Image distortion correction method and device

Abstract

The present application discloses a method and device for correcting image distortion, which obtain an image obtained by photographing a sample image through a lens; determine the calculation weight of each preset graphic in the image according to a preset weight configuration rule; determine the intensity correction value and / or scaling correction value of the image according to each preset graphic in the image and the calculation weight of each preset graphic; and when it is determined that the image does not meet the set distortion requirements, adjust the initial intensity parameter and / or initial scaling parameter according to the intensity correction value and / or scaling correction value until the image meets the set distortion requirements, and save the corresponding correction intensity parameter and / or correction scaling parameter after the distortion correction is finally performed on the image corresponding to the sample image. When the same lens is used to take pictures later, the saved correction intensity parameter and / or correction scaling parameter can be used to correct the captured image, thereby achieving the purpose of quickly and accurately correcting the distorted image without manual intervention.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of image processing technology, and in particular to a method and device for correcting image distortion. Background Art

[0002] With the continuous development of society, security deployment and the collection of relevant information through cameras are becoming increasingly important to various industries. For example, traffic departments can analyze vehicle traffic problems using video streams captured by highway cameras, and public security departments can analyze criminal cases using video streams captured by surveillance cameras. However, the quality of current lenses varies greatly. For example, some low-end lenses can easily cause image distortion when used, seriously affecting the image's usability.

[0003] Therefore, there is an urgent need for a method that can quickly and accurately correct the distorted image captured before the camera device actually outputs the image, so as to improve the usability of the image captured by the lens. Summary of the Invention

[0004] The present application provides a method and device for correcting image distortion, which are used to quickly and accurately correct distorted images taken by a lens, thereby outputting images of better quality and greatly improving the usability of images taken by the lens.

[0005] In a first aspect, an embodiment of the present application provides a method for correcting image distortion, the method comprising: obtaining an image obtained by photographing a sample image through a lens; a plurality of preset graphics are distributed in the sample image; determining the calculation weight of each preset graphic in the image according to a preset weight configuration rule; the preset weight configuration rule indicates that the calculation weight of the preset graphic located in the central area of ​​the image is greater than the calculation weight of the preset graphic located in the edge area of ​​the image; determining an intensity correction value and / or a scaling correction value of the image according to each preset graphic in the image and the calculation weight of each preset graphic; adjusting an initial intensity parameter and / or an initial scaling parameter until the image meets the set distortion requirement according to the intensity correction value and / or the scaling correction value when it is determined that the image does not meet the set distortion requirement, and saving the correction intensity parameter and / or correction scaling parameter of the image when the set distortion requirement is met; the correction intensity parameter and the correction scaling parameter are used to perform distortion correction on the image photographed by the lens.

[0006] To address the problem of distortion in images captured by a lens, the above-mentioned solution proposes using a lens to capture a sample image with multiple preset patterns distributed thereon, and then processing the captured image. This includes first determining the calculation weights of each preset pattern in the image, then determining the intensity correction value and / or scale correction value of the image based on the calculation weights of these preset patterns, and finally determining whether the image meets the set distortion requirements based on these two distortion values. If it is determined that the set distortion requirements are not met, the initial intensity parameters and / or initial scale parameters of the image are iteratively adjusted, thereby achieving the purpose of accurately correcting the distortion of the image captured by the lens. In this method, by saving the corresponding correction intensity parameters and / or correction scale parameters after the distortion correction of the image corresponding to the sample image, when the same lens is used to take pictures later, the saved correction intensity parameters and / or correction scale parameters can be used to correct the captured image, thereby achieving the purpose of quickly and accurately correcting the distorted image without manual intervention.

[0007] In a possible implementation method, the sample graph is a grid network graph composed of a plurality of grids of equal size, wherein each grid has a solid circle at its vertex with the vertex as its center.

[0008] In the above scheme, by designing the sample image as a square grid image composed of multiple squares of equal size, this will help to simplify the subsequent calculation process when calculating the calculation weight of the preset graphics; wherein, by setting any vertex of these squares as a solid circle with the vertex as the center, this helps to accurately identify each square in the image distortion scene through the solid circle, thereby laying a calculation foundation for calculating the calculation weight of the preset graphics and determining the intensity correction value and / or scaling correction value of the image.

[0009] In one possible implementation method, the calculation weights of each preset graphic in the image are determined according to a preset weight configuration rule, including: taking the central pixel point of the image as a reference, determining multiple areas with different distances from the central pixel point; for any one of the multiple areas, setting the weights of each vertex pixel point of each grid in the area; wherein the weight of any vertex pixel point is negatively correlated with the distance from the area where the vertex pixel point is located to the central pixel point; for any one of the grids, using the weight of the vertex pixel point of the grid at the preset position as the calculation weight of the grid.

[0010] The above scheme specifically describes how to determine the calculation weights of each preset graphic in the image according to the preset weight configuration rules, including first taking the central pixel point of the image as the benchmark, determining multiple areas with different distances from the central pixel point, and then, for any one of these multiple areas, setting the weights of each vertex pixel point of each square in the area according to the distance of the area from the central pixel point. If the area is closer to the central pixel point, the weights of the vertex pixel points of each square in the area are set to be larger; if the area is farther from the central pixel point, the weights of the vertex pixel points of each square in the area are set to be smaller. Finally, according to the preset rules, the weights of the vertex pixel points of each square at the same preset position are selected as the calculation weights of the square. This method sets the weight of the pixel points in the central area of ​​the image to be slightly greater than the weight of the pixel points in the edge area of ​​the image. This is mainly due to the fact that the image in the central area is more likely to be used than the image in the edge area in actual use. In this way, by keeping the weight of the central area of ​​the image larger, the intensity parameter and / or scaling parameter after final correction can be tilted towards the central area. Therefore, the correction accuracy of the central area of ​​the distorted image will be superior to that of the edge area under the premise that the distorted image has been well corrected, which can well meet the user's needs for image usage.

