Field angle measuring method and system and medium

Abstract

The embodiment of the invention provides a field angle measuring method and system and a medium. The field angle measuring method is used for measuring the field angle of the virtual reality display equipment. The method comprises the following steps: acquiring a standard image acquired by a camera; obtaining a camera identification angle corresponding to each feature point in a standard image card feature point set, wherein the standard image is an image formed by the standard image card in the camera; according to the standard image card feature points and the camera identification angles corresponding to the standard image card feature points, establishing a mathematical model of the coordinates of the feature points in the standard image and the camera identification angles; obtaining a test image displayed by the virtual reality display device and collected by the camera; and determining the field angle of the display device of the virtual reality device according to the mathematical model and the feature point set of the test image.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of field of view angle measurement. More specifically, the embodiments of the present application relate to a method for measuring a field of view angle, a system for measuring a field of view angle, and a computer-readable storage medium. Background Art

[0002] With the development and application of Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) technologies, various display devices based on these technologies have gradually appeared in the public's field of vision. They are usually shaped like helmets, supplemented by special display screens and signal sources to present images in front of the user, such as VR head-mounted display devices, AR head-mounted display devices, and head-up display devices (HUD). The field of view refers to the angle between the edge of the image and the line connecting the observation point (the center of the pupil of the human eye) that can be observed by the human eye in the image formed by the aforementioned virtual image display devices. The size of the field of view directly affects the size of the image viewed by the user of the head-mounted display device.

[0003] At present, the commonly used method for measuring the field of view angle is to use a brightness meter to measure the brightness at different angles. The field of view angle is measured according to the angle at which the brightness meter is rotated. However, this method is easily affected by the external environment, which reduces the accuracy of the measurement result.

[0004] In view of this, it is necessary to provide a new technical solution to solve the above technical problems. Summary of the invention

[0005] The first purpose of this application is to provide a new technical solution for measuring field of view angle.

[0006] The second purpose of this application is to provide a new technical solution for the field of view angle measurement system

[0007] The third objective of this application is to provide a new technical solution for computer-readable storage media.

[0008] In a first aspect, the present application provides a method for measuring a field of view angle. The method for measuring a field of view angle is used to measure the field of view angle of a virtual reality display device. The method for measuring a field of view angle comprises:

[0009] Get the standard image captured by the camera;

[0010] Obtaining a camera recognition angle corresponding to each feature point in a feature point set of a standard image card, wherein the standard image is an image formed by the standard image card on the camera;

[0011] According to the characteristic points of the standard image card and the corresponding camera recognition angles, a mathematical model of the characteristic point coordinates in the standard image and the camera recognition angle is established;

[0012] Acquire a test image displayed by a virtual reality display device captured by a camera;

[0013] The field of view angle of a display device of a virtual reality device is determined according to the mathematical model and the feature point set of the test image.

[0014] Optionally, obtaining the camera recognition angle corresponding to each feature point in the standard image card feature point set specifically includes:

[0015] Obtaining the straight-line distance between the exit pupil position of the camera and the standard chart;

[0016] According to the straight-line distance and the coordinates of the feature points of the standard image card, the camera recognition angle corresponding to the feature points of the standard image card is determined.

[0017] Optionally, determining the camera recognition angle corresponding to the feature point of the standard image card according to the straight-line distance and the coordinates of the feature point of the standard image card specifically includes:

[0018] According to the straight-line distance and the vertical coordinate of the feature point of the standard chart, the height angle of camera recognition corresponding to the feature point of the standard chart is determined.

[0019] Optionally, determining the camera recognition angle corresponding to the feature point of the standard image card according to the straight-line distance and the coordinates of the feature point of the standard image card specifically includes:

[0020] The horizontal angle of camera recognition corresponding to the feature point of the standard chart is determined according to the straight-line distance and the horizontal coordinate of the feature point of the standard chart.

[0021] Optionally, before establishing the mathematical model of the feature point coordinates and the camera recognition angle in the standard image according to the feature points of the standard image card and the corresponding camera recognition angle, the method further includes:

[0022] According to the coordinates of each feature point of the standard image card and the corresponding camera recognition angle, the coordinates of the feature point of the standard image corresponding to the coordinates of each feature point of the standard image card are determined.

[0023] Optionally, according to each feature point of the standard image card and the camera recognition angle corresponding thereto, establishing a mathematical model of the feature point coordinates and the camera recognition angle in the standard image specifically includes:

[0024] Determine the camera recognition angle corresponding to the coordinates of the feature point of the standard image according to the camera recognition angle corresponding to the coordinates of each feature point of the standard image card;

[0025] By fitting the coordinates of the feature points in the standard image and the corresponding camera recognition angles, a mathematical model of the coordinates of the feature points in the standard image and the camera recognition angles is established.

[0026] Optionally, according to the camera recognition angle corresponding to each feature point of the standard image card, establishing a mathematical model of feature point coordinates and camera recognition angles in the standard image further specifically includes:

[0027] Determine the camera recognition height angle corresponding to the vertical coordinate of each feature point of the standard image according to the camera recognition height angle corresponding to the vertical coordinate of each feature point of the standard image card;

[0028] By fitting the vertical coordinates of the feature points in the standard image and the corresponding height angles recognized by the camera, a mathematical model of the vertical coordinates of the feature points in the standard image and the height angles recognized by the camera is established.

