Distortion detection methods, systems, electronic devices, and storage media

Abstract

This application relates to a distortion detection method, system, electronic device, and storage medium. It involves acquiring a test image obtained from a reference device displayed on the device under test (DUT), the test image containing an array of multiple marker points; determining the positions of the multiple marker points in a reference coordinate system; determining target marker points corresponding to the target field of view based on the positions of the multiple marker points in the reference coordinate system; and calculating the degree of distortion of the target field of view based on the target marker points. This solves the problem in related technologies that it is impossible to detect distortion under different fields of view at once.

Description

Technical Field

[0001] This application relates to the field of image quality inspection, and in particular to a distortion detection method, system, electronic device, and storage medium. Background Technology

[0002] The presence of display distortion in XR glasses can lead to image distortion and visual discomfort, affecting the immersive experience of using XR glasses. Therefore, it is necessary to detect display distortion in XR glasses. As a product of high coupling between optics and software, the optical design of the glasses introduces image distortion, which the software compensates for by performing anti-distortion processing on the displayed image. Generally speaking, the distortion is greater closer to the edge of the field of view, and the amount of distortion varies at different fields of view. Therefore, a method is needed to detect image distortion at different field of view sizes and locations.

[0003] Traditional distortion detection methods typically use white-field images or nine-dot images as markers. These two types of marker images operate on similar principles: extracting the positional information of the white-field edges and corners or the nine-dot center to calculate distortion for a single field of view. However, this method is limited in that it can only detect distortion for one field of view at a time. To calculate distortion for other field of view sizes, the size of the white-field image or the position of points in the nine-dot image needs to be adjusted, increasing the number of measurements and image cropping operations and reducing detection efficiency.

[0004] There is currently no effective solution to the problem that related technologies cannot detect distortion under different fields of view at once. Summary of the Invention

[0005] This embodiment provides a distortion detection method, system, electronic device, and storage medium to solve the problem in related technologies that it is impossible to detect distortion under different fields of view at once.

[0006] Firstly, this embodiment provides a distortion detection method, including:

[0007] The test image obtained by the reference device displayed by the device under test is acquired, and the test image contains an array of multiple marker points;

[0008] Determine the positions of the plurality of marker points in the reference coordinate system;

[0009] Based on the positions of the plurality of marker points in the reference coordinate system, target marker points corresponding to the target field of view are determined, and the degree of distortion of the target field of view is calculated based on the target marker points.

[0010] In some embodiments, determining the positions of the plurality of marker points in a reference coordinate system includes:

[0011] A reference point is determined from the plurality of marker points, and the reference point is numbered.

[0012] Starting from the reference point and based on the adjacency relationship between the multiple marker points, the multiple marker points are sequentially numbered.

[0013] In some embodiments, starting from the reference point and based on the adjacency relationships between the plurality of marker points, the plurality of marker points are sequentially numbered, including:

[0014] Obtain the current first identifier point and its number, wherein the current first identifier point is in the same row or column as the reference point;

[0015] In the first arrangement direction of the array, determine the neighboring points that are directly adjacent and / or indirectly adjacent to the current first identifier point, and number the neighboring points according to the adjacency relationship between the neighboring points and the current first identifier point;

[0016] In the second arrangement direction of the array, a marker point directly adjacent to the current first marker point is determined, and the marker point directly adjacent to the current first marker point is numbered and used as the next first marker point.

[0017] In some embodiments, determining a reference point among the plurality of marker points includes:

[0018] Three reference points are determined from the plurality of marker points, and the reference points and the remaining marker points have different attributes;

[0019] Identify the common neighbor points that are directly adjacent to all three reference points, and use these common neighbor points as the reference points.

[0020] In some embodiments, after determining three reference points from the plurality of marker points, the method further includes:

[0021] The tilt of the test image is calculated based on the three reference points;

[0022] The test image is tilted according to the degree of tilt.

[0023] In some embodiments, after acquiring the test image obtained by the reference device displayed by the device under test, the method further includes:

[0024] Remove markers whose size exceeds a preset range from the plurality of markers.

[0025] In some embodiments, determining the target marker point corresponding to the target field of view based on the position of the plurality of marker points in the reference coordinate system includes:

[0026] Determine the field coefficients corresponding to the target field of view size;

[0027] The target marker points are determined based on the field of view coefficients. The target marker points include points located directly above and below the target field of view, points directly to the left and right, and vertices on the diagonal.

