WO2023179221A1 - 照明光谱生成方法、光谱匹配方法及装置、设备、介质 - Google Patents

照明光谱生成方法、光谱匹配方法及装置、设备、介质 Download PDF

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WO2023179221A1
WO2023179221A1 PCT/CN2023/074902 CN2023074902W WO2023179221A1 WO 2023179221 A1 WO2023179221 A1 WO 2023179221A1 CN 2023074902 W CN2023074902 W CN 2023074902W WO 2023179221 A1 WO2023179221 A1 WO 2023179221A1
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color
information
hsb
spectrum
spectral
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PCT/CN2023/074902
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English (en)
French (fr)
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刘国良
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智谋纪(深圳)科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G06F16/24564—Applying rules; Deductive queries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
    • Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection

Definitions

  • This application relates to the field of lighting technology, and in particular to lighting spectrum generation methods, spectrum matching methods and devices, equipment, and media.
  • choosing different lighting spectra for different illuminated objects can make the entire lighting environment more realistic, thereby better expressing the illuminated objects and making the entire light environment more in line with people's expectations.
  • a lighting spectrum with a color temperature of 3500K for men's clothing it is recommended to use a lighting spectrum with a white color temperature of 3000K for women's clothing, and a lighting spectrum of 3200K for children's clothing.
  • the beef area should use a lighting spectrum with a color temperature of 1800K
  • areas such as vegetables and fruits should use a reddish lighting disc spectrum of 3500K, so as to better express the illuminated objects.
  • the main purpose of the embodiments of this application is to propose an illumination spectrum generation method, a spectrum matching method and a device, equipment, and a medium, so that the HSB information of the illuminated object can be spectrally matched through the established spectral formula library and spectral matching rule library to determine The unique, high-quality lighting spectrum formula for the illuminated object.
  • an illumination spectrum generation method including:
  • HSB color model Obtain a preset HSB color model, and use the HSB color model to divide the HSB information to be matched into primary colors to determine the primary color information uniquely corresponding to the HSB information;
  • the sample parameters corresponding to the color sample set are collected by a color sampling device, wherein the sample parameters include the first spectral power distribution function corresponding to the color sample set;
  • a similarity judgment is performed on the similarity value according to the regression function threshold to determine the lighting spectrum formula uniquely corresponding to the primary color information from a preset spectral formula library.
  • the method further includes:
  • the formula color patch that uniquely corresponds to the primary color information of the HSB information is determined.
  • using a regression function to perform similarity fitting processing on the first spectral power distribution function and the second spectral power distribution function to obtain a similarity value includes:
  • a regression function is used to perform similarity fitting processing on the first spectral power distribution function and the second spectral power distribution function to obtain a similarity value.
  • the similarity judgment is performed on the similarity value according to the regression function threshold to determine the lighting spectrum formula uniquely corresponding to the primary color information from a preset spectral formula library, including:
  • the illumination spectrum formula uniquely corresponding to the primary color information is determined from the preset spectral formula library according to the fitting ratio information.
  • obtaining multiple color samples corresponding to the primary color information according to the HSB color model, and forming the multiple color samples into a color sample set includes:
  • the primary color information is divided into hues according to the HSB color model to obtain the primary color hue uniquely corresponding to the primary color information.
  • the primary color hue includes any one of cool tones, neutral tones and warm tones;
  • each color sample contains a different material and a different reflection coefficient
  • the plurality of color samples constitute a color sample set.
  • the second aspect of the embodiment of the present application proposes an illumination spectrum matching method, including:
  • Spectrum matching is performed on the HSB information according to the spectrum matching rule library and the spectrum formula library to determine the lighting spectrum formula uniquely corresponding to the HSB information.
  • the third aspect of the embodiment of the present application proposes an illumination spectrum generating device, including:
  • a primary color information acquisition module is used to obtain a preset HSB color model, and divide the HSB information to be matched by primary colors through the HSB color model, and determine the primary color information uniquely corresponding to the HSB information;
  • a sample set building module used to obtain multiple color samples corresponding to the primary color information according to the HSB color model, And the plurality of color samples constitute a color sample set;
  • a first parameter acquisition module configured to collect sample parameters corresponding to the color sample set through a color sampling device, where the sample parameters include the first spectral power distribution function corresponding to the color sample set;
  • the second parameter acquisition module is used to acquire the second spectral power distribution function under the fitted light source and the preset regression function threshold;
  • a similarity fitting module configured to perform similarity fitting processing on the first spectral power distribution function and the second spectral power distribution function using a regression function to obtain a similarity value
  • a spectral formula generation module is used to perform similarity judgment on the similarity value according to the regression function threshold, so as to determine the lighting spectrum formula uniquely corresponding to the primary color information from a preset spectral formula library.
