Image clarity evaluation method, device, equipment and storage medium

Abstract

The embodiment of the present invention discloses an image clarity evaluation method, device, equipment and storage medium. The method comprises: obtaining a target image to be evaluated for image clarity, and converting the target image into a single-channel grayscale image; dividing the grayscale image into at least one grayscale sub-image block, and performing brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image; performing foreground image and background image division processing on the brightness-corrected spliced ​​image, and determining a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image respectively; and determining an image clarity evaluation value of the target image according to the foreground evaluation value and the background evaluation value. The method can accurately evaluate the image clarity, avoid the influence of the ambient brightness on the image clarity evaluation, and improve the accuracy and robustness of the image clarity evaluation.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of image processing technology, and in particular to an image clarity evaluation method, device, equipment and storage medium. Background Art

[0002] During the focusing process of an industrial camera, as the camera lens angle changes, it is necessary to calculate the image clarity index in real time to evaluate the image.

[0003] In the prior art, the clarity of an image is usually judged by subjective comparison using human eye observation, or by using an image clarity evaluation function to calculate the clarity index of an image in real time, such as using a variance function, a Brenner gradient function, a Tenergrad gradient function, or a Laplace gradient function.

[0004] However, the way human eyes observe is highly subjective, and the evaluation results of image clarity vary from person to person; there are also limitations, and it is impossible to determine the optimal clarity index of the image. When the image clarity evaluation function is used, the clarity of the image depends on the pixel value of the midpoint of the image. When the brightness in the environment changes, the pixel value will also change. Increasing or decreasing the contrast of the image will have a certain impact on the evaluation of image clarity, resulting in poor robust performance of image clarity evaluation. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, equipment and storage medium for evaluating image clarity, which can avoid the influence of ambient brightness on image clarity evaluation and improve the accuracy and robustness of image clarity evaluation.

[0006] In a first aspect, an embodiment of the present invention provides a method for evaluating image clarity, the method comprising:

[0007] Acquire a target image to be evaluated for image clarity, and convert the target image into a single-channel grayscale image;

[0008] Dividing the grayscale image into at least one grayscale sub-image block, and performing brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image;

[0009] Performing foreground image and background image division processing on the brightness-corrected spliced ​​image, and determining a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image respectively;

[0010] An image clarity evaluation value of the target image is determined according to the foreground evaluation value and the background evaluation value.

[0011] In a second aspect, an embodiment of the present invention further provides an image clarity evaluation device, the device comprising:

[0012] A grayscale image conversion module, used for acquiring a target image to be evaluated for image clarity, and converting the target image into a single-channel grayscale image;

[0013] A brightness-corrected spliced ​​image determination module, used for dividing the grayscale image into at least one grayscale sub-image block, and performing brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image;

[0014] A foreground evaluation value and background evaluation value determination module, used to perform foreground image and background image division processing on the brightness corrected spliced ​​image, and determine the foreground evaluation value and background evaluation value corresponding to the foreground image and the background image respectively;

[0015] The image definition evaluation value determination module is used to determine the image definition evaluation value of the target image according to the foreground evaluation value and the background evaluation value.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, the device comprising:

[0017] one or more processors;

[0018] a storage device for storing one or more programs,

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement an image clarity evaluation method as described in any embodiment of the present invention.

[0020] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an image clarity evaluation method as described in any embodiment of the present invention.

[0021] The embodiment of the present invention obtains a target image to be evaluated for image clarity and converts the target image into a single-channel grayscale image; divides the grayscale image into at least one grayscale sub-image block, and performs brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image; divides the brightness-corrected spliced ​​image into a foreground image and a background image, and determines a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image, respectively; determines an image clarity evaluation value of the target image according to the foreground evaluation value and the background evaluation value, thereby solving the problem that the image clarity evaluation is easily affected by the ambient brightness and the evaluation index is inaccurate, and avoiding the influence of the ambient brightness on the image clarity evaluation, thereby improving the accuracy and robustness of the image clarity evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of an image clarity evaluation method provided in Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of an image clarity evaluation method provided in Embodiment 2 of the present invention;

[0024] Figure 3 is a structural schematic diagram of an image clarity evaluation device provided in Embodiment 3 of the present invention;

[0025] Figure 4 It is a structural schematic diagram of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0027] Embodiment 1

[0028] Figure 1 1 is a flow chart of an image clarity evaluation method provided by the first embodiment of the present invention. This embodiment is applicable to the case where the clarity index of an image is calculated in real time during the focusing process of an industrial camera. The method can be executed by an image clarity evaluation device, which can be implemented by software and / or hardware. The device can be integrated in an electronic device such as an industrial camera, such as Figure 1 As shown, the method specifically includes:

[0029] Step 110: Acquire a target image for image clarity evaluation and convert the target image into a single-channel grayscale image.

[0030] The target image may be an image taken by an industrial camera. Specifically, it may be different images generated during the focusing process of the industrial camera as the focal length of the lens changes. The user can select the focal length corresponding to the clearest image to take a photo based on the image clarity evaluated by the method of the embodiment of the present invention, thereby achieving the best photo taking method.

[0031] In an embodiment of the present invention, converting the target image into a single-channel reply image may be performed by graying the target image to obtain a gray image represented by only a single-channel gray value. Specifically, there may be a variety of graying methods, the purpose of which is to represent R, G, and B in the RGB (red, green, and blue) model with the same gray value. For example, the grayed R, G, and B may be set to the mean of the target image R, G, and B, that is, the grayed R, G, and B may be "(R of the target image + G of the target image + B of the target image) / 3". For another example, the grayed R, G, and B may be set to the weighted sum of the target image R, G, and B under a preset gray weight coefficient. Exemplarily, the grayed R, G, and B may be set to "R×0.3 of the target image + G×0.59 of the target image + B×0.11 of the target image".

