Method for evaluating color dispersion degree of projection imaging of AR (Augmented Reality) glasses

Abstract

The invention relates to a method for evaluating the projection imaging color dispersion degree of AR glasses, and the method comprises the steps: 1, projecting a designed pattern through a light machine of the AR glasses, and obtaining an image through a human eye camera; 2, detecting the positions of all line segments in the obtained image, and cutting out an n * m region of interest by taking the central point of each line segment as a central reference point; 3, carrying out BGR three-channel separation on each cut region, and carrying out pairwise subtraction to obtain three regions of interest after subtraction, and obtaining three brightness difference values; 4, performing binarization processing on the three regions of interest after subtraction, and obtaining the number of non-zero pixel values of each region; 5, calculating a dispersion value between every two color gamuts to obtain three dispersion values; and 6, taking a mean value of the three dispersion values as a final dispersion value of the region of interest. According to the invention, the evaluation result can be quickly obtained, and the projection imaging quality of the AR glasses ray machine can be objectively evaluated.

Description

Technical Field

[0001] The present invention relates to the field of augmented reality (AR) technology, and in particular to a method for evaluating the degree of color discreteness of AR glasses projection imaging. Background Art

[0002] Augmented Reality (AR) technology integrates the virtual environment generated by the computer with the real environment around the user by means of optoelectronic display technology, interactive technology, multiple sensor technologies, computer graphics and multimedia technology, so that the user can be convinced from the sensory effect that the virtual environment is a part of the real environment around him. Augmented reality has the new characteristics of combining virtual and real, real-time interaction, and three-dimensional registration.

[0003] With the rapid development of AR technology, AR glasses, as an important display device, have a direct impact on user experience through their imaging quality. In the prior art, the evaluation methods for the imaging quality of AR glasses are often subjective or complex, and there is a lack of a fast, objective, and quantifiable evaluation method, especially for the evaluation of color discreteness. Summary of the invention

[0004] The purpose of the present invention is to provide a method for evaluating the color discreteness of AR glasses projection imaging, which can quickly obtain evaluation results and objectively evaluate the AR glasses optical and mechanical projection imaging quality.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for evaluating the color discreteness of AR glasses projection imaging, characterized by comprising the following steps:

[0007] Step 1: Use the optical machine of AR glasses to project the designed pattern and obtain the image through the human eye camera;

[0008] Step 2: Detect the positions of all line segments in the acquired image, and cut out a region of interest with a size of n*m using the center point of each line segment as the center reference point, where n and m are the preset numbers of pixels, and n is the length of the line segment to be cut;

[0009] Step 3: Separate the BGR three channels of each cropped area and subtract them two by two to obtain three subtracted regions of interest and three brightness difference values. The formula is as follows:

[0010] Diff BG =(ROI_B-ROI_G)+(ROI_G-ROI_B)

[0011] Diff RG =(ROI_G-ROI_R)+(ROI_R-ROI_G)

[0012] Diff RB =(ROI_R-ROI_B)+(ROI_B-ROI_R)

[0013] Among them: ROI_B is the brightness value of the B channel of the region of interest, ROI_G is the brightness value of the G channel of the region of interest, and ROI_R is the brightness value of the R channel of the region of interest;

[0014] Diff BG is the brightness difference between the B channel and the G channel, Diff RG is the brightness difference between the R channel and the G channel, Diff RB is the brightness difference between the R channel and the B channel;

[0015] Step 4: Binarize the three subtracted regions of interest respectively, and obtain the number of non-zero pixel values ​​in each region. The formula is as follows:

[0016]

[0017] in: t hresh is the preset threshold;

[0018] T_BG is the binarized region of the region of interest after the B channel is subtracted from the G channel, and T_BG(i, j) is the binarized value of the pixel point in the i-th row and j-th column of the region;

[0019] T_RG is the binarized region of the region of interest after the R channel is subtracted from the G channel, and T_RG(i, j) is the binarized value of the pixel point in the i-th row and j-th column of the region;