  • the fourth aspect of the embodiment of the present application proposes an illumination spectrum matching device, including:
  • the HSB information acquisition module is used to sample the color of the object to be matched and obtain the HSB information of the object;
  • the matching rule base acquisition module is used to obtain the spectrum matching rule base established based on the color matching rules
  • a spectral formula library acquisition module used to obtain the spectral formula library in the method described in any one of the embodiments of the first aspect of this application;
  • a spectrum matching module configured to perform spectrum matching on the HSB information according to the spectrum matching rule library and the spectrum formula library to determine the lighting spectrum formula uniquely corresponding to the HSB information.
  • the fifth aspect of the embodiment of the present application provides a computer device.
  • the computer device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is used to implement:
  • the sixth aspect of the embodiment of the present application proposes a computer-readable storage medium that stores a computer program.
  • the computer program When the computer program is executed by a computer, the computer is configured to execute:
  • the illumination spectrum generation method, spectrum matching method and device, equipment, and medium proposed in the embodiments of this application obtain the preset HSB color model, and use the HSB color model to divide the HSB information to be matched into primary colors to determine the unique corresponding HSB information.
  • Base color information Multiple color samples corresponding to the primary color information are obtained according to the HSB color model, and the multiple color samples are formed into a color sample set. Afterwards, the sample parameters corresponding to the color sample set are collected through the color sampling device, where the sample parameters include the first spectral power distribution function corresponding to the color sample set.
  • the second spectral power distribution function under the fitted light source and the preset regression function threshold use the regression function to perform similarity fitting processing on the first spectral power distribution function and the second spectral power distribution function, and obtain a similarity value.
  • the similarity value is judged based on the threshold of the regression function to determine the lighting spectrum formula uniquely corresponding to the primary color information from the preset spectral formula library.
  • This application can perform spectral matching on the HSB information of the illuminated object through the established spectral formula library and spectral matching rule library to determine the unique properties of the illuminated object. A unique, high-quality lighting spectrum formula, so that the illuminated object can be better represented according to the lighting spectrum formula.
  • Figure 1 is a flow chart of an illumination spectrum generation method provided by an embodiment of the present application.
  • Figure 2 is a flow chart of an illumination spectrum generation method provided by another embodiment of the present application.
  • FIG. 3 is a flow chart of step S150 in Figure 1;
  • FIG. 4 is a flow chart of step S120 in Figure 1;
  • Figure 5 is a flow chart of an illumination spectrum matching method provided by an embodiment of the present application.
  • Figure 6 is a detailed schematic diagram of the corresponding information between the HSB information data range and the spectral formula library and formula color patch information provided by the embodiment of the present application;
  • Figure 7 is a detailed schematic diagram of the spectral formula library and the corresponding formula color block area in the CI E color coordinate range provided by the embodiment of the present application;
  • Figure 8 is a schematic diagram of the hardware structure of a computer device provided by an embodiment of the present application.
  • HSB Human-Saturation-Brightness
  • H chroma
  • S saturation
  • B brightness
  • Saturation S and brightness B are expressed as percentage values (0%-100%), and chroma is expressed as angles (0°-360°).
  • Saturation S represents the purity of color. When the saturation is zero, it is gray. White, black and other grayscale colors have no saturation. The greater the saturation, the purer the color.
  • Brightness B refers to the brightness of the color. When the brightness is zero, it is black. The maximum brightness is the most vivid state of the color.
  • choosing different lighting spectra for different illuminated objects can make the entire lighting environment more realistic, thereby better expressing the illuminated objects and making the entire light environment more in line with people's expectations.
  • a lighting spectrum with a color temperature of 3500K for men's clothing it is recommended to use a lighting spectrum with a white color temperature of 3000K for women's clothing, and a lighting spectrum of 3200K for children's clothing.
  • the beef area should use a lighting spectrum with a color temperature of 1800K, and areas such as vegetables and fruits should use a reddish lighting light of 3500K. disk spectrum, thereby better expressing the illuminated object.