[0011] In one possible implementation method, the central pixel point of the image is used as a reference to determine multiple areas with different distances from the central pixel point, including: drawing a circle with the central pixel point of the image as the center and ir as the radius; wherein r represents the set radius, i represents the multiple value, i=1, 2, 3...; for any two adjacent circles in the image, the non-overlapping areas of the two circles are regarded as one area.

[0012] In the above scheme, by designing each area at different distances from the central pixel point of the image as a circle with the central pixel point of the image as the center, and the width between any two adjacent circles is equal, the calculation weights of the squares in the central area of ​​the image and the calculation weights of the squares in the edge area of ​​the image can be distributed more evenly, which will be fed back to the correction of image distortion, and the correction effect will be more coordinated and unified.

[0013] In one possible implementation method, determining the intensity correction value of the image based on each preset graphic in the image and the calculation weight of each preset graphic includes: determining the average distance error of any square in the image; determining the intensity deviation of the square based on the average distance error of the square and the calculation weight of the square; the average distance error of the square represents the overall distortion degree of the square; and obtaining the intensity correction value of the image based on the intensity deviation of each square in the image.

[0014] The above scheme specifically describes how to determine the intensity correction value of an image based on each preset pattern in the image and the calculated weight of each preset pattern. When the preset pattern is a grid, this involves first determining the average distance error of each grid. Then, for any grid, the intensity deviation of that grid can be determined based on the average distance error and the calculated weight of that grid. Finally, the intensity correction value of the image can be obtained based on the intensity deviation of each grid. In this method, by first determining the average distance error of the grid, since this average distance error represents the overall degree of distortion of the grid, the average distance error of each grid is continuously accumulated. Of course, by also considering the calculated weight of each grid, the intensity deviation of the grid can be reasonably and accurately determined. This is of great significance for calculating the intensity correction value of the image, and ultimately, the feedback on whether the intensity correction of the image is performed will also be reasonable and accurate.

[0015] In one possible implementation method, determining the average distance error of any one of the squares in the image includes: determining, for any edge of any one of the squares in the image, at least two pixel points located on the edge; determining, based on the at least two pixel points, a fitted straight line passing through the at least two pixel points; determining the average distance error of the edge based on the distances from each pixel point on the edge to the fitted straight line; and determining the average distance error of the square based on the average distance error of each edge of the square.

[0016] The above scheme specifically describes how to determine the average distance error of a square. This method may include first determining at least two pixels on any side of the square, then fitting a straight line to the at least two pixels to obtain a fitted line about the at least two pixels, then calculating the distances from all pixels on the side to the fitted line to obtain the average distance error for the side, and finally determining the average distance error for the square based on the average distance error for each side of the square. In this method, by determining the fitted line for each side of the square, the degree of distortion of the side can be well and accurately determined, and thus the overall degree of distortion of the square can be determined. This is of great significance for the subsequent determination of intensity correction values, making the calculation of intensity correction values ​​for the image more accurate and reasonable.

[0017] In one possible implementation method, the scaling correction value includes a horizontal scaling correction value and a vertical scaling correction value; determining the scaling correction value of the image based on the preset graphics in the image and the calculated weights of the preset graphics includes: determining, for any square in the image, a first distance error between two sides of the square in the horizontal direction and a second distance error between two sides of the square in the vertical direction; determining a first deviation of the square in the horizontal direction based on the first distance error and the calculated weight of the square, and determining a second deviation of the square in the vertical direction based on the second distance error and the calculated weight of the square; obtaining the horizontal scaling correction value of the image based on the first deviation of each square in the image in the horizontal direction, and obtaining the vertical scaling correction value of the image based on the second deviation of each square in the image in the vertical direction.

[0018] In the above scheme, it is specifically described how to determine the zoom correction value of the image based on the preset graphics in the image and the calculation weight of each preset graphics, specifically how to determine the horizontal zoom correction value and the vertical zoom correction value of the image. Under the condition that the preset graphics are squares, it includes first determining, for any square, the distance error between the two sides of the square in the horizontal direction (i.e., the first distance error) and the distance error between the two sides of the square in the vertical direction (i.e., the second distance error), then determining the deviation of the square in the horizontal direction (i.e., the first deviation) through the first distance error and the calculation weight of the square, and determining the deviation of the square in the vertical direction (i.e., the second deviation) through the second distance error and the calculation weight of the square, and finally determining the horizontal zoom correction value and the vertical zoom correction value based on the respective horizontal and vertical zoom correction values ​​of the squares. The first deviation in the horizontal direction can be used to obtain the horizontal scaling correction value of the image, and the numerical scaling correction value of the image can be obtained according to the second deviation of each grid in the vertical direction. In this method, the first distance error and the second distance error of the grid are first determined. Since the first distance error represents the length change of the grid in the horizontal direction, and the second distance error represents the length change of the grid in the vertical direction, the first distance error and the second distance error of each grid are continuously accumulated. Of course, by considering the calculation weight of each grid, the horizontal scaling correction value and the vertical scaling correction value of the grid can be reasonably and accurately determined. This is of great significance for calculating the scaling correction value of the image, and the final feedback on whether the scaling correction of the image is performed will also be reasonable and accurate.

[0019] In one possible implementation method, when it is determined that the image does not meet the set distortion requirement based on the intensity correction value and / or the scaling correction value, the initial intensity parameter and / or the initial scaling parameter are adjusted until the image meets the set distortion requirement, including: when it is determined that the image does not meet the set intensity distortion requirement based on the intensity correction value and when it is determined that the image does not meet the set scaling distortion requirement based on the scaling correction value, the initial intensity parameter of the image is adjusted; when the intensity parameter of the image is adjusted to meet the set intensity distortion requirement or the number of iterative adjustments of the intensity parameter meets the set threshold value of the number of iterative adjustments of the intensity parameter, the initial scaling parameter of the image is adjusted until the scaling parameter of the image is adjusted to meet the set scaling distortion requirement or the number of iterative adjustments of the scaling parameter meets the set threshold value of the number of iterative adjustments of the scaling parameter.

