WO2017107524A1 - 虚拟现实头盔的成像畸变测试方法及装置 - Google Patents

虚拟现实头盔的成像畸变测试方法及装置 Download PDF

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WO2017107524A1
WO2017107524A1 PCT/CN2016/096628 CN2016096628W WO2017107524A1 WO 2017107524 A1 WO2017107524 A1 WO 2017107524A1 CN 2016096628 W CN2016096628 W CN 2016096628W WO 2017107524 A1 WO2017107524 A1 WO 2017107524A1
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black
white
checkerboard image
distortion
white checkerboard
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PCT/CN2016/096628
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English (en)
French (fr)
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张修宝
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乐视控股（北京）有限公司
乐视致新电子科技（天津）有限公司
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Publication of WO2017107524A1 publication Critical patent/WO2017107524A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30204—Marker
    • G06T2207/30208—Marker matrix

Definitions

  • Embodiments of the present invention relate to the field of virtual reality technologies, and in particular, to an imaging distortion testing method and apparatus for a virtual reality helmet.
  • VR Virtual Reality
  • a helmet-type display also known as a virtual reality helmet
  • the virtual reality helmet is used to close the visual and auditory sense of the outside world and guide the user to create a feeling in the virtual environment.
  • the display principle is that the left and right eye screens respectively display the images of the left and right eyes, and the human eye obtains such a difference information and generates a stereoscopic effect in the mind.
  • the virtual reality helmet has the characteristics of small size and strong sealing, and has wide application in military training, virtual driving and virtual city projects.
  • the inventors have found that the positions corresponding to the binoculars in the existing virtual reality helmet are respectively provided with convex lenses.
  • the image formed by the convex lens may be pincushion-shaped, so that the prior art generally adopts
  • the original image is first subjected to barrel deformation processing, and then subjected to convex lens imaging to make the image obtained by the user approximate the original image.
  • the technique of performing barrel deformation processing on the original image is already a very mature technology.
  • the image finally obtained by the user is only an approximate image of the original image, and there is currently no quantitative evaluation of the imaging effect of the approximate image obtained by this processing method.
  • Embodiments of the present invention provide an imaging distortion testing method and apparatus for a virtual reality helmet, so as to overcome an image obtained by a user in the prior art that is only an approximate image of an original image, and currently lacks This processing method yields a technical problem of quantitative evaluation of the imaging effect of the approximate image.
  • the invention provides an imaging distortion testing method for a virtual reality helmet.
  • a lens for image enlargement is arranged between the screen of the virtual reality helmet and the human eye, and the black and white checkerboard image displayed on the virtual reality helmet is black and white.
  • the composition of the grid includes:
  • the calculating the root mean square error of the intersection coordinates of each black and white grid of the anti-distortion black and white checkerboard image and the intersection coordinates of the corresponding black and white grid of the ideal black and white checkerboard image and before include:
  • Boundary detection is performed on the ideal black and white checkerboard image to determine the coordinates of each black and white grid intersection of the ideal black and white checkerboard image.
  • determining, according to the horizontal center line and the vertical center line of the anti-distortion black and white checkerboard image, the corresponding ideal black and white checkerboard image comprises:
  • the ideal black and white checkerboard image is determined according to the horizontal grid number and the vertical grid number based on the horizontal center line and the vertical center line on the anti-distortion black and white checkerboard image.
  • n is the number of intersections of the black and white grids
  • i is the number of the intersection of the black and white grids
  • dx i is the error of the horizontal direction of the i-th intersection of the black and white checkerboard
  • dy i is the black and white The vertical direction error of the i-th intersection of the checkerboard.
  • the invention also provides an imaging distortion testing device for a virtual reality helmet, comprising:
  • An acquisition module configured to obtain an original black and white checkerboard image displayed on a virtual reality helmet screen
  • a deformation module configured to perform barrel deformation processing on the original black and white checkerboard image to obtain a black and white checkerboard image after barrel deformation processing
  • the acquiring module is further configured to acquire an anti-distortion black and white checkerboard image after lens imaging, and determine intersection coordinates of the black and white grid on the anti-distortion black and white checkerboard image;
  • a determining module according to the horizontal center line and the vertical center line of the anti-distortion black and white checkerboard image, determining a coordinate of the corresponding ideal black and white checkerboard image and the black and white grid intersection on the ideal black and white checkerboard image;
  • the calculation module calculates a root mean square error of each black and white grid intersection coordinate of the anti-distortion black and white checkerboard image and a corresponding black and white grid intersection coordinate of the ideal black and white checkerboard image to determine the size of the imaging distortion.
  • calculation module is specifically configured to:
  • Boundary detection is performed on the ideal black and white checkerboard image to determine the coordinates of each black and white grid intersection of the ideal black and white checkerboard image.
  • the determining module is specifically configured to:
  • the ideal black and white checkerboard image is determined according to the horizontal grid number and the vertical grid number based on the horizontal center line and the vertical center line on the anti-distortion black and white checkerboard image.
  • root mean square error calculation formula is as follows:
  • n is the number of intersections of the black and white grids
  • i is the number of the intersection of the black and white grids
  • dx i is the error of the horizontal direction of the i-th intersection of the black and white checkerboard
  • dy i is the black and white The vertical direction error of the i-th intersection of the checkerboard.
  • the method and device for detecting distortion of a virtual reality helmet using the particularity of a black and white checkerboard image, first obtaining a barrel-shaped black and white checkerboard corresponding to the original image The image, then obtain the anti-distortion black and white checkerboard image displayed through the lens, and then determine the ideal black and white checkerboard image. According to the root mean square error of the black and white grid intersection point, the image distortion of the virtual reality helmet can be tested.
  • an embodiment of the present invention further provides an imaging distortion testing device for a virtual reality helmet, and a lens for image enlargement is disposed between a screen of the virtual reality helmet and a human eye, and the virtual reality helmet is displayed on the helmet.