[0028] In some embodiments, calculating the degree of distortion of the target field of view based on the target marker points includes:

[0029] Based on the target marker, calculate the degree of vertical distortion and / or horizontal distortion of the target field of view.

[0030] In some embodiments, the method includes: determining the lengths of a first boundary and a second boundary on the left and right sides of the target field of view based on vertices located on the diagonal of the target field of view;

[0031] Determine the vertical diameter length inside the target field of view based on the points located directly above and below the target field of view;

[0032] Calculate the degree of vertical distortion of the target field of view based on the first boundary length, the second boundary length, and the vertical diameter; and / or,

[0033] Based on the vertices located on the diagonal of the target field of view, determine the lengths of the third boundary and the fourth boundary on the upper and lower sides of the target field of view;

[0034] Determine the horizontal diameter of the target field of view based on the points located directly to the left and right of the target field of view;

[0035] The degree of horizontal distortion of the target field of view is calculated based on the length of the third boundary, the length of the fourth boundary, and the horizontal diameter.

[0036] Secondly, this embodiment provides a distortion detection system, including: a detection device, a device under test, and a reference device displayed by the device under test; wherein,

[0037] The reference device is used to capture test images for the device under test, and the reference device is provided with an array of multiple marker points;

[0038] The detection equipment is used to perform the distortion detection method described in the first aspect above.

[0039] In some embodiments, the plurality of marker points includes three reference points, which have different attributes from the remaining marker points.

[0040] In some embodiments, the field of view of the detection device is larger than the field of view of the device under test.

[0041] In some embodiments, the distortion detection system further includes a motion mechanism connected to the detection device and / or the device under test, for adjusting the position of the detection device and / or the device under test.

[0042] Thirdly, this embodiment provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the distortion detection method described in the first aspect above.

[0043] Fourthly, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the distortion detection method described in the first aspect above.

[0044] The aforementioned distortion detection method, system, electronic device, and storage medium only require acquiring the test image obtained by the reference device displayed on the device under test once. It can then select target markers corresponding to one or more fields of view from the dot matrix of the test image, thereby detecting distortions in one or more fields of view. This solves the problem of not being able to detect distortions in different fields of view at once. Compared with the traditional method of acquiring images of different fields of view one by one for detection, it greatly improves detection efficiency and accuracy.

[0045] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0046] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0047] Figure 1 This is a hardware structure block diagram of the terminal of the distortion detection method in one embodiment;

[0048] Figure 2 Here is a flowchart of a distortion detection method in one embodiment;

[0049] Figure 3 This is a schematic diagram of a reference device in one embodiment;

[0050] Figure 4 This is a schematic diagram of a target marker point corresponding to a viewpoint in one embodiment;

[0051] Figure 5This is a flowchart of a vertical distortion calculation method in one embodiment;

[0052] Figure 6 This is a flowchart of a horizontal distortion calculation method in one embodiment;

[0053] Figure 7 This is a flowchart of point sorting in one embodiment;

[0054] Figure 8 A flowchart for point sorting in another embodiment;

[0055] Figure 9 This is a schematic diagram of the distortion detection system in one embodiment;

[0056] Figure 10 This is a schematic diagram of the distortion detection system in another embodiment;

[0057] Figure 11 This is a flowchart of the distortion detection system in one embodiment;

[0058] Figure 12 Here is a flowchart of the distortion detection system in another embodiment;

[0059] Figure 13 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0060] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0061] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning as understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these,” used in this application, do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to such processes, methods, products, or devices. The terms “connected,” “linked,” and “coupled,” used in this application, are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. The term “multiple” used in this application refers to two or more. The "and / or" operator describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: A alone, A and B simultaneously, and B alone. Typically, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," and "third," etc., used in this application are merely for distinguishing similar objects and do not represent a specific ordering of the objects.

[0062] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a hardware structure block diagram of a terminal for a distortion detection method according to an embodiment of this application. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0063] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the distortion detection method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0064] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0065] In one embodiment, such as Figure 2 As shown, a distortion detection method is provided, which is applied to... Figure 1 Taking the terminal in the example, the following steps are included:

[0066] Step S101: Acquire a test image obtained by the reference device displayed by the device under test. The test image contains an array of multiple marker points.

