High-precision sub-pixel edge caliper search method, electronic device and storage medium

Abstract

The embodiment of the application relates to the technical field of digital image analysis, and particularly relates to a high-precision sub-pixel edge caliper search method, electronic equipment and a storage medium. The method comprises the following steps: obtaining image data of a sampling image to obtain initial image analysis data; performing affine transformation on a unit caliper to obtain affine caliper data; obtaining a first gradient transformation curve of a projected gray scale transformation curve; determining the position of a first extreme point according to the first gradient transformation curve to obtain the actual rough position of an edge position; performing projection and gradient transformation processing on a two-dimensional array and calculating to obtain the position of a second extreme point and determine the edge position of the image in the unit caliper. The method has high precision, requires less time during execution, improves the edge position determination speed in image analysis under the premise of ensuring precision, can be applied to high-precision and high-timeliness application scenarios, and has wide applicability.

Description

Technical Field

[0001] This invention relates to the technical field of digital image analysis, specifically to a high-precision subpixel edge caliper search method, electronic device, and storage medium. Background Technology

[0002] In the field of digital image analysis, calipers are commonly used to precisely locate the edge information of an image. A caliper is a rectangular area with an angle, including both the projection direction and the scanning direction. During precise localization, the caliper extracts a specific region of interest from the entire image based on the position of its rectangular area for image analysis.

[0003] Current image analysis typically employs multiple affine transformations to determine image edge locations. While this method is efficient, it introduces an error of 0.5-1 pixel, resulting in imprecise edge location X, making it unsuitable for high-precision positioning applications. On the other hand, some methods directly perform sub-pixel calculations on calipers to obtain image edge locations, but this approach is slow and inefficient, failing to meet the demands of real-time applications. Summary of the Invention

[0004] To address the technical problems of low accuracy, slow analysis speed, and low efficiency that often occur in image analysis, this invention provides a high-precision subpixel edge caliper search method, electronic device, and storage medium.

[0005] According to one aspect of the present invention, the present invention provides a high-precision sub-pixel edge caliper search method, the method comprising:

[0006] Acquire image data from the sampled image, extract a specific region of interest, and obtain initial data for image analysis;

[0007] Using a caliper from the initial data of image analysis as a unit, perform an affine transformation on the unit caliper to obtain the affine caliper data.

[0008] The region of interest in the affine caliper data is sequentially projected and gradient transformed to obtain the first gradient transformation curve of the grayscale transformation curve after projection.

[0009] The position of the first extreme point is determined based on the first gradient transformation curve, and the actual approximate position of the edge is obtained based on the inverse transformation relationship of the affine transformation.

[0010] Multiple grid points are generated at a first set distance in the area around the actual rough location. Sub-pixel values ​​are calculated for the grid points and stored in a two-dimensional array.

[0011] The two-dimensional array is projected and gradient transformed to obtain the second gradient transformation curve. The extreme value is calculated based on the second gradient transformation curve to obtain the position of the second extreme point. The edge position of the image in the unit caliper is determined based on the sub-pixel position corresponding to the extreme point.

[0012] In one alternative approach, acquiring image data from the sampled image, extracting a specific region of interest, and obtaining initial image data includes:

[0013] Obtain the initial image data of the sampled image;

[0014] The location of blurred edges in the sampled image is determined based on the initial image data;

[0015] Based on the location of the blurred edges in the sampled image, a specific region of interest is extracted from the initial image data using a caliper tool to obtain the initial data for image analysis.

[0016] In one alternative approach, the affine transformation of the unit caliper to obtain the affine caliper data includes:

[0017] By converting the scanning direction of the region of interest to horizontal as an affine condition, the affine relation is obtained;

[0018] The affine transformation of the unit caliper is performed based on the affine relation to obtain the affine caliper data.

[0019] In one optional approach, the process of sequentially projecting and gradient transforming the region of interest in the affine caliper data to obtain the first gradient transform curve of the projected grayscale curve includes:

[0020] Along the projection direction of the caliper tool, the region of interest in the affine caliper data is averaged and calculated to obtain the first grayscale transformation curve.