[0020] In the above scheme, after the intensity correction value and scale correction value of the image have been determined, if it is determined that the intensity correction value of the image does not meet the set intensity distortion requirement and the scale correction value of the image does not meet the set scale distortion requirement, the initial intensity parameter of the image can be adjusted first. If and only if the intensity parameter of the image is adjusted to the set intensity distortion requirement or the number of iterations of adjusting the intensity parameter meets the threshold of the number of intensity iteration adjustments, the initial scale parameter of the image is adjusted again until the scale parameter of the image is adjusted to the set scale distortion requirement or the number of iterations of adjusting the scale parameter meets the threshold of the number of scale iteration adjustments, then the distortion correction process of the image is terminated. In this method, intensity correction is first performed so that the edges of the curved squares in the image are straightened after correction, and then horizontal and vertical scale adjustments are made to make the horizontal and vertical edges of the squares consistent in length, so that the overall distortion effect is better.

[0021] In a second aspect, an embodiment of the present application provides an image distortion correction device, which includes: an image acquisition unit for acquiring an image obtained by photographing a sample image through a lens; a plurality of preset graphics are distributed in the sample image; a calculation weight determination unit for determining the calculation weight of each preset graphic in the image according to a preset weight configuration rule; the preset weight configuration rule indicates that the calculation weight of the preset graphic located in the center area of ​​the image is greater than the calculation weight of the preset graphic located in the edge area of ​​the image; a correction value determination unit for determining an intensity correction value and / or a scaling correction value of the image according to each preset graphic in the image and the calculation weight of each preset graphic; a distortion correction unit for adjusting an initial intensity parameter and / or an initial scaling parameter until the image meets the set distortion requirement based on the intensity correction value and / or the scaling correction value when it is determined that the image does not meet the set distortion requirement, and saving the correction intensity parameter and / or correction scaling parameter of the image when the set distortion requirement is met; the correction intensity parameter and the correction scaling parameter are used to perform distortion correction on the image photographed by the lens.

[0022] In a third aspect, an embodiment of the present application provides a computing device, including:

[0023] a memory for storing program instructions;

[0024] The processor is used to call the program instructions stored in the memory and execute any implementation method of the first aspect according to the obtained program.

[0025] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute any implementation method of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0027] Figure 1 A schematic diagram of a method for correcting image distortion provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of a multi-region image provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of a multi-region image provided in an embodiment of the present application;

[0030] Figure 4 A schematic diagram of a grid provided in an embodiment of the present application;

[0031] Figure 5 A schematic diagram of an image distortion correction device provided in an embodiment of the present application;

[0032] Figure 6 A schematic diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of this application more clear, this application will be further described in detail below with reference to the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0034] Aiming at the problem that the image captured by the current lens is distorted and the image utilization rate is low, the embodiment of the present application provides a method for correcting image distortion. Figure 1 The figure shows a schematic diagram of an image distortion correction method provided by an embodiment of the present application. The method can be performed by an image distortion correction device. The image distortion correction device can be disposed within a camera to perform distortion correction on each image captured by the lens of the current camera, and output an image whose distortion meets a preset distortion requirement after correction, i.e., an image with high usability. Alternatively, the image distortion correction device can be an image distortion processing component disposed on an image processing device outside the camera, or a dedicated image distortion processing device, to perform distortion correction on images captured by each lens. The image distortion correction device will locally maintain a mapping relationship, which is used to indicate that different lenses will each correspond to their own set of image distortion correction parameters, so that after the image distortion correction device completes the image distortion correction, it will output an image with high usability.

[0035] Considering that images captured using a lens are prone to distortion, which can render the image unusable, in an embodiment of the present application, after the lens is used to capture an image, the image is not immediately output directly. Instead, the image is first corrected for image distortion. Only when the image correction meets the preset distortion correction requirements is the image after distortion correction output, thereby largely ensuring the usability of the image.

[0036] See also Figure 1, the image distortion correction method includes the following steps:

[0037] Step 101: Acquire an image obtained by photographing a sample image through a lens; the sample image includes a plurality of preset graphics.

[0038] In this step, the lens can be any type of lens for a camera. This application does not limit the distribution of the preset graphics in the sample diagram, nor does it limit the continuity of the distribution of the preset graphics in the sample diagram. For example, the preset graphics can be a square, rectangle, equilateral triangle, hexagon, parallelogram, and other shapes; the preset graphics can be laid out in a continuous distribution style in the sample diagram, or in a discontinuous distribution style (i.e., interval distribution).

[0039] Furthermore, this application does not limit the state of the lens when capturing the sample image. For example, the lens may capture the entire sample image, meaning that the resulting image includes the complete sample image; or the lens may capture a portion of the sample image, meaning that the resulting image only includes a portion of the sample image, rather than the complete sample image.

[0040] Step 102: Determine the calculation weight of each preset graphic in the image according to a preset weight configuration rule; the preset weight configuration rule indicates that the calculation weight of the preset graphic located in the central area of ​​the image is greater than the calculation weight of the preset graphic located in the edge area of ​​the image.

[0041] In this step, when the image obtained after taking a photo of the sample image is obtained, due to the quality of the lens, the captured image may be distorted. For this reason, this application processes the image by combining the preset weight configuration rules. The specific processing steps can be found below. In this way, the calculation weights of each preset graphic in the image can be obtained.

[0042] Among them, combined with the actual use of images by users, that is, the possibility of the central area of ​​an image being used will be greater than the edge area of ​​the image. Therefore, in this application, the calculation weight of the preset graphic of the central area of ​​the image is designed to be greater than the calculation weight of the preset graphic of the edge area of ​​the image. In this way, the intensity parameter and / or scaling parameter after the final correction can be tilted towards the central area, so that the correction accuracy of the central area of ​​the distorted image will be superior to the correction accuracy of the edge area under the premise that the distorted image has been well corrected, which will well meet the user's needs for image use.

