CN105869142A - Method and device for testing imaging distortion of virtual reality helmets - Google Patents
Method and device for testing imaging distortion of virtual reality helmets Download PDFInfo
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- G06T2207/30168—Image quality inspection
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Abstract
The invention provides a method and device for testing imaging distortion of virtual reality helmets. The method comprises the following steps: inputting an original black/white checkerboard image; carrying out barrel distortion on the original black/white checkerboard image through a virtual reality helmet to obtain a barrel distortion processed black/white checkerboard image, and displaying the barrel distortion processed black/white checkerboard image on a screen; obtaining a lens imaged anti-distortion black/white checkerboard image, and determining black/white checker cross point coordinates containing distortion; determining a relatively ideal cross point coordinate according to cross points on a horizontal center line and a vertical center line of the anti-distortion black/white checkerboard image; calculating sum of root-mean-square errors of corresponding points in above two types of cross points so as to determine the size of the imaging distortion. According to the method and device for testing imaging distortion of virtual reality helmets, the particularity of the black/white checkerboard image is utilized to firstly obtain the anti-distortion black/white checkerboard image and then determine the cross point coordinates containing distortion and the relatively ideal cross point coordinate, and according to the size of corresponding errors of above two types of cross point coordinates, the image distortion sizes of the virtual reality helmets can be tested.
Description
Technical Field
The invention relates to the technical field of virtual reality, in particular to a method and a device for testing imaging distortion of a virtual reality helmet.
Background
With the rapid development of Virtual Reality technology (VR), a head-mounted display, also called a Virtual Reality helmet, appears. By utilizing the virtual reality helmet, the vision and the hearing of people to the outside are sealed, and the user is guided to generate a feeling of the user in a virtual environment. The display principle is that the left and right eye screens respectively display images of the left and right eyes, and the human eyes generate stereoscopic impression in the brain after acquiring the information with the difference. The virtual reality helmet is used as a virtual reality display device, has the characteristics of small size and strong sealing property, and has wide application in military training, virtual driving, virtual cities and other projects.
The positions corresponding to two eyes in the existing virtual reality helmet are respectively provided with a convex lens, and according to the characteristics of the convex lenses, the image imaged by the convex lenses can generate pillow-shaped deformation, so that the prior art usually adopts a method of firstly performing barrel-shaped deformation processing on the original image and then imaging by the convex lenses, so that the image obtained by a user is similar to the original image. The technique of barrel-warping the original image is already a well-established technique.
However, the image finally obtained by the user is only an approximate image of the original image, and the quantitative evaluation of the imaging effect of the approximate image obtained by the processing method is lacked at present.
Disclosure of Invention
The invention provides a method and a device for testing imaging distortion of a virtual reality helmet, which aim to overcome the technical problem that in the prior art, an image finally obtained by a user is only an approximate image of an original image, and quantitative evaluation on the imaging effect of the approximate image obtained by the processing method is lacked.
The invention provides an imaging distortion test method of a virtual reality helmet, wherein a lens for image amplification is arranged between a screen and human eyes of the virtual reality helmet, a black-and-white checkerboard image displayed on the virtual reality helmet is composed of alternate black-and-white checks, and the method comprises the following steps:
acquiring an original black-white checkerboard image displayed on a virtual reality helmet screen;
barrel-shaped deformation processing is carried out on the original black-and-white checkerboard image to obtain a barrel-shaped deformation processed black-and-white checkerboard image;
acquiring an inverse distortion black-white checkerboard image imaged by a lens, and determining the coordinates of intersection points of black and white grids on the inverse distortion black-white checkerboard image;
determining the coordinates of the corresponding ideal black-white checkerboard image and the black-white grid intersection point on the ideal black-white checkerboard image according to the horizontal center line and the vertical center line of the anti-distortion black-white checkerboard image;
and calculating the root mean square error sum of each black and white grid intersection point coordinate of the anti-distortion black and white checkerboard image and the corresponding black and white grid intersection point coordinate of the ideal black and white checkerboard image so as to determine the imaging distortion.
