CN110781849A - Image processing method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses an image processing method, an image processing device, image processing equipment and a storage medium. The method is performed by a graphics processor and comprises: receiving an image to be identified sent by a central processing unit; preprocessing the image to be identified to obtain a preprocessed image; based on an image processing model, according to the number of single processing data, the preprocessed image is processed in parallel to obtain an image processing result; and determining the number of the single-time processing data according to the data bits of the preprocessed image and the data bit threshold value of the parallel processing. The embodiment of the invention realizes the parallel processing of a large number of images to be identified by deploying the image preprocessing part in the image processor, thereby improving the processing speed, and fully utilizes the resources of the image processor by processing the preprocessed images according to the single-time data processing number of the image processor, thereby realizing the parallel processing of the preprocessed images and improving the image processing efficiency.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to an image processing method, an image processing apparatus, an image processing device, and a storage medium.
Background
With the development of the times and networks, users can acquire network information through various ways, and some vulgar and useless images or videos often appear in the network information, so that the physical and mental health of the users is seriously influenced, and particularly the physical and mental health of teenager groups is influenced.
Deep learning, which is typically applied to image processing and speech recognition, is a leading field of machine learning research and is motivated by the establishment and simulation of neural networks for analytic learning of the human brain, which imitates the mechanism of the human brain to interpret data such as images, sounds and text. The network information is subjected to image processing through deep learning, and bad images or videos in the network information can be analyzed, so that the network information is screened.
Since the deep learning has a large amount of calculation and is complicated in calculation, it is difficult to realize real-time image processing in edge devices such as jetson nano, and image processing efficiency is low.
Disclosure of Invention
The embodiment of the invention provides an image processing method, an image processing device, image processing equipment and a storage medium, which are used for realizing parallel processing of images and improving the image processing efficiency.
In a first aspect, an embodiment of the present invention provides an image processing method, where the method is performed by a graphics processor, and includes:
receiving an image to be identified sent by a central processing unit;
preprocessing the image to be identified to obtain a preprocessed image;
based on an image processing model, according to the number of single processing data, the preprocessed image is processed in parallel to obtain an image processing result; and determining the number of the single-time processing data according to the data bits of the preprocessed image and the data bit threshold value of the parallel processing.
In a second aspect, an embodiment of the present invention provides an image processing apparatus configured in a graphics processor, the apparatus including:
the updating model determining module is used for taking the acquired first historical image and the processing result of the first historical image as training samples and training the training samples based on at least one machine learning algorithm to obtain at least one updating model; the first historical image is an image received from a first historical moment to a current moment;
the candidate model selection module is used for selecting a candidate model from at least one updating model and distributing the currently acquired image to be processed to the candidate model and the target model in use for processing;
and the new target model determining module is used for determining a new target model from the candidate models and the target model in use according to the processing result, and is used for processing the new image by adopting the new target model.
In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus includes:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement an image processing method as in any one of the embodiments of the present invention.
In a fourth aspect, the embodiment of the present invention further provides a 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 the embodiments of the present invention.
In the embodiment of the invention, the image to be recognized sent by the central processing unit is received, and the image to be recognized is preprocessed by the image processor, so that a large number of images to be recognized are processed in parallel, and the processing speed is improved.
Drawings
Fig. 1 is a flowchart of an image processing method according to an embodiment of the present invention;
fig. 2 is a flowchart of an image processing method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an image processing apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of an image processing method according to an embodiment of the present invention. The image processing method provided by the embodiment can be applied to the situation of processing images, and typically, the embodiment of the invention can be applied to the situation that images to be recognized are processed in a device such as JetsonNano which comprises a central processing unit and a graphic processing unit so as to analyze different types of images. The method may particularly be performed by an image processing apparatus, which may be implemented by means of software and/or hardware, which may be integrated in a device, particularly in a graphics processor. Referring to fig. 1, the method of the embodiment of the present invention specifically includes:
and S110, receiving the image to be identified sent by the central processing unit.