  • the main purpose of the embodiments of the present application is to propose an illumination spectrum generation method, a spectrum matching method and a device, equipment, and medium, so as to efficiently and accurately determine the lighting conditions of objects of different colors by establishing a spectral formula library and a spectral matching rule library.
  • the lighting spectrum formula is used to better express the illuminated object according to the lighting spectrum formula.
  • the illumination spectrum generation method according to the first aspect of the embodiment of the present application includes but is not limited to step S110 to step S160.
  • S110 Obtain the preset HSB color model, and use the HSB color model to divide the HSB information to be matched into primary colors to determine the unique primary color information corresponding to the HSB information;
  • S120 Obtain multiple color samples corresponding to the primary color information according to the HSB color model, and construct a color sample set from the multiple color samples;
  • S160 Perform similarity judgment on the similarity value according to the regression function threshold to determine the lighting spectrum formula uniquely corresponding to the primary color information from the preset spectral formula library.
  • step S110 a preset HSB color model is obtained, and the HSB information to be matched is defined through the HSB color model.
  • a preset HSB color model is established according to the HSB color model, and the photographed objects to be matched are defined according to the HSB color model.
  • the color of the object delimits the corresponding range, that is, the HSB information used to define the target color, and the primary color information uniquely corresponding to the HSB information is determined, which is recorded as primary color s.
  • the HSB color model divides the HSB information to be matched into primary colors and determines the primary color s uniquely corresponding to the HSB information.
  • the color parameter forms include RGB (Red-Green-Blue, red-green-blue), HSL (Hue-Saturation-Lightness, hue-saturation-brightness).
  • the obtained color parameters in different forms are converted into corresponding HSB information equivalent to the HSB color mode.
  • the color parameters also include the reflection spectrum distribution function of the primary color object, pixel value, RGB value, HSB value, etc.
  • step S120 in order to obtain the sample parameters corresponding to the target color more accurately and comprehensively, according to the HSB color model The method obtains multiple color samples corresponding to the primary color information, and constructs a color sample set from the multiple color samples.
  • step S130 sample parameters corresponding to the color sample set are collected through the color sampling device, where the sample parameters include the first spectral power distribution function f s corresponding to the color sample set.
  • the color sampling device includes a radiation spectrometer, an integrating sphere (dark room), a standard light source, and sampling software. It should be noted that this application is not limited to the above-mentioned color sampling device. Color sampling devices with the same color sampling function are also applicable to This application will not be repeated here.
  • the sample parameters corresponding to the color sample set are collected through the color sampling device. Specifically, after multiple color samples corresponding to each primary color s are placed into an integrating sphere with a reflection coefficient of R98, for example, a standard light source is placed, and the integrating sphere is placed. sphere and radiation spectrometer, and the integrating sphere is connected to the radiation spectrometer. At the same time, the spectroradiometer needs to be separated by a baffle to prevent the light from the standard light source from directly entering the probe. Among them, in order to avoid strong reflection of light, the integrating sphere needs to be as large as possible, and the standard light source does not directly illuminate the color sample, but illuminates it at an angle of 15 degrees to the color sample.
  • the second spectral power distribution function f c under the fitted light source and the preset regression function threshold are obtained, and the regression function is used to perform similarity between the first spectral power distribution function and the second spectral power distribution function.
  • Degree fitting processing is to establish the functional relationship between the first spectral power distribution function f s and the second spectral power distribution function f c , solve the functional relationship expression, and obtain the similarity value.
  • a similarity judgment is performed on the similarity value according to the preset regression function threshold to determine the lighting spectrum formula uniquely corresponding to the primary color information from the preset spectral formula library.
  • the illumination spectrum formula uniquely corresponding to the base color information is determined from the preset spectral formula library, and the mapping relationship between the base color information and the corresponding illumination spectrum formula in the spectral formula library is established, that is, it is constructed based on the multiple illumination spectrum formulas obtained.
  • Spectral recipe library
  • each primary color s is divided into chromaticity according to the chroma (H), saturation (S) and brightness (B) in the HSB information.
  • Chroma is specifically divided into any of cool colors (cw), neutral colors (nw) and warm colors (ww), that is, the primary color s has a unique corresponding primary color hue.
  • standard light sources that conform to three tones were defined, namely A light source, U35 light source and D50 light source.