[0067] The device under test (DUT) can be an XR (Extended Reality) camera, a camera, or other device with optical imaging capabilities. "Acquiring a test image from a reference device displayed on the DUT" means that the terminal captures the image displayed on the reference device via the DUT, thus obtaining a test image. The reference device has an array of multiple marker points, meaning the test image is a raster image. A marker point can be understood as a point in the image with a specific location and characteristics; for example, it can include areas of certain shapes (e.g., circles, rectangles, triangles), colors, or textures. These marker points are arranged in rows and columns to form an array. The reference coordinate system is a standard reference system used to measure and compare the positions of the marker points; it can be a two-dimensional plane coordinate system.

[0068] In some embodiments, the plurality of marker points includes three reference points. These reference points and the remaining marker points have different attributes (e.g., size, shape, color, or texture). The three reference points are used to locate the center point of the test image and identify its orientation. The center point of the test image is a common neighbor point that is directly adjacent to all three reference points. Directly adjacent means that there are no other marker points between two marker points. Correspondingly, indirectly adjacent means that there are other marker points between two marker points. The descriptions of "directly adjacent" and "indirectly adjacent" in the following text can be understood with reference to this definition.

[0069] As an example, Figure 3 A schematic diagram of one alternative example of a reference device, such as Figure 3 As shown, the reference device can be a target plate, which contains an image composed of small dots distributed from a field of view of 0 to 1.0, referred to as the target plate image. The small dots are markers, with the radii of the adjacent dots above, below, and to the right of the center dot being twice the radius of the other dots. These three large dots serve as reference points, used to locate the center dot and identify the direction of the target plate image. It should be noted that when referring to test image, target plate image, and dot matrix image in the embodiments, all can refer to the image displayed by the device under test and can be used interchangeably.

[0070] Step S102: Determine the positions of multiple marker points in the reference coordinate system.

[0071] By acquiring a dot matrix image and determining the position of the marker points in the reference coordinate system, an accurate image position model can be established. Then, by selecting the target marker points corresponding to the target field of view, the degree of distortion in the target field of view can be calculated to reflect the imaging quality of the device under test in the target field of view.

[0072] Step S103: Based on the positions of multiple marker points in the reference coordinate system, determine the target marker point corresponding to the target field of view, and calculate the distortion degree of the target field of view based on the target marker point.

[0073] Field of view refers to the angular range corresponding to the display area or visible area. Distortion refers to the phenomenon of compression, stretching, offset, and twisting of the geometric position of image pixels relative to a reference system during the imaging process, causing changes in the geometric position, size, shape, and orientation of the image. A target field of view can be one or more. When it is necessary to detect the degree of distortion in at least two fields of view, target markers corresponding to different fields of view can be selected, and the degree of distortion in each field of view can be calculated separately to reflect the imaging quality of the device under test under different fields of view. Different fields of view can be understood as image scenes seen from different observation angles or distances.

[0074] When extracting target markers, the field coefficient corresponding to the target field of view size can be determined first. Based on the field coefficient, the target markers are determined. The target markers include the points directly above and below the corresponding field of view, the points directly to the left and right, and the vertices on the diagonal. Figure 4 This is a schematic diagram of a target marker point corresponding to a viewpoint, such as... Figure 4 As shown, the target markers corresponding to this field of view are: P5(-k,0) directly above and P7(k,0) directly below; P8(0,-k) directly to the left and P6(0,k) directly to the right; and the vertices on the two diagonal lines, including the upper left vertex P1(-k,-k), upper right vertex P2(k,-k), lower right vertex P3(k,k), and lower left vertex P4(-k,k), where k represents a coefficient corresponding to the size of the field of view, such as 0.1F corresponding to k=1. After obtaining the target markers, the degree of distortion of the target field of view can be calculated based on the target markers. The vertical distortion and / or horizontal distortion of the target field of view can be calculated based on the target markers. The calculation methods for vertical and horizontal distortion will be introduced below.

[0075] Figure 5 A flowchart of the vertical distortion calculation method is given, such as... Figure 5 As shown, the vertical distortion calculation method is as follows:

[0076] Step S201: Based on vertices P1, P2, P3, and P4 located on the diagonal of the target field of view, determine the length of the first boundary L14 and the length of the second boundary L23 on the left and right sides of the target field of view; where L14 is the distance between vertices P1 and P4, and L23 is the distance between vertices P2 and P3.