[0029] Optionally, according to the camera recognition angle corresponding to each feature point of the standard image card, establishing a mathematical model of feature point coordinates and camera recognition angles in the standard image further specifically includes:

[0030] Determine the horizontal angle recognized by the camera corresponding to the horizontal coordinate of each feature point of the standard image according to the horizontal angle recognized by the camera corresponding to the horizontal coordinate of each feature point of the standard image card;

[0031] By fitting the horizontal coordinates of the feature points in the standard image and the corresponding horizontal angles recognized by the camera, a mathematical model of the horizontal coordinates of the feature points in the standard image and the horizontal angles recognized by the camera is established.

[0032] Optionally, determining the field of view angle of a display device of a virtual reality device according to the mathematical model and the feature point set of the test image specifically includes:

[0033] The feature point set of the test image includes two feature point sets located at the horizontal edges of the test image;

[0034] The horizontal field of view of the virtual reality display device is determined according to the camera recognition horizontal angle corresponding to each feature point on the horizontal edge of the test image.

[0035] Optionally, determining the field of view angle of a display device of a virtual reality device according to the mathematical model and the feature point set of the test image specifically includes:

[0036] The feature point set of the test image includes two feature point sets located at the vertical edges of the test image;

[0037] The vertical field of view of the virtual reality display device is determined according to the camera recognition height angle corresponding to each feature point of the vertical edge of the test image.

[0038] Optionally, the standard chart is one of a checkerboard chart, a circular dot array chart or a crosshair chart.

[0039] In a second aspect, the embodiment of the present application further provides a field of view angle measurement system. The field of view angle measurement system is used to measure the field of view angle of a virtual reality display device. The field of view angle measurement system includes:

[0040] An image acquisition module, used to acquire standard images of standard image cards and test images displayed by a virtual reality display device;

[0041] An acquisition module, used to acquire a camera recognition angle corresponding to each feature point in a feature point set of a standard image card, wherein the standard image is an image formed by the standard image card on the camera;

[0042] A first processing module, used to establish a mathematical model of the coordinates of the feature points in the standard image and the camera recognition angle according to the feature points of the standard image card and the camera recognition angles corresponding thereto;

[0043] The second processing module is used to determine the field of view angle of the virtual reality device display device according to the mathematical model and the feature point set of the test image.

[0044] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0045] According to the embodiments of the present application, the method for measuring the field of view angle combines image analysis and geometric relationships, and through precise measurement and calculation, the field of view angle of the VR display device can be accurately measured through the camera lens. Compared with the prior art, the method for measuring the field of view angle provided in the embodiments of the present application is not affected by the external environment, and the accuracy of the measurement results is improved.

[0046] Other features and advantages of the present specification will become apparent from the following detailed description of exemplary embodiments of the present specification with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the specification.

[0048] Figure 1 Shown is a flow chart of a method for measuring a field of view angle provided in an embodiment of the present application.

[0049] Figure 2 Shown is a structural block diagram of a field of view angle measurement system provided in an embodiment of the present application.

[0050] Figure 3 Shown is a schematic diagram of a camera acquiring a standard image.

[0051] Figure 4 Shown is a schematic diagram of a standard chart.

[0052] Figure 5 The figure shows a schematic diagram of a single grid of a standard chart being imaged by a camera.

[0053] Figure 6 The figure shows the camera imaging diagram of the grid at the M*N position of the standard chart.

[0054] Figure 7 Shown is a test image displayed by a virtual reality display device.

[0055] Description of reference numerals:

[0056] 1. Camera; 2. Standard picture card;

[0057] 300, a field of view angle measurement system; 301, an image acquisition module; 302, an acquisition module; 303, a first processing module; 304, a second processing module. DETAILED DESCRIPTION

[0058] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application.

[0059] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0060] Techniques and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the techniques and equipment should be considered part of the specification.

[0061] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0062] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0063] The FOV (Field of view) of a VR optical module refers to the angle between the edge of the image that can be observed by the human eye and the line connecting the center of the pupil of the human eye in the image formed by the VR display device. The FOV field of view can usually be divided into the horizontal field of view (HFOV) and the vertical field of view (VFOV). The horizontal field of view refers to the maximum range that the user can see in the left and right directions, while the vertical field of view refers to the maximum range that the user can see in the up and down directions.

[0064] The embodiment of the present application provides a method for measuring a field of view angle. The method for measuring a field of view angle can measure the field of view angle of a virtual reality display device. Generally, the method for measuring a field of view angle can measure the horizontal field of view angle and the vertical field of view angle of a virtual reality display device.

[0065] Hereinafter, various embodiments and examples according to the present disclosure are described with reference to the drawings.

[0066] <Method Example>

[0067] Reference Figure 1 The method for detecting the field of view angle specifically includes steps S1 to S5.

[0068] S1: Get the standard image captured by camera 1;

[0069] S2: Obtaining the recognition angle of the camera 1 corresponding to each feature point in the feature point set of the standard image card 2, wherein the standard image is the image formed by the standard image card 2 on the camera 1;

[0070] S3: establishing a mathematical model of the coordinates of the feature points in the standard image and the recognition angles of the camera 1 according to the feature points of the standard image card 2 and the corresponding recognition angles of the camera 1;

[0071] S4: Acquire a test image displayed by the virtual reality display device captured by camera 1;

[0072] S5: Determine the field of view angle of the display device of the virtual reality device according to the mathematical model and the feature point set of the test image.

[0073] In step S1 of the present application, the standard image card 2 is photographed by the camera 1 to obtain an image containing all the feature points of the standard image card 2, which is the standard image. This step is to obtain an image of a standard image card 2 with known feature points in the camera 1 lens, laying the foundation for the subsequent establishment of the relationship between the coordinates of the feature points of the standard image and the recognition angle of the camera 1.