Further optionally, the calculating a root mean square error sum of the coordinates of the intersection point of each black and white lattice of the anti-distortion black and white checkerboard image and the coordinates of the intersection point of the corresponding black and white lattice of the ideal black and white checkerboard image further comprises:
carrying out boundary detection on the anti-distortion black-white checkerboard image so as to determine coordinates of intersection points of black and white grids of the anti-distortion black-white checkerboard image;
and carrying out boundary detection on the ideal black-white checkerboard image so as to determine the coordinates of each black-white grid intersection point of the ideal black-white checkerboard image.
Further optionally, the determining, according to the horizontal center line and the vertical center line of the anti-distortion black-and-white checkerboard image, a corresponding ideal black-and-white checkerboard image includes:
detecting the horizontal grid number and the vertical grid number of the anti-distortion black-and-white checkerboard image;
and determining the ideal black-white checkerboard image according to the horizontal grid number and the vertical grid number by taking the horizontal center line and the vertical center line on the anti-distortion black-white checkerboard image as a reference.
Further optionally, the root mean square error sum is calculated as follows:
wherein n is the number of the black and white grid crossing points, i is the serial number of the black and white grid crossing points, dxiError in horizontal direction, dy, for the i-th crossing of said black and white checkerboardiIs the error of the vertical direction of the ith intersection point of the black and white checkerboard.
The invention also provides an imaging distortion testing device of the virtual reality helmet, which comprises:
the acquisition module is used for acquiring an original black-white checkerboard image displayed on a virtual reality helmet screen;
the deformation module is used for carrying out barrel-shaped deformation processing on the original black-and-white checkerboard image to obtain a barrel-shaped deformation processed black-and-white checkerboard image;
the acquisition module is further used for acquiring the anti-distortion black-white checkerboard image imaged by the lens and determining the coordinates of the intersection points of the black grids and the white grids on the anti-distortion black-white checkerboard image;
the determining module is used for determining the corresponding ideal black-white checkerboard image and the coordinates of the black-white grid intersection point on the ideal black-white checkerboard image according to the horizontal center line and the vertical center line of the anti-distortion black-white checkerboard image;
and the calculating module is used for calculating the root mean square error sum of each black and white grid intersection point coordinate of the anti-distortion black and white checkerboard image and the corresponding black and white grid intersection point coordinate of the ideal black and white checkerboard image so as to determine the imaging distortion.
Further optionally, the calculation module is specifically configured to:
carrying out boundary detection on the anti-distortion black-white checkerboard image so as to determine coordinates of intersection points of black and white grids of the anti-distortion black-white checkerboard image;
and carrying out boundary detection on the ideal black-white checkerboard image so as to determine the coordinates of each black-white grid intersection point of the ideal black-white checkerboard image.
Further optionally, the determining module is specifically configured to:
detecting the horizontal grid number and the vertical grid number of the anti-distortion black-and-white checkerboard image;
and determining the ideal black-white checkerboard image according to the horizontal grid number and the vertical grid number by taking the horizontal center line and the vertical center line on the anti-distortion black-white checkerboard image as a reference.
Further optionally, the root mean square error calculation formula is as follows:
whereinN is the number of the black and white grid cross points, i is the serial number of the black and white grid cross points, dxiError in horizontal direction, dy, for the i-th crossing of said black and white checkerboardiIs the error of the vertical direction of the ith intersection point of the black and white checkerboard.
