CN109141640A - Acetes chinensis method, system, equipment and storage medium based on machine vision - Google Patents
Acetes chinensis method, system, equipment and storage medium based on machine vision Download PDFInfo
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- CN109141640A CN109141640A CN201810878391.5A CN201810878391A CN109141640A CN 109141640 A CN109141640 A CN 109141640A CN 201810878391 A CN201810878391 A CN 201810878391A CN 109141640 A CN109141640 A CN 109141640A
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- 238000000034 method Methods 0.000 title claims abstract description 9
- 241000114727 Acetes chinensis Species 0.000 title abstract 5
- 238000001514 detection method Methods 0.000 claims abstract description 29
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- 238000004364 calculation method Methods 0.000 claims description 7
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- 238000007639 printing Methods 0.000 abstract description 3
- 238000004737 colorimetric analysis Methods 0.000 abstract 1
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- 238000006243 chemical reaction Methods 0.000 description 3
- 239000003086 colorant Substances 0.000 description 3
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
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Abstract
The acetes chinensis method based on machine vision that the invention discloses a kind of, comprising the following steps: obtain target object image information；Choose region to be compared；Color difference is calculated using CIE colour difference formula；And it is compared and judges with preset threshold.A kind of acetes chinensis system based on machine vision, comprising: image collection module obtains target object image information for executing step；Colorimetry module chooses region to be compared for executing step；Color difference is calculated using CIE colour difference formula；Color difference judgment module, for executing step and being compared and judge with preset threshold.And a kind of acetes chinensis equipment and readable storage medium storing program for executing based on machine vision.Acetes chinensis method, system, equipment and storage medium provided by the invention based on machine vision, the system operatio that this programme provides is simple, intuitive, it can be with the difference of Accurate Determining product surface color, detection efficiency is improved, can be used for printing, silk, injection molding etc. is to the every profession and trade of color difference strict control.
Description
Technical Field
The invention relates to the field of image processing, in particular to a color difference detection method and system based on machine vision.
Background
In the production of industrial products, the color appearance of different batches of products often varies. The reasons for the difference mainly include color ingredients, production process and the like. Currently, most of product detection adopts a manual detection method, and is easily influenced by the physiology and the psychology of people.
Therefore, it is necessary to incorporate machine vision into color testing to objectively quantify the differences between the product and the standard palette.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the invention aims to provide a color difference detection method and system based on machine vision.
The technical scheme adopted by the invention is as follows:
the invention provides a color difference detection method based on machine vision, which comprises the following steps:
acquiring image information of a target object;
selecting an area to be compared;
calculating the color difference by using a CIE color difference formula;
and comparing and judging with a preset threshold value.
As an improvement of the technical scheme, the target object image information is converted into an XYZ color space from an RGB color space; the XYZ color space is then converted to an LAB color space to obtain an LAB color space for the image information.
As an improvement of the technical solution, the calculating the color difference specifically includes:
calculating lightness L, chroma a and b and psychological chroma C in LAB color spaceab；
Then calculate L ', a', b ', hue h'abA regulatory factor G;
calculating lightness difference delta L and chroma difference delta CabColor difference Δ Hab；
Next, a weighting function S is calculatedL、SC、SHAnd a rotation function RT、RC；
And selecting a correction factor KL、KC、KHTo perform color difference calculation.
Further, the threshold value is a statistical value obtained through experiments.
In another aspect, the present invention further provides a color difference detection system based on machine vision, including:
the image acquisition module is used for executing the steps to acquire the image information of the target object;
the color difference calculation module is used for executing the steps to select the area to be compared; calculating the color difference by using a CIE color difference formula;
and the color difference judging module is used for executing the steps and comparing and judging the steps with a preset threshold value.
In still another aspect, the present invention further provides a color difference detection apparatus based on machine vision, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method.
In a fourth aspect, the present invention also provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method.
The invention has the beneficial effects that: the invention provides a color difference detection method, a system, equipment and a storage medium based on machine vision. By adopting the scheme, the efficiency and the accuracy of chromatic aberration detection are improved. The system provided by the scheme is simple and visual to operate, can accurately measure the difference of the surface color of the product, improves the detection efficiency, and can be used in various industries for strictly controlling the color difference, such as printing, silk, injection molding and the like.
Drawings
Fig. 1 is a control diagram of an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The scheme adopts a color image processing method, namely a vivid color synthesis technology. The color image can be decomposed into 256 levels of red, green and blue, total 1677 ten thousand color components, different colors are defined according to the level difference of the colors, and then an algorithm which can detect the image structure similar points and the image change points according to the definition is created. Compared with the monochrome image processing mode of only 256-level gray level identification, the identification capability of the vivid color synthesis mode is improved by about 65000 times. In this way, subtle color differences that cannot be distinguished originally can be detected with high precision at the same speed as in the monochrome image processing method.