  • the black and white checkerboard image consists of a phased black and white grid, the device comprising: one or more processors;
  • One or more programs the one or more programs being stored in the memory, when executed by the one or more processors:
  • the imaging distortion testing device of the virtual reality helmet of the embodiment of the present invention firstly obtains a barrel-shaped black and white checkerboard image corresponding to the original image by using the speciality of the black and white checkerboard image, and then obtains the anti-distortion displayed through the lens.
  • the black and white checkerboard image is used to determine the ideal black and white checkerboard image.
  • the image distortion of the virtual reality helmet can be tested according to the root mean square error and the size of the black and white grid intersection coordinates.
  • the non-transitory computer readable storage medium provided by the embodiment obtains the barrel-shaped black and white checkerboard image corresponding to the original image by using the speciality of the black and white checkerboard image, and then obtains the anti-distortion displayed through the lens.
  • the black and white checkerboard image is used to determine the ideal black and white checkerboard image.
  • the image distortion of the virtual reality helmet can be tested according to the root mean square error and the size of the black and white grid intersection coordinates.
  • Embodiments of the present invention also provide a computer program product, the computer program product comprising a computing program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer When the computer is executed:
  • the computer program product provided by the embodiment takes advantage of the particularity of the black and white checkerboard image. Taking the barrel-shaped black and white checkerboard image corresponding to the original image, and obtaining the anti-distortion black and white checkerboard image displayed through the lens, and then determining the ideal black and white checkerboard image, according to the root mean square error of the black and white grid intersection coordinates The size of the image can be tested for the size of the image distortion of the virtual reality helmet.
  • Figure 1 is a schematic view of an ideal black and white checkerboard image of the present invention
  • FIG. 2 is a schematic view of a black and white checkerboard image after barrel deformation according to the present invention
  • Embodiment 3 is a schematic diagram of Embodiment 1 of an imaging distortion testing method for a virtual reality helmet according to the present invention
  • Embodiment 4 is a schematic diagram of Embodiment 2 of an imaging distortion testing method for a virtual reality helmet according to the present invention
  • FIG. 5 is a schematic diagram of Embodiment 1 of an imaging distortion testing apparatus for a virtual reality helmet according to the present invention.
  • FIG. 6 is a schematic diagram showing the hardware structure of an imaging distortion testing device of a virtual reality helmet according to an embodiment of the present invention.
  • the original image needs to be pre-processed first, that is, the original image is first subjected to barrel deformation processing, and the image after the barrel deformation processing passes.
  • the lens can be reversed to become an anti-distorted black and white checkerboard image that is close to the original image.
  • the present invention proposes an imaging distortion detection method and apparatus for a virtual reality helmet, which utilizes the particularity of a black and white checkerboard image, because the black and white checkerboard can accurately determine each black and white grid intersection even after deformation.
  • the original black and white checkerboard image is shown in Figure 1.
  • the black and white checkerboard image after barrel deformation is shown in Figure 2. Shown.
  • the specific implementation is as follows:
  • FIG. 3 is a schematic diagram of a first embodiment of an imaging distortion test method for a virtual reality helmet according to the present invention. As shown in FIG. 3, the method for testing an imaging distortion of the virtual reality helmet of the present embodiment may specifically include the following steps:
  • the embodiment utilizes the particularity of the black and white checkerboard image. Since the black and white checkerboard can accurately determine the coordinates of each black and white grid intersection even after the deformation, it can be known according to the distortion of the intersection. Distortion of the entire image. The first thing you need to get on the virtual reality helmet screen is the original black and white checkerboard image that has not been processed.
  • S12 Perform barrel deformation processing on the original black and white checkerboard image to obtain a black and white checkerboard image after barrel deformation processing.
  • the lens disposed in the virtual reality helmet is generally a convex lens
  • the convex surface of the convex lens faces the screen image, and the other surface faces the human eye.
  • pincushion deformation occurs in an image displayed after passing through a convex lens.
  • the original black and white checkerboard image is subjected to barrel deformation processing in advance to cancel the pincushion deformation generated by the convex lens.
  • the anti-distortion black and white checkerboard image is actually the image received by the human eye, and this image can be determined according to parameters such as the refractive index of the lens. According to the computer image processing method, the intersection coordinates of the black and white grid on the anti-distortion black and white checkerboard image can also be determined.
  • the center point of the image deformed by the barrel is outwardly protruded, and the periphery is inwardly contracted, but the horizontal center line and the vertical center line are not deformed.
  • the anti-distortion black and white checkerboard image is processed by the anti-distortion of the lens, it is still an error image.
  • the number of the black and white checkerboard is determined even after the deformation, maintaining the horizontal centerline and vertical. The center line is unchanged, and the entire image is equally divided into the number of cells in the black and white checkerboard to obtain an ideal black and white checkerboard image.
  • the intersection coordinates of each black and white grid of the anti-distortion black and white checkerboard image and the intersection coordinates of the black and white grid of the corresponding ideal black and white checkerboard image can be determined, and can be calculated according to the coordinates of the intersection point.
  • the root mean square error sum of all intersections the larger the root mean square error, the larger the distortion, the smaller the root mean square error and the smaller the distortion.
  • the imaging distortion test method of the virtual reality helmet of the embodiment takes the speciality of the black and white checkerboard image, first obtains the barrel-shaped black and white checkerboard image corresponding to the original image, and then obtains the ideal black and white displayed through the lens.
  • the checkerboard image can be used to test the image distortion of the virtual reality helmet according to the coordinate error of the black and white grid intersection.
  • FIG. 4 is a flowchart of Embodiment 2 of an imaging distortion testing method for a virtual reality helmet according to the present invention.
  • the imaging distortion testing method of the virtual reality helmet of the present embodiment is further described in more detail on the basis of the first embodiment.
