CN108804656B - Information processing method and device, electronic equipment and computer readable storage medium - Google Patents
Information processing method and device, electronic equipment and computer readable storage medium Download PDFInfo
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- CN108804656B CN108804656B CN201810585914.7A CN201810585914A CN108804656B CN 108804656 B CN108804656 B CN 108804656B CN 201810585914 A CN201810585914 A CN 201810585914A CN 108804656 B CN108804656 B CN 108804656B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
Abstract
The application relates to an information processing method and device, an electronic device and a computer readable storage medium. The method comprises the following steps: when the application program is detected to be started, an object list corresponding to the application program is obtained, the object list comprises object type information and the number corresponding to the object type information, the time when the object type information with the highest number is recorded in the object list is obtained, and if the time is not within the preset time length from the current time, the object type information corresponding to the time is recommended. By acquiring the object list and recommending the object information according to the object type information recorded at the moment when the object requests, the accuracy of information recommendation is improved, and the accuracy of information processing is further improved.
Description
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
With the development of internet technology, electronic devices are becoming more powerful and can provide various network services, for example, electronic devices can shop through the internet, listen to music, and the like. The electronic equipment can also process information through the Internet according to the historical behaviors of the user and send some recommendation information, so that the convenience of information recommendation is improved.
However, the conventional information processing method has the problem of inaccurate information processing.
Disclosure of Invention
The embodiment of the application provides an information processing method and device, electronic equipment and a computer readable storage medium, which can improve the accuracy of information processing.
An information processing method comprising:
when detecting that an application program is started, acquiring an object list corresponding to the application program, wherein the object list comprises object type information and the number corresponding to the object type information;
acquiring the time when the object type information with the highest quantity is recorded in the object list;
and if the time is not within a preset time length from the current time, recommending the object type information corresponding to the time.
An information processing apparatus comprising:
the system comprises a list acquisition module, a list acquisition module and a display module, wherein the list acquisition module is used for acquiring an object list corresponding to an application program when the application program is detected to be started, and the object list comprises object type information and the number corresponding to the object type information;
the time obtaining module is used for obtaining the time when the object type information with the highest quantity is recorded in the object list;
and the information recommendation module is used for recommending the object type information corresponding to the moment if the distance between the moment and the current moment is not within a preset time length.
An electronic device comprising a memory and a processor, the memory having stored therein computer-readable instructions that, when executed by the processor, cause the processor to perform the steps of:
when detecting that an application program is started, acquiring an object list corresponding to the application program, wherein the object list comprises object type information and the number corresponding to the object type information;
acquiring the time when the object type information with the highest quantity is recorded in the object list;
and if the time is not within a preset time length from the current time, recommending the object type information corresponding to the time.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
when detecting that an application program is started, acquiring an object list corresponding to the application program, wherein the object list comprises object type information and the number corresponding to the object type information;
acquiring the time when the object type information with the highest quantity is recorded in the object list;
and if the time is not within a preset time length from the current time, recommending the object type information corresponding to the time.
According to the information processing method and device, the electronic device and the computer readable storage medium, when the application program is detected to be started, the object list corresponding to the application program is obtained, the object list comprises the object type information and the number corresponding to the object type information, the time when the object type information with the highest number is recorded in the object list is obtained, and if the time is not within the preset time length from the current time, the object type information corresponding to the time is recommended. By acquiring the object list and recommending the object information according to the object type information recorded at the moment when the object requests, the accuracy of information recommendation is improved, and the accuracy of information processing is further improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of an electronic device in one embodiment;
FIG. 2 is a flow diagram of a method of information processing in one embodiment;
FIG. 3 is a flow diagram of a method for obtaining a list of objects in one embodiment;
FIG. 4 is a schematic diagram of an interface for a list of objects in one embodiment;
FIG. 5 is a diagram illustrating a preview image captured by a camera in one embodiment;
FIG. 6 is a flow diagram of a method for turning an object detection model on or off in one embodiment;
FIG. 7 is a flow diagram of a method for training an object detection model in accordance with one embodiment;
FIG. 8 is a flow diagram of a method for recommending a next highest number of object category information in an object list, under an embodiment;
FIG. 9 is a block diagram showing the configuration of an information processing apparatus according to an embodiment;
FIG. 10 is a block diagram showing the construction of an information processing apparatus according to another embodiment;
FIG. 11 is a schematic diagram of an image processing circuit in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, 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 merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in FIG. 1, a schematic diagram of an internal structure of an electronic device is provided. The electronic equipment comprises a processor, a memory, a camera and a network interface which are connected through a system bus. Wherein, the processor is used for providing calculation and control capability and supporting the operation of the whole electronic equipment. The memory is used for storing data, programs, instruction codes and/or the like, and at least one computer program is stored on the memory, and the computer program can be executed by the processor to realize the information processing method suitable for the electronic device provided in the embodiment of the 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-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 execution environment for the operating system and computer programs in the non-volatile storage medium. The camera may be used to capture images. The network interface may be an ethernet card or a wireless network card, etc. for communicating with an external electronic device, such as a server.