Preferably, the system comprises a computer, an industrial camera, a roof lamp light source, an integrated bracket and chromatic aberration detection software for analyzing the acquired image, and the industrial camera can be used for accurately acquiring the appearance color of the product. The light source has the hemisphere inner wall of the sphere integral effect, can reflect the light ray that 360 degrees emission come out from the bottom evenly, makes the illuminance of whole image very even. The integrated bracket is used to fix the camera and adjust the position between the roof light and the camera. The color difference detection software is written by MSVisualstudio 2003.
According to the scheme, a proper color space is selected at first, and then a proper color difference calculation formula is selected to calculate the color difference.
Selection of a color space
The color space includes 3 types of RGB color space, XYZ color space, and LAB color space. Where the RGB color space is not uniform and cannot be used to calculate color differences. The XYZ color space eliminates the negative number of r, g, and b, but is also an uneven color space and cannot be used for calculating the chromatic aberration. The LAB color model is composed of 3 elements of L (lightness), A (color), and B (color). Where a denotes a range from red to green, and B denotes a range from yellow to blue. The LAB color space is a uniform color space that conforms to the human visual perception. When the difference of the colors is recognized by human eyes and the difference is smaller than the color difference value of two adjacent stages, the actual feeling of the product by the observer can be reflected.
Therefore, the RGB color space is first converted into the XYZ color space, and the conversion formula from the RGB color space to the XYZ color space is:
secondly, the XYZ color space is converted to the LAB color space by the conversion formula:
a＝500x(f(X|Xn)-f(Y|Yn)) (3)
b＝200x(f(Y|Yn)-f(Z|Zn)) (4)
wherein, Xn＝95.04，Yn＝100.00，Zn108.89 CIE Standard illuminant D65White light tristimulus values of (2).
Selection of color difference formula
After conversion to the LAB color space, a color difference formula for the LAB color space can be obtained:
where is the color difference value in the LAB color space. CIE1976LAB, however, are not a uniform color space for the detection of small color differences. Therefore, in 2001, the international commission on illumination proposed the CIEDE2000 color difference formula on the basis of the LAB color difference formula, which well solved the detection of small color differences. Therefore, the CIEDE2000 color difference formula is adopted as follows:
the first step is as follows: calculating lightness L, chroma a and b and psychological chroma C in LAB color spaceab。
The second step is that: calculating L ', a', b ', hue h'abAnd a regulatory factor G.
Wherein,is a product C to be testedab1And a standard color plate Cab2Is calculated as the arithmetic mean of (1).
The third step: calculating lightness difference delta L and chroma difference delta CabColor difference Δ Hab。
The fourth step: calculating a weighting function SL、SC、SHAnd a rotation function RT、RC
RT＝-sin(2Δθ)xRC
The fifth step: selection of KL、KC、KHWherein, K isL、KC、KHIs a correction coefficient determined according to actual use conditions. Taking K under the condition of CIE standard observationL＝KC＝KH＝1。
And a sixth step: and calculating the color difference.
Calculating the color difference Δ E using equation (7)00The size of (2).
The color difference detection control flow is shown in fig. 1. A color difference detection method based on machine vision comprises the following steps:
acquiring image information of a target object;
selecting an area to be compared;
calculating the color difference by using a CIE color difference formula;
and comparing and judging with a preset threshold value.
As an improvement of the technical scheme, the target object image information is converted into an XYZ color space from an RGB color space; the XYZ color space is then converted to an LAB color space to obtain an LAB color space for the image information.
As an improvement of the technical solution, the calculating the color difference specifically includes:
calculating lightness L, chroma a and b and psychological chroma C in LAB color spaceab；
Then calculate L ', a', b ', hue h'abA regulatory factor G;
calculating lightness difference delta L and chroma difference delta CabColor difference Δ Hab；
Next, a weighting function S is calculatedL、SC、SHAnd a rotation function RT、RC；
And selecting a correction factor KL、KC、KHTo perform color difference calculation.
Further, the threshold value is a statistical value obtained through experiments.
And starting the camera to read in pictures. Selecting a comparison area and calculating the color difference. Compared with a threshold value and a judgment is made. The threshold value is a statistical value obtained in a large number of detection tests, and when the calculated color difference is larger than the threshold value, the color of the product is judged to be unqualified. Otherwise, below this threshold, the product color is considered acceptable.
