CN110189242B - Image processing method and device - Google Patents
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Abstract
The application provides an image processing method and device, wherein the method comprises the following steps: acquiring a frame image to be processed; judging whether the frame image to be processed is a preset key frame image or not; if the frame image to be processed is not the key frame image, determining a target key frame image corresponding to the frame image to be processed; acquiring a pixel difference between a frame image to be processed and a target key frame image; and inputting the pixel difference into a preset neural network model to calculate a target characteristic vector, splicing the target characteristic vector and a key characteristic vector corresponding to a target key frame image to generate a combined vector, and performing data analysis according to the combined vector. Therefore, the technical problems that image processing in the prior art consumes a large amount of computing resources and the image processing efficiency is low are solved, the processing effect is guaranteed by performing full-frame computing on the key frame, only changed pixels are computed on a changed frame, the computing amount is greatly reduced, and the image processing efficiency is improved.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to an image processing method and apparatus.
Background
At present, in order to improve driving safety and driving experience, a camera is installed on a vehicle to acquire information inside and outside the vehicle, and the information inside and outside the vehicle is analyzed to perform corresponding processing.
In the related art, the acquired image is calculated to acquire the corresponding image characteristics for analysis, and the method consumes a large amount of calculation resources and has low image processing efficiency.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the application provides an image processing method and an image processing device, which are used for solving the technical problems that image processing in the prior art consumes a large amount of computing resources and the image processing efficiency is relatively low.
To achieve the above object, an embodiment of a first aspect of the present application provides an image processing method, including:
acquiring a frame image to be processed;
judging whether the frame image to be processed is a preset key frame image or not;
if the frame image to be processed is not the key frame image, determining a target key frame image corresponding to the frame image to be processed;
acquiring a pixel difference between the frame image to be processed and the target key frame image;
inputting the pixel difference into a preset neural network model to obtain a target characteristic vector, splicing the target characteristic vector and a key characteristic vector corresponding to the target key frame image to generate a combined vector, and performing data analysis according to the combined vector.
In the image processing method of the embodiment, a frame image to be processed is acquired; judging whether the frame image to be processed is a preset key frame image, determining a target key frame image corresponding to the frame image to be processed when the frame image to be processed is not the key frame image, acquiring a pixel difference between the frame image to be processed and the target key frame image, inputting the pixel difference into a preset neural network model to calculate a target feature vector, splicing the target feature vector and a key feature vector corresponding to the target key frame image to generate a combined vector, and performing data analysis according to the combined vector. Therefore, the technical problems that image processing in the prior art consumes a large amount of computing resources and the image processing efficiency is low are solved, the processing effect is guaranteed by performing full-frame computing on the key frame, only changed pixels are computed on a changed frame, the computing amount is greatly reduced, and the image processing efficiency is improved.
To achieve the above object, a second aspect of the present application provides an image processing apparatus comprising:
the first acquisition module is used for acquiring a frame image to be processed;
the judging module is used for judging whether the frame image to be processed is a preset key frame image or not;
a determining module, configured to determine a target key frame image corresponding to the frame image to be processed if the frame image to be processed is not the key frame image;
the second acquisition module is used for acquiring the pixel difference between the frame image to be processed and the target key frame image;
and the processing module is used for inputting the pixel difference into a preset neural network model to calculate a target characteristic vector, splicing the target characteristic vector and a key characteristic vector corresponding to the target key frame image to generate a combined vector, and performing data analysis according to the combined vector.
The image processing apparatus of the embodiment acquires a frame image to be processed; judging whether the frame image to be processed is a preset key frame image, determining a target key frame image corresponding to the frame image to be processed when the frame image to be processed is not the key frame image, acquiring a pixel difference between the frame image to be processed and the target key frame image, inputting the pixel difference into a preset neural network model to calculate a target feature vector, splicing the target feature vector and a key feature vector corresponding to the target key frame image to generate a combined vector, and performing data analysis according to the combined vector. Therefore, the technical problems that image processing in the prior art consumes a large amount of computing resources and the image processing efficiency is low are solved, the processing effect is guaranteed by performing full-frame computing on the key frame, only changed pixels are computed on a changed frame, the computing amount is greatly reduced, and the image processing efficiency is improved.