[0021] The first gradient transformation curve is obtained by taking the first derivative of the first grayscale transformation curve.

[0022] In one alternative approach, determining the position of the first extreme point based on the first gradient transformation curve, and obtaining the actual approximate position of the edge position based on the inverse affine transformation relationship, includes:

[0023] Find the extreme values ​​of the first gradient transformation curve to obtain the location of the first extreme point;

[0024] Based on the affine relations of affine transformation, the inverse affine transformation relations are obtained;

[0025] The inverse transformation relationship of affine transformation is used to perform an inverse transformation on the index position to obtain the position corresponding to the first extreme point, which serves as the actual coarse position of the edge position.

[0026] In one alternative approach, generating multiple grid points at first predetermined distances in the region surrounding the actual coarse location, calculating sub-pixel values ​​for the grid points, and storing them in a two-dimensional array includes:

[0027] Multiple sub-pixel grid points are generated at a first set distance in an area of ​​3x3, 5x5, or 7x7 at an actual approximate location.

[0028] The sub-pixel values ​​are calculated one by one for each sub-pixel grid dot matrix, and the sub-pixel values ​​are stored in a two-dimensional array.

[0029] In one alternative approach, the first set distance is any one of 0.1mm, 0.2mm, 0.3mm, 0.4mm, and 0.5mm.

[0030] In one optional approach, the projection and gradient transformation of the two-dimensional array to obtain a second gradient transformation curve, the extreme value calculation based on the second gradient transformation curve to obtain the position of the second extreme point, and the determination of the edge position of the image in the unit caliper based on the sub-pixel position corresponding to the extreme point include:

[0031] Projecting the two-dimensional array vertically yields the second grayscale transformation curve;

[0032] The second gradient transformation curve is obtained by taking the first derivative of the second grayscale transformation curve.

[0033] Find the extreme values ​​of the second gradient transform curve to obtain the location of the second extreme point;

[0034] The sub-pixel position corresponding to the second extreme point is obtained by transforming the position of the second extreme point, and the edge position of the image in the unit caliper is determined.

[0035] According to a second aspect of the present invention, an electronic device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0036] The memory is used to store at least one executable instruction that causes the processor to perform operations such as the high-precision subpixel edge caliper search method described above.

[0037] According to a third aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction, which, when executed on an electronic device, causes the electronic device to perform the operation of the high-precision subpixel edge caliper search method described above.

[0038] This invention first uses affine transformation to preliminarily determine the edge position of the image acquired by calipers, and then uses this rough position as the base point to generate a regional grid. Sub-pixel calculation is performed on the grid to obtain the precise edge position. This method has high accuracy and requires less time during execution. While ensuring accuracy, it improves the speed of edge position determination in image analysis, and can be applied to high-precision and high-time-efficiency application scenarios, with wide applicability. Attached Figure Description

[0039] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0040] Figure 1 A flowchart illustrating the structure of the high-precision subpixel edge caliper search method provided in Embodiment 1 of the present invention is shown.

[0041] Figure 2 The flowchart showing the specific structure of step 101 of the search method provided in Embodiment 1 of the present invention is shown.

[0042] Figure 3 The flowchart showing the specific structure of step 102 of the search method provided in Embodiment 1 of the present invention is shown.

[0043] Figure 4 The flowchart showing the specific structure of step 103 of the search method provided in Embodiment 1 of the present invention is shown.

[0044] Figure 5 The flowchart showing the specific structure of step 104 of the search method provided in Embodiment 1 of the present invention is shown.

[0045] Figure 6 A flowchart illustrating the specific structure of step 105 of the search method provided in Embodiment 1 of the present invention is shown.

[0046] Figure 7 A flowchart illustrating the specific structure of step 106 of the search method provided in Embodiment 1 of the present invention is shown.

[0047] Figure 8 A flowchart of a specific implementation process in Embodiment 1 of the present invention is shown.

[0048] Figure 9 A structural block diagram of the electronic device provided in Embodiment 2 of the present invention is shown. Detailed Implementation

[0049] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0050] Example 1:

[0051] Figure 1 A flowchart illustrating the structure of the high-precision subpixel edge caliper search method provided in Embodiment 1 is shown.