The imaging distortion testing method and the imaging distortion testing device of the virtual reality helmet utilize the particularity of the black-white checkerboard image, firstly obtain the black-white checkerboard image which corresponds to the original image and is subjected to barrel-shaped deformation, then obtain the anti-distortion black-white checkerboard image displayed through the lens, then determine the ideal black-white checkerboard image, and can test the image distortion of the virtual reality helmet according to the root mean square error sum of the coordinates of the intersection points of the black-white checkerboard and the white-white checkerboard.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of an ideal black and white checkerboard image according to the present invention;
FIG. 2 is a schematic view of a barrel-shaped deformed black-and-white checkerboard image according to the present invention;
FIG. 3 is a schematic diagram of a first embodiment of a method for testing imaging distortion of a virtual reality headset according to the present invention;
fig. 4 is a schematic diagram of a second embodiment of the method for testing imaging distortion of a virtual reality helmet according to the present invention;
fig. 5 is a schematic view of a first embodiment of an imaging distortion testing apparatus of a virtual reality helmet according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to prevent the original image on the screen from generating pincushion distortion after passing through the lens, the existing virtual reality helmet needs to firstly preprocess the original image, namely, firstly carry out barrel-shaped deformation processing on the original image, and the image after barrel-shaped deformation processing can be reversely distorted into a reverse-distortion black-white checkerboard image which is close to the original image after passing through the lens. However, for the lack of quantitative evaluation of the distortion correction condition of the anti-distortion black-and-white checkerboard image, the invention provides an imaging distortion test method and device for a virtual reality helmet, which utilize the particularity of the black-and-white checkerboard image, and because the black-and-white checkerboard can accurately determine the coordinates of each black-and-white checkerboard intersection point even after the black-and-white checkerboard is deformed, the anti-distortion condition of the head portrait displayed by the whole virtual reality helmet can be known according to the distortion condition of the intersection points, the original black-and-white checkerboard image is shown in fig. 1, and the barrel-shaped deformed black-and-white checkerboard image is shown in fig. 2. The specific embodiment is as follows:
fig. 3 is a schematic view of a first method for testing imaging distortion of a virtual reality helmet according to an embodiment of the present invention, and as shown in fig. 3, the method for testing imaging distortion of a virtual reality helmet according to the embodiment may specifically include the following steps:
and S11, acquiring the original black-white checkerboard image displayed on the virtual reality helmet screen.
In specific implementation, the embodiment utilizes the particularity of the black-white checkerboard image, and the coordinates of each intersection point of the black-white checkerboard can be accurately determined even after the black-white checkerboard is deformed, so that the distortion condition of the whole image can be known according to the distortion condition of the intersection points. Firstly, the original black-and-white checkerboard image which is not processed is displayed on the virtual reality helmet screen.
And S12, barrel-shaped deformation processing is carried out on the original black-and-white checkerboard image to obtain the black-and-white checkerboard image after barrel-shaped deformation processing.
In the implementation, the lenses arranged in the virtual reality helmet are generally convex lenses, and the convex surfaces of the convex lenses face the screen image, and the other surfaces of the convex lenses face human eyes. It is known from the common knowledge that the image displayed through the lenticular lens is pincushion-shaped. In order to obtain a normally displayed image, barrel-shaped deformation processing is performed on the original black-white checkerboard image in advance to offset pincushion deformation generated by the convex lens.
S13, acquiring the anti-distortion black-white checkerboard image imaged by the lens, and determining the coordinates of the intersection points of the black and white grids on the anti-distortion black-white checkerboard image.
In the implementation, the anti-distortion black-and-white checkerboard image is actually the image received by human eyes, and the image can be determined according to parameters such as the refractive index of the lens. The coordinates of the intersection points of the black and white grids on the anti-distortion black and white chessboard image can be determined according to a computer image processing method.
And S14, determining the coordinates of the corresponding ideal black-white checkerboard image and the black-white grid intersection point on the ideal black-white checkerboard image according to the horizontal center line and the vertical center line of the anti-distortion black-white checkerboard image.
In practice, the centre point of the barrel-deformed image is projected outwards and the periphery is retracted inwards, but the horizontal and vertical centre lines are not deformed, according to the common knowledge. The anti-distortion black-white checkerboard image is an image with errors even though the anti-distortion processing is carried out on the image through the lens, according to the characteristics of the black-white checkerboard, the number of the black-white checkerboard is determined even after the image is deformed, the horizontal central line and the vertical central line are kept unchanged, the whole image is equally divided into the whole image according to the number of the black-white checkerboard, and the ideal black-white checkerboard image can be obtained.