[0074] In a specific example, the standard image card 2 is placed in front of the camera 1, ensuring that the distance between the standard image card 2 and the lens of the camera 1 is fixed, and the center of the standard image card 2 is aligned with the center of the lens of the camera 1. Then, the standard image card 2 is photographed by the camera 1 to obtain a standard image.

[0075] In one example, refer to Figure 3 and Figure 5 , standard chart 2 is a checkerboard chart, the center of camera 1 is aligned with the center of the checkerboard chart, the distance from the exit pupil position of camera 1 to the checkerboard chart is fixed, and the distance from the exit pupil position of camera 1 to standard chart 2 is A.

[0076] In another example, the standard chart 2 is a circular dot array chart, the center of the camera 1 is aligned with the center of the circular dot array chart, and the distance from the exit pupil position of the camera 1 to the circular dot array chart is fixed.

[0077] In another example, the standard chart 2 is a crosshair chart, the center of the camera 1 is aligned with the center of the crosshair chart, and the distance from the exit pupil position of the camera 1 lens to the crosshair chart is fixed.

[0078] In step S2 of the present application, the feature point set of the standard chart 2 is a point set with clear and known positions and attributes used in fields such as image processing, camera 1 calibration, and field of view angle measurement.

[0079] In other words, the feature point set of the standard chart 2 is a set consisting of a series of feature points distributed on the standard chart 2. These points have clear positions, shapes, colors or other easily identifiable attributes so that they can be quickly and accurately located in the image.

[0080] In this step, the feature point set of the standard chart 2 is used in the field of view angle measurement of the virtual reality display device. The feature points of the standard chart 2 are key points of the standard chart 2, such as corner points or other points with significant local information.

[0081] In step S2 , the recognition angle of each feature point on the standard image card 2 in the camera 1 lens is determined, which is a key step in establishing the relationship between the coordinates of the feature point of the standard image and the recognition angle of the camera 1 .

[0082] In a specific example, feature points in a standard image are identified through image processing technology, and the identification angle of each feature point in the lens of camera 1 is recorded. This usually involves the use of image processing software, such as imatest, to accurately measure the position and angle of the feature points.

[0083] In one example, refer to Figure 5 and Figure 6 In step S2, obtaining the recognition angle of the camera 1 corresponding to each feature point in the feature point set of the standard image card 2 specifically includes:

[0084] S21: Obtaining the straight-line distance between the exit pupil position of the camera 1 and the standard chart 2;

[0085] S22: Determine the recognition angle of the camera 1 corresponding to the feature point of the standard chart 2 according to the straight-line distance and the coordinates of the feature point of the standard chart 2.

[0086] In a specific embodiment, in step S22, determining the recognition angle of the camera 1 corresponding to the feature point of the standard chart 2 according to the straight-line distance and the coordinates of the feature point of the standard chart 2 specifically includes:

[0087] S221: Determine the height angle recognized by the camera 1 corresponding to the feature point of the standard chart 2 according to the distance and the vertical coordinate of the feature point of the standard chart 2.

[0088] In this embodiment, step S22 (determining the camera 1 recognition angle corresponding to the feature point of the standard chart 2 based on the straight-line distance and the coordinates of the feature point of the standard chart 2) is further refined into sub-step S221, focusing on determining the height angle corresponding to the feature point.

[0089] For example, using the principle of similar triangles, the following relationship can be established: tan(θ)=y / A, where y is the vertical coordinate of the feature point of the standard chart 2, and A is the distance between the camera 1 and the chart.

[0090] But since what we actually need is the angle θ, we need to transform the above relationship: θ=arctan(y / A).

[0091] In actual operation, this step is more concerned with calculating the angle by directly measuring the distance and coordinates, rather than relying on the internal parameters of the camera 1.

[0092] In another specific embodiment, in step S22, determining the recognition angle of the camera 1 corresponding to the feature point of the standard chart 2 according to the distance and the coordinates of the feature point of the standard chart 2 specifically includes:

[0093] S222: Determine the horizontal angle recognized by the camera 1 corresponding to the feature point of the standard chart 2 according to the distance and the horizontal coordinate of the feature point of the standard chart 2.

[0094] In this embodiment, step S22 (determining the camera 1 recognition angle corresponding to the feature point of the standard image card 2 based on the straight-line distance and the coordinates of the feature point of the standard image card 2) is further refined into sub-step S222, which focuses on determining the horizontal angle corresponding to the feature point of the standard image card 2.

[0095] For example, using the principle of similar triangles, the following relationship can be established: tan(θ)=x / A, where x is the horizontal coordinate of the feature point of the standard chart 2, and A is the distance between the camera 1 and the chart.

[0096] However, since what we actually need is the angle θ, we need to transform the above relationship: θ=arctan(x / A).

[0097] In actual operation, this step is more concerned with calculating the angle by directly measuring the distance and coordinates, rather than relying on the internal parameters of the camera 1.

[0098] In this embodiment, in the process of field of view angle measurement, step S2 (obtaining the camera 1 recognition angle corresponding to each feature point in the feature point set of the standard chart 2) is crucial. It involves accurately measuring the position information of each feature point on the standard chart 2 from the perspective of the camera 1 and converting it into the camera 1 recognition angle.

