A method for calibrating user parameters in regional eye-tracking

Abstract

This application provides a method for calibrating user parameters for regional eye tracking, including: acquiring a calibration eye map of each calibration point; determining the calibration pupil features and calibration spot features of each calibration point; calculating the calibration corneal curvature radius and calibration corneal curvature center of each calibration point based on the calibration pupil features and calibration spot features; determining the calibration optical axis direction and the distance from the calibration pupil center to the calibration corneal curvature center of each calibration point based on the calibration corneal curvature center and calibration pupil center of each calibration point; calculating the angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point; acquiring an actual eye map, and calculating the actual visual axis direction of the actual eye map based on the angle and the actual optical axis direction of the actual eye map. By performing regional calibration on each calibration point, the accuracy and precision of eye tracking are improved.

Description

Technical Field

[0001] The embodiments of this application belong to the field of eye-tracking technology, and in particular relate to a method for calibrating user parameters for regional eye-tracking. Background Technology

[0002] As one of the body's most important organs, the eye receives 80% of the information the brain acquires in daily life. Therefore, gaze estimation has become a hot research area. A common method for gaze estimation is to place two or more light sources in front of the eye. When light rays from these sources pass through the eye, they are reflected and refracted at the outer edge of the cornea. In eye-tracking technology, because the eye parameters of each individual vary significantly, it is necessary to calibrate parameters such as the corneal radius of curvature (R) and the Kappa angle between the optical axis and the visual axis before eye tracking. Currently, the main calibration method is to use a fixed number of calibration points (usually 5-11) to calibrate each eye parameter, acquiring only one set of calibration parameters for each eye. During eye tracking, the mathematical model of the eye is often assumed to be a relatively ideal bispherical model or a relatively regular geometric model. However, the actual eye is irregular, and the parameters of the eye relative to the viewpoint are different when looking in different directions. Therefore, the accuracy of eye tracking is relatively low, resulting in a poor user experience. Summary of the Invention

[0003] To address or mitigate the technical problems in the prior art, this application proposes a regional eye parameter calibration method. Different eye parameters are taken when the eyes look in different directions to estimate the gaze, thereby improving the accuracy of eye tracking and enhancing the user experience.

[0004] This application provides a method for calibrating user parameters for regional eye-tracking, including:

[0005] Collect calibration eye diagrams of the human eye fixating on each calibration point;

[0006] Determine the calibration pupil features and calibration spot features for each calibration point, wherein the pupil features include the calibration pupil center;

[0007] Calculate the calibrated corneal curvature radius and calibrated corneal curvature center for each calibration point based on the calibrated pupil characteristics and calibrated light spot characteristics for each calibration point;

[0008] The calibration optical axis direction and the distance from the calibration pupil center to the calibration corneal curvature center are determined based on the calibration corneal curvature center and calibration pupil center of each calibration point;

[0009] Calculate the angle between the calibration optical axis direction and the calibration visual axis direction at each calibration point;

[0010] Acquire actual eye images, and calculate the actual optical axis direction of the actual eye images based on the actual pupil characteristics, actual light spot characteristics, and calibrated pupil characteristics at each calibration point, the calibrated corneal curvature radius, and the distance from the calibrated pupil center to the calibrated corneal curvature center.

[0011] The visual axis direction of the actual eye diagram is calculated based on the optical axis direction of the actual eye diagram and the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point.

[0012] As a preferred embodiment of this application, determining the calibration pupil features and calibration spot features for each calibration point includes:

[0013] Each frame of the calibrated eye map is input into the pupil target detection network to detect the pupil position, and the pupil region image is cropped out; the pupil region image is input into the pupil feature detection model to detect the calibrated pupil features;

[0014] The pupil region image is input into the spot detection model to detect and calibrate spot features;

[0015] The calibration pupil features and calibration spot features of each calibration point are calculated based on the calibration pupil features and calibration spot features of the calibration eye diagram in each frame.