[0052] Please see Figure 1 This embodiment provides a high-precision sub-pixel edge caliper search method, which includes:

[0053] 101. Obtain image data from the sampled image, extract the specific region of interest, and obtain the initial data for image analysis.

[0054] The sampled image is the image to be precisely located. This image information is acquired by image acquisition devices such as cameras and output to electronic devices through signal transmission, where it is obtained by the processor in the electronic devices.

[0055] The caliper tool is an angled rectangular area that includes both the projection and scanning directions. Specifically, during fine positioning, the caliper tool extracts a specific region of interest from the entire image based on the position of its rectangular area for image analysis.

[0056] 102. Using a caliper from the initial data of image analysis as a unit, perform an affine transformation on the unit caliper to obtain the affine caliper data.

[0057] The caliper tool includes multiple calipers, and the image is extracted and processed using calipers as units. In this embodiment, the unit caliper does not refer to just one caliper. Since multiple calipers may be processed simultaneously during the process, the unit caliper in this embodiment refers to a unit composed of one or more calipers, with a single caliper as the base.

[0058] Affine transformation refers to changing the angle of a caliper unit. Since the image edges are not straight lines during image analysis, the caliper has multiple angles during acquisition. To facilitate processing, affine transformation is used to adjust it to a horizontal position before processing.

[0059] 103. The region of interest in the affine caliper data is sequentially projected and gradient transformed to obtain the first gradient transformation curve of the grayscale transformation curve after projection.

[0060] The projection process uses average projection to calculate the grayscale curve of the region of interest. Gradient transformation is then performed on the grayscale curve and used to determine subsequent poles.

[0061] 104. Determine the position of the first extreme point based on the first gradient transformation curve, and obtain the actual rough position of the edge position based on the inverse transformation relationship of the affine transformation.

[0062] After the position is determined following the affine transformation, the actual coarse position is obtained. The actual coarse position has a pixel value deviation of 0.5-1 points. In order to obtain a more accurate position, the coarse position is further processed.

[0063] 105. Generate multiple grid points in the area around the actual rough location at a first set distance, calculate the sub-pixel values ​​of the grid points, and store them in a two-dimensional array.

[0064] The area surrounding the actual coarse location refers to the region around the actual coarse location as a base point. For example, a 3x3 network matrix is ​​recommended with the actual coarse location as the center. A two-dimensional array is used to store sub-pixel values ​​for subsequent calculations.

[0065] 106. Project and gradient transform the two-dimensional array to obtain the second gradient transform curve. Calculate the extreme value based on the second gradient transform curve to obtain the position of the second extreme point. Determine the edge position of the image in the unit caliper based on the sub-pixel position corresponding to the extreme point.

[0066] Among them, the two-dimensional array is used for projection and gradient transformation processing with only a small number of grid points. It does not require the generation of a large number of sub-pixel grid points throughout the entire region of interest, which can effectively reduce processing time and computing power and ensure the high timeliness of image analysis.

[0067] The advantage of this embodiment is that the method first determines the edge position of the image acquired by the caliper through affine transformation, and then uses the rough position as the base point to generate a regional grid. Sub-pixel calculation is performed on the grid to obtain the precise edge position. This method has high accuracy and requires less time during execution. While ensuring accuracy, it improves the speed of edge position determination in image analysis, and can be applied to high-precision and high-time-efficiency application scenarios, with wide applicability.

[0068] Figure 2 The specific structural flowchart of step 101 in this embodiment is shown.

[0069] Please see Figure 2 Step 101 in this embodiment specifically includes the following steps:

[0070] 201. Obtain the initial image data of the sampled image.

[0071] 202. Determine the location of the blurred edges in the sampled image based on the initial image data.

[0072] 203. Based on the location of the blurred edges in the sampled image, a specific region of interest is extracted from the initial image data using a caliper tool to obtain the initial data for image analysis.

[0073] Among them, the blurred edge position refers to the edge position of the image information, which can be initially determined by the change in gray value between the two.