[0032] Step 120: Divide the grayscale image into at least one grayscale sub-image block, and perform brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image.

[0033] The grayscale sub-image block may be a part of the grayscale image. For example, the grayscale image may be divided into different grayscale sub-image blocks in a row-column division manner. Each grayscale sub-image block may be mutually exclusive, that is, each grayscale sub-image block may belong to a different part of the grayscale image. All grayscale sub-image blocks may together constitute a grayscale image.

[0034] In an embodiment of the present invention, brightness correction and image block splicing processing can be performed on grayscale sub-image blocks. That is, the technical solution of the embodiment of the present invention can perform brightness correction (local brightness correction processing) on ​​grayscale sub-image blocks instead of directly performing brightness correction (global brightness correction processing) on ​​grayscale images, which can achieve the purpose of accurate brightness correction of different parts of the grayscale image instead of unified brightness correction. Especially in the actual focusing process of industrial cameras, when local shadow blocks or excessive local brightness appear, local brightness correction processing instead of global brightness correction processing can achieve a good brightness correction effect. Specifically, the local brightness correction processing method of the embodiment of the present invention can avoid the situation where the local brightness of the image after brightness correction is too large or too small when the global brightness of the image is corrected, thereby causing the image clarity evaluation to be affected by the interference of brightness and deteriorate.

[0035] Specifically, there are many ways to divide a grayscale image into multiple grayscale sub-image blocks. In order to reduce the complexity of image block splicing and improve the splicing quality, the technical solution of the embodiment of the present invention, in an optional implementation of the embodiment of the present invention, divides the grayscale image into at least one grayscale sub-image block, and performs brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness corrected spliced ​​image, including: dividing the grayscale image into a m ×a m grayscale sub-image blocks, and according to a preset selection rule, adjacent b rows and b columns of grayscale sub-image blocks are selected as target stitching sub-images; wherein a, m, and b are integers greater than or equal to 2, and b is greater than or equal to a; brightness correction and image stitching processing are performed on each target stitching sub-image to obtain a brightness-corrected stitching image.

[0036] In the embodiment of the present invention, the grayscale image can be evenly divided into rows and columns, such as a row and a column. m Sub-blocks, each sub-block is a grayscale sub-image block. According to this division method, when the image blocks are spliced, they can be spliced ​​in a square form, which can improve the efficiency of splicing, reduce complexity and improve the splicing quality.

[0037] When splicing specific image blocks, the preset selection rule can be set according to the splicing order. For example, the splicing can be performed from left to right, from top to bottom or in other regular order. Exemplarily, the first splicing can be started from the b rows and b columns of grayscale sub-image blocks in the upper left corner; the next splicing can continue to splice the b rows and b columns of grayscale sub-image blocks below the current spliced ​​image block, and after the splicing below is completed, the b rows and b columns of grayscale sub-image blocks to the right of the current spliced ​​image block can continue to be spliced, and then the image block below it is spliced, and so on, until the image block in the lower right corner is spliced. The above splicing process can be repeatable, that is, the large block generated by splicing (the image block composed of b rows and b columns of grayscale sub-image blocks) can continue to be spliced ​​until a complete brightness-corrected spliced ​​image is generated. In specific applications, b can be a power of a, for example, a and b can both be set to 2; or, a is 2, b is 4, etc.

[0038] In a specific application example, the grayscale image can be divided into two equal parts according to rows and columns. m ×2 mFor the grayscale sub-image blocks, you can select 4 grayscale sub-image blocks with 2 adjacent rows and 2 columns as the target splicing sub-images in the order of selection from the upper left corner to the lower right corner, and perform brightness correction and image splicing processing. For the image blocks generated by splicing 4 blocks, you can continue to select 4 grayscale sub-image blocks with 2 adjacent rows and 2 columns as the target splicing sub-images in the order of selection from the upper left corner to the lower right corner, and perform brightness correction and image splicing processing until a brightness-corrected splicing image is generated.

[0039] In another specific application example, the grayscale image can be divided into two equal parts according to rows and columns. m ×2 m Grayscale sub-image blocks can be selected in the order from the upper left corner to the lower right corner to select the adjacent 2 m Row 2 m Total 2 columns m ×2 m The grayscale sub-image blocks are used as target stitching sub-images, and brightness correction and image stitching processing are performed to generate a brightness-corrected stitching image.

[0040] In order to make the local brightness correction processing effect of the embodiment of the present invention better, in an optional implementation of the embodiment of the present invention, brightness correction and image stitching processing are performed on each target stitching sub-image to obtain a brightness-corrected stitching image, including: brightness correction is performed on each target stitching sub-image, and a first grayscale representative value formed by each target stitching sub-image and a second grayscale representative value of a target stitching image block stitched by each target stitching sub-image are determined; when the first grayscale representative value and the second grayscale representative value meet a preset grayscale condition, image stitching processing is performed on each target stitching sub-image after brightness correction to obtain a brightness-corrected stitching sub-image; otherwise, a target sub-image block corresponding to the target stitching image block is extracted from the grayscale image, and brightness correction is performed to obtain a brightness-corrected stitching sub-image; based on the brightness-corrected stitching sub-image, a brightness-corrected stitching image is obtained.