The image to be recognized may be an image acquired through a network or an image acquisition device. The central processing unit may be a central processing unit in a Jetson Nano device or the like, and in the device according to the embodiment of the present invention, the central processing unit may include both a central processing unit and a graphics processing unit. The core number of the central processing unit is generally in the order of single digit, and the core number of the graphics processing unit is generally in the order of hundreds digit, so that the graphics processing unit can process a large amount of data in parallel and has high processing efficiency. Due to the fact that the number of the images to be recognized is large, if all the images to be recognized are deployed in the central processing unit to be executed, the processing speed is low, and the processing real-time performance of the images to be recognized is low. Therefore, in the embodiment of the present invention, the graphics processor receives the image to be recognized sent by the central processing unit, and processes the image to be recognized. For example, if the graphics processor is a 128-core graphics processor, 128 processes can be established to process the image to be recognized simultaneously, thereby improving the processing speed and efficiency.
Optionally, before receiving the image to be recognized sent by the central processing unit, the method further includes: and receiving the image processing model sent by the central processing unit, and storing the image processing model for subsequent processing of the image to be identified.
Optionally, in the implementation of the present invention, the establishing process of the model is described by taking the recognition and classification of the image as an example, but the embodiment of the present invention is not limited to be applied to the above scenario. Illustratively, the image data samples are collected, including the first type image, the second type image, the third type image and the fourth type image, and the data samples are preprocessed, such as enhancement processing, normalization processing, smooth denoising processing and the like. Labeling the data samples, for example, 0 represents a first type image, 1 represents a second type image, 2 represents a third type image, and 3 represents a fourth type image; the number of various images is selected according to the ratio of 1:1:1:1, and 70 thousands of samples can be selected; training the deep learning model by using the image data samples and a pruning technology in machine learning, and obtaining an image processing model through repeated iteration, wherein the deep learning model can select a residual error network model.
And S120, preprocessing the image to be identified to obtain a preprocessed image.
Specifically, in order to improve the accuracy and efficiency of identifying the image to be identified, the image to be identified needs to be preprocessed before the image to be identified is processed, so as to improve the quality of the image to be identified and facilitate identification.
Optionally, the preprocessing the image to be recognized to obtain a preprocessed image includes: converting the data type of the image to be recognized from a first data type into a second data type; wherein the data bits of the first data type are smaller than the data bits of the second data type.
Illustratively, in order to increase the transmission rate of the image to be recognized, the central processing unit sends the image to be recognized of a first data type, such as char type data, to the graphics processing unit, and since the char type data is 8-bit data, the data transmission rate is high because the data bits are fewer. And after the image to be recognized is received by the graphics processor, when the image to be recognized is processed based on the image processing model, the data type of the image to be recognized is float type data so as to realize normal processing of the image to be recognized, therefore, the image to be recognized is converted from a first data type to a second data type, and the char type data is converted into float type data, so that the processing efficiency of the image to be recognized is improved.
Optionally, the preprocessing the image to be recognized to obtain the preprocessed image further includes performing scaling processing, normalization processing, gaussian smoothing processing, denoising processing, and the like on the image to be recognized, so as to improve the quality of the image to be recognized, facilitate recognition of the image to be recognized, and improve the accuracy of the recognition.
S130, based on the image processing model, according to the number of single processing data, the preprocessed image is processed in parallel to obtain an image processing result; and determining the number of the single-time processing data according to the data bits of the preprocessed image and the data bit threshold value of the parallel processing.
The number of data processed in a single time is the maximum number of data processed in parallel in a single time by the graphics processor, and the data bit threshold value of the parallel processing may be the maximum number of data bits that the graphics processor can perform the parallel processing. The number of single-processing data is determined according to the data bits of the pre-processing image and the data bit threshold of the parallel processing, for example, if the data bit threshold of the parallel processing by the graphics processor is 128 bits, and the data bits of the pre-processing image are 32 bits, the number of single-processing data is 128/32-4, that is, the graphics processor can process the image data of 4 pre-processing images in parallel.