[0043] Step 103 : determining an intensity correction value and / or a scaling correction value of the image according to each preset pattern in the image and a calculation weight of each preset pattern.

[0044] After obtaining the calculation weights of each preset graphic in the image based on the aforementioned step 102, in this step, by considering the deformation of each preset graphic in the image and combining the calculation weights of each preset graphic in the distortion correction process, the intensity correction value and the scaling correction value of the image can be determined.

[0045] Step 104: When it is determined that the image does not meet the set distortion requirement, based on the intensity correction value and / or the scaling correction value, adjust the initial intensity parameter and / or the initial scaling parameter until the image meets the set distortion requirement, and save the correction intensity parameter and / or correction scaling parameter of the image when the set distortion requirement is met; the correction intensity parameter and the correction scaling parameter are used to perform distortion correction on the image captured by the lens.

[0046] Based on the intensity correction value and scaling correction value of the image obtained in the aforementioned step 103, it can be determined whether the image meets the set distortion requirements. If it is determined that the set distortion requirements are not met, the initial intensity parameters and / or initial scaling parameters of the image are adjusted until the corrected image meets the set distortion requirements. At this time, the image distortion correction device will save the current correction intensity parameters and / or correction scaling parameters of the image, so that when the same lens used to take the image is used to take pictures later, the saved correction intensity parameters and / or correction scaling parameters can be used to correct the captured image. In this way, the purpose of quickly and accurately correcting the distorted image without manual intervention can be achieved.

[0047] The following will explain the above steps in detail with examples.

[0048] In one implementation of the above step 101, the sample graph is a grid network graph consisting of a plurality of grids of equal size, wherein each grid has a solid circle at its vertex with the vertex as its center.

[0049] For example, in this application, a square grid map with M rows and N columns can be produced and printed, that is, the grid map includes a total of M*N small squares of equal size. For the convenience of description, they are uniformly referred to as squares below, and this application does not limit the side length of each square; further, in order to facilitate the rapid positioning of each square in the grid map during the distortion correction process, the background of the square grid map can be set to white and the foreground to black in this application; in addition, in this application, a black solid circle with a radius of 9 pixels will be set at the vertex position of each square, and the center of the black solid circle coincides with the center of the intersection. This design facilitates the identification of square vertices at the edges of images with severe distortion.

[0050] Therefore, for the printed grid map, it can be set in front of a lens to be corrected (the lens to be corrected in this application refers to the image taken by the lens for distortion correction), and the grid can be controlled to cover the entire screen. By triggering the photo button, the image corresponding to the grid map can be obtained, and this image is the sample image.

[0051] In one implementation of the above-mentioned step 102, the calculation weight of each preset graphic in the image is determined according to a preset weight configuration rule, including: taking the central pixel point of the image as a reference, determining multiple areas with different distances from the central pixel point; for any one of the multiple areas, setting the weight of each vertex pixel point of each grid in the area; wherein the weight of any vertex pixel point is negatively correlated with the distance from the area where the vertex pixel point is located to the central pixel point; for any one of the grids, using the weight of the vertex pixel point of the grid at the preset position as the calculation weight of the grid.

[0052] Continuing with the previous example, suppose a camera is used to take a picture of a grid pattern. Then, for the resulting grid pattern image, the calculation weight of each square in the image can be determined. Specifically, the calculation weight of each square in the image is determined as follows:

[0053] First, the central pixel of the image is used as a reference. Based on this reference, multiple preset shapes of different sizes can be drawn at a distance of 1 unit length, 2 unit lengths, 3 unit lengths, etc. from the reference. For example, the preset shape is a square; Figure 2 As shown, a schematic diagram of a multi-region image provided by an embodiment of the present application, wherein a square with a length of 1 unit from the center pixel point can be referred to as region 1, a region with a length of 1 unit to 2 units from the center pixel point can be referred to as region 2, and a region with a length of 2 units to 3 units from the center pixel point can be referred to as region 3. At the same time, the weight of region 1 is set to 100%, the weight of region 2 is set to 80%, and the weight of region 3 is set to 60%. This application does not list other regions and their corresponding weights one by one. Then, for any square located in region 1, if a vertex A of the square is located in region 1, the weight of the pixel located at the vertex A will be 100%, and if a vertex B of the square is located in region 2, the weight of the pixel located at the vertex B will be 80%. According to this logic, the weights of the vertex pixels of each square in the image can be determined one by one. Furthermore, if it is assumed that the weight of the vertex pixel in the lower right corner of the square is used as the calculation weight of the current square, the calculation weights of each square in the image can be determined one by one.

[0054] In certain implementations of the present application, the central pixel point of the image is used as a reference to determine multiple areas with different distances from the central pixel point, including: drawing a circle with the central pixel point of the image as the center and ir as the radius; wherein r represents the set radius, i represents the multiple value, i = 1, 2, 3...; for any two adjacent circles in the image, the non-overlapping areas of the two circles are regarded as one area.

[0055] like Figure 3 As shown, it is a schematic diagram of a multi-region image provided by an embodiment of the present application, wherein, with the central pixel point as the center of the circle, a circle with a radius of 1 unit distance is drawn, a circle with a radius of 2 unit distances is drawn, and a circle with a radius of 3 unit distances is drawn. Then, the circle with the central pixel point as the center and a radius of 1 unit distance can be made into region 1, the region with a distance of 1 unit to 2 units from the central pixel point can be made into region 2, and the region with a distance of 2 units to 3 units from the central pixel point can be made into region 3. At the same time, the weight of region 1 is made into 100%, the weight of region 2 is made into 80%, and the weight of region 3 is made into 100%. 60%. This application no longer lists other areas and the corresponding weights of other areas one by one. Then, for any square located in area 1, if a vertex A of the square is located in area 1, the weight of the pixel point located at the vertex A will be 100%. If a vertex B of the square is located in area 2, the weight of the pixel point located at the vertex B will be 80%. According to this logic, the weights of the vertex pixel points of each square in the image can be determined one by one. Furthermore, if it is assumed that the weight of the vertex pixel point in the lower right corner of the square is used as the calculation weight of the current square, the calculation weights of the squares in the image can be determined one by one.