[0077] Step S202: Determine the vertical diameter L57 inside the target field of view based on point P5 directly above and point P7 directly below the target field of view.

[0078] Step S203: Based on the first boundary length L14, the second boundary length L23, and the vertical diameter L57, calculate the degree of vertical distortion of the target's field of view. The calculation formula is as follows:

[0079]

[0080] Figure 6 A flowchart of the horizontal distortion calculation method is given, such as... Figure 6 As shown, the method for calculating horizontal distortion is as follows:

[0081] Step S301: Based on vertices P1, P2, P3, and P4 located on the diagonal of the target field of view, determine the lengths of the third boundary L12 and the fourth boundary L43 on the upper and lower sides of the target field of view; where L12 is the distance between vertices P1 and P2, and L43 is the distance between vertices P3 and P4.

[0082] Step S302: Determine the horizontal diameter L86 inside the target field of view based on point P8 located to the left and point P6 located to the right of the target field of view.

[0083] Step S303: Based on the length of the third boundary L12, the length of the fourth boundary L43, and the horizontal diameter L86, calculate the degree of horizontal distortion of the target's field of view. The calculation formula is as follows:

[0084]

[0085] It should be noted that the steps shown in the above process or the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although this application provides method operation steps as shown in the above embodiments or accompanying drawings, the method may include more or fewer operation steps based on conventional or non-inventive effort. For steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. For example, steps S201 and S202 can be interchanged or executed synchronously, and steps S301 and S302 can be interchanged or executed synchronously; this embodiment does not impose any limitations.

[0086] In steps S101 to S103 above, it is only necessary to acquire the test image obtained by the display reference device of the device under test once. Then, the target marker point corresponding to one or more fields of view can be selected in the dot matrix of the test image, thereby detecting the distortion under one or more fields of view. This solves the problem that it is impossible to detect the distortion under different fields of view at one time. Compared with the traditional method of acquiring different field of view images one by one for detection, it greatly improves the detection efficiency and accuracy.

[0087] In one embodiment, step S102 can be implemented as follows: determining a reference point among multiple marker points and numbering the reference point; starting from the reference point and based on the adjacency relationship between the multiple marker points and the reference point, sequentially numbering the multiple marker points.

[0088] The reference point can be the center point of the test image, the origin of the reference coordinate system, or other marker points. The reference point can be determined based on several reference points; that is, three reference points are selected from multiple marker points, and these reference points have different attributes from the remaining marker points. A common neighbor point directly adjacent to all three reference points is then identified and used as the reference point. As an example, the reference... Figure 3 The reference point is the center point of the test image, i.e., the exact center circle. In the test image, the radii of the adjacent circles above, below, and to the right of the exact center circle are twice the radii of the other circles. These three large circles are used as reference points. The common neighbor points that are directly adjacent to these three reference points are determined and used as the reference points.

[0089] Among these methods, when sequentially numbering multiple marker points starting from a reference point and based on the adjacency relationships between them, it is possible to... Figure 7 The method shown is implemented. Figure 7 This is a flowchart of the point sorting process in this embodiment, as follows: Figure 7 As shown, the process includes the following steps:

[0090] Step S401: Obtain the current first identifier point and its number. The current first identifier point is in the same row or column as the reference point. This step is used to locate the row or column that needs to be numbered. The first identifier point refers to the starting point of the numbering in the current row or column. It should be noted that when performing the first round of point sorting (one round is defined as performing all steps from S401 to S403), the current first identifier point is the reference point. However, when performing subsequent rounds of point sorting, this first identifier point may not be the reference point; it may be a point in a row or column of the array.

[0091] Step S402: Determine the directly adjacent and / or indirectly adjacent neighbor points of the current first identifier point in the first arrangement direction of the array, and number the neighbor points according to the adjacency relationship between the neighbor points and the current first identifier point. This step can be used to number the points in the located row or column.

[0092] Step S403: Determine the marker directly adjacent to the current first marker in the second arrangement direction of the array, number the marker directly adjacent to the current first marker and use it as the next first marker. This step can be used to locate the next row or column that needs to be numbered.

[0093] The first arrangement direction can be the horizontal arrangement direction of the array, i.e., the row direction, and the second arrangement direction can be the column arrangement direction of the array, i.e., the column direction. Of course, the first arrangement direction can also be the vertical arrangement direction of the array, i.e., the column direction, and the second arrangement direction can be the horizontal arrangement direction of the array, i.e., the row direction.