To achieve the above object, a third aspect of the present application provides a computer device, including: a processor and a memory; wherein the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the image processing method according to the embodiment of the first aspect.
To achieve the above object, a fourth aspect of the present application provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the image processing method according to the first aspect.
To achieve the above object, a fifth aspect of the present application provides a computer program product, where instructions of the computer program product, when executed by a processor, implement the image processing method according to the first aspect.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of an image processing method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another image processing method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of another image processing apparatus according to an embodiment of the present disclosure; and
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
An image processing method and apparatus of an embodiment of the present application are described below with reference to the drawings.
Fig. 1 is a schematic flowchart of an image processing method according to an embodiment of the present disclosure.
As shown in fig. 1, the image processing method may include the steps of:
In practical application, one or more cameras are mounted on a vehicle to monitor vehicle-mounted scenes such as scenes in the vehicle or environmental scenes outside the vehicle, or shop video monitoring scenes, and it can be understood that most of the scenes are repeated data, and that processing most of data on images is repeated calculation, which consumes a large amount of calculation resources and has low image processing efficiency, for example, in the process of driving the vehicle by a driver, most of the images in the vehicle are not changed and are changed only when passengers exist; for another example, when a driver drives a vehicle, the road image is not changed or the image of a preceding vehicle is not changed for a period of time, and the road image is changed when changing lanes.
Therefore, the application provides an image processing method, taking a vehicle-mounted scene as an example, firstly considering that the performance of the residual electric quantity in the vehicle-mounted scene is limited, the resources are limited, and the functions of an instrument panel, vehicle-mounted system control, navigation, voice broadcast, audio program playing and the like are supported, so that the resource occupation of a driver detection system is strongly limited, moreover, the scene change in the vehicle is small, the scene is single, a large number of pixels in the vehicle-mounted scene are unchanged, deep learning reasoning is carried out by using a interframe comparison mode, and only the changed pixels are processed due to the fact that the interframes of a large number of pixels are unchanged, the consumption of computing resources can be greatly reduced, and the image processing efficiency is improved.
Specifically, the frame image to be processed is obtained, it can be understood that different cameras obtain images at different angles, the cameras obtain each frame image all the time during operation, and the image frames can be used as the frame image to be processed, and can be selected according to actual application requirements.
And 102, judging whether the frame image to be processed is a preset key frame image.
And 103, if the frame image to be processed is not the key frame image, determining a target key frame image corresponding to the frame image to be processed.
Specifically, in order to improve the image processing efficiency, the key frame images are set in advance according to the acquisition time and the preset time interval, so that whether the frame image to be processed is the preset key frame image or not is judged firstly, if the frame image to be processed is the preset key frame image, the key feature vector corresponding to the preset key frame image is directly obtained to perform data analysis, and if the frame image to be processed is not the key frame image, the target key frame image corresponding to the frame image to be processed is determined.
It should be noted that the key frame image needs to be preset, the key frame image can be set in different manners for different scenes, and as a possible implementation manner, the key frame image is set according to the acquisition time and the preset time interval, and specifically, the key frame image can be selectively set according to the actual application needs.
There are many ways to determine the target key frame image corresponding to the frame image to be processed, for example, as follows:
in a first example, target acquisition time corresponding to a frame image to be processed is obtained, key acquisition time corresponding to each key frame image is obtained, and a target key frame image corresponding to the frame image to be processed is determined according to the target acquisition time and key acquisition time.
Specifically, a target key frame image which is relatively close to the acquisition time of the frame image to be processed is determined, so that the accuracy of image processing is improved.
In a second example, a key frame image is randomly selected from preset key frame images as a target key frame image corresponding to a frame image to be processed.
And step 104, acquiring a pixel difference between the frame image to be processed and the target key frame image.
Specifically, the frame image to be processed is not a key frame image, that is, the frame image to be processed having a pixel difference with the target key frame image, so that the pixel difference between the frame image to be processed and the target key frame image may be obtained by processing the frame image to be processed and the target key frame image through an image comparison or a preset algorithm, and may be selected according to actual application needs, for example, as follows:
in a first example, the target key frame image is an nth frame image, the frame image to be processed is an N + mth frame image, where N and M are positive integers, and the nth frame image and the N + mth frame image are compared to obtain a pixel difference between the nth frame image and the N + mth frame image.
