CN113722672A - Method for detecting and calculating stray light noise of VR Lens - Google Patents
Method for detecting and calculating stray light noise of VR Lens Download PDFInfo
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- CN113722672A CN113722672A CN202110818940.1A CN202110818940A CN113722672A CN 113722672 A CN113722672 A CN 113722672A CN 202110818940 A CN202110818940 A CN 202110818940A CN 113722672 A CN113722672 A CN 113722672A
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- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000001514 detection method Methods 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 4
- 230000002093 peripheral effect Effects 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 8
- 201000009310 astigmatism Diseases 0.000 claims description 5
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000011179 visual inspection Methods 0.000 abstract description 4
- 230000004438 eyesight Effects 0.000 abstract description 2
- 239000011521 glass Substances 0.000 abstract description 2
- 238000003908 quality control method Methods 0.000 abstract description 2
- 238000013517 stratification Methods 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 abstract description 2
- 230000001419 dependent effect Effects 0.000 abstract 1
- 238000005192 partition Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
Abstract
The invention provides a method for detecting and calculating stray light noise of VR Lens, which comprises the following steps: s1, acquiring an image; s2, processing the image and capturing astigmatic noise; s3, dividing the area where the astigmatic noise is located, calculating the area of each divided area, and determining the value of the astigmatic noise according to the area. The detection of VR Lens is mainly still dependent on visual inspection, the glasses are close to the product during detection, only the Lens on one side of the VR product can be detected through visual inspection at each time, the efficiency is low, operators are easy to fatigue, and the eyesight can be influenced for a long time. Personnel's acutance and quality control ability are different, are difficult to guarantee higher detectable rate, and can not carry out subregion and accurate judgement bad stratification degree to Lens, and the testing process is traceable yet. The invention realizes the intelligent detection of VR Lens by effectively analyzing the VR image and capturing astigmatic noise.
Description
Technical Field
The invention discloses automatic optical detection, and relates to a detection and calculation method for VR Lens stray light noise.
Background
The VR Lens is mainly used at present, because of the abnormal of the screw thread structure or the sawtooth structure on the surface of the Fresnel Lens, the image of the VR display screen when reaching the eyes of people through the VR Lens appears, the stray light/noise phenomenon appears, no device corresponding to the type is applied to a VR production line on the market at present, the detection of the VR Lens is mainly in a visual inspection mode in the production process of VR products, the glasses are close to the products during detection, only the Lens on one side of the VR product can be detected in each visual inspection, the efficiency is low, operators are easy to fatigue, and the vision can be influenced for a long time. Personnel's acutance and quality control ability are different, are difficult to guarantee higher detectable rate, and can not carry out subregion and accurate judgement bad stratification degree to Lens, and the testing process is traceable yet.
Disclosure of Invention
The invention provides a method for detecting and calculating stray light noise of VR Lens, which is used for realizing intelligent detection of VR Lens.
The invention provides a method for detecting and calculating stray light noise of VR Lens, which comprises the following steps:
s1, acquiring an image;
s2, processing the image and capturing astigmatic noise;
s3, dividing the area where the astigmatic noise is located, calculating the area of each divided area, and determining the value of the astigmatic noise according to the area.
Further, the S1 includes:
s101, setting an image format to have grid lines;
s102, photographing is carried out by adopting a camera, and an image is obtained.
Further, the image format in S101 is specifically a black background and green grid line.
Further, the step S101 includes setting a central ring shape and a peripheral ring shape in sequence from outside to inside with the lens facing position in the image format as a center, so that both the central ring shape and the peripheral ring shape intersect with the grid line.
Further, the S2 specifically includes:
s201, carrying out gray level conversion on the image;
s202 sets a gradation threshold value, and captures the astigmatism noise exceeding the gradation threshold value.
Further, the S3 includes:
s301, dividing the image into n detection areas according to a gray threshold, and determining detection areas Block1, Block2, … … and Block n;
s302, collecting the pixel Area exceeding the gray threshold in each detection Area, and calculating the Total pixel Area.