[0056] like Figure 4 The figure shows a schematic diagram of a grid provided in an embodiment of the present application. The vertices of the grid are denoted as a, b, c, and d, respectively. Due to lens distortion, the sides ab, bc, cd, and da of the grid are susceptible to distortion. For example, the sides ab, bc, cd, and da are no longer straight line segments, but are distorted (e.g., convex outward or concave inward). Furthermore, the two horizontal sides (e.g., sides ab and cd) are susceptible to being unequal in length, and the two vertical sides (e.g., sides da and bc) are susceptible to being unequal in length. The problem of the sides ab, bc, cd, and da in the grid abcd no longer being straight line segments can be defined as intensity distortion in the image processing field. The problem of the two horizontal sides or the two vertical sides in the grid abcd being unequal in length can be defined as scaling distortion in the image processing field.

[0057] Regarding the aforementioned problem of image intensity distortion, this application proposes the following solution to determine the image's intensity correction value. With this solution, it is possible to determine whether the image actually has intensity distortion. If this is determined to be the case, the image's intensity parameters can be corrected to restore the image to normal. The solution is as follows:

[0058] In one implementation of the above-mentioned step 103, determining the intensity correction value of the image based on each preset graphic in the image and the calculation weight of each preset graphic includes: determining the average distance error of any square in the image; determining the intensity deviation of the square based on the average distance error of the square and the calculation weight of the square; the average distance error of the square represents the overall distortion degree of the square; and obtaining the intensity correction value of the image based on the intensity deviation of each square in the image.

[0059] In certain embodiments of the present application, determining the average distance error of any one of the squares in the image includes: determining at least two pixel points located on any edge of any one of the squares in the image; determining a fitting straight line passing through the at least two pixel points based on the at least two pixel points; determining the average distance error of the edge based on the distances from each pixel point on the edge to the fitting straight line; and determining the average distance error of the square based on the average distance error of each edge of the square.

[0060] If combined Figure 4 ,set up Figure 4 The square shown is a square in the image to be corrected, so for Figure 4 The grid shown is assumed to be Figure 4 Take the edge da in as an example, traverse at least two pixels located on the edge, such as pixel points p and q are two pixel points located on the edge da, then a straight line can be fitted based on the two pixel points p and q, and the fitted straight line is set as l. Then, for all pixel points on the edge da, there are 100 pixel points, and the distance from each pixel point to the fitted straight line l can be calculated one by one. Then, by averaging the 100 distances, the average distance error of the edge da can be obtained; according to this logic, the other edges of the square abcd can be calculated in turn, that is, the average distance error of the edges ab, bc and cd can be calculated. Finally, by averaging the average distance errors of the four edges, the average distance error of the square abcd can be obtained.

[0061] The above uses a square in an image as an example to illustrate the calculation process of the average distance error of the square; in this way, according to the same logic, the average distance error of all squares in the image can be calculated; further, after obtaining the average distance error of all squares in the image, for any square in the image, by considering the calculation weight of the square, such as multiplying the average distance error of the square by the calculation weight of the square, the product is the intensity deviation of the square; finally, by accumulating the intensity deviations of all squares in the image and averaging the sum of the intensity deviations, the intensity correction value of the image can be obtained.

[0062] Refer to the following formula (1):

[0063]

[0064] Where δ2 represents the intensity correction value of the image, n represents the number of squares in the image, and W i Indicates the calculation weight of each square, Indicates intensity deviation.

[0065] Regarding the issue of image scaling distortion described above, this application proposes the following solution to determine the image scaling correction value. With this solution, it is possible to determine whether the image has actually been distorted. If this is determined to be the case, the image scaling parameters can be corrected to restore the image to normal. The solution is as follows:

[0066] In one implementation of step 103, the scaling correction value includes a horizontal scaling correction value and a vertical scaling correction value. Determining the scaling correction value of the image based on the preset graphics in the image and the calculated weights of the preset graphics includes: determining, for any one of the squares in the image, a first distance error between two sides of the square in the horizontal direction and a second distance error between two sides of the square in the vertical direction; determining a first deviation of the square in the horizontal direction based on the first distance error and the calculated weight of the square, and determining a second deviation of the square in the vertical direction based on the second distance error and the calculated weight of the square; obtaining the horizontal scaling correction value of the image based on the first deviation of each square in the horizontal direction, and obtaining the vertical scaling correction value of the image based on the second deviation of each square in the vertical direction.

[0067] For example, continue to refer to Figure 4For the grid abcd shown, use the right-hand screw rule to arrange the order of the grid vertices in the clockwise direction, and then calculate the distance between each grid vertex in the clockwise direction, which are di0, di1, di2, and di3 respectively. Calculate the horizontal distance error (i.e., the first distance error) ΔH of the grid i =(|d i0 -d i1 |+|d i2 -d i3 |) / 2, and calculate the vertical distance error (i.e., the second distance error) ΔV of the grid i =(|d i1 -d i2 |+|d i0 -d i3 |) / 2, where i is the square number. Using the same logic, we can calculate the horizontal and vertical distance errors for all squares in the image. Finally, by averaging the horizontal distance errors for all squares in the image, we can obtain the horizontal scaling correction value for the image. Similarly, by averaging the vertical distance errors for all squares in the image, we can obtain the vertical scaling correction value for the image.