Those skilled in the art will appreciate that the architecture shown in fig. 1 is a block diagram of only a portion of the architecture associated with the subject application, and does not constitute a limitation on the electronic devices to which the subject application may be applied, and that a particular electronic device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, an information processing method is provided and exemplified by being applied to the electronic device described above, as shown in fig. 2, the method includes the following steps:
An application is a computer program designed to perform one or more specific tasks, which can run in a user mode, interact with a user, and have a visual user interface. The applications may be of a variety of categories, for example, the applications may include shopping categories, food categories, video categories, music categories, and the like. The electronic device may classify the application.
The electronic device can detect the running state of the application program, and when the electronic device detects that the application program is started, the electronic device can acquire an object list corresponding to the started application program. The object list may include object type information and a number corresponding to the object type information. The object type information may be type information of an object related to the application program, for example, when the application program is shopping, the object type information included in the object list corresponding to the shopping application program may be type information of an object to be purchased, such as a jacket, trousers, slippers, skirts, and bags; when the application program is a gourmet food, the object type information included in the object list corresponding to the gourmet food application program may be food type information such as noodles, fried dishes, barbecue, hot pot, pastries, and western-style meals.
For example, when the application program is a food category, the object category information and the number corresponding to the object category information included in the object list corresponding to the food category application program may be the category information of food such as noodles 5, fried dishes 7, barbecued dishes 8, hot pots 6, pastry 2, and western-style dishes 1 and the number corresponding to the food category. When detecting that the application program is started, the electronic device may acquire an object list corresponding to the started application program.
The object list includes object type information and a number corresponding to the object type information, and the electronic device can acquire the object type information with the highest number. Meanwhile, the electronic equipment can also acquire the specific time when the highest object type information is recorded in the object list. For example, the object category information and the corresponding highest quantity in the object list acquired by the electronic device are barbecue 8, the moment when the barbecue 8 is recorded in the object list is 11 am at 4/30/2018 for 2 min, and the moment when the barbecue 8 is recorded in the object list is 11 am at 4/30/2018 for 11 am.
And step 206, recommending object type information corresponding to the moment if the distance from the moment to the current moment is not within a preset time length.
The preset duration may be a duration set by a user, or a duration set by the electronic device. After the electronic equipment acquires the time when the highest number of object type information is recorded in the object list, the electronic equipment can also acquire the current time and calculate the time period difference between the time when the highest number of object type information is recorded in the object list and the current time. The electronic device may further compare the calculated time period difference with a preset time period, and when the calculated time period difference is not within the preset time period, the electronic device may recommend the highest number of object category information corresponding to the time recorded in the object list.
For example, the preset time duration is 24 hours, the highest number of object type information and the corresponding number of the object type information acquired by the electronic device are barbecue 8, the barbecue 8 acquired by the electronic device is recorded at 11 am on 30 am on 4 months in 2018, the current time is 2 pm on 1 st 1 month in 2018, the time period difference between the recording time and the current time is 27 hours, and the 27 hours are not within the time duration range of 24 hours, so that the electronic device can recommend the object type information recorded at 11 am on 30 am on 4 months in 2018, i.e., the electronic device can recommend barbecue.
When the application program is detected to be started, an object list corresponding to the application program is obtained, the object list comprises object type information and the number corresponding to the object type information, the time when the object type information with the highest number is recorded in the object list is obtained, and if the time is not within the preset time length from the current time, the object type information corresponding to the time is recommended. By acquiring the object list and recommending the object information according to the object type information recorded at the moment when the object requests, the accuracy of information recommendation is improved, and the accuracy of information processing is further improved.
