Tobacco leaf image quality evaluation method and system, storage, and electronic device

A quality evaluation and image quality technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of poor stability and long time consumption, and achieve the effect of small calculation amount, excellent stability and short calculation time

Active Publication Date: 2018-09-07
SHANGHAI TOBACCO GRP CO LTD +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide an evaluation method, system, memory, and electronic equipment for tobacco leaf image quality, which are used to solve the problem of poor stability when evaluating the quality of tobacco leaf image in the existing tobacco leaf image quality evaluation method. time-consuming issues

Method used

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  • Tobacco leaf image quality evaluation method and system, storage, and electronic device
  • Tobacco leaf image quality evaluation method and system, storage, and electronic device
  • Tobacco leaf image quality evaluation method and system, storage, and electronic device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] See figure 1 This embodiment provides a method for evaluating tobacco image quality with high stability and short calculation time, which mainly includes the following steps:

[0031] S1: Extract the component values ​​of each pixel of the tobacco leaf image to be evaluated in the RGB color space.

[0032] It should be noted that the RGB color space here is a new color space converted from the traditional machine vision RGB (R, G, B) space according to RGB (R+G, 0.7B, 0.3B). As we all know, R, G, and B are red, green and blue spaces, and the basic color of the color theme of tobacco leaves is yellow. The character of yellow is determined by (R+G) / B. The larger the value, the closer the color is to yellow. So you need to split RGB into R+G and B during color conversion. In addition, because the B value is also an indicator of the brightness of the shooting environment, the B value also has a certain meaning, so the B value also needs to be converted separately. The final co...

Embodiment 2

[0042] according to figure 1 The method flow shown is figure 2 To evaluate the quality of tobacco leaf images, the specific steps are as follows:

[0043] Step 1: Extract the component values ​​in the RGB color space of each pixel of the tobacco leaf image to be evaluated:

[0044] When i=1: R 1 =RGB(1,1)=336.22; G 1 =RGB(1,2)=22.32; B 1 =RGB(1,3)=9.56.

[0045] When i=2: R 2 =RGB(2,1)=310.37; G 2 =RGB(2,2)=22.89; B 2 =RGB(2,3)=9.76.

[0046] When i=3: R 3 =RGB(3,1)=325.22; G 3 =RGB(3,2)=23.13; B 3 =RGB(3,3)=9.32.

[0047] ………………

[0048] When i=n: R n =RGB(n,1)=325.22; G n =RGB(n,2)=23.76; B n =RGB(n,3)=9.18.

[0049] Step 2: Solve the color gamut index of each pixel of the tobacco leaf image to be evaluated:

[0050] When i=1: CGIR 1 =R 1 / (G 1 +B 1 ) = 10.546; CGIG 1 =G 1 / (R 1 +B 1 ) = 0.0645; CGIB 1 =B 1 / (R 1 +G 1 )=0.02941.

[0051] When i=2: CGIR 2 =R 2 / (G 2 +B 2 ) = 9.506; CGIG 2 =G 2 / (R 2 +B 2 ) = 0.0715; CGIB 2 =B 2 / (R 2 +G 2 )=0.02852.

[0052] When i=3: CGIR 3 =R 3 / (G 3 +B ...

Embodiment 3

[0066] use figure 1 The method flow shown, right Figure 3a~3d The quality of the four tobacco leaf images is evaluated and sorted, and the quality evaluation of each tobacco leaf image is obtained The values ​​and their sorting are shown in Table 1. visible, Figure 3d of The value is the largest, and the ranking is first, indicating that the quality of the tobacco leaf image is the best; Figure 3b of The smallest value indicates that the quality of the tobacco leaf image is the worst. Actually, Figure 3d For high-quality camera + high-quality light source conditions to capture images, and Figure 3b It can be seen that the quality evaluation result given by the present invention is also in line with the subjective perception and actual objective factors of human vision.

[0067] Table 1

[0068]

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Abstract

The present invention provides a tobacco leaf image quality evaluation method and system, a storage, and an electronic device. The method comprises the steps of: extracting the component value of eachpixel of a tobacco leaf image to be evaluated in a color space, wherein the color space is formed by yellow color gamut components, bright color gamut components and a black color gamut components; calculating the color gamut indexes of the pixels according to the component values of pixels; determining the quality evaluation indexes of the pixel according to the color gamut indexes of the pixels; and calculating an average value of the quality evaluation indexes, comparing the average value with a preset quality evaluation index, and determining the quality condition of the tobacco leaf image to be evaluated. The tobacco leaf image quality evaluation method and system, the storage, and the electronic device employ the color gamut indexes to perform tobacco leaf image quality evaluation,and are lower in complexity, smaller in computing quantity, shorter in calculation time and better in quality evaluation result stability compared to a current tobacco leaf image quality evaluation method.

Description

Technical field [0001] The invention belongs to the technical field of tobacco image processing, and in particular relates to a method, system, storage medium, and electronic equipment for evaluating the quality of tobacco leaf image based on color gamut index. Background technique [0002] Tobacco image quality evaluation is the basis and key for evaluating tobacco imaging equipment, grading, appearance quality testing and pattern recognition. At present, the evaluation methods of tobacco image quality mainly include peak signal-to-noise ratio method PSNR and information entropy method SSEQ (Ji Jiangtao, Deng Mingli, He Zhitao, et al. Gaussian denoising method based on OpenCV for flue-cured tobacco image[J]. Jiangsu Agricultural Sciences, 2016 , 44(11): 373-376.): (1) Peak signal-to-noise ratio method PSNR: This method has many researches and is the most mature, such as the invention patent of Tsinghua University Dai Qionghai, Ma Xiao, Cao Xun, etc. "A machine-based Learning ob...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/90
CPCG06T7/0004G06T7/90
Inventor 蔡宪杰薛超群张军窦家宇宋纪真薛庆逾郭文卢晓华张伟峰沈钢程森顾毓敏高远牟文君
Owner SHANGHAI TOBACCO GRP CO LTD
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