For example, the target key frame image is a 1 st frame image, the frame image to be processed is a 3 rd frame image, and the 1 st frame image and the 3 rd frame image are directly compared to obtain a pixel difference between the 1 st frame image and the 3 rd frame image.
In a second example, the target key frame image is an nth frame image, the frame image to be processed is an N + mth frame image, where N and M are positive integers, and pixel differences between the image frames are sequentially obtained according to preset interval frames; and adding the M pixel differences to obtain the pixel difference between the frame image to be processed and the target key frame image.
For example, the target key frame image is a 1 st frame image, the frame image to be processed is a 3 rd frame image, the preset interval frame is 1, the 1 st frame image is compared with the 2 nd frame image to obtain a pixel difference 1, the 2 nd frame image is compared with the 3 rd frame image to obtain a pixel difference 2, and the pixel difference 1 and the pixel difference 2 are added to obtain a pixel difference between the 1 st frame image and the 3 rd frame image.
And 105, inputting the pixel difference into a preset neural network model to calculate to obtain a target feature vector, splicing the target feature vector and a key feature vector corresponding to a target key frame image to generate a combined vector, and performing data analysis according to the combined vector.
The preset neural network model can be a convolutional neural network model, a deep neural network model and the like, and can be selected according to the actual application requirements.
For example, the pixel difference is input into a convolutional neural network model to calculate a target feature vector, the target feature vector is spliced with a key feature vector corresponding to a target key frame image to generate a combined vector, and data analysis is performed according to the combined vector.
The key feature vector corresponding to the target key frame image is obtained by inputting the key frame image into a preset neural network model when the key frame image is set, and calculating the key feature vector corresponding to the key frame image.
It should be noted that, data analysis according to the combined vector may be selected according to actual application requirements, for example, the combined vector is used to extract eye fatigue features and mouth fatigue features of a driver, and compare the extracted eye fatigue features and mouth fatigue features with preset features, so as to perform fatigue early warning; and for example, the combination vector is used for extracting the license plate characteristics of the front vehicle, so that the tracking of the front vehicle is realized.
In the image processing method of the embodiment, a frame image to be processed is acquired; judging whether the frame image to be processed is a preset key frame image, determining a target key frame image corresponding to the frame image to be processed when the frame image to be processed is not the key frame image, acquiring a pixel difference between the frame image to be processed and the target key frame image, inputting the pixel difference into a preset neural network model to calculate a target feature vector, splicing the target feature vector and a key feature vector corresponding to the target key frame image to generate a combined vector, and performing data analysis according to the combined vector. Therefore, the technical problems that image processing in the prior art consumes a large amount of computing resources and the image processing efficiency is low are solved, the processing effect is guaranteed by performing full-frame computing on the key frame, only changed pixels are computed on a changed frame, the computing amount is greatly reduced, and the image processing efficiency is improved.
Fig. 2 is a schematic flowchart of another image processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the image processing method may include the steps of:
Specifically, each frame of image has its corresponding acquisition time, for example, 5 frames of image, the acquisition times are 14 points at 22 months in 2019, 14 points at 0 min 01 s at 22 months in 4 months in 2019, 14 points at 0 min 02 s at 22 days in 4 months in 2019, 14 points at 0 min 03 s at 22 days in 4 months in 2019, 0 min 04 s at 14 points at 22 days in 4 months in 2019, and 14 points at 0 min 04 s at 22 days in 4 months in 2019, and the preset time interval is 3 seconds, and then the image frames corresponding to 14 points at 22 months in 4 months in 2019 and 14 points at 0 min 03 s at 22 months in 4 months in 2019 are determined as the key frame images.
Further, after the key frame image is set, the key frame image is input into a preset neural network model to calculate a key feature vector corresponding to the key frame image.
The preset neural network model can be a convolutional neural network model, a deep neural network model and the like, and can be selected according to the actual application requirements.
And 204, if the frame image to be processed is not the key frame image, acquiring target acquisition time corresponding to the frame image to be processed, acquiring key acquisition time corresponding to each key frame image, and determining the target key frame image corresponding to the frame image to be processed according to the target acquisition time and the key acquisition time.