[0099] In step S21, the actual physical distance between the camera 1 lens (especially the exit pupil position) and the standard chart 2 is determined. This distance is a key parameter for the subsequent calculation of the recognition angle of the camera 1 corresponding to the feature point.

[0100] In this step, for example, a precise measuring tool (such as a laser rangefinder, a ruler, etc.) is used to measure the straight-line distance between the exit pupil position of the camera 1 and the plane of the standard chart 2. During the measurement process, it is necessary to ensure that the positions of the camera 1 and the standard chart 2 are fixed during the measurement process to avoid any movement or tilt, so as to ensure the accuracy of the measurement result.

[0101] For example, refer to Figure 5 and Figure 6 , the straight-line distance between the exit pupil position of camera 1 and the plane of standard chart 2 is A.

[0102] In step S22 , the known distance information and the precise coordinates of the feature points on the standard chart 2 are used to determine the recognition angle of each feature point in the field of view of the camera 1 through geometric calculation.

[0103] In a specific embodiment, according to the precise coordinates of each feature point on the standard chart 2 (which is usually determined in the design and manufacturing stage of the standard chart 2), the angle of each feature point in the field of view of the camera 1 is calculated according to the distance between the exit pupil of the camera 1 and the standard chart 2 and the coordinates of the feature point by using the trigonometric principle or the geometric similarity principle. Finally, a list or data set containing the recognition angles of the camera 1 corresponding to all feature points can be obtained.

[0104] For example, standard chart 2 is a checkerboard chart. Figure 4 , the horizontal length of a single grid in the chessboard chart is L and the height width is H, the number of grids in the horizontal direction is M, and the number of grids in the height direction is N. The precise coordinates of each feature point on the standard chart 2 can be determined based on the grid size and the number of grids.

[0105] That is to say, in this step, some complex mathematical calculations are involved, such as the use of the inverse tangent function (arctan).

[0106] In one example, referring to Figure 5 , A represents the distance from the camera 1 exit pupil to the chart, H represents the height of a single grid, θ 1 Represents the camera 1 recognition height angle corresponding to the feature point with coordinates (L1, H1) in the standard chart 2, θ 2 The horizontal angle recognized by camera 1 corresponding to the feature point with coordinates (L1, H1) in standard chart 2. Figure 5 In the figure, the recognition angle of camera 1 corresponding to the feature point with coordinates (L1, H1) in the standard chart 2 is θ 1 and θ 2 θ 1 Corresponding to the following formula 1), θ 2 Corresponding to the following formula 2).

[0107]

[0108] In another example, refer to Figure 6 , the recognition angle of camera 1 corresponding to the feature point with coordinates (L1*M, H1*N) in standard chart 2 is θ n and θ m θ n Represents the camera 1 recognition height angle corresponding to the feature point with coordinates (L1*M, H1*N) in the standard chart 2, θ m Represents the horizontal angle recognized by camera 1 corresponding to the feature point with coordinates (L1*M, H1*N) in standard chart 2. θ n Corresponding to the following formula 3), θ m Corresponding to the following formula 4).

[0109]

[0110] It should be noted that in actual operation, due to factors such as the lens distortion of camera 1 and the nonlinearity of the image sensor, the recognition angle directly obtained through geometric calculation may have certain errors. Therefore, in actual applications, it may be necessary to further correct these errors through steps such as camera 1 calibration to improve the accuracy of the measurement results. For example, the distortion of the camera 1 lens can be calibrated in advance.

[0111] In step S3 of the embodiment of the present application, a mathematical model is established based on the feature points on the standard image card 2 and the corresponding recognition angles of the camera 1 to describe the relationship between the coordinates of the feature points of the standard image and the recognition angles of the camera 1 .

[0112] In a specific embodiment, a mathematical model is established based on the coordinates of the feature points of the standard image and the corresponding recognition angles of the camera 1 using mathematical methods such as linear regression and polynomial fitting. This mathematical model will be used in subsequent steps to calculate the field of view of the VR display device based on the coordinates of the feature points of the test image.

[0113] In one embodiment, before establishing a mathematical model of the coordinates of the feature points in the standard image and the recognition angles of the camera 1 according to the feature points of the standard image card 2 and the corresponding recognition angles of the camera 1 in step S3, the method further includes:

[0114] S3 ′: according to the coordinates of each feature point of the standard chart 2 and the corresponding recognition angle of the camera 1 , determine the coordinates of the feature point of the standard image corresponding to the coordinates of each feature point of the standard chart 2 .

[0115] In this embodiment, in the embodiment of the field of view angle measurement, the preprocessing step S3 ′ before the step S3 plays a crucial role, and is responsible for converting the feature point coordinates on the standard chart 2 into the feature point coordinates in the standard image.

[0116] Specifically, when the precise coordinates of each feature point on the standard chart 2 and the recognition angle of the camera 1 corresponding to each feature point on the standard chart 2 are known, a mapping relationship between the object space coordinates and the image coordinates can be established according to the imaging principle of the camera 1.

[0117] Among them, the imaging principle of camera 1 points out that the projection position of an object on the image sensor is related to its position in three-dimensional space.

[0118] Here, the mapping relationship between the object space coordinates and the image coordinates is established by using the known coordinates of the feature points of the standard chart 2 and the recognition angle of the camera 1, as well as the internal parameters of the camera 1 (such as focal length, principal point position, etc., although these parameters may not be directly used for calculation in S3', they are the basis for understanding the imaging process).

[0119] In fact, this step may involve some complex mathematical transformations, such as perspective transformation, affine transformation, etc., which can describe the projection process from three-dimensional space to two-dimensional plane.