And S15, calculating the root mean square error sum of the coordinates of each intersection point of the black and white grids of the anti-distortion black and white checkerboard image and the coordinates of the corresponding intersection points of the black and white grids of the ideal black and white checkerboard image to determine the imaging distortion.
In specific implementation, the coordinates of the intersection point of each black and white grid of the anti-distortion black and white checkerboard image and the coordinates of the intersection point of the corresponding black and white grid of the ideal black and white checkerboard image can be determined according to the prior art, the root mean square error sum of all the intersection points can be calculated according to the coordinates of the intersection points, the larger the root mean square error sum is, the larger the distortion is, the smaller the root mean square error sum is, and the smaller the distortion is. According to the imaging distortion testing method of the virtual reality helmet, by utilizing the particularity of the black-and-white checkerboard image, the barrel-shaped deformed black-and-white checkerboard image corresponding to the original image is obtained, then the ideal black-and-white checkerboard image displayed through the lens is obtained, and the image distortion of the virtual reality helmet can be tested according to the coordinate error of the intersection point of the black-and-white checkerboard image.
Fig. 4 is a flowchart of a second embodiment of the method for testing imaging distortion of a virtual reality helmet according to the present invention, and the method for testing imaging distortion of a virtual reality helmet according to the present embodiment further introduces the technical solution of the present invention in more detail on the basis of the first embodiment. As shown in fig. 4, the imaging distortion testing method for the virtual reality helmet of the embodiment may specifically include the following steps:
and S21, acquiring the original black-white checkerboard image displayed on the virtual reality helmet screen.
In specific implementation, the embodiment utilizes the particularity of the black-white checkerboard image, and the coordinates of each intersection point of the black-white checkerboard can be accurately determined even after the black-white checkerboard is deformed, so that the distortion condition of the whole image can be known according to the distortion condition of the intersection points. Firstly, the original black-and-white checkerboard image which is not processed is displayed on the virtual reality helmet screen.
And S22, barrel-shaped deformation processing is carried out on the original black-and-white checkerboard image to obtain the black-and-white checkerboard image after barrel-shaped deformation processing.
In the implementation, in order to counteract the deformation of the original image caused by the lens, the original black-and-white checkerboard image is subjected to barrel-shaped deformation in advance. So as to obtain an ideal black and white checkerboard image through the lens.
S23, acquiring the anti-distortion black-white checkerboard image imaged by the lens, and determining the coordinates of the intersection points of the black and white grids on the anti-distortion black-white checkerboard image.
In the implementation, the anti-distortion black-and-white checkerboard image is actually the image received by human eyes, and the image can be determined according to parameters such as the refractive index of the lens. The coordinates of the intersection points of the black and white grids on the anti-distortion black and white chessboard image can be determined according to a computer image processing method.
And S24, detecting the horizontal grid number and the vertical grid number of the black-white chessboard image after barrel-shaped deformation processing.
In practical implementation, although the image is deformed, elements in the image are not lost, and only deformation occurs at the boundary. For the black-and-white checkerboard image, the horizontal grid number and the vertical grid number of the black-and-white checkerboard image after deformation are kept unchanged.
And S25, determining an ideal black-and-white checkerboard image according to the horizontal grid number and the vertical grid number by taking the horizontal center line and the vertical center line on the anti-distortion black-and-white checkerboard image as a reference.
In practice, the centre point of the barrel-deformed image is projected outwards and the periphery is retracted inwards, but the horizontal and vertical centre lines are not deformed, according to the common knowledge. According to the characteristics of the barrel-shaped deformed image and the characteristics of the black and white checkerboards, namely the number of the black and white checkerboards is determined even after deformation, the ideal black and white checkerboard image can be determined. The horizontal central line and the vertical central line can be kept unchanged, and the whole image is equally divided into equal parts according to the grid number of the black and white checkerboard, so that the ideal black and white checkerboard image can be obtained.