  • Technical solution. As shown in FIG. 4, the method for testing the imaging distortion of the virtual reality helmet of the embodiment may specifically include the following steps:
  • the embodiment utilizes the particularity of the black and white checkerboard image. Since the black and white checkerboard can accurately determine the coordinates of each black and white grid intersection even after the deformation, it can be known according to the distortion of the intersection. Distortion of the entire image. The first thing you need to get on the virtual reality helmet screen is the original black and white checkerboard image that has not been processed.
  • S22 Perform barrel deformation processing on the original black and white checkerboard image to obtain a black and white checkerboard image after the barrel deformation process.
  • the original black and white checkerboard image is deformed in a barrel shape in advance.
  • the ideal black and white checkerboard image is thus obtained through the lens.
  • S23 Obtain an anti-distortion black and white checkerboard image after lens imaging, and determine intersection coordinates of the black and white grid on the anti-distortion black and white checkerboard image.
  • the anti-distortion black and white checkerboard image is actually the image received by the human eye, and this image can be determined according to parameters such as the refractive index of the lens.
  • the computer image processing method The intersection coordinates of the black and white grid on the anti-distortion black and white checkerboard image can be determined.
  • the image is deformed, there is no loss of elements in the image, but only deformation occurs at the boundary.
  • the horizontal and vertical grid numbers of the deformed black and white checkerboard image remain unchanged.
  • the center point of the image deformed by the barrel is outwardly protruded, and the periphery is inwardly contracted, but the horizontal center line and the vertical center line are not deformed.
  • the ideal black and white checkerboard image can be determined.
  • the horizontal center line and the vertical center line can be kept unchanged, and the entire image is equally divided into the number of squares of the black and white checkerboard to obtain an ideal black and white checkerboard image.
  • S26 Perform boundary detection on the anti-distortion black and white checkerboard image to determine the coordinates of the intersection of the black and white grid of the anti-distortion black and white checkerboard image.
  • boundary detection is a basic problem in image processing and computer vision.
  • the purpose of boundary detection is to identify points in the digital image where the brightness changes significantly.
  • the boundary detection method can be used to obtain the coordinates of the intersection points of the black and white grids in the anti-distortion black and white checkerboard image.
  • S27 Perform boundary detection on the ideal black and white checkerboard image to determine the coordinates of the intersection of each black and white grid of the ideal black and white checkerboard image.
  • the coordinates of each black and white grid intersection of the ideal black and white checkerboard image can also be determined.
  • the black and white grid on the ideal black and white checkerboard image is determined.
  • the root mean square error of the intersection coordinates is determined.
  • root mean square error calculation formula is as follows:
  • n is the number of intersections of black and white grids
  • i is the number of the intersection of black and white grids
  • dx i is the error of the horizontal direction of the i-th intersection of the black and white checkerboard
  • dy i is the i-th intersection of the black and white checkerboard The error in the vertical direction.
  • the imaging distortion testing method and device of the virtual reality helmet of the present invention utilizes the particularity of the black and white checkerboard image to obtain the barrel-shaped black and white checkerboard image corresponding to the original image, and then obtain the ideal image displayed through the lens.
  • the black and white checkerboard image can be used to determine the coordinates of the black and white grid intersection points and the intersections of the black and white grids in the ideal black and white checkerboard image by using the boundary detection method, so that the error of each black and white grid intersection in the ideal black and white checkerboard can be calculated, and then calculated. Root mean square error of all black and white grid intersections.
  • FIG. 5 is a schematic diagram of a first embodiment of an imaging distortion test method for a virtual reality helmet according to the present invention.
  • the imaging distortion test method of the virtual reality helmet of the present embodiment may include an acquisition module 11, a deformation module 12, and a determination. Module 13 and calculation module 14.
  • the obtaining module 11 is configured to obtain an original black and white checkerboard image displayed on the virtual reality helmet screen; and the deformation module 12 is connected to the obtaining module 11 for performing barrel deformation processing on the original black and white checkerboard image to obtain a barrel deformation process.
  • the imaging distortion testing device of the virtual reality helmet of the embodiment takes the speciality of the black and white checkerboard image, first obtains the barrel-shaped black and white checkerboard image corresponding to the original image, and then obtains the ideal black and white displayed through the lens.
  • the checkerboard image can be used to test the image distortion of the virtual reality helmet according to the coordinate error of the black and white grid intersection.
  • the schematic diagram of the second embodiment of the imaging distortion detecting device of the virtual reality helmet of the present embodiment is the same as that of FIG. 5 .
  • the imaging distortion testing device of the virtual reality helmet of the embodiment is Based on the first embodiment shown in FIG. 5, the technical solution of the present invention will be further described in more detail.
  • the imaging distortion testing device of the virtual reality helmet of the present embodiment may include an obtaining module 11, a deformation module 12, a determining module 13, and a calculating module 14.
  • the obtaining module 11 is configured to obtain an original black and white checkerboard image displayed on the virtual reality helmet screen; and the deformation module 12 is connected to the obtaining module 11 for performing barrel deformation processing on the original black and white checkerboard image to obtain a barrel deformation process.
  • calculation module 14 is specifically configured to:
  • Boundary detection is performed on the ideal black and white checkerboard image to determine the coordinates of each black and white grid intersection of the ideal black and white checkerboard image.
  • the determining module 13 is specifically configured to:
  • the ideal black and white checkerboard image is determined according to the horizontal grid number and the vertical grid number.
  • root mean square error calculation formula is as follows:
  • n is the number of intersections of black and white grids
  • i is the number of the intersection of black and white grids
  • dx i is the error of the horizontal direction of the i-th intersection of the black and white checkerboard
  • dy i is the i-th intersection of the black and white checkerboard The error in the vertical direction.