Further, in S302, the formula is Total Area = Area1+ Area2+ … … + Area n.
Further, the gray threshold is 40-120.
Still further, the step S302 further includes calculating an astigmatism mean gray value:
and calculating the pixel Average Gray level Gray _ Average Value corresponding to the stray light/noise of each Block, summing the Average Gray levels in each detection area, and averaging to obtain the Total Average Gray level Total Gray _ Average Value.
Further, the total average gray level calculation formula is: total Gray _ Average Value = (Gray _ Average Value1+ Gray _ Average Value2+ … … + Gray _ Average Value n)/n.
Compared with the prior art, the VR Lens intelligent detection method has the advantages that the VR images are effectively analyzed, astigmatic noise is captured, and the VR Lens intelligent detection is realized.
Drawings
FIG. 1 is a diagram of an image format according to an embodiment of the present invention;
FIG. 2 is a photograph of a camera according to an embodiment of the present invention;
FIG. 3 is a diagram of an image format having a central annular shape and a peripheral annular shape in accordance with an embodiment of the present invention;
FIG. 4 illustrates an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
The invention provides a method for detecting and calculating stray light noise of VR Lens, which comprises the following steps:
s1, acquiring an image;
s2, processing the image and capturing astigmatic noise;
s3, dividing the area where the astigmatic noise is located, calculating the area of each divided area, and determining the value of the astigmatic noise according to the area.
According to the embodiment of the invention, the VR Lens is effectively analyzed, and astigmatic noise is captured, so that the intelligent detection of VR Lens is realized. Meanwhile, the embodiment of the invention partitions the area where the astigmatic noise is located at S3, so that the partitioned quantitative analysis of the VR Lens detection is realized, and the accurate OK NG judgment and grade division of the VR Lens are effectively achieved.
Optionally, the S1 includes:
s101, setting an image format to have grid lines;
as shown in fig. 1, the image format in S101 is specifically a pattern of black background and green grid lines;
s102, photographing is carried out by adopting a camera, and an image is obtained.
As shown in fig. 2, the camera is a camera using a small-aperture lens.
The embodiment of the invention can partition the image by adopting the grid lines, thereby facilitating the effective detection of each part in the image.
Specifically, the step S101 includes setting a central ring shape and a peripheral ring shape in sequence from outside to inside with the position where the lens faces in the image format as a center, so that both the central ring shape and the peripheral ring shape intersect with the grid line.
As shown in fig. 3, the central ring and the peripheral ring are both blue ellipses. The green line intersects the blue line as shown in fig. 3. The centers of the central ring and the peripheral ring are superposed and are opposite to the lens of the camera.
According to the embodiment of the invention, the central ring and the peripheral ring are arranged, so that the image format can be subjected to curvature change according to the central ring and the peripheral ring, and the image format is matched with the shot image of the camera.
Optionally, the S2 specifically includes:
s201, carrying out gray level conversion on the image;
s202 sets a gradation threshold value, and captures the astigmatism noise exceeding the gradation threshold value.
Wherein, the gray threshold value after capturing the green part in the image and converting into the gray scale is in the range of Graysscale 40-120 (can be self-defined according to the actual situation) and is partitioned.
Specifically, the S3 includes:
s301, dividing the image into n detection areas according to a gray threshold, and determining detection areas Block1, Block2, … … and Block n;
s302, collecting the pixel Area exceeding the gray threshold in each detection Area, and calculating the Total pixel Area.
In the embodiment of the present invention, an image is divided into 9 interested detection regions, 4 corner points of each of 9 blocks are searched, a corresponding Mask is constructed, 9 ROI regions can be obtained by using the Mask, an average value of gray values is calculated for the 9 regions respectively (Block 1, Block2, Block3, Block4, Block5, Block6, Block7, Block8, Block9, and the number of partitions can be set according to requirements, in the embodiment of the present invention, 9 interested regions are taken as an example), and 4 minimum frames in the image form one Block, and the effect is as shown in fig. 4.