S26, boundary detection is performed on the inverse distorted black-and-white checkerboard image to determine coordinates of intersections of black and white squares of the inverse distorted black-and-white checkerboard image.
In particular, boundary detection is a fundamental problem in image processing and computer vision, and the purpose of boundary detection is to identify points in digital images where changes in brightness are significant. Therefore, the coordinates of each black and white grid intersection point in the anti-distortion black and white checkerboard image can be obtained by adopting a boundary detection method.
S27, boundary detection is performed on the ideal black and white checkerboard image to determine coordinates of each intersection of black and white squares of the ideal black and white checkerboard image.
In a specific implementation, the coordinates of each intersection of black and white grids of the ideal black and white checkerboard image may also be determined according to the boundary detection method described in step S25.
And S28, calculating the root mean square error sum of the coordinates of each intersection point of the black and white grids of the anti-distortion black and white checkerboard image and the coordinates of the corresponding intersection points of the black and white grids of the ideal black and white checkerboard image to determine the intersection points of the black and white grids at the intersection points of the black and white grids with the imaging distortion.
In specific implementation, the root mean square error of the coordinates of the black and white grid intersections on the ideal black and white checkerboard image is determined according to the corresponding relationship between the coordinates of each black and white grid intersection of the black and white checkerboard image after barrel-shaped deformation processing and the coordinates of the black and white grid intersections of the ideal black and white checkerboard image.
Specifically, the root mean square error calculation formula is as follows:
where n is the number of black and white grid intersections, i is the number of black and white grid intersections, dxiError in the horizontal direction, dy, for the ith crossing of the black and white checkerboardiIs the error in the vertical direction of the ith intersection of the black and white checkerboard.
The imaging distortion testing method and the device of the virtual reality helmet utilize the particularity of the black-white checkerboard image, firstly obtain the black-white checkerboard image which corresponds to the original image and is subjected to barrel-shaped deformation, then obtain the ideal black-white checkerboard image displayed through the lens, and utilize a boundary detection method to determine the coordinates of the black-white checkerboard intersection points and the coordinates of the black-white checkerboard intersection points in the ideal black-white checkerboard image, thereby calculating the error of each black-white checkerboard intersection point in the ideal black-white checkerboard, and further calculating the root-mean-square error of all the black-white checkerboard intersection points.
Fig. 5 is a schematic view of a first embodiment of the method for testing imaging distortion of a virtual reality helmet according to the present invention, and as shown in fig. 5, the method for testing imaging distortion of a virtual reality helmet according to the present embodiment may include an obtaining module 11, a deforming module 12, a determining module 13, and a calculating module 14.
Wherein,
the acquiring module 11 is configured to acquire an original black-and-white checkerboard image displayed on a virtual reality helmet screen; the deformation module 12 is connected with the acquisition module 11 and is used for carrying out barrel-shaped deformation processing on the original black-and-white checkerboard image to obtain a barrel-shaped deformation processed black-and-white checkerboard image; the obtaining module 11 is further configured to obtain an inverse distortion black-and-white checkerboard image imaged by the lens, and determine coordinates of intersection points of black and white squares on the inverse distortion black-and-white checkerboard image; the determining module 13 is connected with the deforming module 12 and is used for determining the corresponding ideal black-white checkerboard image and the coordinates of the black-white grid intersection points on the ideal black-white checkerboard image according to the horizontal center line and the vertical center line of the anti-distortion black-white checkerboard image; the calculating module 14 is connected to the determining module 13, and is configured to calculate a root mean square error sum of each intersection coordinate of black and white grids of the anti-distortion black and white checkerboard image and a corresponding intersection coordinate of black and white grids of the ideal black and white checkerboard image, so as to determine the magnitude of the imaging distortion.