  • the imaging distortion testing device of the virtual reality helmet of the embodiment realizes the realization mechanism of the imaging distortion test of the virtual reality helmet by using the above module and the virtual reality of the embodiment shown in FIG. 4 described above.
  • the implementation of the imaging distortion test of the helmet is the same. For details, refer to the description of the embodiment shown in FIG. 4, and details are not described herein again.
  • FIG. 6 is a schematic diagram of a hardware structure of an imaging distortion testing device for a virtual reality helmet according to an embodiment of the present application. As shown in FIG. 6, the imaging distortion testing device includes:
  • processors 610 and memory 620 one processor 610 is taken as an example in FIG.
  • the imaging distortion testing device may further include an input device 630 and an output device 640.
  • the processor 610, the memory 620, the input device 630, and the output device 640 may be connected by a bus or other means, as exemplified by a bus connection in FIG.
  • the memory 620 is used as a non-transitory computer readable storage medium, and can be used for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as the imaging distortion test method of the virtual reality helmet in the embodiment of the present application.
  • Program instructions/modules eg, acquisition module 11, deformation module 12, determination module 13, and calculation module 14 shown in FIG. 5.
  • the processor 610 performs related various functional applications and data processing by running non-transitory software programs, instructions, and modules stored in the memory 620, that is, the imaging distortion testing method of the virtual reality helmet in the above method embodiment.
  • the memory 620 can include a storage program area and a storage data area, wherein the storage program area can store an operating system, an application required by at least one function;
  • the storage data area can store data and the like created according to the use of the imaging distortion test apparatus of the virtual reality helmet.
  • memory 620 can include high speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device.
  • the memory 620 can optionally include memory remotely located relative to the processor 610 that can be connected to the imaging distortion testing device of the virtual reality helmet via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • Input device 630 can receive input numeric or character information and generate key signal inputs related to user settings and function control of the imaging distortion testing device of the virtual reality helmet.
  • the output device 640 can include a display device such as a display screen.
  • the one or more modules are stored in the memory 620, and when executed by the one or more processors 610, perform the virtual reality helmet in any of the above method embodiments. Like the distortion test method.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).
  • the storage medium may be a magnetic disk, an optical disk, a read only memory (ROM), or a random access memory (RAM).

Abstract