It can be understood that different cameras acquire images at different angles, the cameras acquire each frame of image all the time when working, and the image frames can be used as frame images to be processed and can be selected according to actual application requirements.
Further, whether the frame image to be processed is the preset key frame image or not is judged, when the frame image to be processed is not the key frame image, the target key frame image corresponding to the frame image to be processed is determined according to the target acquisition time corresponding to the frame image to be processed and the key acquisition time corresponding to each key frame, for example, the target acquisition time corresponding to the frame image to be processed is 0 minute 10 seconds at 14 points of 4 and 22 months in 2019, 14 points of 22 and 14 points of 0 minute 03 seconds at 14 days of 4 and 22 months in 2019, the image frame corresponding to 14 points of 22 and 22 months in 2019 is determined as the key frame image, and at this time, the key frame image corresponding to 14 points of 0 minute 03 seconds at 22 months in 4 and 22 months in 2019 can be determined as the target key frame image.
Namely, the target key frame image which is relatively close to the acquisition time of the frame image to be processed is determined, so as to improve the accuracy of image processing.
For example, the target key frame image is a 1 st frame image, the frame image to be processed is a 3 rd frame image, and the 1 st frame image and the 3 rd frame image are directly compared to obtain a pixel difference between the 1 st frame image and the 3 rd frame image. Therefore, the pixel difference between the image frames is rapidly acquired, and the image processing efficiency is improved.
And step 206, inputting the pixel difference into a preset neural network model to calculate a target feature vector, splicing the target feature vector and a key feature vector corresponding to a target key frame image to generate a combined vector, and performing data analysis according to the combined vector.
The preset neural network model can be a convolutional neural network model, a deep neural network model and the like, and can be selected according to the actual application requirements.
For example, the pixel difference is input into a convolutional neural network model to calculate a target feature vector, the target feature vector is spliced with a key feature vector corresponding to a target key frame image to generate a combined vector, and data analysis is performed according to the combined vector.
The key feature vector corresponding to the target key frame image is obtained by inputting the key frame image into a preset neural network model when the key frame image is set, and calculating the key feature vector corresponding to the key frame image.
It should be noted that, data analysis according to the combined vector may be selected according to actual application requirements, for example, the combined vector is used for extracting eye fatigue features and mouth fatigue features of a driver, and comparing the eye fatigue features and the mouth fatigue features with preset features, so as to perform fatigue early warning; and for example, the combination vector is used for extracting the license plate characteristics of the front vehicle, so that the tracking of the front vehicle is realized.
The image processing method of the embodiment includes setting key frame images according to acquisition time and a preset time interval, inputting the key frame images into a preset neural network model, calculating key feature vectors corresponding to the key frame images, obtaining frame images to be processed, judging whether the frame images to be processed are the preset key frame images, if the frame images to be processed are not the key frame images, obtaining target acquisition time corresponding to the frame images to be processed, obtaining key acquisition time corresponding to each key frame image, determining target key frame images corresponding to the frame images to be processed according to the target acquisition time and key acquisition time, inputting the pixel differences into the preset neural network model to obtain the target feature vectors, comparing the N frame images with the N + M frame images, obtaining pixel differences between the N frame images and the N + M frame images, and analyzing data combination according to the key feature vectors generated by splicing the target feature vectors and the key frame images. Therefore, the technical problems that image processing in the prior art consumes a large amount of computing resources and the image processing efficiency is low are solved, the processing effect is guaranteed by performing full-frame computing on the key frame, only changed pixels are computed on a changed frame, the computing amount is greatly reduced, and the image processing efficiency is improved.
In order to implement the above embodiments, the present application also provides an image processing apparatus.
Fig. 3 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present disclosure.
As shown in fig. 3, the image processing apparatus may include: a first obtaining module 310, a determining module 320, a determining module 330, a second obtaining module 340, and a processing module 350. Wherein, the first and the second end of the pipe are connected with each other,
the first obtaining module 310 is configured to obtain a frame image to be processed.
The determining module 320 is configured to determine whether the frame image to be processed is a preset key frame image.