[0041] The first grayscale representative value may be formed by the grayscale representative values ​​of each target stitching sub-image. For example, the grayscale representative value of the target stitching sub-image 1 is f 1 , the grayscale representative value of the target spliced ​​sub-image 2 is f 2 , the grayscale representative value of the target spliced ​​sub-image 3 is f 3 , the grayscale representative value of the target spliced ​​sub-image 4 is f 4 , the first grayscale representative value can be f 1 、f 2 、f 3 and f 4 There are many specific ways of forming. For example, 1 、f2 、f 3 and f 4 The first grayscale representative value is formed by taking the form of the mean, median or weighted sum of f. In the embodiment of the present invention, in order to make the first grayscale representative value better reflect the grayscale value of each target spliced ​​sub-image, f can be used. 1 、f 2 、f 3 and f 4 The median value is taken as the first grayscale representative value α 1 .

[0042] In the embodiment of the present invention, the target stitching image block may be stitched together by each target stitching sub-image. For example, the target stitching image block may be a large image block stitched together by four target stitching sub-images. Determine the grayscale representative value of each target stitching sub-image, and determine the second grayscale representative value α of the target stitching image block. 2 , it can be a method of using the pixel value of a representative position in the image as the grayscale representative value. For example, the pixel value of a position such as the center point or the center of gravity point in the image can be used as the grayscale representative value.

[0043] In the embodiment of the present invention, the preset grayscale condition may be a condition for determining the order of brightness correction of the target spliced ​​sub-images and image splicing processing. By setting the order of image brightness correction and image splicing processing by the preset grayscale condition, the grayscale value can be made continuous without black edges appearing at the connection, which can improve the image splicing quality and avoid affecting the image clarity evaluation.

[0044] Specifically, the preset grayscale condition can be to determine whether the absolute value of the difference between the first grayscale representative value and the second grayscale representative value is less than or equal to a preset threshold, that is, |α 1 -α 2 |≤T. In the embodiment of the present invention, when the first grayscale representative value and the second grayscale representative value satisfy |α 1 -α 2 |≤T, each target splicing sub-image can be subjected to brightness correction before image splicing to obtain a brightness-corrected splicing sub-image; when the first grayscale representative value and the second grayscale representative value do not satisfy |α 1 -α 2 |≤T, the target sub-image block corresponding to the target spliced ​​image block can be extracted from the grayscale image, and brightness correction can be performed to obtain a brightness-corrected spliced ​​sub-image. That is, when the grayscale value is continuous, the image splicing process can be performed on each target spliced ​​sub-image after brightness correction; when the grayscale value is discontinuous, brightness correction can be directly performed on the target sub-image block at the position corresponding to each target spliced ​​sub-image in the grayscale image after splicing, so as to avoid the black edge phenomenon caused by discontinuous grayscale value during splicing, which affects the image quality.

[0045] Step 130 : performing foreground image and background image segmentation processing on the brightness corrected spliced ​​image, and determining a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image, respectively.

[0046] Among them, the technical solution of the embodiment of the present invention can distinguish the image information corresponding to the region of interest (ROI) in the image by dividing the brightness corrected spliced ​​image into a foreground image and a background image. The foreground evaluation value and the background evaluation value can be determined according to the pixel points in the foreground image and the background image, respectively. According to the foreground evaluation value and the background evaluation value corresponding to the foreground image and the background image, respectively, the clarity of the image can be evaluated more accurately based on the ROI, thereby improving the accuracy of the image clarity evaluation.

[0047] Specifically, the foreground image may be an image corresponding to the ROI, and the background image may be an image of the background to which the ROI belongs. For example, in an image containing an apple, the image corresponding to the apple may be understood as the foreground image, and the background of the apple, such as an image of the background color, may be understood as the background image. There are many ways to divide the image into the foreground image and the background image, for example, the division may be performed according to the layers in the image processing, or the division may be performed according to the positions of the pixels, etc.

[0048] In order to ensure the reliability of the division of the foreground image and the background image, in an optional implementation of the embodiment of the present invention, the brightness corrected spliced ​​image is divided into the foreground image and the background image, including: using a threshold segmentation method to divide the brightness corrected spliced ​​image into the foreground image and the background image.

[0049] The threshold segmentation method may be the Otsu threshold segmentation method (OTSU). The OTSU threshold segmentation method is a method for performing image binary segmentation. The inter-class variance between the foreground image and the background image is the largest. The image can be divided into the foreground image and the background image by the OTSU threshold segmentation method, and is not affected by the image brightness and contrast.

[0050] Step 140: Determine the image clarity evaluation value of the target image according to the foreground evaluation value and the background evaluation value.

[0051] In the embodiment of the present invention, the foreground evaluation value and the background evaluation value can be combined to comprehensively determine the image clarity evaluation value of the target image. There are many ways to combine. For example, multiple forms such as mean, cumulative value or weighted sum can be used.

[0052] In order to ensure the accuracy of the image clarity evaluation value, in an optional implementation of the embodiment of the present invention, the image clarity evaluation value of the target image is determined based on the foreground evaluation value and the background evaluation value, including: weighted summing the foreground evaluation value and the background evaluation value according to a second preset weight coefficient to obtain the image clarity evaluation value of the target image.

[0053] Specifically, according to formula C I =w 1 *C f +w 2 *C b Get the image clarity evaluation value of the target image. Among them, C I represents the image clarity evaluation value of the target image, C f represents the foreground evaluation value of the foreground image, w 1 Represents the weight coefficient of the foreground image, C b represents the background evaluation value of the background image, w 2 represents the weight coefficient of the background image. The second preset weight coefficient satisfies the condition {w 1 ∈[0,1],w 2 ∈[0,1],w 1 +w 2 =1}, w 1 Greater than w 2 .