一种虚拟现实头盔的成像畸变测试方法及装置，所述方法包括：输入原始黑白棋盘格图像，获取经虚拟现实头盔对其桶形变形处理后显示在屏幕上，且经透镜成像后的反畸变黑白棋盘格图像，由其确定含畸变的黑白格交叉点坐标，并由其水平中心线和垂直中心线上的交叉点确定相对理想的交叉点坐标，计算上述两类交叉点中对应点的均方根误差和，以确定成像畸变的大小。虚拟现实头盔的成像畸变测试方法及装置利用黑白棋盘格图像的特殊性，先获取反畸变黑白棋盘格图像，再确定含畸变的交叉点坐标和相对理想的交叉点坐标，根据上述两类交叉点坐标对应误差的大小，可以测试出虚拟现实头盔的图像畸变大小。

Description

虚拟现实头盔的成像畸变测试方法及装置
本申请要求在2015年12月21日提交中国专利局、申请号201510964756.2、发明名称为“虚拟现实头盔的成像畸变测试方法及装置”的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本发明实施例涉及虚拟现实技术领域，尤其涉及一种虚拟现实头盔的成像畸变测试方法及装置。
背景技术
随着虚拟现实技术(Virtual Reality,简称VR)的迅速发展，出现了一种头盔式显示器，也称为虚拟现实头盔。利用虚拟现实头盔，将人的对外界的视觉、听觉封闭，引导用户产生一种身在虚拟环境中的感觉。其显示原理是左右眼屏幕分别显示左右眼的图像，人眼获取这种带有差异的信息后在脑海中产生立体感。虚拟现实头盔作为虚拟现实的显示设备，具有小巧和封闭性强的特点，在军事训练、虚拟驾驶及虚拟城市等项目中具有广泛的应用。
发明人在实现本发明的过程中发现现有的虚拟现实头盔中与双眼对应的位置分别设置有凸透镜，根据凸透镜的特性，经过凸透镜成像的图像会发生枕形形变，因此现有技术中通常采用先将原始图像进行桶形变形处理，再经过凸透镜成像的方法，使用户得到的图像近似于原始图像。将原始图像进行桶形变形处理的技术已经是非常成熟的技术。
但是，用户最终得到的图像只是原始图像的近似图像，目前缺乏对于这种处理方法得到的近似图像的成像效果的量化的评价。
发明内容
本发明实施例提供一种虚拟现实头盔的成像畸变测试方法及装置，以克服现有技术中用户最终得到的图像只是原始图像的近似图像，目前缺乏对于 这种处理方法得到的近似图像的成像效果的量化的评价的技术问题。
本发明提供一种虚拟现实头盔的成像畸变测试方法，所述虚拟现实头盔的屏幕与人眼之间设置有用于图像放大的透镜，所述虚拟现实头盔上显示的黑白棋盘格图像由相间的黑白格组成，包括：
获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；
对所述原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；
获取经透镜成像后的反畸变黑白棋盘格图像，并确定所述反畸变黑白棋盘图像上黑白格的交叉点坐标；
根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及所述理想黑白棋盘格图像上黑白格交叉点的坐标；
计算所述反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
进一步可选地，所述计算所述反畸变黑白棋盘格图像的每个黑白格的交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格的交叉点坐标的均方根误差和之前还包括：
对所述反畸变黑白棋盘格图像进行边界检测，以确定所述反畸变黑白棋盘格图像的黑白格的交叉点的坐标；
对所述理想黑白棋盘格图像进行边界检测，以确定所述理想黑白棋盘格图像的每个黑白格交叉点的坐标。
进一步可选地，所述根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像包括：
检测所述反畸变黑白棋盘格图像的水平格数和竖直格数；
以所述反畸变黑白棋盘格图像上的水平中心线和竖直中心线为基准，按照所述水平格数和竖直格数确定所述理想黑白棋盘格图像。
进一步可选地，所述均方根误差和的计算公式如下：
Figure PCTCN2016096628-appb-000001
其中，n为所述黑白格交叉点的数量，i为所述黑白格交叉点的序号，dxi 为所述黑白棋盘格的第i个交叉点的水平方向的误差，dyi为所述黑白棋盘格的第i个交叉点的垂直方向的误差。
本发明还提供一种虚拟现实头盔的成像畸变测试装置，包括：
获取模块，用于获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；
变形模块，用于对所述原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；
所述获取模块，还用于获取经透镜成像后的反畸变黑白棋盘格图像，并确定所述反畸变黑白棋盘图像上黑白格的交叉点坐标；
确定模块，根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及所述理想黑白棋盘格图像上黑白格交叉点的坐标；
计算模块，计算所述反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
进一步可选地，所述计算模块具体用于：
对所述反畸变黑白棋盘格图像进行边界检测，以确定所述反畸变黑白棋盘格图像的黑白格的交叉点的坐标；
对所述理想黑白棋盘格图像进行边界检测，以确定所述理想黑白棋盘格图像的每个黑白格交叉点的坐标。
进一步可选地，所述确定模块具体用于：
检测所述反畸变黑白棋盘格图像的水平格数和竖直格数；
以所述反畸变黑白棋盘格图像上的水平中心线和竖直中心线为基准，按照所述水平格数和竖直格数确定所述理想黑白棋盘格图像。
进一步可选地，所述均方根误差计算公式如下：
Figure PCTCN2016096628-appb-000002
其中，n为所述黑白格交叉点的数量，i为所述黑白格交叉点的序号，dxi为所述黑白棋盘格的第i个交叉点的水平方向的误差，dyi为所述黑白棋盘格的第i个交叉点的垂直方向的误差。
本发明实施例的虚拟现实头盔的成像畸变测试方法及装置，利用黑白棋盘格图像的特殊性，先获取与原始图像对应的经过桶形形变后的黑白棋盘格 图像，再获取透过透镜显示的反畸变黑白棋盘格图像，再确定理想黑白棋盘格图像，根据黑白格交叉点坐标均方根误差和的大小，可以测试出虚拟现实头盔的图像畸变大小。
为实现上述目的，本发明实施例还提供了一种虚拟现实头盔的成像畸变测试设备，所述虚拟现实头盔的屏幕与人眼之间设置有用于图像放大的透镜，所述虚拟现实头盔上显示的黑白棋盘格图像由相间的黑白格组成，所述设备包括：一个或者多个处理器；
存储器；
一个或者多个程序，所述一个或者多个程序存储在所述存储器中，当被所述一个或者多个处理器执行时：
获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；
对所述原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；
获取经透镜成像后的反畸变黑白棋盘格图像，并确定所述反畸变黑白棋盘图像上黑白格的交叉点坐标；
根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及所述理想黑白棋盘格图像上黑白格交叉点的坐标；
计算所述反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