Wherein the camera is mainly used for collecting images and transmitting the collected images to the operation window. In the operation window, a user selects 2 rectangular areas on the collected picture, the rectangular areas are respectively arranged on the standard color plate and the product to be detected, and then the color difference between the standard color plate and the product to be detected can be calculated. The detection personnel can judge whether the product is qualified according to the detected color difference. It was found experimentally that when 2 rectangular areas were selected on a standard color plate, the color difference was found to be small.
In another aspect, the present invention further provides a color difference detection system based on machine vision, including:
the image acquisition module is used for executing the steps to acquire the image information of the target object;
the color difference calculation module is used for executing the steps to select the area to be compared; calculating the color difference by using a CIE color difference formula;
and the color difference judging module is used for executing the steps and comparing and judging the steps with a preset threshold value.
In still another aspect, the present invention further provides a color difference detection apparatus based on machine vision, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method.
In a fourth aspect, the present invention also provides a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method.
The invention provides a color difference detection method, a system, equipment and a storage medium based on machine vision. By adopting the scheme, the efficiency and the accuracy of chromatic aberration detection are improved. The system provided by the scheme is simple and visual to operate, can accurately measure the difference of the surface color of the product, improves the detection efficiency, and can be used in various industries for strictly controlling the color difference, such as printing, silk, injection molding and the like.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A color difference detection method based on machine vision is characterized by comprising the following steps:
acquiring image information of a target object;
selecting an area to be compared;
calculating the color difference by using a CIE color difference formula;
and comparing and judging with a preset threshold value.
2. The machine-vision-based color difference detection method according to claim 1, further comprising: converting the target object image information from an RGB color space to an XYZ color space; the XYZ color space is then converted to an LAB color space to obtain an LAB color space for the image information.
3. Machine vision based color difference detection method according to claim 1 or 2, characterized in that said calculating color differences specifically comprises:
calculating lightness L, chroma a and b and psychological chroma C in LAB color spaceab；
Then calculate L ', a', b ', hue h'abA regulatory factor G;
calculating lightness difference delta L and chroma difference delta CabColor difference Δ Hab；
Next, a weighting function S is calculatedL、SC、SHAnd a rotation function RT、RC；
And selecting a correction factor KL、KC、KHTo perform color difference calculation.
4. The machine-vision-based color difference detection method according to claim 3, wherein the threshold value is a statistical value obtained through experiments.
5. A machine vision based chromatic aberration detection system, comprising:
the image acquisition module is used for executing the steps to acquire the image information of the target object;
the color difference calculation module is used for executing the steps to select the area to be compared; calculating the color difference by using a CIE color difference formula;
and the color difference judging module is used for executing the steps and comparing and judging the steps with a preset threshold value.
6. A machine vision-based chromatic aberration detection apparatus, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 4.
7. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 4.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110108362A (en) * | 2019-04-17 | 2019-08-09 | 江苏理工学院 | The adaptive online test method of color difference and device based on SLIC super-pixel segmentation |
| CN110378393A (en) * | 2019-06-26 | 2019-10-25 | 江苏理工学院 | A kind of mixing printing product acetes chinensis method based on PSO-GSA-SVM |
| CN110426222A (en) * | 2019-07-16 | 2019-11-08 | 北京中家智信信息技术有限公司 | A kind of washing machine Anti-fade performance test methods |
| CN111579505A (en) * | 2020-04-23 | 2020-08-25 | 河南中烟工业有限责任公司 | Cigarette packaging material color difference detection method based on digital image processing |
