicker detection, black level compensation, and lens 1112 shading correction. The control logic 1150 may include a processor and / or a microcontroller that executes one or more routines (such as firmware). The one or more routines may determine the control parameters of the imaging device 1110 and the ISP processing based on the received statistical data. 1140 control parameters. For example, the control parameters of the imaging device 1110 may include sensor 1120 control parameters (such as gain, integration time for exposure control, image stabilization parameters, etc.), camera flash control parameters, lens 1112 control parameters (such as focus distance for focusing or zooming), or these A combination of parameters. The ISP control parameters may include gain levels and color correction matrices for automatic white balance and color adjustment (eg, during RGB processing), and lens 1112 shading correction parameters.

In this embodiment, the image processing method in FIG. 11 can be used to implement the foregoing image processing method.

Any reference to memory, storage, database, or other media used in this application may include non-volatile and / or volatile memory. Non-volatile memory may 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 is used as external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDR, SDRAM), enhanced SDRAM (ESDRAM), synchronous 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 only express several implementation manners of the present application, and their descriptions are more specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be noted that, for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of this application patent shall be subject to the appended claims.

Claims

An information processing method, comprising:

When it is detected that the application program is opened, obtaining an object list corresponding to the application program, the object list includes object category information and a quantity corresponding to the object category information;

Obtaining the moment when the highest number of object category information is recorded in the object list; and

If the time is not within a preset duration from the current time, then object category information corresponding to the time is recommended.
The method according to claim 1, wherein before detecting that the application is opened, the method further comprises:

When it is detected that the camera is turned on, use an object detection model to detect a preview image collected by the camera to obtain object category information;

Record the object category information, and update the quantity of the stored object category information to obtain the updated quantity of the object category information; and

Sort the object category information according to the updated quantity of the object category information to obtain an object list.
The method according to claim 2, further comprising:

Obtain switch control instructions for the object detection model; and

The object detection model is turned on or off according to the switch control instruction.
The method according to claim 2, further comprising:

Obtaining training images containing object category information; and

The training image is input to an object detection model, and the object detection model is trained to obtain an object detection model for detecting object category information.
The method according to claim 2, wherein after obtaining the updated quantity of the object category information, the method further comprises:

Automatically stop detecting preview images collected by the camera, and exit the object detection model.
The method according to claim 5, wherein after exiting the object detection model, the method further comprises:

When it is detected that the camera is turned on again, the object detection model is kept in an exit state.
The method according to any one of claims 1 to 6, further comprising:

If the time is within a preset time from the current time, obtaining the next highest number of object category information in the object list; and

The second-highest number of object category information in the object list is recommended.
An information processing device, comprising:

A list obtaining module, configured to obtain a list of objects corresponding to the application when an application program is detected to be opened, where the list of objects includes object category information and a quantity corresponding to the object category information;

A time acquisition module, configured to acquire the time when the highest number of object category information is recorded in the object list;

An information recommendation module is configured to recommend the object category information corresponding to the time if the time is not within a preset duration from the current time.
An electronic device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor causes the processor to perform the following steps:

When it is detected that the application program is opened, obtaining an object list corresponding to the application program, the object list includes object category information and a quantity corresponding to the object category information;

Obtaining the moment when the highest number of object category information is recorded in the object list; and

If the time is not within a preset duration from the current time, then object category information corresponding to the time is recommended.
The electronic device according to claim 9, wherein the processor is further configured to execute an object detection model on the preview image collected by the camera when the camera is detected to be turned on before detecting that the application is turned on. Detection to obtain object category information;

Record the object category information, and update the quantity of the stored object category information to obtain the updated quantity of the object category information; and

Sort the object category information according to the updated quantity of the object category information to obtain an object list.
The electronic device according to claim 10, wherein the processor is further configured to execute a switch control instruction for acquiring an object detection model; and turn the object detection model on or off according to the switch control instruction.
The electronic device according to claim 10, wherein the processor is further configured to execute a training image that includes object category information; and input the training image to an object detection model, Training is performed to obtain an object detection model for detecting object category information.
The electronic device according to claim 10, wherein the processor is further configured to automatically stop detecting a preview image collected by the camera after obtaining the updated quantity of the object category information, and exit the camera. The object detection model is described.
The electronic device according to claim 13, wherein the processor is further configured to keep the object detection model in an exit state when it is detected that the camera is turned on again after exiting the object detection model. .
The electronic device according to any one of claims 9 to 14, wherein the processor is further configured to execute, if the time is within a preset duration from the current time, obtaining the second highest number in the list of objects Object category information; and

The second-highest number of object category information in the object list is recommended.
A computer-readable storage medium having stored thereon a computer program, characterized in that when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.