本发明实施例的虚拟现实头盔的成像畸变测试设备，利用黑白棋盘格图像的特殊性，先获取与原始图像对应的经过桶形形变后的黑白棋盘格图像，再获取透过透镜显示的反畸变黑白棋盘格图像，再确定理想黑白棋盘格图像，根据黑白格交叉点坐标均方根误差和的大小，可以测试出虚拟现实头盔的图像畸变大小。
本发明实施例还提供了一种非暂态计算机可读存储介质，所述非暂态计算机可读存储介质存储计算机指令，所述计算机指令用于使所述计算机执行：
获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；
对所述原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；
获取经透镜成像后的反畸变黑白棋盘格图像，并确定所述反畸变黑白棋盘图像上黑白格的交叉点坐标；
根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及所述理想黑白棋盘格图像上黑白格交叉点的坐标；
计算所述反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
本实施例提供的非暂态计算机可读存储介质，利用黑白棋盘格图像的特殊性，先获取与原始图像对应的经过桶形形变后的黑白棋盘格图像，再获取透过透镜显示的反畸变黑白棋盘格图像，再确定理想黑白棋盘格图像，根据黑白格交叉点坐标均方根误差和的大小，可以测试出虚拟现实头盔的图像畸变大小。
本发明实施例还提供了一种计算机程序产品，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行：
获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；
对所述原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；
获取经透镜成像后的反畸变黑白棋盘格图像，并确定所述反畸变黑白棋盘图像上黑白格的交叉点坐标；
根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及所述理想黑白棋盘格图像上黑白格交叉点的坐标；
计算所述反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
本实施例提供的计算机程序产品，利用黑白棋盘格图像的特殊性，先获 取与原始图像对应的经过桶形形变后的黑白棋盘格图像，再获取透过透镜显示的反畸变黑白棋盘格图像，再确定理想黑白棋盘格图像，根据黑白格交叉点坐标均方根误差和的大小，可以测试出虚拟现实头盔的图像畸变大小。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本发明的理想的黑白棋盘格图像的示意图；
图2为本发明的桶形变形后的黑白棋盘格图像的示意图；
图3为本发明的虚拟现实头盔的成像畸变测试方法的实施例一的示意图；
图4为本发明的虚拟现实头盔的成像畸变测试方法的实施例二的示意图；
图5为本发明的虚拟现实头盔的成像畸变测试装置的实施例一的示意图。
图6本发明实施例提供的虚拟现实头盔的成像畸变测试设备的硬件结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
现有的虚拟现实头盔，为防止屏幕上原始图像经过透镜后产生枕形畸变，需要先对原始图像进行预处理，即先对原始图像进行桶形变形处理，该桶形变形处理后的图像经过透镜后可反畸变成与原始图像相接近的反畸变黑白棋盘格图像。但是对于该反畸变黑白棋盘格图像的畸变校正情况缺乏量化的评 测，本发明针对此问题，提出一种虚拟现实头盔的成像畸变测试方法及装置，利用黑白棋盘格图像的特殊性，由于黑白棋盘格即使在变形以后也能够准确地确定每个黑白格交叉点的坐标，因此根据交叉点的畸变情况，即可获知整个虚拟现实头盔所显示头像的反畸变情况，原始的黑白棋盘格图像如图1所示，桶形形变后的黑白棋盘格图像如图2所示。具体实施例如下：
图3为本发明的虚拟现实头盔的成像畸变测试方法实施例一的示意图，如图3所示，本实施例的虚拟现实头盔的成像畸变测试方法，具体可以包括如下步骤：
S11，获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像。
在具体实施时，本实施例利用黑白棋盘格图像的特殊性，由于黑白棋盘格即使在变形以后也能够准确地确定每个黑白格交叉点的坐标，因此根据交叉点的畸变情况，即可获知整个图像的畸变情况。首先需要获取虚拟现实头盔屏幕上显示的是没有经过处理的原始黑白棋盘格图像。
S12，对原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像。
在具体实施时，由于虚拟现实头盔中设置的透镜一般为凸透镜，且该凸透镜的凸面朝向屏幕图像，另一面朝向人眼。根据公知常识可知，经过凸透镜后显示的图像会发生枕形变形。为获得正常显示的图像，预先对原始黑白棋盘格图像进行桶形变形处理，以抵消凸透镜所产生的枕形变形。
S13，获取经透镜成像后的反畸变黑白棋盘格图像，并确定反畸变黑白棋盘图像上黑白格的交叉点坐标。
在具体实施时，反畸变黑白棋盘格图像实际就是人眼所接收到的图像，这个图像据透镜的折射率等参数是可以确定的。根据计算机图像处理方法还可以确定反畸变黑白棋盘图像上黑白格的交叉点坐标。
S14，根据反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及理想黑白棋盘格图像上黑白格交叉点的坐标。
在具体实施时，根据公知常识，经过桶形变形的图像中心点向外突，周边向内缩，但是其水平中心线和竖直中心线却不会变形。反畸变黑白棋盘格图像虽然经过透镜的反畸变处理，但仍是存在误差的图像，根据黑白棋盘格的特征，黑白棋盘格的格数即使变形后仍是确定的，保持水平中心线和竖直 中心线不变，将整个图像按照黑白棋盘格的格数等分整个图像，即可获得理想黑白棋盘格图像。
S15，计算反畸变黑白棋盘格图像的每个黑白格的交叉点坐标与对应的理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
在具体实施时，根据现有技术即可确定反畸变黑白棋盘格图像的每个黑白格的交叉点坐标与对应的理想黑白棋盘格图像的黑白格的交叉点坐标，根据交叉点坐标可计算出所有交叉点的均方根误差和，均方根误差和越大，畸变越大，均方根误差和越小，畸变越小。本实施例的虚拟现实头盔的成像畸变测试方法，利用黑白棋盘格图像的特殊性，先获取与原始图像对应的经过桶形形变后的黑白棋盘格图像，再获取透过透镜显示的理想的黑白棋盘格图像，根据黑白格交叉点坐标误差的大小，可以测试出虚拟现实头盔的图像畸变大小。
图4为本发明的虚拟现实头盔的成像畸变测试方法的实施例二的流程图，本实施例的虚拟现实头盔的成像畸变测试方法在上述实施例一的基础上，进一步更加详细地介绍本发明的技术方案。如图4所示，本实施例的虚拟现实头盔的成像畸变测试方法，具体可以包括如下步骤：
S21，获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像。
在具体实施时，本实施例利用黑白棋盘格图像的特殊性，由于黑白棋盘格即使在变形以后也能够准确地确定每个黑白格交叉点的坐标，因此根据交叉点的畸变情况，即可获知整个图像的畸变情况。首先需要获取虚拟现实头盔屏幕上显示的是没有经过处理的原始黑白棋盘格图像。
S22，对原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像。
在具体实施时，为抵消透镜对原始图像发生的变形，预先使原始的黑白棋盘格图像发生桶形变形。从而透过透镜获得理想的黑白棋盘格图像。