[0016] As a preferred embodiment of this application, the step of calculating the calibration pupil features and calibration spot features of each calibration point based on the calibration pupil features and calibration spot features of each frame of the calibration eye diagram includes:

[0017] The calibrated pupil feature positions of each frame of the calibrated eye map obtained at each calibration point are divided into N groups of data according to the x, y, z coordinate axes;

[0018] After clustering the N sets of data for each calibration point using a clustering algorithm, the calibration pupil features and calibration spot features for each calibration point are obtained.

[0019] As a preferred embodiment of this application, the step of calculating the calibrated corneal curvature radius and calibrated corneal curvature center of each calibrated point based on the calibrated pupil characteristics and calibrated light spot characteristics of each calibrated point includes:

[0020] The calibrated corneal curvature radius and calibrated corneal curvature center of each frame of the calibrated eye diagram are calculated based on the calibrated pupil features and calibrated spot features of each frame of the calibrated eye diagram.

[0021] The calibrated corneal curvature radius and calibrated corneal curvature center of each frame of the calibrated eye map obtained at each calibrated point are divided into M groups of data according to the x, y, z coordinate axes;

[0022] After clustering the M sets of data for each calibration point using a clustering algorithm, the calibrated corneal curvature radius and the calibrated corneal curvature center of each calibration point are obtained.

[0023] As a preferred embodiment of this application, determining the calibration optical axis direction of each calibration point based on the calibration corneal curvature center and calibration pupil center of each calibration point includes:

[0024] Based on the calibrated corneal curvature center and calibrated pupil center of each frame of the calibrated eye diagram, determine the calibrated optical axis direction and the distance from the calibrated corneal curvature center to the calibrated pupil center of each frame of the calibrated eye diagram;

[0025] The calibration optical axis direction and the distance from the calibration corneal curvature center to the calibration pupil center of each frame of the calibration eye map are clustered using a clustering algorithm to obtain the distance from the calibration corneal curvature center to the pupil center and the calibration optical axis direction of each calibration point.

[0026] As a preferred embodiment of this application, the calculation of the actual optical axis direction of the actual eye diagram includes:

[0027] A real eye image is acquired, and the actual pupil features and actual light spot features are obtained through a pupil feature detection model and a light spot detection model. The actual pupil features include the actual pupil center.

[0028] The weight is calculated by multiplying the weight by the distance between the actual pupil center of the actual eye diagram and the calibrated pupil center of each calibration point, and then summing the products of the weights and the calibrated corneal curvature radius of each calibration point to obtain the actual corneal curvature radius.

[0029] The actual distance from the actual pupil center to the actual corneal curvature center of the actual eye diagram is obtained by summing the products of the weights and the distance from the calibrated corneal curvature center to the calibrated pupil center at each calibration point.

[0030] The actual optical axis direction of the actual eye diagram is obtained by the direction of the line connecting the actual corneal curvature center and the actual pupil center.

[0031] As a preferred embodiment of this application, the step of calculating the actual visual axis direction of the actual eye diagram based on the actual optical axis direction of the actual eye diagram and the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point includes:

[0032] The weight is calculated based on the reciprocal of the distance between the actual optical axis direction of the actual eye diagram and the calibration optical axis direction of each calibration point. The vertical component of the actual optical axis direction and the actual visual axis direction of the actual eye diagram is obtained by summing the product of the weight and the vertical component of the angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point.

[0033] The horizontal components of the actual optical axis direction and the actual visual axis direction of the actual eye diagram are obtained by summing the product of the weight and the horizontal component of the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point;

[0034] The actual visual axis direction of the actual eye diagram is calculated based on the horizontal and vertical components of the actual optical axis direction and the actual visual axis direction of the actual eye diagram.