| CN111798528A (en) * | 2020-07-01 | 2020-10-20 | 西交利物浦大学 | Fabric color difference detection method, device, storage medium and equipment |
| CN111862186A (en) * | 2020-06-29 | 2020-10-30 | 湖北中烟工业有限责任公司 | Color difference control method and device for cigarette wrapping paper |
| CN112200242A (en) * | 2020-10-09 | 2021-01-08 | 陕西科技大学 | Color classification method for drawing Thangka image, computer equipment, terminal and medium |
| CN112529970A (en) * | 2019-10-22 | 2021-03-19 | 卜风雷 | Mobile on-site big data analysis platform |
| CN112766187A (en) * | 2021-01-25 | 2021-05-07 | 佛山科学技术学院 | Rapeseed oil heating time prediction system and method |
| CN112798107A (en) * | 2021-01-18 | 2021-05-14 | 北京印刷学院 | Method for calculating color difference threshold of cigarette packet holographic printed matter |
| CN112861920A (en) * | 2021-01-15 | 2021-05-28 | 佛山科学技术学院 | Palm oil heating time prediction system and method |
| CN113546874A (en) * | 2021-07-15 | 2021-10-26 | 厦门强力巨彩光电科技有限公司 | SMT PCB ink color classification method |
| CN113670443A (en) * | 2021-07-09 | 2021-11-19 | 北京中科慧眼科技有限公司 | Color difference measuring method and system based on device-independent color space and intelligent terminal |
| CN113781451A (en) * | 2021-09-13 | 2021-12-10 | 长江存储科技有限责任公司 | Wafer detection method and device, electronic equipment and computer readable storage medium |
| CN114184278A (en) * | 2021-12-20 | 2022-03-15 | 湘潭大学 | Door and window color difference detection device and color difference detection algorithm |
| CN117524432A (en) * | 2023-11-09 | 2024-02-06 | 中国人民解放军总医院京南医疗区 | Blood transport system and method based on ai image processing technology |
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| CN110108362A (en) * | 2019-04-17 | 2019-08-09 | 江苏理工学院 | The adaptive online test method of color difference and device based on SLIC super-pixel segmentation |
| CN110378393A (en) * | 2019-06-26 | 2019-10-25 | 江苏理工学院 | A kind of mixing printing product acetes chinensis method based on PSO-GSA-SVM |
| CN110426222A (en) * | 2019-07-16 | 2019-11-08 | 北京中家智信信息技术有限公司 | A kind of washing machine Anti-fade performance test methods |
| CN110426222B (en) * | 2019-07-16 | 2021-03-02 | 北京中家智信信息技术有限公司 | Washing machine anti-fading performance testing method |
| CN112529970A (en) * | 2019-10-22 | 2021-03-19 | 卜风雷 | Mobile on-site big data analysis platform |
| CN111579505A (en) * | 2020-04-23 | 2020-08-25 | 河南中烟工业有限责任公司 | Cigarette packaging material color difference detection method based on digital image processing |
| CN111862186A (en) * | 2020-06-29 | 2020-10-30 | 湖北中烟工业有限责任公司 | Color difference control method and device for cigarette wrapping paper |
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| CN111798528A (en) * | 2020-07-01 | 2020-10-20 | 西交利物浦大学 | Fabric color difference detection method, device, storage medium and equipment |
| CN112200242B (en) * | 2020-10-09 | 2023-06-20 | 陕西科技大学 | Color classification method, computer equipment, terminal and medium for drawing Thangka image |
| CN112200242A (en) * | 2020-10-09 | 2021-01-08 | 陕西科技大学 | Color classification method for drawing Thangka image, computer equipment, terminal and medium |
| CN112861920A (en) * | 2021-01-15 | 2021-05-28 | 佛山科学技术学院 | Palm oil heating time prediction system and method |
| CN112798107A (en) * | 2021-01-18 | 2021-05-14 | 北京印刷学院 | Method for calculating color difference threshold of cigarette packet holographic printed matter |
| CN112798107B (en) * | 2021-01-18 | 2023-06-20 | 北京印刷学院 | Color difference threshold calculation method for holographic printed matter of tobacco package |
| CN112766187A (en) * | 2021-01-25 | 2021-05-07 | 佛山科学技术学院 | Rapeseed oil heating time prediction system and method |
| CN113670443A (en) * | 2021-07-09 | 2021-11-19 | 北京中科慧眼科技有限公司 | Color difference measuring method and system based on device-independent color space and intelligent terminal |
| CN113670443B (en) * | 2021-07-09 | 2024-01-12 | 北京中科慧眼科技有限公司 | Color difference measurement method and system based on device-independent color space and intelligent terminal |
| CN113546874A (en) * | 2021-07-15 | 2021-10-26 | 厦门强力巨彩光电科技有限公司 | SMT PCB ink color classification method |
| CN113781451A (en) * | 2021-09-13 | 2021-12-10 | 长江存储科技有限责任公司 | Wafer detection method and device, electronic equipment and computer readable storage medium |
| CN113781451B (en) * | 2021-09-13 | 2023-10-17 | 长江存储科技有限责任公司 | Wafer detection method, device, electronic equipment and computer readable storage medium |
| CN114184278A (en) * | 2021-12-20 | 2022-03-15 | 湘潭大学 | Door and window color difference detection device and color difference detection algorithm |
| CN117524432A (en) * | 2023-11-09 | 2024-02-06 | 中国人民解放军总医院京南医疗区 | Blood transport system and method based on ai image processing technology |
| CN117524432B (en) * | 2023-11-09 | 2024-05-31 | 中国人民解放军总医院京南医疗区 | Blood transport system and method based on ai image processing technology |
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