As shown in fig. 3, in an embodiment, the information processing method may further include a process of acquiring an object list, and the specific steps include:
The electronic equipment can also detect the state of the camera, when the electronic equipment detects that the camera is opened, the electronic equipment can control the camera to collect preview pictures, and the electronic equipment can also detect the preview pictures collected by the camera by using the object detection model.
The object detection model may be a model trained based on a neural network and used for detecting the type of an object, and the object detection model may be a food detection model, for example, when a preview picture acquired by the electronic device and captured by a camera contains food, the electronic device may detect the food in the preview picture by using the object detection model, and specifically, the type of the food may include noodles, fried dishes, barbecues, hot pots, pastries, western-style meals, and the like. The electronic device can detect the food in the preview picture through the object detection model, so as to obtain the category information of the food such as noodles, fried dishes, barbecue, hot pot, pastries, western-style food and the like.
And 304, recording the object type information, and updating the quantity of the stored object type information to obtain the quantity of the updated object type information.
The stored object type information refers to object type information that has been stored on the electronic device before the electronic device records the object information, and the stored object type information may include a number corresponding to the stored object type information. The electronic device may record the obtained object classification information on the basis that the object classification information has been stored, and update the number of the object classification information to obtain the updated number of the object classification information. For example, the stored object type information and the corresponding number are noodle 5, barbecue 8, and hot pot 6, the object type information obtained by the electronic device is barbecue, the electronic device may update the number of the stored object type information, and the updated object type information and the corresponding number may be noodle 5, barbecue 9, and hot pot 6.
And step 306, sorting the object type information according to the number of the updated object type information to obtain an object list.
The electronic device may also rank the object class information by the number of updated object class information. For example, the updated object type information and the corresponding number obtained by the electronic device are the noodles 5, the barbecue 9 and the hot pot 6, the electronic device may sort the object type information according to the updated number of the object types, the electronic device may sort the object type information in an ascending order, and the sorted object type information and the number of the object type information are the noodles 5, the hot pot 6 and the barbecue 9; the electronic equipment can also sort the food according to descending order, and the sorted object type information and the number of the object type information are barbecue 9, hot pot 6 and noodle 5. The electronic device may generate an object list according to the sorted object category information and the number of the corresponding object category information, and the electronic device may obtain the generated object list.
When the camera is detected to be opened, detecting a preview picture acquired by the camera by using an object detection model to obtain object type information, recording the object type information, updating the quantity of the stored object type information to obtain the quantity of the updated object type information, and sequencing the object type information according to the quantity of the updated object type information to obtain an object list. The electronic equipment detects the preview picture acquired by the camera by using the object detection model and updates the quantity of the object type information according to the detection result, so that an object list is generated, and the accuracy of the object type information detection result is improved.
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 number corresponding to the object category information, for example, the electronic device may sort the object category information in a descending order according to the number of the object category information, so as to obtain the object list 410. As shown in fig. 4, the object list 410 may include object type information and a corresponding number of object type information, e.g., 8; chafing dish, corresponding number is 7; cooking, the corresponding number is 6; noodles, corresponding in number to 5; the number of the corresponding points is 2; western meals, the corresponding number is 1. The electronic device may acquire the generated object list, and may also acquire the times at which the object category information is recorded in the object list, respectively.
As shown in fig. 5, in one embodiment, the electronic device may acquire a preview screen captured by a camera. As shown in fig. 5, the preview screen 510 acquired by the electronic device through the camera includes object type information, which is a hot pot. When the electronic device detects that the camera is opened, the preview picture 510 collected by the camera can be detected by using an object detection model, and the obtained object type information is a hot pot. The electronic equipment can also record the obtained object type information, namely record the hot pot, and update the number of the stored hot pots. The electronic device may sort according to the updated quantity to obtain the list of objects.
In an embodiment, as shown in fig. 6, the provided information processing method may further include a process of turning on or off the object detection model, and the specific steps include:
The opening or closing of the object detection model can be controlled through a button or a control on the electronic equipment, and can also be triggered by the electronic equipment through opening a camera. The electronic equipment can acquire the on-off control instruction of the object detection model by a key, a control or a mode of starting the triggering of a camera.