[0094] In this embodiment, the points can be sorted row by row or column by column. Once a row or column is sorted, the next nearest row or column is searched for sorting, and this process is repeated. It can be understood that steps S401 to S403 can be executed cyclically; the more iterations, the more points will be sorted and numbered. Optionally, if only the distortion level of one or a few fields of view needs to be detected, only a subset of the marker points needs to be sorted and numbered, requiring only a few iterations. Optionally, if multiple fields of view or even the entire field of view need to be detected, all marker points need to be sorted and numbered, meaning the iteration continues until all marker points in the test image are numbered.

[0095] Considering the unfavorable situations that may arise during actual image acquisition, such as incomplete image display (edge ​​and corner dots cannot be fully captured due to display limitations of the device under test), dot obstruction (dirt on the lens partially obscures dots), and image tilt (structural interference prevents the measurement position from being at the eye level or causes image rotation), some embodiments select non-edge and non-corner markers as the reference point to address these issues. In other embodiments, to achieve the best point sorting effect, the reference point is selected as the center point of the test image. This setting allows for the detection and sorting of acquired markers as much as possible, thereby improving the robustness of the algorithm and providing greater tolerance for the display performance of the device under test and the test scenario.

[0096] As an example, Figure 8 A flowchart is provided to sort all marker points starting from the center point of the test image. For example... Figure 8 As shown, starting from the center point of the test image, neighboring points are searched to the left and right simultaneously. The j-value decreases when the numbering moves left and increases when it moves right. After the current row is sorted, neighboring points are searched upwards from the center point of the current row, i.e., the row above the current row is taken as the current row, so the center point of the current row is (ik,0). Starting from the center point of the current row, neighboring points are searched to the left and right simultaneously, and the found neighboring points are marked with row and column numbers (i,j). Similarly, the center point of each row is searched upwards, and neighboring points are searched to the left and right from the center point of each row until all the marked points in the upper half are marked. Similarly, starting from the center point (0,0) again, the row and column numbers (i,j) of all the marked points are marked downwards according to the proximity relationship.

[0097] Figure 8 The proposed solution is to first number the upper half of the image row by row, and then number the lower half of the image row by row. In some embodiments, this can be modeled after... Figure 8The process involves first numbering the lower half of the image row by row, then numbering the upper half row by row. Alternatively, the left half of the image can be numbered column by column first, then the right half column by column. Or, the right half of the image can be numbered column by column first, then the left half column by column. It should be understood that these are all technical solutions covered by the point-sorting method provided in this application. Several modifications and improvements can be made without departing from the concept of this application, and these all fall within the scope of protection of this application.

[0098] In some embodiments, in order to make the distortion detection results more accurate, the test image can be distorted before step S102 above, according to the distortion parameters calibrated by the device under test.

[0099] Furthermore, before determining the positions of multiple marker points in the reference coordinate system, marker points exceeding a preset size range are filtered out, i.e., marker points that are too large or too small are filtered out. Taking circular marker points as an example, the test image can first be binarized; then, morphological operations can be performed on the image to remove interference from other discrete information in the environment; spot detection can be performed to detect all circles in the image and record the center and radius of each circle; based on the size information of all detected circles, circles that are too large or too small are filtered out.

[0100] After determining three reference points from multiple marker points, the tilt of the test image can be calculated based on the three reference points; and the tilt of the test image can be corrected based on the tilt.

[0101] In one embodiment, a distortion detection system is also provided. Figure 9 This is a schematic diagram of the distortion detection system in this embodiment, as shown below. Figure 9 As shown, the distortion detection system includes: a detection device 1, a device under test (DUT), and a reference device 3 displayed by the DUT. The reference device 3 is used to display an image on the DUT, and is equipped with an array of multiple marker points. The detection device 1 acquires the image displayed by the DUT, obtains a test image, and performs the distortion detection method of any of the above embodiments based on the test image. The multiple marker points include three reference points, which have different attributes (e.g., size, shape, color, or texture) compared to the other marker points. The three reference points are used to locate the center point of the test image and identify its orientation. The center point of the test image is a common neighbor point directly adjacent to all three reference points. To detect full-field distortion, the field of view of the detection device can be larger than the field of view of the DUT.