[0068] Refer to the following formula (2):

[0069]

[0070] Where δ0 represents the horizontal scaling correction value of the image, n represents the number of squares in the image, and W i Indicates the calculation weight of each square, ΔH i Indicates the first distance error.

[0071] Refer to the following formula (3):

[0072]

[0073] Where δ1 represents the vertical scaling correction value of the image, n represents the number of squares in the image, and W i Indicates the calculation weight of each square, ΔH i Indicates the second distance error.

[0074] In one implementation of the above-mentioned step 104, when it is determined that the image does not meet the set distortion requirement based on the intensity correction value and / or the scaling correction value, the initial intensity parameter and / or the initial scaling parameter are adjusted until the image meets the set distortion requirement, including: when it is determined that the image does not meet the set intensity distortion requirement based on the intensity correction value and when it is determined that the image does not meet the set scaling distortion requirement based on the scaling correction value, the initial intensity parameter of the image is adjusted; when the intensity parameter of the image is adjusted to meet the set intensity distortion requirement or the number of iterative adjustments of the intensity parameter meets the set threshold value of the number of iterative adjustments of the intensity parameter, the initial scaling parameter of the image is adjusted until the scaling parameter of the image is adjusted to meet the set scaling distortion requirement or the number of iterative adjustments of the scaling parameter meets the set threshold value of the number of iterative adjustments of the scaling parameter.

[0075] After the processing of step 103, the intensity correction value and the scaling correction value of the image can be determined. Then, in step 104, the intensity correction value is compared with the set intensity distortion requirement, and the scaling correction value is compared with the set scaling distortion requirement. Specifically, the horizontal scaling correction value is compared with the set horizontal scaling distortion requirement, and the vertical scaling correction value is compared with the set vertical scaling distortion requirement, so as to determine whether the image needs to be distorted. This includes the following situations:

[0076] Case 1: Only the intensity correction value does not meet the set intensity distortion requirement.

[0077] For example, in combination with the above example, case 1 corresponds to δ2>T1, where T1 represents the set intensity distortion requirement. That is, if the calculated intensity correction value δ2 of the image is greater than T1, it means that the image does not meet the set intensity distortion requirement.

[0078] Case 2: Only the scaling correction value does not meet the set scaling distortion requirements. This case can be specifically divided into the following cases:

[0079] In case 2.1, only the horizontal scaling correction value does not meet the set horizontal scaling distortion requirement.

[0080] For example, in combination with the above example, case 2.1 corresponds to δ0>T0, where T0 represents the set horizontal scaling distortion requirement. That is, if the calculated horizontal scaling correction value δ0 of the image is greater than T0, it means that the image does not meet the set horizontal scaling distortion requirement.

[0081] Case 2.2: Only the vertical scaling correction value does not meet the set vertical scaling distortion requirement.

[0082] For example, combining the above example, case 2.2 corresponds to δ1>T0, where T0 represents the set vertical scaling distortion requirement. That is, if the calculated vertical scaling correction value δ1 of the image is greater than T0, it means that the image does not meet the set vertical scaling distortion requirement.

[0083] Case 2.3: The horizontal scaling correction value does not meet the set horizontal scaling distortion requirement and the vertical scaling correction value does not meet the set vertical scaling distortion requirement.

[0084] It should be noted that no further examples are given here.

[0085] Case 3: The intensity correction value does not meet the set intensity distortion requirement and the scaling correction value does not meet the set scaling distortion requirement.

[0086] It should be noted that examples will not be given one by one here.

[0087] For the above situation 1, it is only necessary to adjust the intensity parameters of the image so that δ2 continues to converge and meets the set threshold T1. At the same time, the number of iterations is set. When δ2 encounters a situation where it cannot converge during the convergence process, it can be based on the set number of iterations, that is, after the number of iterations is met, the correction of the intensity parameters of the image can be stopped.

[0088] Regarding the above situation 2, this application only describes situation 2.1. For the above situation 2.1, it is only necessary to adjust the horizontal scaling parameters of the image so that δ0 continues to converge and meets the set threshold T0. At the same time, the number of iterations is set. If δ0 encounters a situation where it cannot converge during the convergence process, the correction of the horizontal scaling parameters of the image can be stopped based on the set number of iterations, that is, after the number of iterations is met.

[0089] It should be noted that this application will not elaborate on the processing procedures for the above-mentioned situations 2.2 and 2.3.

[0090] For the above situation 3, if it is determined that the intensity correction value of the image does not meet the set intensity distortion requirement and the scaling correction value of the image does not meet the set scaling distortion requirement, the present application can first adjust the image intensity parameter so that δ2 continues to converge and meets the set threshold value T1; by setting the number of iterations, when δ2 cannot converge to the set threshold value T1, the correction of the intensity parameter will be stopped based on the number of iterations reached; then by adjusting the horizontal scaling parameter and / or the vertical scaling parameter, δ0 and / or δ1 continue to converge; by setting the number of iterations, in the scenario where δ0 and / or δ1 cannot converge, the correction of the scaling parameter will be stopped based on the number of iterations reached, so that the image distortion correction process is completed. In this method, intensity correction is first performed so that the edges of the curved squares in the image are straightened after correction, and then the horizontal and vertical scaling adjustments are made to make the horizontal and vertical edges of the squares consistent in length, so that the overall distortion effect is better.