The determining module 330 is configured to determine a target key frame image corresponding to the frame image to be processed if the frame image to be processed is not a key frame image.
The second obtaining module 340 is configured to obtain a pixel difference between the frame image to be processed and the target key frame image.
The processing module 350 is configured to input the pixel difference into a preset neural network model to calculate a target feature vector, splice the target feature vector and a key feature vector corresponding to a target key frame image to generate a combined vector, and perform data analysis according to the combined vector.
In a possible implementation manner of the embodiment of the present application, as shown in fig. 4, on the basis of fig. 3, the method further includes: a setup module 360 and a calculation module 370.
A setting module 360 for setting the key frame image 370 according to the acquisition time and the preset time interval
The calculating module 370 is configured to input the key frame image into a preset neural network model to calculate a key feature vector corresponding to the key frame image.
In a possible implementation manner of the embodiment of the present application, the determining module 330 is specifically configured to: acquiring target acquisition time corresponding to a frame image to be processed; acquiring key acquisition time corresponding to each key frame image; and determining a target key frame image corresponding to the frame image to be processed according to the target acquisition time and the key acquisition time.
In a possible implementation manner of the embodiment of the present application, the target key frame image is an nth frame image, and the second obtaining module 340 is specifically configured to: and comparing the N frame image with the (N + M) frame image to obtain a pixel difference between the N frame image and the (N + M) frame image.
In a possible implementation manner of the embodiment of the application, the target key frame image is an nth frame image, and the second obtaining module is further specifically configured to: sequentially acquiring pixel differences among all image frames according to a preset interval frame; and adding the M pixel differences to obtain the pixel difference between the frame image to be processed and the target key frame image.
It should be noted that the foregoing explanation of the embodiment of the image processing method is also applicable to the image processing apparatus of the embodiment, and the implementation principle is similar, and is not described herein again.
The image processing device of the embodiment of the application acquires a frame image to be processed; judging whether the frame image to be processed is a preset key frame image, determining a target key frame image corresponding to the frame image to be processed when the frame image to be processed is not the key frame image, acquiring a pixel difference between the frame image to be processed and the target key frame image, inputting the pixel difference into a preset neural network model to calculate a target feature vector, splicing the target feature vector and a key feature vector corresponding to the target key frame image to generate a combined vector, and performing data analysis according to the combined vector. Therefore, the technical problems that image processing in the prior art consumes a large amount of computing resources and the image processing efficiency is low are solved, the processing effect is guaranteed by performing full-frame computing on the key frame, only changed pixels are computed on a changed frame, the computing amount is greatly reduced, and the image processing efficiency is improved.
In order to implement the foregoing embodiment, the present application further provides a computer device, including: a processor and a memory. Wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, for implementing the image processing method as described in the foregoing embodiments.
FIG. 5 is a block diagram of a computer device provided in an embodiment of the present application, illustrating an exemplary computer device 90 suitable for use in implementing embodiments of the present application. The computer device 90 shown in fig. 5 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present application.
As shown in fig. 5, the computer device 90 is in the form of a general purpose computer device. The components of computer device 90 may include, but are not limited to: one or more processors or processing units 906, a system memory 910, and a bus 908 that couples the various system components (including the system memory 910 and the processing unit 906).
Computer device 90 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 90 and includes both volatile and nonvolatile media, removable and non-removable media.
The system Memory 910 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 911 and/or cache Memory 912. The computer device 90 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 913 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard disk drive"). Although not shown in FIG. 5, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 908 by one or more data media interfaces. System memory 910 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
Program/utility 914 having a set (at least one) of program modules 9140 may be stored, for example, in system memory 910, such program modules 9140 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination of these examples may comprise an implementation of a network environment. Program modules 9140 generally perform the functions and/or methods of embodiments described herein.
The computer device 90 may also communicate with one or more external devices 10 (e.g., keyboard, pointing device, display 100, etc.), one or more devices that enable a user to interact with the terminal device 90, and/or any device (e.g., network card, modem, etc.) that enables the computer device 90 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interfaces 902. Moreover, computer device 90 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via Network adapter 900. As shown in FIG. 5, network adapter 900 communicates with the other modules of computer device 90 via bus 908. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with computer device 90, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 906 executes various functional applications and image processing based on the in-vehicle scene by running a program stored in the system memory 910, for example, implementing the image processing method mentioned in the foregoing embodiment.