[0102] Those skilled in the art will understand that Figure 9 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the distortion detection system may also include... Figure 9 The more or fewer components shown, or having the same Figure 9 The different configurations shown are illustrated.

[0103] In one embodiment, another distortion detection system is provided. Figure 10 This is a schematic diagram of the distortion detection system in this embodiment, as shown below. Figure 10 As shown, the distortion detection system includes: a detection device 1, a device under test 2, a reference device 3, and a motion mechanism 4. The motion mechanism 4 is connected to the detection device 1 and / or the device under test 2, and is used to adjust the position of the detection device 1 and / or the device under test 2.

[0104] In some embodiments, the device under test 2 can be XR (Extended Reality) glasses, a camera, or other devices with optical imaging capabilities. XR glasses are devices that integrate VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality) technologies. Distortion issues in XR glasses can lead to image distortion and visual discomfort, affecting the user's immersion experience; therefore, it is necessary to detect distortion in XR glasses.

[0105] One type of XR inspection device is a point-scanning inspection device based on a high-pixel angular resolution, small field-of-view lens. The distortion detection scheme of this type of device is similar to traditional distortion detection methods. The target image is also a white field image. A moving robotic arm is aligned with the edges and corners of the white field, and distortion is calculated based on the angle of the robotic arm's movement. This method has the same limitations: it can only detect distortion within one field of view at a time, and because it requires robotic arm movement for detection, its detection efficiency is lower than traditional distortion detection methods.

[0106] The following will introduce an example of using the distortion detection system provided in this application to perform distortion detection on XR glasses.

[0107] The distortion detection system in this embodiment can be referenced. Figure 10 The detection device 1 and the device under test 2 are compatible. If it is necessary to detect full field-of-view distortion, the field of view of the detection device 1 can be set to be larger than the field of view of the device under test 2. As an example, the field of view of the detection device 1 used in this embodiment is 144° diagonally.

[0108] Regarding reference device 3, please refer to Figure 3 The full-field distortion detection target image consists of small dots distributed from the 0 field of view to the 1.0 field of view. The radii of the three adjacent dots above, below, and to the right of the center dot are twice the radii of the other dots. These three large dots serve as reference points, used to locate the center dot and identify the orientation of the target image.

[0109] The device under test (DUT) 2 captures an image of the reference device 3 and displays the target image. Either the detection device 1 or the DUT 2 can be fixed to the motion mechanism 4. The detection position is adjusted via the motion mechanism 4 so that the center 11 of the probe tip of the detection device 1 aligns with the eye point position 21 of the DUT 2. Once the center 11 of the probe tip of the detection device 1 aligns with the eye point position 21 of the DUT 2, the detection device 1 acquires the target image displayed by the DUT 2, calls the distortion detection method provided in this application, performs full-field distortion detection, and outputs detection results for different field-of-view sizes.

[0110] Figure 11 This is a flowchart of the distortion detection system in this embodiment, as follows: Figure 11 As shown, the process includes the following steps:

[0111] Step S501: Adjust the relative positions of the testing equipment and the device under test;

[0112] Step S502: The device under test displays the target image;

[0113] Step S503: The detection equipment acquires images of the target plate;

[0114] Step S504: Detect the degree of distortion across the entire field of view based on the target image;

[0115] Step S505: Output the result.

[0116] To further understand the workflow of the above distortion detection system, specifically, Figure 12 This is a flowchart of another distortion detection system in this embodiment, as shown below. Figure 12 As shown, the process includes the following steps:

[0117] Step S61: Obtain the image of the target plate captured by the detection device.

[0118] Step S62: Based on the calibrated camera distortion parameters, perform distortion correction on the calibration plate image.

[0119] Step S63, extract all dots. This includes the following steps:

[0120] Step S631: Perform binarization on the target image;

[0121] Step S632: Perform morphological operations on the target image to remove interference from other discrete information in the environment;

[0122] Step S633: Perform spot detection on the target image, detect all circles in the image, and record the center and radius of the circles;

[0123] Step S634: Based on the size information of all detected circles, filter out circles that are too large or too small.