[0120] However, in the context of S3', we are more concerned with finding the feature points in the standard image corresponding to the feature points of the standard chart 2 directly through image processing techniques (such as feature point matching, template matching, etc.), and recording the image coordinates of these feature points.

[0121] In a specific embodiment, in step S3, according to the camera 1 recognition angle corresponding to each feature point of the standard image card 2, establishing a mathematical model of the feature point coordinates in the standard image and the camera 1 recognition angle specifically includes:

[0122] S31: determining the camera 1 recognition angle corresponding to the coordinates of the feature point of the standard image according to the coordinates of each feature point of the standard image card 2 and the corresponding camera 1 recognition angle;

[0123] S32: Establishing a mathematical model of the coordinates of the feature points in the standard image and the recognition angle of the camera 1 by fitting the coordinates of the feature points in the standard image and the recognition angle of the camera 1 corresponding thereto.

[0124] In this embodiment, the details of establishing the mathematical model of the feature point coordinates in the standard image and the recognition angle of the camera 1 in step S3 can be further refined into two sub-steps S31 and S32. The following is a detailed explanation of these two sub-steps:

[0125] S31: Determine the recognition angle of camera 1 corresponding to the coordinates of the feature points of the standard image

[0126] The purpose of this step is to match the coordinates of each feature point on the standard image with its corresponding angle obtained during the recognition process of camera 1.

[0127] For example, feature points corresponding to feature points on the standard chart 2 are found in the standard image by image processing techniques (such as feature point detection, etc.). These feature points are usually positions with significant features (such as corner points, edge points, etc.) in the image.

[0128] For each matched feature point, find its corresponding angle obtained during the recognition process of camera 1. These angles are usually calculated by the sensor and image processing algorithm of camera 1, and reflect the direction or position information of the feature point in the field of view of camera 1.

[0129] The matched feature point coordinates and the corresponding camera 1 recognition angles are sorted to form a data set for the subsequent fitting process.

[0130] In one example, refer to Figure 5 and Figure 6 , the feature point coordinates of the standard image corresponding to the feature point with coordinates (L1, H1) in the standard chart 2 are: (x1, y1). The feature point coordinates of the standard image corresponding to the feature point with coordinates (L1*M, H1*N) in the standard chart 2 are: (xm, yn).

[0131] According to the above formula 1) and formula 2), the camera 1 recognizes the horizontal angle θ corresponding to the feature point (x1, y1) of the standard image x1 , the camera 1 recognition height angle θ corresponding to the feature point (x1, y1) of the standard image y1 .

[0132]

[0133] According to the above formula 3) and formula 4), the camera 1 recognizes the horizontal angle θ corresponding to the feature point (xm, yn) of the standard image xm , the camera 1 recognition height angle θ corresponding to the feature point (x1, y1) of the standard image yn .

[0134]

[0135] S32: Establishing mathematical models through fitting

[0136] In step S32, the data set obtained in step S31 is used to fit a mathematical model that can describe the relationship between the coordinates of the feature points in the standard image and the recognition angle of camera 1. This generally involves the following steps:

[0137] 1) In the scenario of field of view angle measurement, a linear model, a polynomial model or other more complex nonlinear model may be used to describe the relationship between the coordinates of the standard image feature points and the recognition angle of the camera 1.

[0138] 2) Use data fitting techniques (such as least squares method, nonlinear optimization, etc.) to solve the model parameters. These parameters will enable the model to output a camera 1 recognition angle close to the actual one when given the input (feature point coordinates).

[0139] 3) Verify the accuracy and reliability of the model by comparing the model prediction results with the actual recognition angle of Camera 1. If there is a large difference between the model prediction results and the actual values, it may be necessary to adjust the model or reselect a more appropriate model.

[0140] 4) Once the model is verified to be effective, it can be applied to the subsequent field of view angle measurement process. By inputting the coordinates of the feature points in the test image, the model can predict the corresponding camera 1 recognition angle, which is then used to calculate the field of view angle of camera 1.

[0141] In one example, the horizontal coordinate value of the feature point in the standard image is used as the abscissa, and the horizontal angle recognized by the camera 1 is used as the ordinate to draw a curve, and a fitting curve f(x)=θx is obtained through fitting. The fitting curve f(x)=θx is a mathematical model between the horizontal coordinate of the feature point in the standard image and the horizontal angle recognized by the camera 1, and the mathematical model is a quadratic equation.

[0142] Similarly, the vertical coordinate value of the feature point in the standard image is used as the horizontal coordinate, and the height angle recognized by the camera 1 is used as the vertical coordinate to draw a curve, and the fitting curve f(y)=θy is obtained through fitting. The fitting curve f(y)=θy is a mathematical model between the vertical coordinate of the feature point in the standard image and the height angle recognized by the camera 1, and the mathematical model is a quadratic equation.

[0143] In a specific embodiment, in step S3, according to the camera 1 recognition angle corresponding to each feature point of the standard image card 2, establishing a mathematical model of the feature point coordinates in the standard image and the camera 1 recognition angle further specifically includes:

[0144] S311: determining the height angle recognized by the camera 1 corresponding to the vertical coordinate of each feature point of the standard image according to the height angle recognized by the camera 1 corresponding to the vertical coordinate of each feature point of the standard image card 2;

[0145] S321: Establishing a mathematical model of the vertical coordinates of the feature points in the standard image and the height angles recognized by the camera 1 by fitting the vertical coordinates of the feature points in the standard image and the corresponding height angles recognized by the camera 1.