S23，获取经透镜成像后的反畸变黑白棋盘格图像，并确定反畸变黑白棋盘图像上黑白格的交叉点坐标。
在具体实施时，反畸变黑白棋盘格图像实际就是人眼所接收到的图像，这个图像据透镜的折射率等参数是可以确定的。根据计算机图像处理方法还 可以确定反畸变黑白棋盘图像上黑白格的交叉点坐标。
S24，检测经过桶形变形处理后的黑白棋盘格图像的水平格数和竖直格数。
在具体实施时，图像虽然经过变形，但是图像中的元素不会有损失，只是在边界处发生变形。对于黑白棋盘格图像来说，发生变形后的黑白棋盘格图像的水平格数和竖直格数是保持不变的。
S25，以反畸变黑白棋盘格图像上的水平中心线和竖直中心线为基准，按照水平格数和竖直格数确定理想黑白棋盘格图像。
在具体实施时，根据公知常识，经过桶形变形的图像中心点向外突，周边向内缩，但是其水平中心线和竖直中心线却不会变形。根据桶形变形图像的特性，再根据黑白棋盘格的特性，即黑白棋盘格的格数即使变形后仍是确定的，即可确定理想黑白棋盘格图像。可保持水平中心线和竖直中心线不变，将整个图像按照黑白棋盘格的格数等分整个图像，即可获得理想黑白棋盘格图像。
S26，对反畸变黑白棋盘格图像进行边界检测，以确定反畸变黑白棋盘格图像的黑白格的交叉点的坐标。
在具体实施时，边界检测是图像处理和计算机视觉中的基本问题，边界检测的目的是标识数字图像中亮度变化明显的点。据此，采用边界检测方法，可以得到反畸变黑白棋盘格图像中各个黑白格交叉点的坐标。
S27，对理想黑白棋盘格图像进行边界检测，以确定理想黑白棋盘格图像的每个黑白格的交叉点的坐标。
在具体实施时，根据在步骤S25中介绍的边界检测法，同样可以确定理想黑白棋盘格图像的每个黑白格交叉点的坐标。
S28，计算反畸变黑白棋盘格图像的每个黑白格的交叉点坐标与对应的理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小黑白格交叉点黑白格交叉点黑白格交叉点。
在具体实施时，根据桶形变形处理后的黑白棋盘格图像的每个黑白格交叉点坐标与理想黑白棋盘格图像的黑白格交叉点坐标的对应关系，确定理想黑白棋盘格图像上的黑白格交叉点坐标的均方根误差。
具体地，均方根误差计算公式如下：
Figure PCTCN2016096628-appb-000003
其中，n为黑白格交叉点的数量，i为黑白格交叉点的序号，dxi为黑白棋盘格的第i个交叉点的水平方向的误差，dyi为黑白棋盘格的第i个交叉点的垂直方向的误差。
本发明的虚拟现实头盔的成像畸变测试方法及装置，利用黑白棋盘格图像的特殊性，先获取与原始图像对应的经过桶形形变后的黑白棋盘格图像，再获取透过透镜显示的理想的黑白棋盘格图像，利用边界检测法可以确定黑白格交叉点坐标和理想黑白棋盘格图像中黑白格交叉点的坐标，从而可以计算出理想黑白棋盘格中各个黑白格交叉点的误差，继而计算出所有黑白格交叉点的均方根误差。
图5为本发明的虚拟现实头盔的成像畸变测试方法的实施例一的示意图，如图5所示，本实施例的虚拟现实头盔的成像畸变测试方法可以包括获取模块11、变形模块12、确定模块13和计算模块14。
其中，
获取模块11，用于获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；变形模块12，与获取模块11相连，用于对原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；获取模块11，还用于获取经透镜成像后的反畸变黑白棋盘格图像，并确定反畸变黑白棋盘图像上黑白格的交叉点坐标；确定模块13，与变形模块12相连，用于根据反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及理想黑白棋盘格图像上黑白格交叉点的坐标；计算模块14与确定模块13相连，用于计算反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
本实施例的虚拟现实头盔的成像畸变测试装置，利用黑白棋盘格图像的特殊性，先获取与原始图像对应的经过桶形形变后的黑白棋盘格图像，再获取透过透镜显示的理想的黑白棋盘格图像，根据黑白格交叉点坐标误差的大小，可以测试出虚拟现实头盔的图像畸变大小。
本实施例的虚拟现实头盔的成像畸变测试装置的实施例二的示意图与图5一致，具体可参见图5，本实施例的虚拟现实头盔的成像畸变测试装置在上 述图5所示的实施例一的基础上，进一步更加详细地介绍本发明的技术方案。如图5所示，本实施例的虚拟现实头盔的成像畸变测试装置可以包括获取模块11、变形模块12、确定模块13和计算模块14。
其中，
获取模块11，用于获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；变形模块12，与获取模块11相连，用于对原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；获取模块11，还用于获取经透镜成像后的反畸变黑白棋盘格图像，并确定反畸变黑白棋盘图像上黑白格的交叉点坐标；确定模块13，与变形模块12相连，用于根据反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及理想黑白棋盘格图像上黑白格交叉点的坐标；计算模块14与确定模块13相连，用于计算反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
进一步可选地，计算模块14具体用于：
对反畸变黑白棋盘格图像进行边界检测，以确定反畸变黑白棋盘格图像的黑白格的交叉点的坐标；
对理想黑白棋盘格图像进行边界检测，以确定理想黑白棋盘格图像的每个黑白格交叉点的坐标。
进一步可选地，确定模块13具体用于：
检测反畸变黑白棋盘格图像的水平格数和竖直格数；
以反畸变黑白棋盘格图像上的水平中心线和竖直中心线为基准，按照水平格数和竖直格数确定理想黑白棋盘格图像。
进一步可选地，均方根误差计算公式如下：
Figure PCTCN2016096628-appb-000004
其中，n为黑白格交叉点的数量，i为黑白格交叉点的序号，dxi为黑白棋盘格的第i个交叉点的水平方向的误差，dyi为黑白棋盘格的第i个交叉点的垂直方向的误差。
本实施例的虚拟现实头盔的成像畸变测试装置，通过采用上述模块实现虚拟现实头盔的成像畸变测试的实现机制与上述图4所示实施例的虚拟现实 头盔的成像畸变测试的实现机制相同，详细可以参考上述图4所示实施例的记载，在此不再赘述。
图6是本申请实施例提供的一种虚拟现实头盔的成像畸变测试设备的硬件结构示意图，如图6所示，该成像畸变测试设备包括：
一个或多个处理器610以及存储器620，图6中以一个处理器610为例。
成像畸变测试设备还可以包括：输入装置630和输出装置640。
处理器610、存储器620、输入装置630和输出装置640可以通过总线或者其他方式连接，图6中以通过总线连接为例。
存储器620作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序、非暂态计算机可执行程序以及模块，如本申请实施例中的虚拟现实头盔的成像畸变测试方法对应的程序指令/模块(例如，附图5所示的获取模块11、变形模块12、确定模块13和计算模块14)。处理器610通过运行存储在存储器620中的非暂态软件程序、指令以及模块，从而执行相关的各种功能应用以及数据处理，即实现上述方法实施例中虚拟现实头盔的成像畸变测试方法。