And step 604, turning on or turning off the object detection model according to the switch control instruction.
After the electronic equipment acquires the switch control instruction of the object detection model, the state of the current object detection model can be detected. If the current object detection model is in an on state, the electronic equipment can switch the state of the object detection model into an off state according to the acquired switch control instruction; if the current object detection model is in the closed state, the electronic device can switch the state of the object detection model into the open state according to the acquired switch control instruction.
And starting or closing the object detection model according to the switch control instruction by acquiring the switch control instruction of the object detection model. The electronic equipment can switch the state of the object detection model according to the acquired switch control instruction, so that resources can be saved.
As shown in fig. 7, in an embodiment, the information processing method provided may further include a process of training an object detection model, and the specific steps include:
The electronic device can acquire a plurality of images containing object class information and use the images as training images. For example, the electronic device may acquire images including noodles, fried dishes, barbecues, hot pots, pastries, and western cuisine, respectively, and use these images as training images.
The electronic device can input the training image into the object detection model, and the training image contains the object type information, so that the electronic device can train the object detection model and obtain the object detection model for detecting the object type information.
The training image containing the object type information is acquired, the training image is input into the object detection model, and the object detection model is trained to obtain the object detection model for detecting the object type information. The object detection model is obtained by training according to the image containing the object class information, and the accuracy of the object detection model for detecting the object class information can be improved.
In an embodiment, the provided information processing method may further include a process of exiting the object detection model after acquiring the object type information and the number, specifically including: and automatically stopping detecting the preview picture acquired by the camera and quitting the object detection model.
The electronic equipment can automatically stop detecting the preview picture collected by the camera after acquiring the object type information and the number of the objects. The electronic device can also detect the object detection model after stopping detecting the preview picture that the camera gathered.
And automatically stopping detecting the preview picture acquired by the camera, and quitting the object detection model. The electronic equipment can obtain the object detection model after acquiring the object type information and the number, so that the resources can be saved, and the power consumption of the electronic equipment can be reduced.
In an embodiment, the provided information processing method may further include a process of maintaining an exit state of the object detection model when the camera is turned on again, and specifically includes: and when the camera is detected to be turned on again, keeping the object detection model in an exit state.
The electronic device can continuously detect the state of the camera after stopping detecting the preview picture collected by the camera and exiting the object detection model. When the electronic equipment detects that the camera is turned on again, the object detection model can be continuously kept in the quitting state. When the object detection model is continuously in the exit state, the electronic equipment can reacquire the switch control instruction of the object detection model, open the object detection model according to the switch control instruction, and detect the preview image collected by the camera again.
In an embodiment, as shown in fig. 8, the provided information processing method may further include a process of recommending object category information of the second highest number in the object list, and the specific steps include:
The electronic device may acquire the time when the highest amount of object category information is recorded in the object list. The electronic device may further calculate a time period difference between the time and the current time, compare the calculated time period difference with a preset time period, and when the calculated time period difference is within the preset time period, the electronic device may obtain the second highest object type information in the object list.
Similarly, the electronic device may further obtain a time at which the highest number of object type information is recorded in the object list, and calculate a time period difference between the time and the current time, the electronic device may further compare the calculated time period difference with a preset time period, and when the calculated time period difference is not within the preset time period, the electronic device may perform step 804; when the calculated time period difference is within the preset time period, the electronic device may acquire the information of the object category with the third highest number in the object list, and so on.
And step 804, recommending the object category information with the highest number in the object list.
When the recording time of the highest-quantity object type information in the object list is within a preset time length from the current time, and the recording time of the second-quantity object type information in the object list is not within the preset time length from the current time, the electronic equipment can recommend the second-quantity object type information in the object list. For example, the object type information and the corresponding number recorded in the object list are barbecue 8, hot pot 6, and noodle 5, respectively, when the time difference between the time when the barbecue 8 is recorded in the object list and the current time is within a preset time period, the electronic device may record the time difference between the time when the hot pot 6 is recorded in the object list and the current time, and if the time difference is not within the preset time period, the electronic device may recommend the hot pot 6 in the object list.