[0054] The technical solution of this embodiment is to obtain a target image for which image clarity evaluation is to be performed, and convert the target image into a single-channel grayscale image; divide the grayscale image into at least one grayscale sub-image block, and perform brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image; divide the brightness-corrected spliced ​​image into a foreground image and a background image, and determine a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image, respectively; determine an image clarity evaluation value of the target image based on the foreground evaluation value and the background evaluation value, thereby solving the problem of real-time clarity calculation of an image when an industrial camera takes an image, avoiding the influence of ambient brightness on the image clarity evaluation, and improving the accuracy and robustness of the image clarity evaluation.

[0055] Embodiment 2

[0056] Figure 2 : is a flow chart of an image clarity evaluation method provided by Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0057] Step 210: Acquire a target image for image clarity evaluation and convert the target image into a single-channel grayscale image.

[0058] Step 220: Divide the grayscale image into a m ×a m grayscale sub-image blocks, and according to a preset selection rule, adjacent b rows and b columns of grayscale sub-image blocks are selected as the target splicing sub-image.

[0059] Wherein, a, m, and b are integers greater than or equal to 2, and b is greater than or equal to a.

[0060] Step 230 : Perform brightness correction on each target stitching sub-image, and determine a first grayscale representative value formed by each target stitching sub-image and a second grayscale representative value of a target stitching image block stitched by each target stitching sub-image.

[0061] Step 240: When the first grayscale representative value and the second grayscale representative value meet the preset grayscale condition, image stitching processing is performed on each target stitching sub-image after brightness correction to obtain a brightness-corrected stitching sub-image; otherwise, a target sub-image block corresponding to the target stitching image block is extracted from the grayscale image, and brightness correction is performed to obtain a brightness-corrected stitching sub-image.

[0062] Step 250: Correct the spliced ​​sub-images according to brightness to obtain a brightness-corrected spliced ​​image.

[0063] In an optional implementation of the embodiment of the present invention, a brightness-corrected stitched image is obtained based on brightness-corrected stitched sub-images, including: when the number of brightness-corrected stitched sub-images is greater than 1, according to a preset selection rule, selecting b adjacent rows and b columns of brightness-corrected stitched sub-images as target stitched sub-images; returning to perform brightness correction on each target stitched sub-image, and determining a first grayscale representative value of the target stitched sub-image, and a second grayscale representative value of a target stitched image block stitched from each target stitched sub-image, until the number of brightness-corrected stitched sub-images obtained is 1; when the number of brightness-corrected stitched sub-images is 1, using the brightness-corrected stitched sub-image as the brightness-corrected stitched image.

[0064] Among them, the process of obtaining a brightness-corrected stitched image based on brightness-corrected stitched sub-images can be similar to the process of performing brightness correction and image stitching processing on each target stitched sub-image to obtain a brightness-corrected stitched sub-image. In short, all grayscale sub-image blocks are stitched into large image blocks in a certain order, and then the large image blocks are stitched again in a certain order until a complete brightness-corrected stitched image is stitched. Among them, whether each block can be spliced ​​depends on whether the first grayscale representative value and the second grayscale representative value meet the preset grayscale condition. The specific process will not be repeated here.

[0065] In an optional implementation of the embodiment of the present invention, brightness correction is performed on each target stitching sub-image, including: if the third grayscale representative value of the target stitching sub-image is greater than a preset grayscale threshold, the first gamma value is used to perform brightness correction on the target stitching sub-image; otherwise, the second gamma value is used to perform brightness correction on the target stitching sub-image; wherein the first gamma value is greater than 1; and the second gamma value is less than 1. A target sub-image block corresponding to the target stitching image block is extracted from the grayscale image, and brightness correction is performed, including: if the fourth grayscale representative value of the target sub-image block is greater than a preset grayscale threshold, the first gamma value is used to perform brightness correction on the target sub-image block; otherwise, the second gamma value is used to perform brightness correction on the target sub-image block.

[0066] Specifically, the formula f(I)=I can be used γ Correct the image brightness. When the coefficient γ>1, the overall gray value of the image will become smaller, and the image will appear darker; when the coefficient γ<1, the overall gray value of the image will become larger, and the image will appear brighter.

[0067] In a specific embodiment, it is possible to use Perform brightness correction. Among them, f(c x ,c y ) is the third grayscale representative value or the fourth grayscale representative value, 2.2 is the first gamma value, 1 / 2.2 is the second gamma value, and 127 is the preset grayscale threshold. The local brightness correction process can be used to coordinate the brightness of the entire image and avoid local shadow blocks or excessive local brightness, so that the clarity evaluation of the image is not affected by the brightness and has better robustness.

[0068] In an optional implementation of an embodiment of the present invention, determining a first grayscale representative value formed by each target stitching sub-image and a second grayscale representative value of a target stitching image block stitched by each target stitching sub-image includes: respectively determining alternative grayscale values ​​corresponding to the center of gravity of each target stitching sub-image, and determining the first grayscale representative value based on each alternative grayscale value; determining the second grayscale representative value corresponding to the center of gravity of the target stitching image block stitched by each target stitching sub-image.

[0069] Among them, the coordinates of the center of gravity can be recorded as (c x ,c y ). The calculation process of the centroid coordinates is: use the OTSU threshold segmentation method to convert the small block into a binary image, and count the number of white pixels in the binary image as S 00 , calculate the cumulative sum of the x coordinates of each white pixel as S 10 , the cumulative sum of the y coordinates of each white pixel is S 01 ; The coordinates of the center of gravity can be obtained by the formula Determine. Specifically, the first grayscale representative value may be the median of the candidate grayscale values ​​corresponding to the centroid of each target spliced ​​sub-image; the second grayscale representative value may be the grayscale value corresponding to the centroid of the target spliced ​​image block. The most representative grayscale value in each target spliced ​​sub-image may be used as the first grayscale representative value; the most representative grayscale value of the target spliced ​​image block may be used as the second grayscale representative value, which may improve the reliability and rationality of image block splicing and avoid the appearance of black edges due to discontinuous grayscale values, thereby affecting image quality.