In order to implement the above embodiments, the present application also proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the image processing method as described in the foregoing embodiments.
In order to implement the foregoing embodiments, the present application also proposes a computer program product, wherein when the instructions in the computer program product are executed by a processor, the image processing method according to the foregoing embodiments is implemented.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (12)
1. An image processing method, characterized by comprising the steps of:
acquiring a frame image to be processed;
judging whether the frame image to be processed is a preset key frame image or not;
if the frame image to be processed is not the key frame image, determining a target key frame image corresponding to the frame image to be processed;
acquiring a pixel difference between the frame image to be processed and the target key frame image;
inputting the pixel difference into a preset neural network model to obtain a target characteristic vector, splicing the target characteristic vector and a key characteristic vector corresponding to the target key frame image to generate a combined vector, and performing data analysis according to the combined vector.
2. The method of claim 1, prior to said obtaining a frame image to be processed, further comprising:
setting the key frame image according to the acquisition time and a preset time interval;
and inputting the key frame image into a preset neural network model to calculate a key feature vector corresponding to the key frame image.
3. The method of claim 1, wherein the determining a target key frame image corresponding to the frame image to be processed comprises:
acquiring target acquisition time corresponding to the frame image to be processed;
acquiring key acquisition time corresponding to each key frame image;
and determining a target key frame image corresponding to the frame image to be processed according to the target acquisition time and the key acquisition time.
4. The method according to claim 1, wherein the target key frame image is an nth frame image, the frame image to be processed is an N + mth frame image, where N and M are positive integers, and the obtaining a pixel difference between the frame image to be processed and the target key frame image comprises:
and comparing the N frame image with the N + M frame image to obtain a pixel difference between the N frame image and the N + M frame image.
5. The method according to claim 1, wherein the target key frame image is an nth frame image, the frame image to be processed is an N + mth frame image, where N and M are positive integers, and the obtaining the pixel difference between the frame image to be processed and the target key frame image comprises:
sequentially acquiring pixel differences among all image frames according to a preset interval frame;
and adding the M pixel differences to obtain the pixel difference between the frame image to be processed and the target key frame image.
6. An image processing apparatus characterized by comprising:
the first acquisition module is used for acquiring a frame image to be processed;
the judging module is used for judging whether the frame image to be processed is a preset key frame image or not;
a determining module, configured to determine a target key frame image corresponding to the frame image to be processed if the frame image to be processed is not the key frame image;
the second acquisition module is used for acquiring the pixel difference between the frame image to be processed and the target key frame image;
and the processing module is used for inputting the pixel difference into a preset neural network model to calculate a target characteristic vector, splicing the target characteristic vector and a key characteristic vector corresponding to the target key frame image to generate a combined vector, and performing data analysis according to the combined vector.
7. The apparatus of claim 6, further comprising:
the setting module is used for setting the key frame images according to the acquisition time and a preset time interval;
and the calculating module is used for inputting the key frame image into a preset neural network model to calculate the key feature vector corresponding to the key frame image.
8. The apparatus of claim 6, wherein the determination module is specifically configured to:
acquiring target acquisition time corresponding to the frame image to be processed;
acquiring key acquisition time corresponding to each key frame image;
and determining a target key frame image corresponding to the frame image to be processed according to the target acquisition time and the key acquisition time.
9. The apparatus according to claim 6, wherein the target key frame image is an nth frame image, the second obtaining module is configured to obtain the frame image to be processed is an N + mth frame image, where N and M are positive integers, and are specifically configured to:
and comparing the N frame image with the N + M frame image to obtain a pixel difference between the N frame image and the N + M frame image.
10. The apparatus of claim 6, wherein the target key frame image is an nth frame image, and the second obtaining module is further configured to:
sequentially acquiring pixel differences among all image frames according to a preset interval frame;
and adding the M pixel differences to obtain the pixel difference between the frame image to be processed and the target key frame image.
11. A computer device comprising a processor and a memory;
wherein the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for implementing the image processing method according to any one of claims 1 to 5.
12. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the image processing method according to any one of claims 1 to 5.
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