[0074] Figure 3 The specific structural flowchart of step 102 in this embodiment is shown.

[0075] Please see Figure 3 Step 102 in this embodiment specifically includes the following steps:

[0076] 301. By converting the scanning direction of the region of interest to horizontal as an affine condition, the affine relation is obtained.

[0077] 302. Perform an affine transformation on the unit caliper according to the affine relation to obtain the affine caliper data.

[0078] Here, the affine relation represents the relative position between the position of the caliper element before affine transformation and the position of the caliper element after affine transformation. In the subsequent inverse affine transformation process, the inverse transformation relation can be derived from this affine relation.

[0079] Figure 4 The specific structural flowchart of step 103 in this embodiment is shown.

[0080] Please see Figure 4 Step 103 in this embodiment specifically includes the following steps:

[0081] 401. Along the projection direction of the caliper tool, perform average projection calculation on the region of interest in the affine caliper data to obtain the first grayscale transformation curve.

[0082] 402. Take the first derivative of the first grayscale transformation curve to obtain the first gradient transformation curve.

[0083] The projection direction of the caliper tool is perpendicular to its scanning direction.

[0084] Figure 5 The specific structural flowchart of step 104 in this embodiment is shown.

[0085] Please see Figure 5 Step 104 in this embodiment specifically includes the following steps:

[0086] 501. Find the extreme values ​​of the first gradient transformation curve to obtain the location of the first extreme point.

[0087] 502. Based on the affine relationship of affine transformation, the inverse affine transformation relationship can be obtained.

[0088] 503. The inverse transformation relationship of affine transformation: Perform an inverse transformation on the index position to obtain the position corresponding to the first extreme point, which is used as the actual rough position of the edge position.

[0089] The index position is the starting position for defining the element caliper before the affine transformation.

[0090] Figure 6 The specific structural flowchart of step 105 in this embodiment is shown.

[0091] Please see Figure 6 Step 105 in this embodiment specifically includes the following steps:

[0092] 601. Generate multiple sub-pixel grid points at a first set distance in an area of ​​3x3, 5x5, or 7x7 at an actual approximate location.

[0093] 602. Calculate the sub-pixel value for each sub-pixel grid dot matrix and store the sub-pixel value in a two-dimensional array.

[0094] The first set distance is any one of 0.1mm, 0.2mm, 0.3mm, 0.4mm, and 0.5mm.

[0095] Figure 7 The specific structural flowchart of step 106 in this embodiment is shown.

[0096] Please see Figure 7 Step 106 in this embodiment specifically includes the following steps:

[0097] 701. Project the two-dimensional array vertically to obtain the second grayscale transformation curve.

[0098] 702. Take the first derivative of the second grayscale transformation curve to obtain the second gradient transformation curve.

[0099] 703. Find the extreme values ​​of the second gradient transformation curve to obtain the location of the second extreme point.

[0100] 704. Based on the position transformation of the second extreme point, obtain the sub-pixel position corresponding to the second extreme point, and determine the edge position of the image in the unit caliper.

[0101] The calculation of the second extreme point is similar to the calculation process in steps 103 and 104, except that its initial data is a two-dimensional array.

[0102] To provide a better user experience, a specific implementation process is provided. Figure 8 A flowchart of the specific implementation process in this embodiment is provided.

[0103] Please see Figure 8 The implementation process is as follows:

[0104] ①. Apply an affine transformation T1 to the unit caliper to change the scanning direction of the region of interest to horizontal;

[0105] ②. Along the projection direction of the caliper tool, perform average projection calculation on the region of interest to obtain the first grayscale transformation curve Cv1;

[0106] ③. Calculate the first derivative of the first grayscale transformation curve to obtain the first gradient transformation curve Cg1;

[0107] ④. Find the extreme values ​​of the first gradient transformation curve and obtain the position E1^ of the first extreme point;

[0108] ⑤. Use the inverse affine transformation T1^ to perform an inverse transformation on the index position to obtain the approximate position E1 of the actual edge position;