And if the time is within a preset time length from the current time, obtaining the object type information with the second highest number in the object list, and recommending the object type information with the second highest number in the object list. When the time is within the preset time length from the current time, the object class information with the second highest recommended quantity can be recommended, so that the information recommendation accuracy can be improved, and the information processing accuracy is improved.
In one embodiment, an information processing method is provided, and the specific steps for implementing the method are as follows:
first, the electronic device may acquire a training image containing object class information. The electronic device can acquire a plurality of images containing object class information and use the images as training images. For example, the electronic device may acquire images including noodles, fried dishes, barbecues, hot pots, pastries, and western cuisine, respectively, and use these images as training images. And inputting the training image into the object detection model, and training the object detection model to obtain the object detection model for detecting the object class information. The electronic device can input the training image into the object detection model, and the training image contains the object type information, so that the electronic device can train the object detection model and obtain the object detection model for detecting the object type information.
Then, when the camera is detected to be turned on, the electronic device can detect the preview picture acquired by the camera by using the object detection model to obtain the object type information. The electronic equipment can also detect the state of the camera, when the electronic equipment detects that the camera is opened, the electronic equipment can control the camera to collect preview pictures, and the electronic equipment can also detect the preview pictures collected by the camera by using the object detection model. The electronic device can also record the object type information and update the quantity of the stored object type information to obtain the quantity of the updated object type information.
Moreover, the electronic device can automatically stop detecting the preview picture collected by the camera after acquiring the object type information and the number of the objects. The electronic device can also detect the object detection model after stopping detecting the preview picture that the camera gathered. The electronic device can continuously detect the state of the camera after stopping detecting the preview picture collected by the camera and exiting the object detection model. When the electronic equipment detects that the camera is turned on again, the object detection model can be continuously kept in the quitting state. When the object detection model is continuously in the exit state, the electronic equipment can reacquire the switch control instruction of the object detection model, open the object detection model according to the switch control instruction, and detect the preview image collected by the camera again.
Then, the electronic device may sort the object category information according to the updated number of the object category information to obtain an object list. The electronic device may also rank the object class information by the number of updated object class information. The electronic device may sort the object category information according to the updated number of object categories, the electronic device may sort in an ascending order, and the electronic device may sort in a descending order. The electronic device may generate an object list according to the sorted object category information and the number of the corresponding object category information, and the electronic device may obtain the generated object list.
When detecting that the application program is started, the electronic device may acquire an object list corresponding to the application program, where the object list includes object category information and a number corresponding to the object category information. The electronic device can detect the running state of the application program, and when the electronic device detects that the application program is started, the electronic device can acquire an object list corresponding to the started application program. The object list may include object type information and a number corresponding to the object type information. The object class information may be category information of an object related to the application. The object list may further include the number corresponding to the object category information, and when the electronic device detects that the application program is started, the electronic device may acquire the object list corresponding to the started application program.
Next, the electronic device may acquire the time at which the highest number of object classification information is recorded in the object list. The object list includes object type information and a number corresponding to the object type information, and the electronic device can acquire the object type information with the highest number. Meanwhile, the electronic equipment can also acquire the specific time when the highest object type information is recorded in the object list.
If the time is not within the preset time length from the current time, the electronic equipment can recommend the object type information corresponding to the time. After the electronic equipment acquires the time when the highest number of object type information is recorded in the object list, the electronic equipment can also acquire the current time and calculate the time period difference between the time when the highest number of object type information is recorded in the object list and the current time. The electronic device may further compare the calculated time period difference with a preset time period, and when the calculated time period difference is not within the preset time period, the electronic device may recommend the highest number of object category information corresponding to the time recorded in the object list.
If the time is within the preset time length from the current time, the electronic equipment can acquire the object type information with the highest number in the object list. The electronic device may acquire the time when the highest amount of object category information is recorded in the object list. The electronic device may further calculate a time period difference between the time and the current time, compare the calculated time period difference with a preset time period, and when the calculated time period difference is within the preset time period, the electronic device may obtain the second highest object type information in the object list. When the recording time of the highest-quantity object type information in the object list is within a preset time length from the current time, and the recording time of the second-quantity object type information in the object list is not within the preset time length from the current time, the electronic equipment can recommend the second-quantity object type information in the object list.