[0035] Compared with the prior art, the embodiments of this application provide a method for calibrating user parameters for regional eye tracking. By calibrating each calibration point in a regional manner, the eye tracking parameters for different visions are refined. In actual use, by selectively using eye parameters, the accuracy and precision of eye tracking can be improved, the customer experience can be enhanced, and more application scenarios can be met. Attached Figure Description

[0036] 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. Some specific embodiments of this application will be described in detail below with reference to the accompanying drawings in an exemplary and non-limiting manner. The same reference numerals in the drawings designate the same or similar parts or components. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0037] Figure 1 This is a flowchart of a method for calibrating user parameters for regional eye tracking, provided in an embodiment of this application. Detailed Implementation

[0038] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort should fall within the scope of protection of the present application.

[0039] like Figure 1 As shown in the embodiment of this application, a method for calibrating user parameters for regional eye-tracking is provided. The method includes:

[0040] Step S101: Collect calibration eye diagrams of the human eye fixating on each calibration point;

[0041] The calibration program needs to be designed first, determining the number and location of calibration points, the position of the infrared lamp relative to the optical center of the camera, and the number of frames required to be acquired at each calibration point during calibration.

[0042] Design a calibration procedure, in which 9 calibration points are determined (the number depends on requirements; generally, more calibration points result in higher accuracy but longer calibration time; typically 5-11 calibration points are used) and their specific coordinates are defined. The locations of the calibration points are the 3D coordinates of the indicator points. .

[0043] The coordinates of the infrared lamp relative to the optical center of the camera can be determined from the structural dimensions of the head-mounted display device. This application provides an embodiment design with 8 infrared lamps (Note: When infrared lamp light shines on the cornea, it will form 8 light spots on the cornea).

[0044] The calibration procedure guides the human eye to focus on each calibration point, and an image is acquired at each calibration point. (Multiple frames of images are collected at each calibration point. Generally, the more frames, the smaller the statistical error. In this embodiment, 20 frames of images are collected at each calibration point.) Parameter calibration is performed.

[0045] Among them, images In this context, 'c' represents the calibration point number. f represents the sequence number of the image frame acquired at each calibration point. .

[0046] Step S102: Determine the calibration pupil characteristics and calibration spot characteristics for each calibration point;

[0047] Specifically, it includes:

[0048] The collected calibration eye maps Input the pupil target detection network to detect the pupil position and crop out the pupil region based on the pupil position (i.e., The purpose of cropping is mainly to reduce interference and improve the speed of model inference in the later stages.

[0049] The pupil detection models mentioned above, typically in architectures like U-Net and DeepLabv3+, can be semantic segmentation-based models. They treat the labeled eye map as a pixel-level binary classification task (pupil vs. non-pupil), outputting a segmentation mask of the same size as the input, where each pixel value represents the probability of belonging to the pupil. The model primarily performs morphological operations and ellipse fitting on the segmented pupil region, obtaining the center coordinates and contour from the fitted ellipse.

[0050] The pupil area obtained above The inputs are fed into the pupil feature detection model and the spot detection and ranking model to obtain the calibrated pupil key points of each frame of the calibrated eye map. ( Includes the center of the pupil The locations of eight key points at the edge of the pupil and the characteristics of the calibrated light spot, i.e. .

[0051] The pupil feature detection model mentioned above refers to a model that uses deep learning to automatically extract and quantify a series of high-dimensional feature vectors describing the morphology, dynamics, and texture characteristics of the pupil from calibrated eye maps. Typical architectures include HRNet or EfficientNet.

[0052] The aforementioned spot detection and ranking model specifically refers to a deep learning model used in ophthalmic imaging (such as corneal topography and surgical microscopy) to automatically detect, identify, and rank the importance of multiple corneal reflective points (spots). Its core objective is to accurately locate the key reference point used to calculate the Kappa angle from multiple potentially interfering reflections.

[0053] The pupil center is determined from the pupil key points obtained above. Each frame of the calibrated eye map has a calibrated pupil center, and the calibrated pupil center of each calibration point is obtained through the k-means clustering algorithm. (Supplementary explanation: For example, the center of the calibration pupil in the 20-frame calibration eye diagram at calibration point 0 is...) The calibrated pupil center was divided into 3 groups of data according to the x, y, z coordinate axes. , and The k-means algorithm was used to cluster the three sets of data, with the number of clusters k being 3 for each set. The centroid of the cluster with the largest number of clusters was taken as the final value. Using triple clustering, the centroid value of the largest cluster is... Therefore, the calibration pupil center at calibration point 0 can be obtained as... The calibration pupil center at other calibration points can be obtained similarly.