[0109] ⑥. Generate an equidistant subpixel grid array ArrayGp1 in a 3x3, 5x5, or 7x7 region around the approximate position E1;

[0110] ⑦. Calculate the subpixel values ​​for each grid dot matrix ArrayGp1 and store the subpixel values ​​in the two-dimensional array ArrayGv1;

[0111] ⑧. Project the two-dimensional array ArrayGv1 vertically to obtain the second grayscale transformation curve Cv2;

[0112] 9. Take the first derivative of the grayscale curve Gv4 to obtain the second gradient transformation curve Cg2;

[0113] ⑩. Find the extreme value of the second gradient transformation curve Cg2 to obtain the second extreme value point E2^, and obtain the corresponding sub-pixel position E2 of the extreme value, which is the actual edge position.

[0114] Example 2:

[0115] Figure 9 A structural block diagram of the electronic device provided in Embodiment 2 of the present invention is shown.

[0116] Please see Figure 9This embodiment provides an electronic device, including: a processor 801, a memory 802, a communication interface 803 and a communication bus 804, wherein the processor 801, the memory 802 and the communication interface 803 communicate with each other through the communication bus 804;

[0117] Memory 802 stores at least one executable instruction that causes processor 801 to perform the operation of a high-precision subpixel edge caliper search method. The high-precision subpixel edge caliper search method includes the following steps:

[0118] Acquire image data from the sampled image, extract a specific region of interest, and obtain initial data for image analysis;

[0119] Using a caliper from the initial data of image analysis as a unit, perform an affine transformation on the unit caliper to obtain the affine caliper data.

[0120] The region of interest in the affine caliper data is sequentially projected and gradient transformed to obtain the first gradient transformation curve of the grayscale transformation curve after projection.

[0121] The position of the first extreme point is determined based on the first gradient transformation curve, and the actual approximate position of the edge is obtained based on the inverse transformation relationship of the affine transformation.

[0122] Multiple grid points are generated at a first set distance in the area around the actual rough location. Sub-pixel values ​​are calculated for the grid points and stored in a two-dimensional array.

[0123] The two-dimensional array is projected and gradient transformed to obtain the second gradient transformation curve. The extreme value is calculated based on the second gradient transformation curve to obtain the position of the second extreme point. The edge position of the image in the unit caliper is determined based on the sub-pixel position corresponding to the extreme point.

[0124] Specifically, the high-precision subpixel edge caliper search method in this embodiment adopts the search method in Embodiment 1.

[0125] Example 3:

[0126] This embodiment provides a computer-readable storage medium storing at least one executable instruction. When executed on an electronic device, the executable instruction causes the electronic device to perform an operation of a high-precision subpixel edge caliper search method. The high-precision subpixel edge caliper search method includes the following steps:

[0127] Acquire image data from the sampled image, extract a specific region of interest, and obtain initial data for image analysis;

[0128] Using a caliper from the initial data of image analysis as a unit, perform an affine transformation on the unit caliper to obtain the affine caliper data.

[0129] The region of interest in the affine caliper data is sequentially projected and gradient transformed to obtain the first gradient transformation curve of the grayscale transformation curve after projection.

[0130] The position of the first extreme point is determined based on the first gradient transformation curve, and the actual approximate position of the edge is obtained based on the inverse transformation relationship of the affine transformation.

[0131] Multiple grid points are generated at a first set distance in the area around the actual rough location. Sub-pixel values ​​are calculated for the grid points and stored in a two-dimensional array.

[0132] The two-dimensional array is projected and gradient transformed to obtain the second gradient transformation curve. The extreme value is calculated based on the second gradient transformation curve to obtain the position of the second extreme point. The edge position of the image in the unit caliper is determined based on the sub-pixel position corresponding to the extreme point.