[0070] Step 260: Using a threshold segmentation method, the brightness-corrected spliced ​​image is divided into a foreground image and a background image, and a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image are determined.

[0071] In an optional implementation of an embodiment of the present invention, determining a foreground evaluation value corresponding to a foreground image includes: extracting high-frequency information and low-frequency information in the foreground image, and generating a high-frequency image and a low-frequency image accordingly; determining a high-frequency evaluation value and a low-frequency evaluation value corresponding to the high-frequency image and the low-frequency image, respectively, and determining a foreground evaluation value corresponding to the foreground image based on the high-frequency evaluation value and the low-frequency evaluation value.

[0072] Among them, the high frequency in the image refers to the area where the pixel frequency changes quickly, that is, the grayscale difference between adjacent pixel areas is large, usually the edge area of ​​the image. In addition, the details of the image are also where the grayscale value changes more dramatically. Low-frequency information refers to the grayscale information of the area of ​​non-high-frequency information in the image, which is manifested as the area where the pixel frequency changes relatively slowly, that is, the area where the pixel changes more continuously.

[0073] In the embodiment of the present invention, the high-frequency information can be extracted by using the Sobel operator. The low-frequency information is obtained by subtracting the high-frequency information from the foreground image. Specifically, in the foreground image, the high-frequency information is extracted using a third-order Sobel operator to obtain an edge contour image, i.e., the "high-frequency image" f h ; Then use the foreground image to subtract f h Image, the resulting image is the "low-frequency image" f l .

[0074] There are many ways to determine the high-frequency evaluation value and the low-frequency evaluation value. For example, they can be determined by an image evaluation function, or by a representative grayscale value in the image.

[0075] Specifically, in an optional implementation of an embodiment of the present invention, high-frequency evaluation values ​​and low-frequency evaluation values ​​corresponding to the high-frequency image and the low-frequency image respectively are determined, and a foreground evaluation value corresponding to the foreground image is determined based on the high-frequency evaluation values ​​and the low-frequency evaluation values, including: taking the average cumulative sum of the high-frequency information in the high-frequency image as the high-frequency evaluation value, and taking the average cumulative sum of the low-frequency information in the low-frequency image as the low-frequency evaluation value; weightedly summing the high-frequency evaluation value and the low-frequency evaluation value according to a first preset weight coefficient to obtain a foreground evaluation value corresponding to the foreground image; determining a background evaluation value corresponding to the background image, including: taking the average cumulative sum of the information in the background image as the background evaluation value.

[0076] In a specific embodiment, the average accumulation sum of high frequency information in the high frequency image can be obtained by the formula It is determined that the average cumulative sum of low-frequency information in the low-frequency image can be obtained by the formula Determine. Where row is the horizontal coordinate of the pixel value in the image, and col is the vertical coordinate of the pixel value in the image; S h is the high frequency evaluation value, S l is the low frequency evaluation value; f h (i, j) is the pixel value at position (i, j) in the high-frequency information, f l (i, j) is the pixel value at position (i, j) in the low-frequency information. It can be obtained by formula C f =w h S h +w l S l Determine the prospect evaluation value. Among them, C f represents the foreground evaluation value of the foreground image, w h Represents the weight coefficient of high-frequency information, w l represents the weight coefficient of low-frequency information, and the first preset weight coefficient satisfies {w l ∈[0,1],w h ∈[0,1],w l +w h =1}, specific w l Greater than w h .

[0077] In another specific embodiment, the background image may be processed using a second-order Sobel operator, and the average accumulation sum of the pixels of the background image is used as the background evaluation value of the background image. For example, by the formula Determine the background evaluation value. Among them, f b(i, j) is the pixel value at position (i, j) in the background image. The image can be better evaluated by averaging the cumulative sum of the pixels in the image. The embodiment of the present invention divides the image into a foreground image and a background image, divides the foreground image into a high-frequency image and a low-frequency image, and uses different weights to determine the clarity evaluation of the image, so that the clarity evaluation of the image can better reflect the information of the ROI area, thereby making the clarity evaluation of the image more accurate.

[0078] Step 270: Perform weighted summation on the foreground evaluation value and the background evaluation value according to a second preset weight coefficient to obtain an image clarity evaluation value of the target image.

[0079] The technical solution of this embodiment is to obtain a target image to be evaluated for image clarity and convert the target image into a single-channel grayscale image; divide the grayscale image into a m ×a m grayscale sub-image blocks are selected, and according to a preset selection rule, adjacent b rows and b columns of grayscale sub-image blocks are selected as target stitching sub-images; brightness correction is performed on each target stitching sub-image, and a first grayscale representative value formed by each target stitching sub-image and a second grayscale representative value of a target stitching image block stitched by each target stitching sub-image are determined; when the first grayscale representative value and the second grayscale representative value meet a preset grayscale condition, image stitching processing is performed on each target stitching sub-image after brightness correction to obtain a brightness-corrected stitching sub-image; otherwise, a target sub-image block corresponding to the target stitching image block is extracted from the grayscale image, and brightness correction is performed. A brightness-corrected stitched sub-image is obtained; a brightness-corrected stitched image is obtained based on the brightness-corrected stitched sub-image; a threshold segmentation method is used to divide the brightness-corrected stitched image into a foreground image and a background image, and a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image are determined, respectively; the foreground evaluation value and the background evaluation value are weightedly summed according to a second preset weight coefficient to obtain an image clarity evaluation value of the target image, thereby solving the problem of real-time clarity calculation of an image when an industrial camera takes an image, avoiding the influence of ambient brightness on image clarity evaluation, and improving the accuracy and robustness of image clarity evaluation.