[0054] Step S103: Calculate the calibrated corneal curvature radius and calibrated corneal curvature center of each calibration point based on the calibration pupil characteristics and calibration spot characteristics of each calibration point;

[0055] Specifically, it includes:

[0056] First, based on the Le Grand full theoretical eye model and the principle of reflected light through light spot characteristics, the center of corneal curvature for each frame of the calibration eye map can be obtained through an iterative minimum value method. and radius of curvature (Note: Determine the center of corneal curvature) and radius of curvature The specific calculation process will not be elaborated in detail, as it is not the focus of this application.

[0057] The principle of reflected light is that infrared light emitted from a light source creates a light spot on the human eye, which is then reflected by the eye and imaged on a camera. Simultaneously, the center of the pupil is also imaged on the camera. By combining the images of the pupil center and the light spot on the camera, the center of corneal curvature for each frame of the calibration eye diagram can be obtained through an iterative minimum-value method. and calibrating the radius of curvature of the cornea .

[0058] The Le Grand mathematical model is a complete set of optical system parameters consisting of multiple optical surfaces (cornea, lens) and their media. Its core purpose is for theoretical calculations (such as retinal image size and aberration analysis), rather than for accurately simulating a specific human eye.

[0059] Subsequently, the calibrated corneal curvature center and calibrated corneal curvature radius at each calibration point are calculated. Each frame of the calibrated eye image obtained in the above steps yields one calibrated corneal curvature center and one calibrated corneal curvature radius. If there are 9 calibration points, then 9 (number of calibration points) * 20 (number of image frames acquired at each calibration point) = 180 calibrated corneal curvature centers and calibrated corneal curvature radii. This step determines the calibrated corneal curvature center (only one coordinate) and curvature radius (only one value) for each calibration point. Now, let's take calibration point 0 as an example for calculation:

[0060] Obtain the corneal curvature center at calibration point 0: Through the above steps, the corneal curvature center of the 20-frame calibration eye image at calibration point 0 is obtained. At the same time, the calibrated corneal curvature center at this time is divided into 3 groups of data according to the x, y, z coordinate axes. , and The three sets of data were clustered using the k-means algorithm, with k=3 clusters. The centroid of the largest cluster was the final value. Therefore, the center of corneal curvature at calibration point 0 was determined. .

[0061] Obtain the radius of curvature at calibration point 0: The radius of curvature of the calibrated cornea is obtained through the above steps. The three sets of data were clustered using the k-means algorithm, with k=3 clusters. The centroid of the largest cluster was the final value, from which the radius of curvature at calibration point 0 could be obtained. .

[0062] Similarly, repeat the above steps at each calibration point to obtain the calibrated corneal curvature center at each calibration point. and the calibrated corneal curvature radius is (Note: The center of corneal curvature is used to calculate other parameters of the eye.)

[0063] Step S104: Determine the calibration optical axis direction and the distance from the calibration pupil center to the calibration corneal curvature center for each calibration point based on the calibration corneal curvature center and calibration pupil center for each calibration point;

[0064] The above steps are used to determine the center of corneal curvature. and calibrating the radius of curvature of the cornea Based on the benchmark, combined with the calibration of key pupil points and calibration spot characteristics Based on the principle of reflected light, the distance from the center of curvature of the calibrated cornea to the center of the calibrated pupil and the direction of the calibrated optical axis of each frame of the calibrated eye diagram are obtained.

[0065] The k-means clustering algorithm was used to cluster the calibration eye images of each frame, based on the distance from the center of the calibrated corneal curvature to the center of the calibrated pupil and the direction of the calibrated optical axis. This yielded the distance from the center of the calibrated corneal curvature to the center of the calibrated pupil at each calibration point. The calibration optical axis direction at each calibration point is... .