[0020] T_RB is the binarized region of the region of interest after the R channel is subtracted from the B channel, and T_RB(i, j) is the binarized value of the pixel point in the i-th row and j-th column of the region;

[0021] Step 5: Calculate the dispersion value between the two color domains and obtain three dispersion values. The formula is as follows:

[0022]

[0023] Where: n is the length of the line segment to be cut;

[0024] dispersion BG is the dispersion value between the B and G color domains, dispersion RG is the dispersion value between the R and G color domains, dispersion RB is the dispersion value between the R and B color domains;

[0025] Step 6: Take the average of the three dispersion values ​​as the final dispersion value of the region of interest. The formula is as follows:

[0026]

[0027] Specifically, in step 2, Hough transform is used to detect the positions of all line segments in the acquired image.

[0028] Specifically, in step 4, the value of thresh is set according to actual needs.

[0029] Specifically, in step 2, the values ​​of n and m are set according to actual needs.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. This method can objectively quantify the color discreteness of AR glasses projection imaging by digitally processing and calculating the image;

[0032] 2. This method has a clear process, efficient and accurate calculation process, and can quickly obtain evaluation results;

[0033] 3. This method can be applied to the evaluation of various AR glasses and has wide applicability;

[0034] 4. This method can make a horizontal comparison of the imaging quality of different AR glasses through a unified evaluation standard. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 It is a flowchart of the main process of the present invention;

[0037] Figure 2 Projecting patterns for light machines;

[0038] Figure 3 This is a schematic diagram of image cropping;

[0039] Figure 4 Schematic diagram of BGR channel separation and subtraction;

[0040] Figure 5 Schematic diagram of ROI_R-ROI_G subtraction;

[0041] Figure 6This is the binary effect diagram of ROI_R-ROI_G result;

[0042] Figure 7 This is a schematic diagram of the algorithm execution effect. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0044] See Figures 1 to 7 A method for evaluating the color discreteness of AR glasses projection imaging, characterized in that it includes the following steps:

[0045] Step 1: Use the optical machine of AR glasses to project the designed pattern and obtain the image through the human eye camera;

[0046] Step 2: Detect the positions of all line segments in the acquired image, and cut out a region of interest with a size of n*m using the center point of each line segment as the center reference point, where n and m are the preset numbers of pixels, and n is the length of the line segment to be cut;

[0047] Step 3: Separate the BGR three channels of each cropped area and subtract them two by two to obtain three subtracted regions of interest and three brightness difference values. The formula is as follows:

[0048] Diff BG =(ROI_B-ROI_G)+(ROI_G-ROI_B)

[0049] Diff RG =(ROI_G-ROI_R)+(ROI_R-ROI_G)

[0050] Diff RB =(ROI_R-ROI_B)+(ROI_B-ROI_R)

[0051] Among them: ROI_B is the brightness value of the B channel of the region of interest, ROI_G is the brightness value of the G channel of the region of interest, and ROI_R is the brightness value of the R channel of the region of interest;

[0052] Diff BG is the brightness difference between the B channel and the G channel, Diff RG is the brightness difference between the R channel and the G channel, Diff RB is the brightness difference between the R channel and the B channel;

[0053] Step 4: Binarize the three subtracted regions of interest respectively, and obtain the number of non-zero pixel values ​​in each region. The formula is as follows:

[0054]

[0055] in: t hresh is the preset threshold;

[0056] T_BG is the binarized region of the region of interest after the B channel is subtracted from the G channel, and T_BG(i, j) is the binarized value of the pixel point in the i-th row and j-th column of the region;

[0057] T_RG is the binarized region of the region of interest after the R channel is subtracted from the G channel, and T_RG(i, j) is the binarized value of the pixel point in the i-th row and j-th column of the region;

[0058] T_RB is the binarized region of the region of interest after the R channel is subtracted from the B channel, and T_RB(i, j) is the binarized value of the pixel point in the i-th row and j-th column of the region;

[0059] Step 5: Calculate the dispersion value between the two color domains and obtain three dispersion values. The formula is as follows:

[0060]

[0061] Where: n is the length of the line segment to be cut;

[0062] dispersion BG is the dispersion value between the B and G color domains, dispersion RG is the dispersion value between the R and G color domains, dispersion RB is the dispersion value between the R and B color domains;

[0063] Step 6: Take the average of the three dispersion values ​​as the final dispersion value of the region of interest. The formula is as follows:

[0064]

[0065] Specifically, in step 2, Hough transform is used to detect the positions of all line segments in the acquired image.