  • the corresponding light source weights of different hue light sources are established as shown in Table 1. According to the combination of the primary color hues of different primary colors s under different standard light sources The weight ratio is used to obtain the most accurate reflection spectrum curve function f r of the primary color s. In order to perform spectral fitting more effectively, the curve wavelength of the obtained reflection spectrum curve function f r is intercepted in the range of 380-780 nm, and the intercepted curve is normalized to obtain the first spectral power distribution function f s curve.
  • the fitting light source can be monochromatic light, composite light, or any combination of monochromatic light and composite light.
  • the fitting light source can be independently controllable and include two or more light sources, and the dominant wavelength of the fitting light source is Between 350nm and 780nm.
  • the fitting light source contains at least one set of composite white light.
  • the color temperature range of the composite white light is 1000K-10000K, and the main wavelength of the composite white light is 350nm to 780nm.
  • the corresponding Ra color rendering index is any value between 0 and 100.
  • fitting The shape and arrangement of the light sources can include any of staggered, interspersed, embedded and irregular free distribution.
  • the packaging form of the fitted light source can be any package such as SMD, COB and other forms, or a combination of any packages.
  • this spectral formula library can cover HSB information applicable to all illuminated objects including single color and multiple colors.
  • the illumination spectrum generation method in the embodiment of the present application also includes but is not limited to step S210 and step S220.
  • Step S210 obtain the mapping relationship between the base color information and the formula color patch in the spectral formula library
  • Step S220 According to the mapping relationship between the primary color information and the formula color patch, determine the formula color patch that uniquely corresponds to the primary color information corresponding to the HSB information.
  • the mapping relationship between the base color information and the formula color blocks in the spectral formula library is obtained, where there is a mapping relationship between the base color information and the corresponding lighting spectrum formula in the spectral formula library, and each lighting in the spectral formula library
  • the spectral formula is expressed in the form of formula color blocks, that is, there is a mapping relationship between the corresponding HSB information and the formula color blocks.
  • the mapping relationship between the primary color information and the formula color patch the formula color patch uniquely corresponding to the primary color information corresponding to the HSB information to be matched is determined.
  • the spectral formula library is divided into color blocks to obtain the formula color block corresponding to each lighting spectrum formula, and the formula color block uniquely corresponds to the base color information.
  • the obtained spectral formula library is divided into color blocks, that is, the CIE color coordinate xy center point combined with the MacAdam ellipse is used as the range to delineate the color blocks of the spectral formula library, and the formula color block corresponding to each lighting spectrum formula is obtained.
  • the HSB information to be matched and the formula color blocks in the spectral formula library form a mapping relationship, and then the corresponding relationship between the color of the illuminated object to be matched and the formula color blocks in the spectral formula library is obtained.
  • step S150 specifically includes but is not limited to step S310 to step S360.
  • Step S310 obtain the first spectral power distribution function and its corresponding first chromaticity information
  • Step S320 obtain the second spectral power distribution function and its corresponding second chromaticity information
  • Step S330 Perform chromaticity judgment processing on the first chromaticity information and the second chromaticity information to obtain a chromaticity comparison result;
  • Step S340 establish a functional relationship between the first spectral power distribution function and the second spectral power distribution function, and obtain the fitting ratio information of the fitting light source;
  • Step S350 adjust the functional relationship according to the chromaticity comparison result to update the fitting ratio information
  • Step S360 Use a regression function to perform similarity fitting processing on the first spectral power distribution function and the second spectral power distribution function to obtain a similarity value.
  • the first spectral power distribution function f s and its corresponding first chromaticity information are obtained, and the second chromaticity information corresponding to the second spectral power distribution function f c is obtained.
  • the correlated color temperature CCT, color coordinates, color gamut index R g and fidelity of the first spectral power distribution function f s are obtained
  • the second spectral power distribution function f c and its corresponding second chromaticity are obtained according to IES TM-30-18Method for Evaluating Light Source Color Rendition (Evaluation Method for Light Source Color Rendition) information. Therefore, chromaticity judgment processing is performed on the acquired first chromaticity information and second chromaticity information to obtain a chromaticity comparison result.
  • step S340 a function between the first spectral power distribution function f s and the second spectral power distribution function f c is established relationship to obtain the fitting ratio information of the fitting light source. Specifically, assuming that there are n groups of independent fitting light sources for spectral fitting (n is a positive integer), the light source spectral power distribution f 1 , f 2 ...f n of each group of fitting light sources is tested through a radiation spectrometer, then The second spectral power distribution function fc is used to represent the spectral power distribution function after fitting of n groups of fitting light sources.