[0146] In this embodiment, in step S311, according to the vertical coordinate (y_card) of each feature point on the standard image card 2 and the corresponding height angle (θy_card) obtained by recognition by the camera 1, feature points corresponding to the feature points on the standard image card 2 are found in the standard image through image processing technology (such as feature point detection, matching, etc.), and the vertical coordinates (y_image) of these feature points are recorded.

[0147] For each feature point found in the standard image, find its height angle (θy_image) obtained during the recognition process of camera 1 by some means (such as lookup table, direct calculation, etc.). This usually involves the process of converting the image coordinates of the feature point back to the camera 1 coordinate system, but here we simplify it to directly obtain the matching angle value.

[0148] The vertical coordinates (y_image) and corresponding height angles (θy_image) of the matched feature points are sorted to form a data set for subsequent fitting.

[0149] In step S321, a suitable mathematical model is selected. In this scenario, a linear model, a polynomial model or other forms of nonlinear models may be used to describe the relationship between the vertical coordinate (y_image) of the feature point and the height angle (θy_image) recognized by the camera 1.

[0150] During the fitting process, data fitting techniques (such as least squares method, nonlinear regression, etc.) are used to solve the model parameters. These parameters will enable the model to output a height angle (θy_image) close to the actual camera 1 recognition when the vertical coordinate (y_image) of the feature point is given as input.

[0151] In another specific embodiment, in step S3, according to the camera 1 recognition angle corresponding to each feature point of the standard image card 2, establishing a mathematical model of the feature point coordinates in the standard image and the camera 1 recognition angle further specifically includes:

[0152] S312: determining the horizontal angle recognized by the camera 1 corresponding to the horizontal coordinate of each feature point of the standard image according to the horizontal angle recognized by the camera 1 corresponding to the horizontal coordinate of each feature point of the standard image card 2;

[0153] S322: Establishing a mathematical model of the horizontal coordinates of the feature points in the standard image and the horizontal angles recognized by the camera 1 by fitting the horizontal coordinates of the feature points in the standard image and the corresponding horizontal angles recognized by the camera 1.

[0154] In this embodiment, in step S312, according to the horizontal coordinate (x_card) of each feature point on the standard image card 2 and the corresponding horizontal angle (θx_card) obtained by recognition by the camera 1, feature points corresponding to the feature points on the standard image card 2 are found in the standard image through image processing technology (such as feature point detection, matching, etc.), and the horizontal coordinates (x_image) of these feature points are recorded.

[0155] For each feature point found in the standard image, find its horizontal angle (θx_image) obtained during the recognition process of camera 1 by some means (such as lookup table, direct calculation, etc.). This usually involves the process of converting the image coordinates of the feature point back to the camera 1 coordinate system, but here we simplify it to directly obtain the matching angle value.

[0156] The vertical coordinates (x_image) and the corresponding horizontal angles (θx_image) of the matched feature points are sorted to form a data set for subsequent fitting.

[0157] In step S322, a suitable mathematical model is selected. In this scenario, a linear model, a polynomial model or other forms of nonlinear models may be used to describe the relationship between the vertical coordinates (x_image) of the feature points and the horizontal angle (θx_image) recognized by the camera 1.

[0158] During the fitting process, data fitting techniques (such as least squares method, nonlinear regression, etc.) are used to solve the model parameters. These parameters will enable the model to output a horizontal angle (θx_image) close to the actual camera 1 recognition when the horizontal coordinate (x_image) of the feature point is given as input.

[0159] In step S4 of the embodiment of the present application, a test image displayed by the VR display device is obtained, and the VR display device is placed in front of the lens of the camera 1 to ensure that the image displayed by the VR device can be clearly captured by the camera 1. Then, the test image displayed by the VR display device is photographed using the camera 1. Figure 7 , the test image displayed by the VR display device captured by camera 1.

[0160] In step S5 of the embodiment of the present application, the field of view angle of the VR display device is calculated using the mathematical model established in step S3 and the feature point set of the test image.

[0161] In a specific example, the feature points in the test image are identified by the test software, and the recognition angle of the camera 1 corresponding to each feature point is calculated using a mathematical model. Then, the field of view of the VR display device is calculated based on these angles.

[0162] In a specific embodiment, in step S5, determining the field of view angle of the display device of the virtual reality device according to the mathematical model and the feature point set of the test image specifically includes:

[0163] S511: the feature point set of the test image includes two feature point sets located at the horizontal edges of the test image;

[0164] S521: Determine the horizontal field of view angle of the virtual reality display device according to the horizontal angle recognized by the camera 1 corresponding to each feature point on the horizontal edge of the test image.

[0165] In this embodiment, in step S511, in the test image, an image processing algorithm (such as a Canny edge detector, a Harris corner detector, etc.) is used to detect the edges and corners of the image. From the detected edges and corners, two feature point sets located at the horizontal edges of the test image are selected. These feature point sets should represent the left and right edges of the field of view, respectively, to ensure that the selected feature point sets accurately reflect the edges of the field of view of the virtual reality display device. For example, referring to Figure 7 , the feature point with coordinates (xl, yl) in the test image is the feature point located at the left edge of the test image. The feature point with coordinates (xr, yr) in the test image is the feature point located at the right edge of the test image.