In calculating the astigmatic Area, the pixel Area corresponding to the stray light/noise of each Block is calculated as shown in fig. 4, and the sum of the Area areas of each Block is the Total Area of the pixel areas of the stray light/noise of the Area to be detected by the next VR Lens, so that the astigmatic areas of all blocks of the product or for a certain Block can be controlled, and the formula is as follows, wherein the Total Area = Area1+ Area2+ Area3+ Area4+ Area5+ Area6+ Area7+ Area8+ Area9 calculates the average astigmatic gray value
In particular, said S302 further comprises the calculation of mean gray value of astigmatism:
and calculating the pixel Average Gray level Gray _ Average Value corresponding to the stray light/noise of each Block, summing the Average Gray levels in each detection area, and averaging to obtain the Total Average Gray level Total Gray _ Average Value.
Specifically, the total average gray level calculation formula is: total Gray _ Average Value = (Gray _ Average Value1+ Gray _ Average Value2+ … … + Gray _ Average Value n)/n.
The Average Gray scale Value of the pixel corresponding to the stray light/noise of each Block is calculated by an algorithm, the sum of each Average Gray scale and the Total number of the blocks is divided by the Total number of the blocks, that is, the Total Average Gray scale Value of the stray light/noise of the area to be detected by the current VR Lens, and as a result, the Average Gray scale Value of the Total product blocks or the stray light of a certain Block can be controlled, and the calculation formula is as follows, i.e., Total Gray scale Value1+ Gray scale Value2+ Gray scale Value3+ Gray scale Value4+ Gray scale Value5+ Gray scale Value6+ Gray scale Value7+ Gray scale Value8+ Gray scale Value 9/9.
The embodiment of the invention is suitable for online production, replaces manpower, provides quick, stable and reliable detection capability, can perform customized partition and quantification on VR Lens detection in a visual field, can customize various card control values, and realizes accurate judgment and grade division of OK NG of products. The process and the result are controllable and traceable.
Finally, it should be noted that the above-mentioned embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the modifications and equivalents of the specific embodiments of the present invention can be made by those skilled in the art after reading the present specification, but these modifications and variations do not depart from the scope of the claims of the present application.
Claims (10)
1. A VR Lens stray light noise detection and calculation method is characterized by comprising the following steps:
s1, acquiring an image;
s2, processing the image and capturing astigmatic noise;
s3, dividing the area where the astigmatic noise is located, calculating the area of each divided area, and determining the value of the astigmatic noise according to the area.
2. The method of claim 1, wherein the step S1 includes:
s101, setting an image format to have grid lines;
s102, photographing is carried out by adopting a camera, and an image is obtained.
3. The method of claim 2, wherein the image format in S101 is black-bottom green grid lines.
4. The method of claim 3, wherein S101 comprises setting a central ring shape and a peripheral ring shape from outside to inside with the Lens facing position in the image format as a center, such that the central ring shape and the peripheral ring shape intersect with the grid lines.
5. The method of claim 1, wherein S2 specifically includes:
s201, carrying out gray level conversion on the image;
s202 sets a gradation threshold value, and captures the astigmatism noise exceeding the gradation threshold value.
6. The method of claim 5, wherein the step S3 includes:
s301, dividing the image into n detection areas according to a gray threshold, and determining detection areas Block1, Block2, … … and Block n;
s302, collecting the pixel Area exceeding the gray threshold in each detection Area, and calculating the Total pixel Area.
7. The method of claim 6, wherein in S302, the calculation formula is Total Area = Area1+ Area2+ … … + Area n.
8. The method of claim 5, wherein the threshold grayscale value is 40-120.
9. The method of claim 6, wherein the step S302 further comprises calculating an average gray value of the scattered light:
and calculating the pixel Average Gray level Gray _ Average Value corresponding to the stray light/noise of each Block, summing the Average Gray levels in each detection area, and averaging to obtain the Total Average Gray level Total Gray _ Average Value.
10. The method of claim 9, wherein the total average gray scale calculation formula is: total Gray _ Average Value = (Gray _ Average Value1+ Gray _ Average Value2+ … … + Gray _ Average Value n)/n.
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