[0133] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A high-precision sub-pixel edge caliper search method, characterized in that, The method includes: Acquire image data from the sampled image, extract a specific region of interest, and obtain initial data for image analysis; Using a caliper from the initial data of image analysis as a unit, perform an affine transformation on the unit caliper to obtain the affine caliper data. The region of interest in the affine caliper data is sequentially projected and gradient transformed to obtain the first gradient transformation curve of the grayscale transformation curve after projection. The position of the first extreme point is determined based on the first gradient transformation curve, and the actual approximate position of the edge is obtained based on the inverse transformation relationship of the affine transformation. Multiple grid points are generated at a first set distance in the area around the actual rough location. Sub-pixel values ​​are calculated for the grid points and stored in a two-dimensional array. Projecting and gradient transformation of the two-dimensional array yields the second gradient transformation curve. Extreme values ​​are calculated based on the second gradient transformation curve to obtain the position of the second extreme point. The edge position of the image in the unit caliper is determined based on the sub-pixel position corresponding to the extreme point. Wherein: the projection and gradient transformation of the two-dimensional array to obtain the second gradient transformation curve, the extreme value calculation based on the second gradient transformation curve to obtain the position of the second extreme point, and the determination of the edge position of the image in the unit caliper based on the sub-pixel position corresponding to the extreme point include: Projecting the two-dimensional array vertically yields the second grayscale transformation curve; The second gradient transformation curve is obtained by taking the first derivative of the second grayscale transformation curve. Find the extreme values ​​of the second gradient transform curve to obtain the location of the second extreme point; The sub-pixel position corresponding to the second extreme point is obtained by transforming the position of the second extreme point, and the edge position of the image in the unit caliper is determined.

2. The high-precision sub-pixel edge caliper search method according to claim 1, characterized in that, The process of acquiring image data from the sampled image and extracting a specific region of interest to obtain initial image data includes: Obtain the initial image data of the sampled image; The location of blurred edges in the sampled image is determined based on the initial image data; Based on the location of the blurred edges in the sampled image, a specific region of interest is extracted from the initial image data using a caliper tool to obtain the initial data for image analysis.

3. The high-precision sub-pixel edge caliper search method according to claim 1, characterized in that, The affine transformation of the unit caliper to obtain the affine caliper data includes: By converting the scanning direction of the region of interest to horizontal as an affine condition, the affine relation is obtained; The affine transformation of the unit caliper is performed based on the affine relation to obtain the affine caliper data.

4. The high-precision sub-pixel edge caliper search method according to claim 1, characterized in that, The process of sequentially projecting and gradient transforming the region of interest in the affine caliper data to obtain the first gradient transformation curve of the projected grayscale curve includes: Along the projection direction of the caliper tool, the region of interest in the affine caliper data is averaged and calculated to obtain the first grayscale transformation curve. The first gradient transformation curve is obtained by taking the first derivative of the first grayscale transformation curve.

5. The high-precision sub-pixel edge caliper search method according to claim 1, characterized in that, The process of determining the position of the first extreme point based on the first gradient transformation curve, and obtaining the approximate actual position of the edge position based on the inverse affine transformation relationship, includes: Find the extreme values ​​of the first gradient transformation curve to obtain the location of the first extreme point; Based on the affine relations of affine transformation, the inverse affine transformation relations are obtained; The inverse transformation relationship of affine transformation is used to perform an inverse transformation on the index position to obtain the position corresponding to the first extreme point, which serves as the actual coarse position of the edge position.

6. The high-precision sub-pixel edge caliper search method according to claim 1, characterized in that, The step of generating multiple grid points at a first predetermined distance in the area surrounding the actual approximate location, calculating sub-pixel values ​​for the grid points, and storing them in a two-dimensional array includes: Multiple sub-pixel grid points are generated at a first set distance in an area of ​​3x3, 5x5, or 7x7 at an actual approximate location. The sub-pixel values ​​are calculated one by one for each sub-pixel grid dot matrix, and the sub-pixel values ​​are stored in a two-dimensional array.

7. The high-precision sub-pixel edge caliper search method according to claim 6, characterized in that, The first set distance is any one of 0.1mm, 0.2mm, 0.3mm, 0.4mm, and 0.5mm.

8. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the high-precision subpixel edge caliper search method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the electronic device, causes the electronic device to perform the operation of the high-precision subpixel edge caliper search method as described in any one of claims 1-7.

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