[0166] In step S521, the image coordinates (usually pixel coordinates) of each feature point in the test image are obtained. Then, these image coordinates are substituted into the previously established mathematical model (the model describes the relationship between the coordinates of the feature points of the standard image and the recognition angle of the camera 1). For the measurement of the horizontal field of view angle, we will pay special attention to the relationship between the horizontal coordinates and the corresponding horizontal angle recognized by the camera 1.

[0167] Based on the horizontal angle of camera 1 output by the mathematical model, we can calculate the horizontal field of view of the virtual reality display device. This usually involves calculating the sum of the angles corresponding to the two feature points, that is, the sum of the angles between the left and right edges of the field of view.

[0168] For example, θl is calculated according to f(x)=θx, where θl is the horizontal angle recognized by camera 1 corresponding to the feature point (xl, yl). θr is calculated according to f(x)=θx, where θr is the horizontal angle recognized by camera 1 corresponding to the feature point (xr, yr). θl+θr is the horizontal field of view angle of the virtual reality display device.

[0169] In another specific embodiment, in step S5, determining the field of view angle of the display device of the virtual reality device according to the mathematical model and the feature point set of the test image specifically includes:

[0170] S512: the feature point set of the test image includes two feature point sets located at the vertical edges of the test image;

[0171] S522: Determine the vertical field of view of the virtual reality display device according to the camera 1 recognition height angle corresponding to each feature point of the vertical edge of the test image.

[0172] In this embodiment, in step S511, in the test image, an image processing algorithm (such as a Canny edge detector, a Harris corner detector, etc.) is used to detect the edges and corners of the image. From the detected edges and corners, two feature point sets located at the vertical edges of the test image are selected. These feature point sets should represent the upper and lower edges of the field of view, respectively, to ensure that the selected feature point sets accurately reflect the edges of the field of view of the virtual reality display device. For example, referring to Figure 7 , the feature point with coordinates (xd, yd) in the test image is located at the lower edge of the test image. The feature point with coordinates (xu, yu) in the test image is located at the upper edge of the test image.

[0173] In step S521, the image coordinates (usually pixel coordinates) of each feature point in the test image are obtained. Then, these image coordinates are substituted into the previously established mathematical model (the model describes the relationship between the coordinates of the standard image feature points and the recognition angle of the camera 1). For the measurement of the vertical field of view angle, we will pay special attention to the relationship between the vertical coordinate and the corresponding camera 1 recognition height angle.

[0174] Based on the camera 1 recognition height angle output by the mathematical model, we can calculate the horizontal field of view of the virtual reality display device. This usually involves calculating the sum of the angles corresponding to the two feature points, that is, the sum of the angles between the upper and lower edges of the field of view.

[0175] For example, θu is calculated according to f(y)=θy, where θu is the height angle recognized by camera 1 corresponding to the feature point (xu, yu), θd is calculated according to f(y)=θy, where θd is the height angle recognized by camera 1 corresponding to the feature point (xd, yd), and θu+θd is the vertical field of view angle of the virtual reality display device.

[0176] The method for measuring the field of view angle provided in the embodiment of the present application combines image analysis and geometric relationships. Through precise measurement and calculation, the field of view angle of the VR display device can be accurately measured through the lens of camera 1. Compared with the prior art, the method for measuring the field of view angle provided in the embodiment of the present application is not affected by the external environment, and the accuracy of the measurement result is improved.

[0177] <System Embodiment>

[0178] The present application embodiment also provides a field of view angle measurement system 300. Figure 2 , the field of view angle measurement system 300 includes:

[0179] Image acquisition module 301, used to acquire the standard image of the standard image card 2 and the test image displayed by the virtual reality display device

[0180] An acquisition module 302 is used to acquire the recognition angle of the camera 1 corresponding to each feature point in the feature point set of the standard image card 2, wherein the standard image is an image formed by the standard image card 2 on the camera 1;

[0181] The first processing module 303 is used to establish a mathematical model of the coordinates of the feature points in the standard image and the recognition angle of the camera 1 according to the feature points of the standard image card 2 and the corresponding recognition angle of the camera 1;

[0182] The second processing module 304 is used to determine the field of view of the display device of the virtual reality device according to the mathematical model and the feature point set of the test image.

[0183] The field of view angle measurement system provided in the embodiment of the present application combines image analysis and geometric processing. The field of view angle of the VR display device can be measured through the image acquisition module 301 combined with the acquisition module 302, the first processing module 303 and the second processing module 304 without being affected by the external environment, thereby improving the accuracy of the measurement results.

[0184] <Medium Example>

[0185] In this embodiment, a computer-readable storage medium is also provided, which stores a computer program that can be read and executed by a computer, and the computer program is used to execute a method for measuring the field of view angle of any of the above method embodiments of the present application when read and executed by the computer.

[0186] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments, but it should be clear to those skilled in the art that the above embodiments can be used alone or in combination with each other as needed. In addition, for the device embodiment, since it corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the corresponding part of the method embodiment. The system embodiment described above is merely illustrative, and the modules described as separate components may or may not be physically separated.

[0187] The present application may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present application.

[0188] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0189] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0190] The computer program instructions for performing the operation of the present application can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer by any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect to the Internet). In some embodiments, by using the state information of computer-readable program instructions to customize electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs) or programmable logic arrays (PLAs), the electronic circuits can execute computer-readable program instructions, thereby realizing various aspects of the present application.

[0191] Various aspects of the present application are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0192] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0193] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0194] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0195] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present application is defined by the appended claims.