The imaging distortion testing device of the virtual reality helmet in the embodiment utilizes the particularity of the black-and-white checkerboard image to firstly acquire the barrel-shaped deformed black-and-white checkerboard image corresponding to the original image, and then acquire the ideal black-and-white checkerboard image displayed through the lens, and according to the coordinate error of the black-and-white checkerboard intersection point, the image distortion of the virtual reality helmet can be tested.
A schematic diagram of a second embodiment of the imaging distortion testing apparatus of the virtual reality helmet in this embodiment is consistent with fig. 5, and specifically, referring to fig. 5, the imaging distortion testing apparatus of the virtual reality helmet in this embodiment further introduces the technical solution of the present invention in more detail on the basis of the first embodiment shown in fig. 5. As shown in fig. 5, the imaging distortion testing apparatus of the virtual reality helmet of the present embodiment may include an obtaining module 11, a deforming module 12, a determining module 13, and a calculating module 14.
Wherein,
the acquiring module 11 is configured to acquire an original black-and-white checkerboard image displayed on a virtual reality helmet screen; the deformation module 12 is connected with the acquisition module 11 and is used for carrying out barrel-shaped deformation processing on the original black-and-white checkerboard image to obtain a barrel-shaped deformation processed black-and-white checkerboard image; the obtaining module 11 is further configured to obtain an inverse distortion black-and-white checkerboard image imaged by the lens, and determine coordinates of intersection points of black and white squares on the inverse distortion black-and-white checkerboard image; the determining module 13 is connected with the deforming module 12 and is used for determining the corresponding ideal black-white checkerboard image and the coordinates of the black-white grid intersection points on the ideal black-white checkerboard image according to the horizontal center line and the vertical center line of the anti-distortion black-white checkerboard image; the calculating module 14 is connected to the determining module 13, and is configured to calculate a root mean square error sum of each intersection coordinate of black and white grids of the anti-distortion black and white checkerboard image and a corresponding intersection coordinate of black and white grids of the ideal black and white checkerboard image, so as to determine the magnitude of the imaging distortion.
Further optionally, the calculation module 14 is specifically configured to:
carrying out boundary detection on the anti-distortion black-white checkerboard image to determine coordinates of intersection points of black and white grids of the anti-distortion black-white checkerboard image;
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.
Further optionally, the determining module 13 is specifically configured to:
detecting the horizontal grid number and the vertical grid number of the anti-distortion black-white checkerboard image;
and determining an ideal black-white checkerboard image according to the horizontal grid number and the vertical grid number by taking the horizontal center line and the vertical center line on the anti-distortion black-white checkerboard image as a reference.
Further optionally, the root mean square error calculation formula is as follows:
wherein n is the number of black and white grid intersectionsQuantity, i is the number of the black and white grid crossing, dxiError in the horizontal direction, dy, for the ith crossing of the black and white checkerboardiIs the error in the vertical direction of the ith intersection of the black and white checkerboard.
In the imaging distortion testing apparatus for a virtual reality helmet in this embodiment, an implementation mechanism for implementing the imaging distortion test of the virtual reality helmet by using the modules is the same as the implementation mechanism for implementing the imaging distortion test of the virtual reality helmet in the embodiment shown in fig. 4, and details of the implementation mechanism can be referred to the description of the embodiment shown in fig. 4, and are not repeated herein.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. An imaging distortion test method of a virtual reality helmet is characterized in that a lens for image amplification is arranged between a screen and human eyes of the virtual reality helmet, and a black-white checkerboard image displayed on the virtual reality helmet is composed of alternate black and white grids, and the method comprises the following steps:
acquiring an original black-white checkerboard image displayed on a virtual reality helmet screen;
barrel-shaped deformation processing is carried out on the original black-and-white checkerboard image to obtain a barrel-shaped deformation processed black-and-white checkerboard image;
acquiring an inverse distortion black-white checkerboard image imaged by a lens, and determining the coordinates of intersection points of black and white grids on the inverse distortion black-white checkerboard image;
determining the coordinates of the corresponding ideal black-white checkerboard image and the black-white grid intersection point on the ideal black-white checkerboard image according to the horizontal center line and the vertical center line of the anti-distortion black-white checkerboard image;
and calculating the root mean square error sum of each black and white grid intersection point coordinate of the anti-distortion black and white checkerboard image and the corresponding black and white grid intersection point coordinate of the ideal black and white checkerboard image so as to determine the imaging distortion.