Specifically, based on the image processing model, the number of image data of the preprocessed image processed in parallel by the graphics processor in a single time is determined according to the number of data processed in a single time, so that the plurality of image data are processed in parallel, and the image processing efficiency is improved.
In the embodiment of the invention, the image to be recognized sent by the central processing unit is received and is preprocessed by the graphics processing unit, so that a large number of images to be recognized are processed in parallel, the processing speed is improved, the preprocessed images are processed according to the number of data processed by the graphics processing unit once, the resources of the graphics processing unit are fully utilized, the graphics processing unit processes the image data of the preprocessed images according to the maximum number of data which can be processed in parallel, the parallel processing of the preprocessed images is realized, and the image processing efficiency is improved.
Example two
Fig. 2 is a flowchart of an image processing method according to a second embodiment of the present invention. The embodiment of the invention is optimized on the basis of the above embodiment, and details which are not described in detail in the embodiment are referred to the above embodiment. Referring to fig. 2, the image processing method provided in this embodiment may include:
s210, receiving the image to be identified sent by the central processing unit.
S220, preprocessing the image to be identified to obtain a preprocessed image.
S230, acquiring a preset number of data to be processed from the preprocessed image, and acquiring a preset number of parameter data from the image processing model; and the preset number is less than or equal to the number of the single-time processing data.
The preset number can be set according to actual conditions. Since the graphics processor can process multiple data simultaneously, in the embodiment of the present invention, multiple pieces of data to be processed in the preprocessed image are obtained, and the multiple pieces of data to be processed are processed simultaneously.
Optionally, if the number of the data to be processed in the preprocessed image is greater than or equal to the number of the data to be processed in a single time, the preset number is made equal to the number of the data to be processed in a single time, so that parallel processing resources of the graphics processor are fully utilized, maximization of resource utilization is achieved, and the digit of the data to be processed is aligned with the digit threshold of the parallel processing of the graphics processor. And if the number of the data to be processed in the preprocessed image is less than the number of the data to be processed in a single time, enabling the preset number to be equal to the number of the data to be processed in the preprocessed image so as to process the residual data to be processed in the preprocessed image.
S240, based on the image processing model, calculating a preset number of data to be processed and parameter data to obtain an image processing result.
For example, if a convolution operation is applied in the training process of the image processing model, a convolution operation is performed on a preset number of data to be processed and parameter data, and an obtained operation result is used as an image processing result.
And S250, sending the image processing result to the central processing unit, and screening the image to be identified by the central processing unit according to the image processing result.
Specifically, the image processor obtains an image processing result, and needs to send the image recognition result to the central processing unit, and the central processing unit performs subsequent processing on the acquired image to be recognized. For example, if the image processing result of the image to be recognized is determined to be that the image to be recognized is the first type image, the image processing result is sent to the central processing unit, the central processing unit determines whether the image to be recognized needs to be retained according to the image processing result, if the image to be recognized is an unclear image or an illegal image, the image to be recognized is deleted, and if the image to be recognized is a clear image or a regular image, the image to be recognized is retained, so that the image to be recognized is screened.
According to the technical scheme of the embodiment of the invention, a preset number of data to be processed are obtained from a preprocessed image, and a preset number of parameter data are obtained from an image processing model; the preset number is less than or equal to the number of the single processing data, the preset number of the data to be processed and the parameter data are calculated based on the image processing model to obtain an image processing result, so that parallel processing resources of the image processor are fully utilized, parallel processing of a plurality of data is realized, the image processing speed and efficiency are improved, the real-time performance of image processing is ensured, and the problem of time delay caused by slow processing process due to fast image transmission is avoided. And the image processing result is sent to the central processing unit, and the central processing unit screens the image to be identified according to the image processing result, so that the screening of the image to be identified is realized.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an image processing apparatus according to a third embodiment of the present invention. The device is suitable for processing the image, and typically, the embodiment of the invention can be suitable for determining the update model according to the newly added image when the image is continuously increased so as to update the model in use. The apparatus may be implemented by software and/or hardware, and the apparatus may be integrated in a device. Referring to fig. 3, the apparatus specifically includes:
the receiving module 310 is configured to receive an image to be identified sent by the central processing unit;
the preprocessing module 320 is configured to preprocess the image to be identified to obtain a preprocessed image;
an image processing result determining module 330, configured to perform parallel processing on the preprocessed image according to the number of single processed data based on an image processing model to obtain an image processing result; and determining the number of the single-time processing data according to the data bits of the preprocessed image and the data bit threshold value of the parallel processing.