[0066] Specifically, in step 4, the value of thresh is set according to actual needs.

[0067] Specifically, in step 2, the values ​​of n and m are set according to actual needs.

[0068] The beneficial effects of the present invention are as follows:

[0069] 1. This method can objectively quantify the color discreteness of AR glasses projection imaging by digitally processing and calculating the image;

[0070] 2. This method has a clear process, efficient and accurate calculation process, and can quickly obtain evaluation results;

[0071] 3. This method can be applied to the evaluation of various AR glasses and has wide applicability;

[0072] 4. This method can make a horizontal comparison of the imaging quality of different AR glasses through a unified evaluation standard.

[0073] The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Although the present invention has been disclosed as above in the form of preferred embodiments, it is not intended to limit the present invention. Any technical personnel in the field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating the color discreteness of AR glasses projection imaging, characterized in that: The following steps are involved: Step 1: Use the optical machine of AR glasses to project the designed pattern and obtain the image through the human eye camera; Step 2: Detect the positions of all line segments in the acquired image, and cut out a region of interest with a size of n*m using the center point of each line segment as the center reference point, where n and m are the preset numbers of pixels, and n is the length of the line segment to be cut; Step 3: Separate the BGR three channels of each cropped area and subtract them two by two to obtain three subtracted regions of interest and three brightness difference values. The formula is as follows: Diff BG =(ROI_B―ROI_G)+(ROI_G―ROI_B) Diff RG =(ROI_G―ROI_R)+(ROI_R―ROI_G) Diff RB =(ROI_R―ROI_B)+(ROI_B―ROI_R) Among them: ROI_B is the brightness value of the B channel of the region of interest, ROI_G is the brightness value of the G channel of the region of interest, and ROI_R is the brightness value of the R channel of the region of interest; Diff BG is the brightness difference between the B channel and the G channel, Diff RG is the brightness difference between the R channel and the G channel, Diff RB is the brightness difference between the R channel and the B channel; Step 4: Binarize the three subtracted regions of interest respectively, and obtain the number of non-zero pixel values ​​in each region. The formula is as follows: Where: thresh is the preset threshold; T_BG is the binarized region of the region of interest after the B channel is subtracted from the G channel, and T_BG(i,j) is the binarized value of the pixel point in the i-th row and j-th column of the region; T_RG is the binarized region of the region of interest after the R channel is subtracted from the G channel, and T_RG(i,j) is the binarized value of the pixel point in the i-th row and j-th column of the region; T_RB is the binarized region of the region of interest after the R channel is subtracted from the B channel, and T_RB(i,j) is the binarized value of the pixel point in the i-th row and j-th column of the region; Step 5: Calculate the dispersion value between the two color domains and obtain three dispersion values. The formula is as follows: Where: n is the length of the line segment to be cut; dispersion BG is the dispersion value between the B and G color domains, dispersion RG is the dispersion value between the R and G color domains, dispersion RB is the dispersion value between the R and B color domains; Step 6: Take the average of the three dispersion values ​​as the final dispersion value of the region of interest. The formula is as follows:

2. The method for evaluating the color discreteness of AR glasses projection imaging according to claim 1 is characterized in that: In step 2, Hough transform is used to detect the positions of all line segments in the acquired image.

3. The method for evaluating the color discreteness of AR glasses projection imaging according to claim 1 is characterized in that: In step 4, the value of thresh is set according to actual needs.

4. The method for evaluating the color discreteness of AR glasses projection imaging according to claim 1 is characterized in that: In step 2, the values ​​of n and m are set according to actual needs.

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