The electronic device may also obtain switch control instructions for the object detection model. The opening or closing of the object detection model can be controlled through a button or a control on the electronic equipment, and can also be triggered by the electronic equipment through opening a camera. The electronic equipment can acquire the on-off control instruction of the object detection model by a key, a control or a mode of starting the triggering of a camera. After the electronic equipment acquires the switch control instruction of the object detection model, the state of the current object detection model can be detected. If the current object detection model is in an on state, the electronic equipment can switch the state of the object detection model into an off state according to the acquired switch control instruction; if the current object detection model is in the closed state, the electronic device can switch the state of the object detection model into the open state according to the acquired switch control instruction.
It should be understood that, although the steps in the respective flowcharts described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
Fig. 9 is a block diagram showing a configuration of an information processing apparatus according to an embodiment, and as shown in fig. 9, the apparatus includes: a list obtaining module 910, a time obtaining module 920 and an information recommending module 930, wherein:
the list obtaining module 910 is configured to, when it is detected that the application is started, obtain an object list corresponding to the application, where the object list includes object category information and a number corresponding to the object category information.
The time obtaining module 920 is configured to obtain a time when the highest amount of object category information is recorded in the object list.
And an information recommending module 930, configured to recommend the object type information corresponding to the time if the time is not within a preset time period from the current time.
In one embodiment, as shown in fig. 10, there is provided an information processing apparatus further including: a category information obtaining module 940, a quantity updating module 950, and a quantity ordering module 960, wherein:
and a category information obtaining module 940, configured to detect a preview picture acquired by the camera by using an object detection model when it is detected that the camera is turned on, so as to obtain object category information.
The quantity updating module 950 is configured to record the object type information, and update the quantity of the stored object type information to obtain the updated quantity of the object type information.
The quantity sorting module 960 is configured to sort the object category information according to the updated quantity of the object category information, so as to obtain an object list.
In one embodiment, the category information obtaining module 940 may further be configured to obtain a switch control instruction for the object detection model, and turn on or off the object detection model according to the switch control instruction.
In an embodiment, the category information obtaining module 940 may be further configured to obtain a training image including object category information, input the training image to the object detection model, and train the object detection model to obtain an object detection model for detecting the object category information.
In one embodiment, the quantity update module 950 can also be used to automatically stop the preview image captured by the detection camera and exit the object detection model.
In one embodiment, the quantity update module 950 may also be configured to keep the object detection model in an exit state when it is detected that the camera is turned on again.
In an embodiment, the information recommending module 930 may be further configured to acquire the second highest object category information in the object list and recommend the second highest object category information in the object list if the time is within a preset time period from the current time.
The division of the modules in the information processing apparatus is only for illustration, and in other embodiments, the information processing apparatus may be divided into different modules as needed to complete all or part of the functions of the information processing apparatus.
For specific limitations of the information processing apparatus, reference may be made to the above limitations of the information processing method, which are not described herein again. Each module in the information processing apparatus described above may be entirely or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The implementation of each module in the information processing apparatus provided in the embodiment of the present application may be in the form of a computer program. The computer program may be run on a terminal or a server. The program modules constituted by the computer program may be stored on the memory of the terminal or the server. Which when executed by a processor, performs the steps of the method described in the embodiments of the present application.
The embodiment of the 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 processors to perform the steps of the information processing method.
A computer program product containing instructions which, when run on a computer, cause the computer to perform an information processing method.
The embodiment of the application also provides the electronic equipment. Included in the electronic device is an Image Processing circuit, which may be implemented 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 one embodiment. As shown in fig. 11, for convenience of explanation, 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 control logic 1150. Image data captured by the imaging device 1110 is first processed by the ISP processor 1140, and the ISP processor 1140 analyzes the image data to capture image statistics that may be used to determine and/or control one or more 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 (e.g., a Bayer filter), and the image sensor 1114 may acquire light intensity and wavelength information captured with each imaging pixel of the image sensor 1114 and provide a set of raw image data that may be processed by the ISP processor 1140. The sensor 1120 (e.g., a gyroscope) may provide parameters of the acquired image processing (e.g., anti-shake parameters) to the ISP processor 1140 based on the type of interface of the sensor 1120. The sensor 1120 interface may utilize an SMIA (Standard Mobile Imaging Architecture) interface, other serial or parallel camera interfaces, or a combination of the above.