[0066] Step S105: Calculate the angle between the calibration optical axis direction and the calibration visual axis direction at each calibration point;

[0067] The optical axis is the center for calibrating the curvature of the cornea. The visual axis is the line connecting the center of the pupil to the center of curvature of the cornea and the fovea on the retina (i.e., the line connecting the center of curvature of the cornea to the center of curvature of the retina). (Continuous direction), based on the projection relationship between the optical axis and the visual axis, the horizontal and vertical components of the Kappa angle can be determined. and .

[0068] Thus, each calibration point has a set of calibration parameters. For example, the calibration parameters for calibration point 0 are: .

[0069] Step S106: Acquire the actual eye diagram and calculate the actual optical axis direction of the actual eye diagram;

[0070] It should be noted that the above calibration parameters are required for actual line-of-sight estimation.

[0071] First, real-time eye images are acquired, and actual pupil features are obtained through pupil feature detection and spot detection algorithms. (Including the actual pupil center) ) and actual light spot characteristics The actual light spot characteristics are the light spot position and light spot number on the actual eye diagram.

[0072] Based on the actual pupil center of this actual eye diagram The weight ww is calculated by the reciprocal of the distance to the calibration pupil center pc1(x,y,z) from the calibration point. i The actual corneal curvature radius of this actual eye image is obtained by weighting. and the distance from the actual pupil center to the actual corneal curvature center .

[0073]

[0074]

[0075] Here, norm is the distance function between two points.

[0076] Step S107: Calculate the visual axis direction of the actual eye diagram based on the actual optical axis direction of the actual eye diagram and the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point.

[0077] Based on actual pupil characteristics Actual light spot characteristics Using the actual corneal curvature radius rr and the actual distance dd from the pupil center to the actual corneal curvature center obtained in the previous step, the actual optical axis direction of this frame's actual eye map can be obtained based on the 3D gaze algorithm. In the embodiments of this application, the 3D gaze algorithm is prior art and will not be described in detail here.

[0078] The weight wp is calculated based on the distance between the actual optical axis direction of the actual eye diagram and the calibration optical axis direction of the calibration point. i The horizontal component Kappa1 and the vertical component Kappa2 of the Kappa angle of the current frame are obtained by weighting:

[0079]

[0080] in, , n = 8, is a C++ library function.

[0081] Based on the Kappa1 and Kappa2 obtained in the previous step, the final eye movement direction or fixation point direction is obtained, thus completing eye tracking.

[0082] This application embodiment refines eye-tracking parameters for different visions through regional calibration. In actual use, selective use of eye parameters improves the accuracy and precision of eye tracking, enhancing the user experience. Furthermore, it reduces errors in eye parameters across different visions, improving the accuracy and precision of gaze estimation, making it suitable for eye tracking in VR / AR headsets.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for calibrating user parameters in regional eye-tracking, characterized in that, include: Collect calibration eye diagrams of the human eye fixating on each calibration point; Determine the calibration pupil features and calibration spot features for each calibration point, wherein the pupil features include the calibration pupil center; Calculate the calibrated corneal curvature radius and calibrated corneal curvature center for each calibration point based on the calibrated pupil characteristics and calibrated light spot characteristics for each calibration point; The calibration optical axis direction and the distance from the calibration pupil center to the calibration corneal curvature center are determined based on the calibration corneal curvature center and calibration pupil center of each calibration point; Calculate the angle between the calibration optical axis direction and the calibration visual axis direction at each calibration point; Acquire actual eye images, and calculate the actual optical axis direction of the actual eye images based on the actual pupil characteristics, actual light spot characteristics, and calibrated pupil characteristics at each calibration point, the calibrated corneal curvature radius, and the distance from the calibrated pupil center to the calibrated corneal curvature center. The visual axis direction of the actual eye diagram is calculated based on the optical axis direction of the actual eye diagram and the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point; The calculation of the actual optical axis direction of the actual eye diagram includes: A real eye image is acquired, and the actual pupil features and actual light spot features are obtained through a pupil feature detection model and a light spot detection model. The actual pupil features include the actual pupil center. The weight is calculated by multiplying the weight by the distance between the actual pupil center of the actual eye diagram and the calibrated pupil center of each calibration point, and then summing the products of the weights and the calibrated corneal curvature radius of each calibration point to obtain the actual corneal curvature radius. The actual distance from the actual pupil center to the actual corneal curvature center of the actual eye diagram is obtained by summing the products of the weights and the distance from the calibrated corneal curvature center to the calibrated pupil center at each calibration point. The actual optical axis direction of the actual eye diagram is obtained by the direction of the line connecting the actual corneal curvature center and the actual pupil center.

2. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The determination of the calibration pupil features and calibration spot features for each calibration point includes: Each frame of the calibrated eye map is input into the pupil target detection network to detect the pupil position, and the pupil region image is cropped out; the pupil region image is input into the pupil feature detection model to detect the calibrated pupil features; The pupil region image is input into the spot detection model to detect and calibrate spot features; The calibration pupil features and calibration spot features of each calibration point are calculated based on the calibration pupil features and calibration spot features of the calibration eye diagram in each frame.

3. The method for calibrating user parameters for regional eye tracking as described in claim 2, characterized in that, The step of calculating the calibration pupil features and calibration spot features for each calibration point based on the calibration pupil features and calibration spot features of each frame of the calibration eye diagram includes: The calibrated pupil feature positions of each frame of the calibrated eye map obtained at each calibration point are divided into N groups of data according to the x, y, z coordinate axes; After clustering the N sets of data for each calibration point using a clustering algorithm, the calibration pupil features and calibration spot features for each calibration point are obtained.

4. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The step of calculating the calibrated corneal curvature radius and calibrated corneal curvature center for each calibrated point based on the calibrated pupil characteristics and calibrated light spot characteristics of each calibrated point includes: The calibrated corneal curvature radius and calibrated corneal curvature center of each frame of the calibrated eye diagram are calculated based on the calibrated pupil features and calibrated spot features of each frame of the calibrated eye diagram. The calibrated corneal curvature radius and calibrated corneal curvature center of each frame of the calibrated eye map obtained at each calibrated point are divided into M groups of data according to the x, y, z coordinate axes; After clustering the M sets of data for each calibration point using a clustering algorithm, the calibrated corneal curvature radius and the calibrated corneal curvature center of each calibration point are obtained.

5. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The step of determining the calibration optical axis direction of each calibration point based on the calibration corneal curvature center and calibration pupil center of each calibration point includes: Based on the calibrated corneal curvature center and calibrated pupil center of each frame of the calibrated eye diagram, determine the calibrated optical axis direction and the distance from the calibrated corneal curvature center to the calibrated pupil center of each frame of the calibrated eye diagram; The calibration optical axis direction and the distance from the calibration corneal curvature center to the calibration pupil center of each frame of the calibration eye map are clustered using a clustering algorithm to obtain the distance from the calibration corneal curvature center to the pupil center and the calibration optical axis direction of each calibration point.

6. The method for calibrating user parameters for regional eye tracking as described in claim 1, characterized in that, The step of calculating the actual visual axis direction of the actual eye diagram based on the actual optical axis direction of the actual eye diagram and the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point includes: The weight is calculated based on the reciprocal of the distance between the actual optical axis direction of the actual eye diagram and the calibration optical axis direction of each calibration point. The vertical component of the actual optical axis direction and the actual visual axis direction of the actual eye diagram is obtained by summing the product of the weight and the vertical component of the angle between the calibration optical axis direction and the calibration visual axis direction of each calibration point. The horizontal components of the actual optical axis direction and the actual visual axis direction of the actual eye diagram are obtained by summing the product of the weight and the horizontal component of the angle between the calibrated optical axis direction and the calibrated visual axis direction at each calibration point; The actual visual axis direction of the actual eye diagram is calculated based on the horizontal and vertical components of the actual optical axis direction and the actual visual axis direction of the actual eye diagram.

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