Optionally, the preprocessing module 320 includes:
the conversion unit is used for converting the data type of the image to be recognized from a first data type to a second data type; wherein the data bits of the first data type are smaller than the data bits of the second data type.
Optionally, the image processing result determining module 330 includes:
the data acquisition unit is used for acquiring a preset number of data to be processed from the preprocessed image and acquiring a preset number of parameter data from the image processing model; wherein the preset number is less than or equal to the number of the single processing data;
and the operation unit is used for operating the preset number of data to be processed and the parameter data based on the image processing model to obtain an image processing result.
Optionally, the method further includes:
and the sending module is used for sending the image processing result to the central processing unit, and the central processing unit screens the image to be identified according to the image processing result.
According to the technical scheme of the embodiment of the invention, the receiving module receives the image to be recognized sent by the central processing unit, the image processor preprocessing module preprocesses the image to be recognized, and the parallel processing of a large number of images to be recognized is realized, so that the processing speed is improved.
Example four
Fig. 4 is a schematic structural diagram of an apparatus according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary device 412 suitable for use in implementing embodiments of the present invention. The device 412 shown in fig. 4 is only an example and should not impose any limitation on the functionality or scope of use of embodiments of the present invention.
As shown in fig. 4, the apparatus 412 includes: one or more processors 416; the memory 428 is used for storing one or more programs, when the one or more programs are executed by the one or more processors 416, so that the one or more processors 416 implement the image processing method provided by the embodiment of the present invention, and the execution of the program by the graphics processor includes:
receiving an image to be identified sent by a central processing unit;
preprocessing the image to be identified to obtain a preprocessed image;
based on an image processing model, according to the number of single processing data, the preprocessed image is processed in parallel to obtain an image processing result; and determining the number of the single-time processing data according to the data bits of the preprocessed image and the data bit threshold value of the parallel processing.
Is expressed in the form of general-purpose equipment. The components of device 412 may include, but are not limited to: one or more processors or processors 416, a system memory 428, and a bus 418 that couples the various system components (including the system memory 428 and the processors 416).
The system memory 428 may include computer system readable storage media in the form of volatile memory, such as Random Access Memory (RAM)430 and/or cache memory 432. The device 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from and write to non-removable, nonvolatile magnetic storage media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic 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 CD-ROM, DVD-ROM, or other optical storage medium) may be provided. In these cases, each drive may be connected to bus 418 by one or more image storage media interfaces. Memory 428 can 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 invention.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in memory 428, such program modules 462 including, but not limited to, an operating system, one or more application programs, other program modules, and a program image, each of which examples or some combination may include an implementation of a network environment. Program modules 462 generally perform the functions and/or methodologies of the described embodiments of the invention.
The device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, display 426, etc.), with one or more devices that enable a user to interact with the device 412, and/or with any devices (e.g., network card, modem, etc.) that enable the device 412 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 422. Also, the device 412 may 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) through the network adapter 420. As shown, network adapter 420 communicates with the other modules of device 412 over bus 418. It should be appreciated that although not shown in FIG. 4, other hardware and/or software modules may be used in conjunction with device 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, image backup storage systems, and the like.
The processor 416 performs various functional applications and image processing, such as implementing an image processing method provided by embodiments of the present invention, by executing at least one of the other programs stored in the system memory 428.