[0080] Embodiment 3

[0081] Figure 3 Schematic diagram of the structure of an image clarity evaluation device provided by Embodiment 3 of the present invention. Figure 3 The device includes: a grayscale image conversion module 310, a brightness correction splicing image determination module 320, a foreground evaluation value and background evaluation value determination module 330 and an image clarity evaluation value determination module 340. Among them:

[0082] A grayscale image conversion module 310 is used to obtain a target image to be evaluated for image clarity and convert the target image into a single-channel grayscale image;

[0083] A brightness-corrected spliced ​​image determination module 320 is used to divide the grayscale image into at least one grayscale sub-image block, and perform brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image;

[0084] A foreground evaluation value and background evaluation value determination module 330 is used to perform foreground image and background image segmentation processing on the brightness corrected spliced ​​image, and determine the foreground evaluation value and background evaluation value corresponding to the foreground image and the background image respectively;

[0085] The image definition evaluation value determination module 340 is used to determine the image definition evaluation value of the target image according to the foreground evaluation value and the background evaluation value.

[0086] Optionally, the brightness corrected spliced ​​image determination module 320 includes:

[0087] The target splicing sub-image selection unit is used to divide the grayscale image into a m ×a m grayscale sub-image blocks, and according to a preset selection rule, select b rows and b columns of adjacent grayscale sub-image blocks as the target splicing sub-image; wherein a, m, and b are integers greater than or equal to 2, and b is greater than or equal to a;

[0088] The brightness-corrected spliced ​​image determining unit is used to perform brightness correction and image splicing processing on each target spliced ​​sub-image to obtain a brightness-corrected spliced ​​image.

[0089] Optionally, a brightness corrected spliced ​​image determination unit comprises:

[0090] A grayscale representative value determination subunit is used to perform brightness correction on each target stitching sub-image, and determine a first grayscale representative value formed by each target stitching sub-image, and a second grayscale representative value of a target stitching image block stitched by each target stitching sub-image;

[0091] The brightness-corrected stitched sub-image determining sub-unit is used to, when the first grayscale representative value and the second grayscale representative value meet a preset grayscale condition, perform image stitching processing on each target stitched sub-image after brightness correction to obtain a brightness-corrected stitched sub-image; otherwise, extract a target sub-image block corresponding to the target stitched image block from the grayscale image, and perform brightness correction to obtain a brightness-corrected stitched sub-image;

[0092] The brightness-corrected spliced ​​image determining subunit is used to obtain a brightness-corrected spliced ​​image according to the brightness-corrected spliced ​​sub-images.

[0093] Optionally, the brightness corrected spliced ​​image determination subunit is specifically used for:

[0094] When the number of brightness-corrected stitching sub-images is greater than 1, according to a preset selection rule, adjacent b rows and b columns of brightness-corrected stitching sub-images are selected as target stitching sub-images;

[0095] Returning to the operation of performing brightness correction on each target stitching sub-image, and determining the first grayscale representative value of the target stitching sub-image, and the second grayscale representative value of the target stitching image block stitched by each target stitching sub-image, until the number of brightness-corrected stitching sub-images obtained is 1;

[0096] When the number of brightness-corrected stitching sub-images is 1, the brightness-corrected stitching sub-images are used as brightness-corrected stitching images.

[0097] Optionally, a brightness corrected spliced ​​image determination unit comprises:

[0098] A first brightness correction sub-unit is used to use a first gamma value to perform brightness correction on the target spliced ​​sub-image if the third grayscale representative value of the target spliced ​​sub-image is greater than a preset grayscale threshold; otherwise, use a second gamma value to perform brightness correction on the target spliced ​​sub-image; wherein the first gamma value is greater than 1; and the second gamma value is less than 1;

[0099] The brightness correction and splicing sub-image determination sub-unit includes:

[0100] The second brightness correction subunit is used to use the first gamma value to perform brightness correction on the target sub-image block if the fourth grayscale representative value of the target sub-image block is greater than a preset grayscale threshold; otherwise, use the second gamma value to perform brightness correction on the target sub-image block.

[0101] Optionally, the grayscale representative value determination subunit is specifically used for:

[0102] Determine the candidate grayscale values ​​corresponding to the center of gravity of each target spliced ​​sub-image respectively, and determine the first grayscale representative value according to each candidate grayscale value;

[0103] A second grayscale representative value corresponding to the center of gravity of a target spliced ​​image block formed by splicing the target spliced ​​sub-images is determined.

[0104] Optionally, the foreground evaluation value and background evaluation value determination module 330 includes:

[0105] The image division processing unit is used to divide the brightness corrected spliced ​​image into a foreground image and a background image by using a threshold segmentation method.

[0106] Optionally, the foreground evaluation value and background evaluation value determination module 330 includes:

[0107] A high-frequency image and a low-frequency image generating unit, used to extract high-frequency information and low-frequency information from the foreground image, and generate a high-frequency image and a low-frequency image accordingly;

[0108] The foreground evaluation value determination unit is used to determine the high frequency evaluation value and the low frequency evaluation value corresponding to the high frequency image and the low frequency image respectively, and determine the foreground evaluation value corresponding to the foreground image according to the high frequency evaluation value and the low frequency evaluation value.