[0091] Based on the same concept, the embodiment of the present application provides an image distortion correction device, such as Figure 5 , which is a schematic diagram of an image distortion correction device provided by an embodiment of the present application, the device includes an image acquisition unit 501, a calculation weight determination unit 502, a correction value determination unit 503, and a distortion correction unit 504;

[0092] An image acquisition unit 501 is configured to acquire an image of a sample image captured by a lens; the sample image includes a plurality of preset graphics;

[0093] A calculation weight determination unit 502 is configured to determine a calculation weight for each preset graphic in the image according to a preset weight configuration rule, wherein the preset weight configuration rule indicates that a calculation weight for a preset graphic located in a central area of ​​the image is greater than a calculation weight for a preset graphic located in an edge area of ​​the image;

[0094] a correction value determining unit 503, configured to determine an intensity correction value and / or a scaling correction value of the image based on each preset pattern in the image and a calculated weight of each preset pattern;

[0095] The distortion correction unit 504 is configured to adjust the initial intensity parameter and / or the initial scaling parameter until the image meets the set distortion requirement based on the intensity correction value and / or the scaling correction value, when it is determined that the image does not meet the set distortion requirement, and save the correction intensity parameter and / or correction scaling parameter of the image when the set distortion requirement is met; the correction intensity parameter and the correction scaling parameter are used to perform distortion correction on the image captured by the lens.

[0096] Furthermore, for the device, the sample graph is a grid network graph composed of a plurality of squares of equal size, wherein each square has a solid circle at its vertex with the vertex as its center.

[0097] Furthermore, for the device, the calculation weight determination unit 502 is specifically used to: determine a plurality of areas with different distances from the central pixel point of the image based on the central pixel point; for any one of the plurality of areas, set the weights of the vertex pixel points of each grid in the area; wherein the weight of any vertex pixel point is negatively correlated with the distance from the area where the vertex pixel point is located to the central pixel point; for any one of the grids, use the weight of the vertex pixel point of the grid at a preset position as the calculation weight of the grid.

[0098] Furthermore, for the device, the calculation weight determination unit 502 is specifically used to: draw a circle with the central pixel point of the image as the center and ir as the radius; wherein r represents the set radius, i represents the multiple value, i = 1, 2, 3...; for any two adjacent circles in the image, the non-overlapping areas of the two circles are regarded as one area.

[0099] Furthermore, for the device, the correction value determination unit 503 is specifically used to: determine the average distance error of any square in the image; determine the intensity deviation of the square based on the average distance error of the square and the calculation weight of the square; the average distance error of the square represents the overall distortion degree of the square; and obtain the intensity correction value of the image based on the intensity deviation of each square in the image.

[0100] Furthermore, for the device, the correction value determination unit 503 is specifically used to: determine at least two pixel points located on any edge of any square in each square in the image; determine a fitting straight line passing through the at least two pixel points based on the at least two pixel points; determine the average distance error of the edge based on the distances from each pixel point on the edge to the fitting straight line; and determine the average distance error of the square based on the average distance error of each edge of the square.

[0101] Further, for the device, the scaling correction value includes a horizontal scaling correction value and a vertical scaling correction value; the correction value determination unit 503 is specifically used to: determine, for any one of the squares in the image, a first distance error between the two sides of the square in the horizontal direction and a second distance error between the two sides of the square in the vertical direction; determine a first deviation of the square in the horizontal direction based on the first distance error and the calculated weight of the square, and determine a second deviation of the square in the vertical direction based on the second distance error and the calculated weight of the square; obtain the horizontal scaling correction value of the image based on the first deviation of each square in the image in the horizontal direction, and obtain the vertical scaling correction value of the image based on the second deviation of each square in the image in the vertical direction.

[0102] Furthermore, for the device, the distortion correction unit 504 is specifically used to: when it is determined according to the intensity correction value that the image does not meet the set intensity distortion requirement and when it is determined according to the scaling correction value that the image does not meet the set scaling distortion requirement, adjust the initial intensity parameter of the image; when the intensity parameter of the image is adjusted to meet the set intensity distortion requirement or the number of iterative adjustments of the intensity parameter meets the threshold value of the set number of iterative adjustments of the intensity parameter, adjust the scaling parameter of the image until the scaling parameter of the image is adjusted to meet the set scaling distortion requirement or the number of iterative adjustments of the scaling parameter meets the threshold value of the set number of iterative adjustments of the scaling parameter.

[0103] The present application also provides a computing device, which may be a desktop computer, a portable computer, a smart phone, a tablet computer, a personal digital assistant (PDA), etc. The computing device may include a central processing unit (CPU), a memory, input / output devices, etc. The input device may include a keyboard, a mouse, a touch screen, etc. The output device may include a display device, such as a liquid crystal display (LCD), a cathode ray tube (CRT), etc.

[0104] The memory may include a read-only memory (ROM) and a random access memory (RAM), and provides the processor with program instructions and data stored in the memory. In an embodiment of the present application, the memory may be used to store program instructions for a method for correcting image distortion;

[0105] The processor is used to call the program instructions stored in the memory and execute the image distortion correction method according to the obtained program.

[0106] like Figure 6 FIG. 1 is a schematic diagram of a computing device provided in an embodiment of the present application, wherein the computing device includes:

[0107] Processor 601, memory 602, transceiver 603, bus interface 604; wherein the processor 601, memory 602 and transceiver 603 are connected via bus 605;

[0108] The processor 601 is configured to read the program in the memory 602 and execute the above-mentioned image distortion correction method;

[0109] Processor 601 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP. It may also be a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0110] The memory 602 is used to store one or more executable programs and can store data used by the processor 601 when performing operations.

[0111] Specifically, the program may include program code, which includes computer operating instructions. Memory 602 may include volatile memory, such as random-access memory (RAM); memory 602 may also include non-volatile memory, such as flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); and memory 602 may also include a combination of the aforementioned types of memory.

[0112] The memory 602 stores the following elements, executable modules or data structures, or a subset or an extension thereof:

[0113] Operation instructions: include various operation instructions, used to implement various operations.

[0114] Operating system: includes various system programs used to implement various basic services and process hardware-based tasks.

[0115] The bus 605 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0116] The bus interface 604 may be a wired communication access port, a wireless bus interface, or a combination thereof. The wired bus interface may be, for example, an Ethernet interface. The Ethernet interface may be an optical interface, an electrical interface, or a combination thereof. The wireless bus interface may be a WLAN interface.