In addition, image sensor 1114 may also send raw image data to sensor 1120, sensor 1120 may provide raw image data to ISP processor 1140 based on the type of interface of sensor 1120, or sensor 1120 may store raw image data in image memory 1130.
The ISP processor 1140 processes the raw 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 ISP processor 1140 may perform one or more image processing operations on the raw image data, collecting statistical information about the image data. Wherein the image processing operations may be performed with the same or different bit depth precision.
The statistics determined by ISP processor 1140 may be sent to control logic 1150. For example, the statistical data may include image sensor 1114 statistics such as auto-exposure, auto-white balance, auto-focus, flicker detection, black level compensation, lens 1112 shading correction, and the like. Control logic 1150 may include a processor and/or microcontroller that executes one or more routines (e.g., firmware) that may determine control parameters of imaging device 1110 and control parameters of ISP processor 1140 based on the received statistical data. For example, the control parameters of imaging device 1110 may include sensor 1120 control parameters (e.g., gain, integration time for exposure control, anti-shake parameters, etc.), camera flash control parameters, lens 1112 control parameters (e.g., focal length for focusing or zooming), or a combination of these parameters. The ISP control parameters may include gain levels and color correction matrices for automatic white balance and color adjustment (e.g., during RGB processing), as well as lens 1112 shading correction parameters.
The image processing method described above can be implemented in this embodiment using the image processing technique of fig. 11.
Any reference to memory, storage, database, or other medium used herein may include non-volatile and/or volatile memory. Non-volatile memory 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 external cache memory. 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), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (14)
1. An information processing method characterized by comprising:
when detecting that an application program is started, acquiring an object list corresponding to the application program, wherein the object list comprises object type information and the number corresponding to the object type information;
acquiring the time when the object type information with the highest quantity is recorded in the object list;
if the time is not within a preset time length from the current time, recommending object type information corresponding to the time;
the acquiring of the object list corresponding to the application program includes:
when the camera is detected to be opened, detecting a preview picture acquired by the camera by using an object detection model to obtain object type information;
recording the object type information, and updating the quantity of the stored object type information to obtain the quantity of the updated object type information;
and sequencing the object type information according to the updated number of the object type information to obtain an object list.
2. The method of claim 1, further comprising:
acquiring a switch control instruction of the object detection model;
and opening or closing the object detection model according to the switch control instruction.
3. The method of claim 1, further comprising:
acquiring a training image containing object category information;
and inputting the training image into an object detection model, and training the object detection model to obtain an object detection model for detecting object class information.
4. The method according to claim 1, wherein after obtaining the updated amount of the object class information, the method further comprises:
and automatically stopping detecting the preview picture acquired by the camera, and quitting the object detection model.
5. The method of claim 4, wherein after exiting the object detection model, the method further comprises:
and when the camera is detected to be started again, keeping the object detection model in an exit state.
6. The method according to any one of claims 1 to 5, further comprising:
if the time is within a preset time length from the current time, acquiring the object type information with the second highest number in the object list;
recommending the object category information with the highest number in the object list.
7. An information processing apparatus characterized by comprising:
the system comprises a list acquisition module, a list acquisition module and a display module, wherein the list acquisition module is used for acquiring an object list corresponding to an application program when the application program is detected to be started, and the object list comprises object type information and the number corresponding to the object type information;
the time obtaining module is used for obtaining the time when the object type information with the highest quantity is recorded in the object list;
the information recommendation module is used for recommending the object type information corresponding to the moment if the distance between the moment and the current moment is not within a preset time length;
the device comprises a category information acquisition module, a preview module and a preview module, wherein the category information acquisition module is used for detecting a preview picture acquired by a camera by using an object detection model when the camera is detected to be started to obtain object category information;
the quantity updating module is used for updating the quantity of the stored object type information to obtain the updated quantity of the object type information;
and the quantity sorting module is used for sorting the object type information according to the updated quantity of the object type information to obtain an object list.
8. The apparatus of claim 7,
the category information acquisition module is also used for acquiring a switch control instruction of the object detection model; and opening or closing the object detection model according to the switch control instruction.
9. The apparatus of claim 7,
the category information acquisition module is also used for acquiring a training image containing object category information;
and inputting the training image into an object detection model, and training the object detection model to obtain an object detection model for detecting object class information.