存储器620可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；
存储数据区可存储根据虚拟现实头盔的成像畸变测试装置的使用所创建的数据等。此外，存储器620可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施例中，存储器620可选包括相对于处理器610远程设置的存储器，这些远程存储器可以通过网络连接至虚拟现实头盔的成像畸变测试装置。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
输入装置630可接收输入的数字或字符信息，以及产生与虚拟现实头盔的成像畸变测试装置的用户设置以及功能控制有关的键信号输入。输出装置640可包括显示屏等显示设备。
所述一个或者多个模块存储在所述存储器620中，当被所述一个或者多个处理器610执行时，执行上述任意方法实施例中的虚拟现实头盔的成 像畸变测试方法。
上述产品可执行本申请实施例所提供的方法，具备执行方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节，可参见本申请实施例所提供的方法。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory,ROM)或随机存储记忆体(Random Access Memory,RAM)等。
最后需要说明的是，本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(ROM)或随机存储记忆体(RAM)等。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。

Claims (11)

  1. 一种虚拟现实头盔的成像畸变测试方法，应用于虚拟现实头盔，所述虚拟现实头盔的屏幕与人眼之间设置有用于图像放大的透镜，所述虚拟现实头盔上显示的黑白棋盘格图像由相间的黑白格组成，其特征在于，包括：
    获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；
    对所述原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；
    获取经透镜成像后的反畸变黑白棋盘格图像，并确定所述反畸变黑白棋盘图像上黑白格的交叉点坐标；
    根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及所述理想黑白棋盘格图像上黑白格交叉点的坐标；
    计算所述反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
  2. 根据权利要求1所述的方法，其特征在于，所述计算所述反畸变黑白棋盘格图像的每个黑白格的交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格的交叉点坐标的均方根误差和之前还包括：
    对所述反畸变黑白棋盘格图像进行边界检测，以确定所述反畸变黑白棋盘格图像的黑白格的交叉点的坐标；
    对所述理想黑白棋盘格图像进行边界检测，以确定所述理想黑白棋盘格图像的每个黑白格交叉点的坐标。
  3. 根据权利要求1所述的方法，其特征在于，所述根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像包括：
    检测所述反畸变黑白棋盘格图像的水平格数和竖直格数；
    以所述反畸变黑白棋盘格图像上的水平中心线和竖直中心线为基准，按照所述水平格数和竖直格数确定所述理想黑白棋盘格图像。
  4. 根据权利要求1-3任一所述的方法，其特征在于，所述均方根误差和 的计算公式如下：
    Figure PCTCN2016096628-appb-100001
    其中，n为所述黑白格交叉点的数量，i为所述黑白格交叉点的序号，dxi为所述黑白棋盘格的第i个交叉点的水平方向的误差，dyi为所述黑白棋盘格的第i个交叉点的垂直方向的误差。
  5. 一种虚拟现实头盔的成像畸变测试装置，其特征在于，包括：
    获取模块，用于获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；
    变形模块，用于对所述原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；
    所述获取模块，还用于获取经透镜成像后的反畸变黑白棋盘格图像，并确定所述反畸变黑白棋盘图像上黑白格的交叉点坐标；
    确定模块，根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及所述理想黑白棋盘格图像上黑白格交叉点的坐标；
    计算模块，计算所述反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
  6. 根据权利要求5所述的装置，其特征在于，所述计算模块具体用于：
    对所述反畸变黑白棋盘格图像进行边界检测，以确定所述反畸变黑白棋盘格图像的黑白格的交叉点的坐标；
    对所述理想黑白棋盘格图像进行边界检测，以确定所述理想黑白棋盘格图像的每个黑白格交叉点的坐标。
  7. 根据权利要求5所述的装置，其特征在于，所述确定模块具体用于：
    检测所述反畸变黑白棋盘格图像的水平格数和竖直格数；
    以所述反畸变黑白棋盘格图像上的水平中心线和竖直中心线为基准，按照所述水平格数和竖直格数确定所述理想黑白棋盘格图像。
  8. 根据权利要求5-7任一所述的装置，其特征在于，所述均方根误差计算公式如下：
    Figure PCTCN2016096628-appb-100002
    其中，n为所述黑白格交叉点的数量，i为所述黑白格交叉点的序号，dxi为所述黑白棋盘格的第i个交叉点的水平方向的误差，dyi为所述黑白棋盘格的第i个交叉点的垂直方向的误差。
  9. 一种虚拟现实头盔的成像畸变测试设备，所述虚拟现实头盔的屏幕与人眼之间设置有用于图像放大的透镜，所述虚拟现实头盔上显示的黑白棋盘格图像由相间的黑白格组成，其特征在于，所述设备包括：一个或者多个处理器；
    存储器；
    一个或者多个程序，所述一个或者多个程序存储在所述存储器中，当被所述一个或者多个处理器执行时：
    获取虚拟现实头盔屏幕上显示的原始黑白棋盘格图像；
    对所述原始黑白棋盘格图像进行桶形变形处理，得到桶形变形处理后的黑白棋盘格图像；
    获取经透镜成像后的反畸变黑白棋盘格图像，并确定所述反畸变黑白棋盘图像上黑白格的交叉点坐标；
    根据所述反畸变黑白棋盘格图像的水平中心线和竖直中心线，确定对应的理想黑白棋盘格图像以及所述理想黑白棋盘格图像上黑白格交叉点的坐标；
    计算所述反畸变黑白棋盘格图像的每个黑白格交叉点坐标与对应的所述理想黑白棋盘格图像的黑白格交叉点坐标的均方根误差和，以确定成像畸变的大小。
  10. 一种非暂态计算机可读存储介质，其特征在于，所述非暂态计算机可读存储介质存储计算机指令，所述计算机指令用于使所述计算机执行权利要求1-4任一所述方法。
  11. 一种计算机程序产品，其特征在于，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行权利要求1-4任一所述的方法。
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