[0124] Step S64: Sort all detected dots, with each dot identified by two numbers (i, j), where i represents the row number and j represents the column number. This includes the following steps:

[0125] Step S641: Based on the size of the dots, find three reference dots with larger radii;

[0126] Step S642: Based on the relative positional relationship between the three reference points, locate the three reference points and distinguish them as points located above, below, and to the right of the center point;

[0127] Step S643: Based on the position information of the three detected reference points, calculate the tilt of the entire image. If the image is tilted, perform tilt correction.

[0128] Step S644: Establish proximity relationships for all dots;

[0129] Step S645: Based on the proximity relationship, find the common neighbor of the three reference circles, which is the center point (0,0);

[0130] Step S646: Starting from the center point of the label image, search for neighboring points to the left and right simultaneously. The j-value decreases when the label is moved to the left and increases when it is moved to the right. After the current row is sorted, start from the center point of the current row and search for neighboring points upwards, i.e., take the row above the current row as the current row. The center point of the current row is (ik,0). Starting from the center point of the current row, search for neighboring points to the left and right simultaneously and mark the row and column numbers (i,j) of the neighboring points found. Similarly, continue to search for the center point of each row upwards, and search for neighboring points to the left and right of the center point of each row until all the label points in the upper half are marked. Similarly, start from the center point (0,0) again and mark the row and column numbers (i,j) of all the labeled points downwards according to the proximity relationship.

[0131] Step S65: Based on the sorting results, extract the points that need to participate in the distortion operation according to the row and column numbers of each point. (Reference) Figure 4 The target markers corresponding to a certain field of view are: point P5(-k,0) directly above the field of view and point P7(k,0) directly below the field of view; point P8(0,-k) directly to the left of the field of view and point P6(0,k) directly to the right of the field of view; and the vertices on the two diagonal lines, including the upper left vertex P1(-k,-k), upper right vertex P2(k,-k), lower right vertex P3(k,k), and lower left vertex P4(-k,k), where k represents the coefficient corresponding to the size of the field of view, such as 0.1F corresponding to k=1.

[0132] Step S66: Calculate the distortion for different fields of view based on the distortion calculation formula.

[0133]

[0134]

[0135] This step can be referenced. Figure 5 , Figure 6 In the embodiment shown, Dv represents vertical distortion and Dh represents horizontal distortion.

[0136] Step S67: Output the distortion results for different field sizes and positions.

[0137] In this embodiment, based on a wide field-of-view camera and dot matrix imagery, distortion detection for different fields of view in XR glasses is achieved in a single operation. The dot matrix imagery allows for the extraction of the center point and the detection and correction of image orientation using a single image. Compared to traditional distortion detection methods based on full white images or nine-dot images, this method completes the detection of all fields of view with just one image, resulting in a significant improvement in detection efficiency. The distortion detection method in this embodiment can detect and sort all captured dots even under adverse conditions such as incomplete image display (edge ​​and corner dots cannot be fully captured due to display limitations of the device under test), occlusion of dots (dirt on the lens obscures some dots), and image tilt (structural interference prevents the measurement position from being at the eye point or causes image rotation). The algorithm is robust and has high tolerance for the display performance of the device under test and the test scenario. The dot extraction-based scheme often has higher positional accuracy than the white field edge extraction scheme. Testing has verified that the distortion detection error for each field of view in this embodiment is less than 0.1%.

[0138] This embodiment also provides an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0139] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0140] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0141] S1, acquire a test image obtained by the device under test from the reference device, the test image containing an array of multiple marker points;

[0142] S2, determine the positions of multiple marker points in the reference coordinate system;

[0143] S3, based on the positions of multiple marker points in the reference coordinate system, determine the target marker points corresponding to at least two different fields of view, and calculate the distortion degree of at least two different fields of view based on the target marker points.

[0144] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0145] Furthermore, in conjunction with the distortion detection methods provided in the above embodiments, this embodiment can also provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any one of the distortion detection methods in the above embodiments.

[0146] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 13 As shown, the computer device includes a processor, memory, communication interface, display unit, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a distortion detection method. The display unit can be a liquid crystal display unit or an e-ink display unit. The input device can be a touch layer covering the display unit, buttons, a trackball, or a touchpad on the computer device casing, or an external keyboard, touchpad, or mouse.

[0147] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0148] It should be understood that the specific embodiments described above are only used to explain the relevant applications and are not intended to limit them. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0149] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0150] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. The acquisition, storage, use, and processing of data involved in the embodiments of this application all comply with the relevant provisions of national laws and regulations.