Claims

1. A method for measuring a field of view angle, characterized in that: For measuring the field of view angle of a virtual reality display device, the method comprises: Get the standard image captured by the camera; Obtaining a camera recognition angle corresponding to each feature point in a feature point set of a standard image card, wherein the standard image is an image formed by the standard image card on the camera; According to the characteristic points of the standard image card and the corresponding camera recognition angles, a mathematical model of the characteristic point coordinates in the standard image and the camera recognition angle is established; Acquire a test image displayed by a virtual reality display device captured by a camera; The field of view angle of a display device of a virtual reality device is determined according to the mathematical model and the feature point set of the test image.

2. The method for measuring the viewing angle according to claim 1, characterized in that: Obtaining the camera recognition angle corresponding to each feature point in the standard image card feature point set specifically includes: Obtaining the straight-line distance between the exit pupil position of the camera and the standard chart; According to the straight-line distance and the coordinates of the feature points of the standard image card, the camera recognition angle corresponding to the feature points of the standard image card is determined.

3. The method for measuring the viewing angle according to claim 2, characterized in that: Determining the camera recognition angle corresponding to the feature point of the standard image card according to the straight-line distance and the coordinates of the feature point of the standard image card specifically includes: According to the straight-line distance and the vertical coordinate of the feature point of the standard chart, the height angle of camera recognition corresponding to the feature point of the standard chart is determined.

4. The method for measuring the viewing angle according to claim 2, characterized in that: Determining the camera recognition angle corresponding to the feature point of the standard image card according to the straight-line distance and the coordinates of the feature point of the standard image card specifically includes: The horizontal angle of camera recognition corresponding to the feature point of the standard chart is determined according to the straight-line distance and the horizontal coordinate of the feature point of the standard chart.

5. The method for measuring the viewing angle according to any one of claims 1 to 4, characterized in that: Before establishing a mathematical model of the coordinates of the feature points in the standard image and the camera recognition angles corresponding thereto, the method further includes: According to the coordinates of each feature point of the standard image card and the corresponding camera recognition angle, the coordinates of the feature point of the standard image corresponding to the coordinates of each feature point of the standard image card are determined.

6. The method for measuring the viewing angle according to claim 5, characterized in that: According to the camera recognition angle corresponding to each feature point of the standard image card, establishing a mathematical model of the feature point coordinates and the camera recognition angle in the standard image specifically includes: Determine the camera recognition angle corresponding to the coordinates of the feature point of the standard image according to the coordinates of each feature point of the standard image card and the camera recognition angle corresponding thereto; By fitting the coordinates of the feature points in the standard image and the corresponding camera recognition angles, a mathematical model of the coordinates of the feature points in the standard image and the camera recognition angles is established.

7. The method for measuring the viewing angle according to claim 6, characterized in that: According to the camera recognition angle corresponding to each feature point of the standard image card, establishing a mathematical model of the feature point coordinates and the camera recognition angle in the standard image further specifically includes: Determine the camera recognition height angle corresponding to the vertical coordinate of each feature point of the standard image according to the camera recognition height angle corresponding to the vertical coordinate of each feature point of the standard image card; By fitting the vertical coordinates of the feature points in the standard image and the corresponding height angles recognized by the camera, a mathematical model of the vertical coordinates of the feature points in the standard image and the height angles recognized by the camera is established.

8. The method for measuring the viewing angle according to claim 6, characterized in that: According to the camera recognition angle corresponding to each feature point of the standard image card, establishing a mathematical model of the feature point coordinates and the camera recognition angle in the standard image further specifically includes: Determine the horizontal angle recognized by the camera corresponding to the horizontal coordinate of each feature point of the standard image according to the horizontal angle recognized by the camera corresponding to the horizontal coordinate of each feature point of the standard image card; By fitting the horizontal coordinates of the feature points in the standard image and the corresponding horizontal angles recognized by the camera, a mathematical model of the horizontal coordinates of the feature points in the standard image and the horizontal angles recognized by the camera is established.

9. The method for measuring the viewing angle according to claim 8, characterized in that: Determining the field of view angle of a display device of a virtual reality device according to the mathematical model and the feature point set of the test image specifically includes: The feature point set of the test image includes two feature point sets located at the horizontal edges of the test image; The horizontal field of view of the virtual reality display device is determined according to the camera recognition horizontal angle corresponding to each feature point on the horizontal edge of the test image.

10. The method for measuring the viewing angle according to claim 7, characterized in that: Determining the field of view angle of a display device of a virtual reality device according to the mathematical model and the feature point set of the test image specifically includes: The feature point set of the test image includes two feature point sets located at the vertical edges of the test image; The vertical field of view of the virtual reality display device is determined according to the camera recognition height angle corresponding to each feature point of the vertical edge of the test image.

11. The method for measuring the viewing angle according to claim 1, characterized in that: The standard chart is one of a checkerboard chart, a circular dot array chart or a crosshair chart.

12. A field of view angle measurement system, characterized in that: For measuring the field of view angle of a virtual reality display device, the system comprises: An image acquisition module, used to acquire standard images of standard image cards and test images displayed by a virtual reality display device; An acquisition module, used to acquire a camera recognition angle corresponding to each feature point in a feature point set of a standard image card, wherein the standard image is an image formed by the standard image card on the camera; A first processing module, used to establish a mathematical model of the coordinates of the feature points in the standard image and the camera recognition angle according to the feature points of the standard image card and the camera recognition angles corresponding thereto; The second processing module is used to determine the field of view angle of the virtual reality device display device according to the mathematical model and the feature point set of the test image.

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

Images