[0109] Optionally, the foreground evaluation value determination unit is specifically used to:

[0110] The average cumulative sum of high-frequency information in the high-frequency image is used as the high-frequency evaluation value, and the average cumulative sum of low-frequency information in the low-frequency image is used as the low-frequency evaluation value;

[0111] The high-frequency evaluation value and the low-frequency evaluation value are weighted and summed according to a first preset weight coefficient to obtain a foreground evaluation value corresponding to the foreground image;

[0112] The foreground evaluation value and background evaluation value determination module 330 includes:

[0113] The background evaluation value determination unit is used to take the average accumulation sum of the information in the background image as the background evaluation value.

[0114] Optionally, the image clarity evaluation value determination module 340 includes:

[0115] The image definition evaluation value determination unit is used to perform weighted summation of the foreground evaluation value and the background evaluation value according to a second preset weight coefficient to obtain the image definition evaluation value of the target image.

[0116] The image clarity evaluation device provided in the embodiment of the present invention can execute the image clarity evaluation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0117] Embodiment 4

[0118] Figure 4 is a schematic diagram of the structure of an electronic device provided by Embodiment 4 of the present invention, such as Figure 4 As shown, the device includes:

[0119] one or more processors 410, Figure 4 A processor 410 is taken as an example;

[0120] Memory 420;

[0121] The device may further include: an input device 430 and an output device 440 .

[0122] The processor 410, memory 420, input device 430 and output device 440 in the device may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0123] The memory 420 is a non-transitory computer-readable storage medium that can be used to store software programs, computer executable programs, and modules, such as program instructions / modules corresponding to an image clarity evaluation method in an embodiment of the present invention (for example, the attached Figure 3 The processor 410 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 420, that is, an image clarity evaluation method of the above method embodiment is implemented, namely:

[0124] Acquire a target image to be evaluated for image clarity, and convert the target image into a single-channel grayscale image;

[0125] Dividing the grayscale image into at least one grayscale sub-image block, and performing brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image;

[0126] Performing foreground image and background image division processing on the brightness-corrected spliced ​​image, and determining a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image respectively;

[0127] An image clarity evaluation value of the target image is determined according to the foreground evaluation value and the background evaluation value.

[0128] The memory 420 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 420 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 420 may optionally include a memory remotely arranged relative to the processor 410, and these remote memories may be connected to the terminal device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0129] The input device 430 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the computer device. The output device 440 may include a display device such as a display screen.

[0130] Embodiment 5

[0131] Embodiment 5 of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, an image clarity evaluation method provided in an embodiment of the present invention is implemented:

[0132] Acquire a target image to be evaluated for image clarity, and convert the target image into a single-channel grayscale image;

[0133] Dividing the grayscale image into at least one grayscale sub-image block, and performing brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image;

[0134] Performing foreground image and background image division processing on the brightness-corrected spliced ​​image, and determining a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image respectively;

[0135] An image clarity evaluation value of the target image is determined according to the foreground evaluation value and the background evaluation value.

[0136] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0137] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0138] 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.

[0139] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0140] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for evaluating image clarity, characterized in that: The method comprises: Acquire a target image to be evaluated for image clarity, and convert the target image into a single-channel grayscale image; Dividing the grayscale image into at least one grayscale sub-image block, and performing brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image; Performing foreground image and background image division processing on the brightness corrected spliced ​​image, and determining a foreground evaluation value and a background evaluation value corresponding to the foreground image and the background image respectively; wherein the foreground evaluation value and the background evaluation value are determined according to pixel points in the foreground image and the background image; Determining an image clarity evaluation value of the target image according to the foreground evaluation value and the background evaluation value; The grayscale image is divided into at least one grayscale sub-image block, and brightness correction and image block splicing processing are performed on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image, including: Divide the grayscale image into a m ×a m grayscale sub-image blocks, and according to a preset selection rule, select b rows and b columns of adjacent grayscale sub-image blocks as the target splicing sub-image; wherein a, m, and b are integers greater than or equal to 2, and b is greater than or equal to a; Performing brightness correction and image stitching processing on each of the target stitching sub-images to obtain a brightness-corrected stitching image; Performing brightness correction and image stitching processing on each of the target stitching sub-images to obtain a brightness corrected stitching image, including: Performing brightness correction on each of the target stitched sub-images, and determining a first grayscale representative value formed by each of the target stitched sub-images, and a second grayscale representative value of a target stitched image block stitched by each of the target stitched sub-images; When the first grayscale representative value and the second grayscale representative value meet a preset grayscale condition, performing image stitching processing on each of the target stitching sub-images after brightness correction to obtain a brightness-corrected stitching sub-image; Otherwise, extracting a target sub-image block corresponding to the target spliced ​​image block from the grayscale image, and performing brightness correction to obtain a brightness-corrected spliced ​​sub-image; Obtaining a brightness-corrected spliced ​​image according to the brightness-corrected spliced ​​sub-images; The preset grayscale condition is that the absolute value of the difference between the first grayscale representative value and the second grayscale representative value is less than or equal to a preset threshold.

2. The method according to claim 1, characterized in that: Obtaining a brightness-corrected spliced ​​image according to the brightness-corrected spliced ​​sub-images, including: When the number of the brightness-corrected stitching sub-images is greater than 1, selecting adjacent b rows and b columns of brightness-corrected stitching sub-images as target stitching sub-images according to the preset selection rule; Returning to the operation of performing brightness correction on each of the target stitched sub-images, and determining a first grayscale representative value of the target stitched sub-image, and a second grayscale representative value of a target stitched image block stitched by each of the target stitched sub-images, until the number of the brightness-corrected stitched sub-images obtained is 1; When the number of the brightness-corrected stitching sub-images is 1, the brightness-corrected stitching sub-images are used as brightness-corrected stitching images.