[0117] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute a method for correcting image distortion.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0122] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0123] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for correcting image distortion, characterized in that: include: Acquire an image obtained by photographing the sample image through a lens; A plurality of preset graphics are distributed in the sample graph; Determining the calculation weight of each preset graphic in the image according to a preset weight configuration rule; The preset weight configuration rule indicates that the calculation weight of the preset graphics located in the central area of ​​the image is greater than the calculation weight of the preset graphics located in the edge area of ​​the image; Determining, for any one of the squares in the image, an intensity deviation of the square based on an average distance error of the square and a calculation weight of the square; obtaining an intensity correction value of the image based on the intensity deviation of each square in the image; wherein the average distance error of the square represents an overall distortion degree of the square; The scaling correction value includes a horizontal scaling correction value and a vertical scaling correction value; for any one of the squares in the image, a first distance error between two sides of the square in the horizontal direction and a second distance error between two sides of the square in the vertical direction are determined; a first deviation of the square in the horizontal direction is determined based on the first distance error and a calculated weight of the square, and a second deviation of the square in the vertical direction is determined based on the second distance error and the calculated weight of the square; a horizontal scaling correction value of the image is obtained based on the first deviation, and a vertical scaling correction value of the image is obtained based on the second deviation; When it is determined based on the intensity correction value and / or the scaling correction value that the image does not meet the set distortion requirement, the initial intensity parameter and / or the initial scaling parameter are adjusted until the image meets the set distortion requirement, and the correction intensity parameter and / or correction scaling parameter of the image when the set distortion requirement is met are saved; the correction intensity parameter and the correction scaling parameter are used to perform distortion correction on the image captured by the lens.

2. The method according to claim 1, wherein The sample graph is a grid network graph consisting of a plurality of grids of equal size, wherein each grid has a solid circle at its vertex with the vertex as its center.

3. The method according to claim 2, wherein The step of determining the calculation weight of each preset graphic in the image according to a preset weight configuration rule includes: Taking a central pixel point of the image as a reference, determining a plurality of regions with different distances from the central pixel point; For any one of the multiple areas, setting a weight for each vertex pixel point of each grid in the area; wherein the weight of any vertex pixel point is negatively correlated with the distance from the area where the vertex pixel point is located to the center pixel point; For any one of the squares, the weight of the vertex pixel of the square at a preset position is used as the calculation weight of the square.

4. The method according to claim 3, wherein The determining of a plurality of regions with different distances from the central pixel point of the image based on the central pixel point includes: Draw a circle with the central pixel of the image as the center and ir as the radius; where r represents the set radius and i represents the multiplication value, i = 1, 2, 3, etc.; For any two adjacent circles in the image, non-overlapping areas of the two circles are regarded as one area.

5. The method according to claim 1, wherein include: For any edge of any square in each square in the image, determine at least two pixel points located on the edge; Determining, based on the at least two pixel points, a fitting straight line passing through the at least two pixel points; Determine the average distance error of the edge according to the distances from each pixel point on the edge to the fitted straight line; The average distance error of the square is determined according to the average distance error of each side of the square.

6. The method according to claim 2, wherein When it is determined that the image does not meet the set distortion requirement based on the intensity correction value and / or the scaling correction value, adjusting the initial intensity parameter and / or the initial scaling parameter until the image meets the set distortion requirement includes: When it is determined according to the intensity correction value that the image does not meet the set intensity distortion requirement and when it is determined according to the scale correction value that the image does not meet the set scale distortion requirement, iteratively adjusting the initial intensity parameter of the image; When the intensity parameter of the image is adjusted to meet the set intensity distortion requirement or the number of iterative adjustments of the intensity parameter meets the threshold of the set number of iterative adjustments of the intensity parameter, the initial scaling parameter of the image is adjusted until the scaling parameter of the image is adjusted to meet the set scaling distortion requirement or the number of iterative adjustments of the scaling parameter meets the threshold of the set number of iterative adjustments of the scaling parameter.

7. A device for correcting image distortion, characterized in that: include: An image acquisition unit, configured to acquire an image obtained by photographing the sample image through a lens; A plurality of preset graphics are distributed in the sample graph; a calculation weight determination unit, configured to determine the calculation weight of each preset graphic in the image according to a preset weight configuration rule; The preset weight configuration rule indicates that the calculation weight of the preset graphics located in the central area of ​​the image is greater than the calculation weight of the preset graphics located in the edge area of ​​the image; a correction value determining unit, configured to determine, based on any one of the squares in the image, an intensity deviation of the square according to an average distance error of the square and a calculation weight of the square; and obtain an intensity correction value of the image according to the intensity deviation of each square in the image; wherein the average distance error of the square represents an overall degree of distortion of the square; The scaling correction value includes a horizontal scaling correction value and a vertical scaling correction value; for any one of the squares in the image, a first distance error between two sides of the square in the horizontal direction and a second distance error between two sides of the square in the vertical direction are determined; a first deviation of the square in the horizontal direction is determined based on the first distance error and a calculated weight of the square, and a second deviation of the square in the vertical direction is determined based on the second distance error and the calculated weight of the square; a horizontal scaling correction value of the image is obtained based on the first deviation, and a vertical scaling correction value of the image is obtained based on the second deviation; A distortion correction unit is configured to adjust, based on the intensity correction value and / or the scaling correction value, an initial intensity parameter and / or an initial scaling parameter until the image meets the set distortion requirement when it is determined that the image does not meet the set distortion requirement, and to save the correction intensity parameter and / or correction scaling parameter of the image when the set distortion requirement is met; the correction intensity parameter and the correction scaling parameter are used to perform distortion correction on the image captured by the lens.

8. A computer device, characterized in that: include: memory for storing computer programs; A processor is configured to call a computer program stored in the memory and execute the method according to any one of claims 1 to 6 according to the obtained program.

9. A computer-readable storage medium, characterized in that The storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

Images