[0152] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0153] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A distortion detection method, characterized in that, include: The test image obtained by the reference device displayed by the device under test is acquired, and the test image contains an array of multiple marker points; Determining the position of the plurality of marker points in the reference coordinate system specifically includes: determining a reference point among the plurality of marker points, the reference point being numbered (0,0); starting from the reference point and based on the adjacency relationship between the plurality of marker points, sequentially numbering the plurality of marker points; Based on the positions of the plurality of marker points in the reference coordinate system, target marker points corresponding to the target field of view are determined, and the degree of distortion of the target field of view is calculated based on the target marker points; Among them, determining the target marker point corresponding to the target field of view based on the position of the plurality of marker points in the reference coordinate system includes: Determine the field-of-view coefficient k corresponding to the target's field-of-view size; Based on the field of view coefficient k, the position of the target marker point is determined in the reference coordinate system. The target marker point includes point P5(-k,0) directly above the target field of view, point P7(k,0) directly below, point P8(0,-k) directly to the left, point P6(0,k) directly to the right, the upper left diagonal vertex P1(-k,-k), the upper right diagonal vertex P2(k,-k), the lower right diagonal vertex P3(k,k), and the lower left diagonal vertex P4(-k,k).

2. The distortion detection method according to claim 1, characterized in that, Starting from the reference point and based on the adjacency relationships between the multiple marker points, the multiple marker points are sequentially numbered, including: Obtain the current first identifier point and its number, wherein the current first identifier point is in the same row or column as the reference point; In the first arrangement direction of the array, determine the neighboring points that are directly adjacent and / or indirectly adjacent to the current first identifier point, and number the neighboring points according to the adjacency relationship between the neighboring points and the current first identifier point; In the second arrangement direction of the array, a marker point directly adjacent to the current first marker point is determined, and the marker point directly adjacent to the current first marker point is numbered and used as the next first marker point.

3. The distortion detection method according to claim 1, characterized in that, Determining a reference point among the plurality of marker points includes: Three reference points are determined from the plurality of marker points, and the reference points and the remaining marker points have different attributes; Identify the common neighbor points that are directly adjacent to all three reference points, and use these common neighbor points as the reference points.

4. The distortion detection method according to claim 3, characterized in that, After determining three reference points from the plurality of marker points, the method further includes: The tilt of the test image is calculated based on the three reference points; The test image is tilted according to the degree of tilt.

5. The distortion detection method according to claim 1, characterized in that, After acquiring the test image obtained by the reference device displayed by the device under test, the method further includes: Remove markers whose size exceeds a preset range from the plurality of markers.

6. The distortion detection method according to claim 1, characterized in that, Calculating the distortion degree of the target field of view based on the target marker points includes: Based on the target marker, calculate the degree of vertical distortion and / or horizontal distortion of the target field of view.

7. The distortion detection method according to claim 6, characterized in that, include: Based on the vertices located on the diagonal of the target field of view, determine the lengths of the first boundary and the second boundary on the left and right sides of the target field of view; Determine the vertical diameter length inside the target field of view based on the points located directly above and below the target field of view; Calculate the degree of vertical distortion of the target field of view based on the first boundary length, the second boundary length, and the vertical diameter; and / or, Based on the vertices located on the diagonal of the target field of view, determine the lengths of the third boundary and the fourth boundary on the upper and lower sides of the target field of view; Determine the horizontal diameter of the target field of view based on the points located directly to the left and right of the target field of view; The degree of horizontal distortion of the target field of view is calculated based on the length of the third boundary, the length of the fourth boundary, and the horizontal diameter.

8. A distortion detection system, characterized in that, include: The device includes a detection device, a device under test, and a reference device displayed by the device under test; wherein the reference device is provided with an array of multiple marker points. The detection equipment is used to perform the distortion detection method according to any one of claims 1 to 7.

9. The distortion detection system according to claim 8, characterized in that, The plurality of marker points includes three reference points, which have different attributes from the remaining marker points.

10. The distortion detection system according to claim 8, characterized in that, The field of view of the detection device is greater than that of the device under test.

11. The distortion detection system according to claim 8, characterized in that, The distortion detection system further includes a motion mechanism connected to the detection device and / or the device under test, used to adjust the position of the detection device and / or the device under test.

12. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the distortion detection method according to any one of claims 1 to 7.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the distortion detection method according to any one of claims 1 to 7.

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