3. The method according to claim 1, characterized in that Performing brightness correction on each of the target spliced ​​sub-images includes: If the third grayscale representative value of the target spliced ​​sub-image is greater than a preset grayscale threshold, performing brightness correction on the target spliced ​​sub-image using the first gamma value; Otherwise, using the second gamma value to perform brightness correction on the target spliced ​​sub-image; Wherein, the first gamma value is greater than 1; the second gamma value is less than 1; Extracting a target sub-image block corresponding to the target spliced ​​image block from the grayscale image and performing brightness correction, including: If the fourth grayscale representative value of the target sub-image block is greater than a preset grayscale threshold, performing brightness correction on the target sub-image block using the first gamma value; Otherwise, the second gamma value is used to perform brightness correction on the target sub-image block.

4. The method according to claim 1, characterized in that Determining a first grayscale representative value formed by each of the target stitched sub-images and a second grayscale representative value of a target stitched image block stitched by each of the target stitched sub-images comprises: Determine the candidate grayscale values ​​corresponding to the center of gravity of each target stitching sub-image respectively, and determine the first grayscale representative value according to each candidate grayscale value; A second grayscale representative value corresponding to the center of gravity of a target spliced ​​image block formed by splicing the target spliced ​​sub-images is determined.

5. The method according to claim 1, characterized in that The brightness-corrected spliced ​​image is processed to be divided into a foreground image and a background image, including: The brightness-corrected spliced ​​image is divided into a foreground image and a background image by using a threshold segmentation method.

6. The method according to claim 1, characterized in that Determine the foreground evaluation value corresponding to the foreground image, including: Extracting high-frequency information and low-frequency information from the foreground image, and generating a high-frequency image and a low-frequency image accordingly; A high-frequency evaluation value and a low-frequency evaluation value corresponding to the high-frequency image and the low-frequency image are determined respectively, and a foreground evaluation value corresponding to the foreground image is determined according to the high-frequency evaluation value and the low-frequency evaluation value.

7. The method according to claim 6, characterized in that Determining a high-frequency evaluation value and a low-frequency evaluation value corresponding to the high-frequency image and the low-frequency image, respectively, and determining a foreground evaluation value corresponding to the foreground image according to the high-frequency evaluation value and the low-frequency evaluation value, including: Taking the average accumulated sum of the high-frequency information in the high-frequency image as the high-frequency evaluation value, and taking the average accumulated sum of the low-frequency information in the low-frequency image as the low-frequency evaluation value; The high-frequency evaluation value and the low-frequency evaluation value are weighted and summed according to a first preset weight coefficient to obtain a foreground evaluation value corresponding to the foreground image; Determine the background evaluation value corresponding to the background image, including: The average cumulative sum of the information in the background image is used as the background evaluation value.

8. The method according to claim 1, characterized in that: Determining the image clarity evaluation value of the target image according to the foreground evaluation value and the background evaluation value includes: The foreground evaluation value and the background evaluation value are weighted and summed according to a second preset weight coefficient to obtain an image clarity evaluation value of the target image.

9. An image clarity evaluation device, characterized in that: include: A grayscale image conversion module, used for acquiring a target image to be evaluated for image clarity, and converting the target image into a single-channel grayscale image; A brightness-corrected spliced ​​image determination module, used for dividing the grayscale image into at least one grayscale sub-image block, and performing brightness correction and image block splicing processing on the grayscale sub-image block to obtain a brightness-corrected spliced ​​image; A foreground evaluation value and background evaluation value determination module, used to perform foreground image and background image segmentation processing on the brightness corrected spliced ​​image, and determine the foreground evaluation value and background evaluation value corresponding to the foreground image and the background image respectively; wherein the foreground evaluation value and the background evaluation value are determined according to the pixel points in the foreground image and the background image; An image definition evaluation value determination module, used to determine the image definition evaluation value of the target image according to the foreground evaluation value and the background evaluation value; The brightness correction splicing image determination module includes: The target splicing sub-image selection unit is used to divide the grayscale image into a m ×a m grayscale sub-image blocks, and according to a preset selection rule, select b rows and b columns of adjacent grayscale sub-image blocks as the target splicing sub-image; wherein a, m, and b are integers greater than or equal to 2, and b is greater than or equal to a; A brightness-corrected spliced ​​image determination unit is used to perform brightness correction and image splicing processing on each target spliced ​​sub-image to obtain a brightness-corrected spliced ​​image; A brightness corrected spliced ​​image determination unit, comprising: A grayscale representative value determination subunit is used to perform brightness correction on each target stitching sub-image, and determine a first grayscale representative value formed by each target stitching sub-image, and a second grayscale representative value of a target stitching image block stitched by each target stitching sub-image; The brightness-corrected stitched sub-image determining sub-unit is used to, when the first grayscale representative value and the second grayscale representative value meet a preset grayscale condition, perform image stitching processing on each target stitched sub-image after brightness correction to obtain a brightness-corrected stitched sub-image; otherwise, extract a target sub-image block corresponding to the target stitched image block from the grayscale image, and perform brightness correction to obtain a brightness-corrected stitched sub-image; A brightness-corrected spliced ​​image determining subunit is used to obtain a brightness-corrected spliced ​​image according to the brightness-corrected spliced ​​sub-images; The preset grayscale condition is that the absolute value of the difference between the first grayscale representative value and the second grayscale representative value is less than or equal to a preset threshold.

10. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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