2. The method according to claim 1, wherein said calculating a root mean square error sum of the coordinates of the intersection point of each black and white grid of said undistorted black and white checkerboard image and the coordinates of the intersection point of the corresponding black and white grid of said ideal black and white checkerboard image further comprises:
carrying out boundary detection on the anti-distortion black-white checkerboard image so as to determine coordinates of intersection points of black and white grids of the anti-distortion black-white checkerboard image;
and carrying out boundary detection on the ideal black-white checkerboard image so as to determine the coordinates of each black-white grid intersection point of the ideal black-white checkerboard image.
3. The method according to claim 1, wherein said determining a corresponding ideal black and white checkerboard image from the horizontal and vertical centerlines of said anti-distorted black and white checkerboard image comprises:
detecting the horizontal grid number and the vertical grid number of the anti-distortion black-and-white checkerboard image;
and determining the ideal black-white checkerboard image according to the horizontal grid number and the vertical grid number by taking the horizontal center line and the vertical center line on the anti-distortion black-white checkerboard image as a reference.
4. A method according to any one of claims 1 to 3, wherein the root mean square error sum is calculated as follows:
wherein n is the number of the black and white grid crossing points, i is the serial number of the black and white grid crossing points, dxiError in horizontal direction, dy, for the i-th crossing of said black and white checkerboardiIs the error of the vertical direction of the ith intersection point of the black and white checkerboard.
5. An imaging distortion testing arrangement of a virtual reality helmet, comprising:
the acquisition module is used for acquiring an original black-white checkerboard image displayed on a virtual reality helmet screen;
the deformation module is used for carrying out barrel-shaped deformation processing on the original black-and-white checkerboard image to obtain a barrel-shaped deformation processed black-and-white checkerboard image;
the acquisition module is further used for acquiring the anti-distortion black-white checkerboard image imaged by the lens and determining the coordinates of the intersection points of the black grids and the white grids on the anti-distortion black-white checkerboard image;
the determining module is used for determining the corresponding ideal black-white checkerboard image and the coordinates of the black-white grid intersection point on the ideal black-white checkerboard image according to the horizontal center line and the vertical center line of the anti-distortion black-white checkerboard image;
and the calculating module is used for calculating the root mean square error sum of each black and white grid intersection point coordinate of the anti-distortion black and white checkerboard image and the corresponding black and white grid intersection point coordinate of the ideal black and white checkerboard image so as to determine the imaging distortion.
6. The apparatus of claim 5, wherein the computing module is specifically configured to:
carrying out boundary detection on the anti-distortion black-white checkerboard image so as to determine coordinates of intersection points of black and white grids of the anti-distortion black-white checkerboard image;
and carrying out boundary detection on the ideal black-white checkerboard image so as to determine the coordinates of each black-white grid intersection point of the ideal black-white checkerboard image.
7. The apparatus of claim 5, wherein the determining module is specifically configured to:
detecting the horizontal grid number and the vertical grid number of the anti-distortion black-and-white checkerboard image;
and determining the ideal black-white checkerboard image according to the horizontal grid number and the vertical grid number by taking the horizontal center line and the vertical center line on the anti-distortion black-white checkerboard image as a reference.
8. The method according to any one of claims 5-7, wherein the root mean square error is calculated as follows:
wherein n is the number of the black and white grid crossing points, i is the serial number of the black and white grid crossing points, dxiError in horizontal direction, dy, for the i-th crossing of said black and white checkerboardiIs the error of the vertical direction of the ith intersection point of the black and white checkerboard.
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