10. The apparatus of claim 7,
and the quantity updating module is also used for automatically stopping detecting the preview pictures acquired by the camera and quitting the object detection model.
11. The apparatus of claim 10,
and the quantity updating module is also used for keeping the object detection model in an exit state when the camera is detected to be started again.
12. The apparatus according to any one of claims 7 to 11,
the information recommendation module is further configured to acquire second-highest object category information in the object list if the time is within a preset time length from the current time;
recommending the object category information with the highest number in the object list.
13. An electronic device comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, causes the processor to perform the steps of the information processing method according to any one of claims 1 to 6.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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| CN201810585914.7A CN108804656B (en) | 2018-06-08 | 2018-06-08 | Information processing method and device, electronic equipment and computer readable storage medium |
| PCT/CN2019/088702 WO2019233309A1 (en) | 2018-06-08 | 2019-05-28 | Information processing method and apparatus, and electronic device and computer-readable storage medium |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649697A (en) * | 2016-12-19 | 2017-05-10 | 蒋子轩 | Online software interactive experiential method |
| CN107480172A (en) * | 2017-06-30 | 2017-12-15 | 深圳天珑无线科技有限公司 | The method, apparatus and terminal of article matching |
| CN108053295A (en) * | 2017-12-29 | 2018-05-18 | 广州品唯软件有限公司 | A kind of method and apparatus of Brand sequence |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7506001B2 (en) * | 2006-11-01 | 2009-03-17 | I3Solutions | Enterprise proposal management system |
| CN101324948B (en) * | 2008-07-24 | 2015-11-25 | 阿里巴巴集团控股有限公司 | A kind of method of information recommendation and device |
| US8239333B2 (en) * | 2009-03-03 | 2012-08-07 | Microsoft Corporation | Media tag recommendation technologies |
| CN103488714B (en) * | 2013-09-11 | 2017-01-18 | 杭州东信北邮信息技术有限公司 | Book recommendation method and system based on social networking |
| CN103870856A (en) * | 2013-11-05 | 2014-06-18 | 高贤荣 | Radio frequency identification technology-based micro personal object management intelligent system and method |
| JP6362465B2 (en) * | 2014-07-23 | 2018-07-25 | 株式会社ソニー・インタラクティブエンタテインメント | Information processing device |
| CN105184618A (en) * | 2015-10-20 | 2015-12-23 | 广州唯品会信息科技有限公司 | Commodity individual recommendation method for new users and system |
| CN105869024A (en) * | 2016-04-20 | 2016-08-17 | 北京小米移动软件有限公司 | Commodity recommending method and device |
| CN106682163A (en) * | 2016-12-26 | 2017-05-17 | 北京小米移动软件有限公司 | Article information recommendation method and device and equipment |
| CN107230136A (en) * | 2017-05-31 | 2017-10-03 | 合肥亿迈杰软件有限公司 | A kind of shopping sequence method for pushing based on big data |
| CN108052519A (en) * | 2017-10-31 | 2018-05-18 | 珠海格力电器股份有限公司 | Information method for displaying and processing and device |
| CN107911491B (en) * | 2017-12-27 | 2019-09-27 | Oppo广东移动通信有限公司 | Information recommendation method, device and storage medium, server and mobile terminal |
| CN108804656B (en) * | 2018-06-08 | 2021-11-16 | Oppo广东移动通信有限公司 | Information processing method and device, electronic equipment and computer readable storage medium |
-
2018
- 2018-06-08 CN CN201810585914.7A patent/CN108804656B/en active Active
-
2019
- 2019-05-28 WO PCT/CN2019/088702 patent/WO2019233309A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649697A (en) * | 2016-12-19 | 2017-05-10 | 蒋子轩 | Online software interactive experiential method |
| CN107480172A (en) * | 2017-06-30 | 2017-12-15 | 深圳天珑无线科技有限公司 | The method, apparatus and terminal of article matching |
| CN108053295A (en) * | 2017-12-29 | 2018-05-18 | 广州品唯软件有限公司 | A kind of method and apparatus of Brand sequence |
Non-Patent Citations (1)
| Title |
|---|
| "基于大数据时代背景下计算机信息处理技术";陈岩;《中国新通信》;20170505;第19卷(第9期);第92页 * |
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