EXAMPLE five
One embodiment of the present invention provides a storage medium containing computer-executable instructions that, when executed by a computer processor, are operable to perform a method of image processing comprising:
receiving an image to be identified sent by a central processing unit;
preprocessing the image to be identified to obtain a preprocessed image;
based on an image processing model, according to the number of single processing data, the preprocessed image is processed in parallel to obtain an image processing result; and determining the number of the single-time processing data according to the data bits of the preprocessed image and the data bit threshold value of the parallel processing.
Computer storage media for embodiments of the present invention can take the form of any combination of one or more computer-readable storage media. The computer readable storage medium may be a computer readable signal storage medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the invention, the computer readable storage medium may be any tangible storage medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal storage medium may include a propagated image signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated image 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 storage medium may also be any computer readable storage 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 storage medium may be transmitted using any appropriate storage 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 invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like 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 device. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An image processing method, performed by a graphics processor, the method comprising:
receiving an image to be identified sent by a central processing unit;
preprocessing the image to be identified to obtain a preprocessed image;
based on an image processing model, according to the number of single processing data, the preprocessed image is processed in parallel to obtain an image processing result; and determining the number of the single-time processing data according to the data bits of the preprocessed image and the data bit threshold value of the parallel processing.
2. The method of claim 1, wherein preprocessing the image to be recognized to obtain a preprocessed image comprises:
converting the data type of the image to be recognized from a first data type into a second data type; wherein the data bits of the first data type are smaller than the data bits of the second data type.
3. The method of claim 1, wherein the parallel processing of the pre-processed images based on the image processing model according to the number of data processed in a single time to obtain the image processing result comprises:
acquiring a preset number of data to be processed from the preprocessed image, and acquiring a preset number of parameter data from the image processing model; wherein the preset number is less than or equal to the number of the single processing data;
and calculating a preset number of data to be processed and parameter data based on the image processing model to obtain an image processing result.
4. The method according to claim 1, wherein after determining the image processing result of the image to be recognized, further comprising:
and sending the image processing result to the central processing unit, and screening the image to be identified by the central processing unit according to the image processing result.
5. An image processing apparatus, disposed in a graphics processor, the apparatus comprising:
the receiving module is used for receiving the image to be identified sent by the central processing unit;
the preprocessing module is used for preprocessing the image to be identified to obtain a preprocessed image;
the image processing result determining module is used for processing the preprocessed images in parallel according to the number of single-time processing data based on an image processing model to obtain an image processing result; and determining the number of the single-time processing data according to the data bits of the preprocessed image and the data bit threshold value of the parallel processing.
6. The apparatus of claim 5, wherein the pre-processing module comprises:
the conversion unit is used for converting the data type of the image to be recognized from a first data type to a second data type; wherein the data bits of the first data type are smaller than the data bits of the second data type.
7. The apparatus of claim 5, wherein the image processing result determining module comprises:
the data acquisition unit is used for acquiring a preset number of data to be processed from the preprocessed image and acquiring a preset number of parameter data from the image processing model; wherein the preset number is less than or equal to the number of the single processing data;
and the operation unit is used for operating the preset number of data to be processed and the parameter data based on the image processing model to obtain an image processing result.
8. The apparatus of claim 5, further comprising:
and the sending module is used for sending the image processing result to the central processing unit, and the central processing unit screens the image to be identified according to the image processing result.
9. An apparatus, characterized in that the apparatus comprises: one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement an image processing method as claimed in any one of claims 1-4.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out an image processing method as claimed in any one of claims 1 to 4.
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| CN113610135A (en) * | 2021-07-30 | 2021-11-05 | 广州文远知行科技有限公司 | Image processing method and device, computer equipment and storage medium |
| CN114463161A (en) * | 2022-04-12 | 2022-05-10 | 之江实验室 | Method and device for processing continuous images through neural network based on memristor |
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