  • the specific method is to obtain multiple sets of typical peaks of the first spectral power distribution function f s , that is, assuming that there are z typical peaks (z is a positive integer), let f s1 , f s2 ...f sz represent the first to The value of the z-th typical peak is shown in formula (1).
  • the z set of values c s11 , c s12 ...c s1n , c s21 , c s22 .. can be obtained.
  • steps S350 to S360 the above functional relationship is adjusted according to the chromaticity comparison result to update the fitting ratio information, and a regression function is used to perform similarity between the first spectral power distribution function f s and the second spectral power distribution function f c degree fitting processing to obtain the similarity value.
  • a regression function is used to perform similarity between the first spectral power distribution function f s and the second spectral power distribution function f c degree fitting processing to obtain the similarity value.
  • the base of the typical peak z in the fitting light source for spectral fitting needs to be adjusted to update the corresponding value of the fitting light source. Fitting ratio information.
  • the base of the typical peak z needs to be increased. Large, that is, increase the number of typical peaks z until the similarity value is greater than the preset regression function threshold.
  • the base of the typical peak z has become very large, it is necessary to increase the base n of independent fitting light sources in spectral fitting until the similarity value is greater than the preset regression function threshold.
  • step S160 specifically includes: performing a similarity judgment on the regression function threshold and the similarity value.
  • the similarity value is greater than the regression function threshold, determine the primary color information from the preset spectral formula library according to the fitting ratio information. The only corresponding lighting spectrum recipe.
  • the functional relationship expression is solved to obtain the similarity value.
  • similarity judgment is made on the regression function threshold and similarity value.
  • the illumination spectrum formula uniquely corresponding to the primary color information is determined from the preset spectral formula library based on the fitting ratio information.
  • the illumination spectrum ratio uniquely corresponding to the primary color information is obtained. square.
  • the regression functions used in the similarity fitting processing of the first spectral power distribution function f s and the second spectral power distribution function f c include CORREL (correlation coefficient function) and RSQ (square function of the correlation coefficient), That is, the value R corresponding to CORREL and the value R 2 corresponding to RSQ are obtained. Therefore, when the similarity value is greater than the preset regression function threshold, that is, the value R corresponding to CORREL and the value R 2 corresponding to RSQ satisfy formula (3), then the lighting spectrum formula uniquely corresponding to the primary color information is obtained based on the fitting ratio information.
  • the base color information can be combined with the spectral formula library matching Based on the experimental data and big data statistics of the public's color preference, a lighting spectrum formula uniquely corresponding to the primary color information to be matched is obtained.
  • step S120 specifically includes but is not limited to steps S410 to step S430.
  • Step S410 divide the primary color information into hues according to the HSB color model to obtain the primary color hue uniquely corresponding to the primary color information.
  • the primary color hue includes any one of cool tones, neutral tones, and warm tones;
  • Step S420 Create multiple color samples corresponding to the primary color information according to the primary color hue corresponding to the primary color information, where each color sample contains different materials and different reflection coefficients;
  • Step S430 Combine multiple color samples into a color sample set.
  • each primary color information is divided into hues according to the HSB color model. Specifically, after the unique corresponding primary color information is defined for the HSB information to be matched, each primary color information is divided into cold tones (cw), neutral tones (nw) and warm tones (ww) according to the corresponding hue in the primary color information. Any one of them, that is, the primary color information has a unique corresponding primary color hue, that is, the HSB information to be matched has a unique corresponding primary color hue.
  • steps S420 to S430 in order to obtain color parameters of a color sample set that is more accurate, more complete, and more in line with actual lighting applications, multiple color samples using different materials and different reflection coefficients are produced according to the primary color hue corresponding to the primary color information.
  • Color samples include leather, fiber, cotton, silk, etc., and the reflection coefficients selected according to different materials also include many types, so that multiple color samples corresponding to the primary color information can be obtained.
  • a color sample set is constructed based on multiple color samples, so that more accurate and comprehensive sample parameters of primary color information can be obtained.
  • This application can perform spectral matching on the HSB information of the illuminated object through the established spectral formula library and spectral matching rule library to determine the unique and high-quality lighting spectrum formula of the illuminated object, so as to match the illuminated object according to the lighting spectrum formula. Things are better expressed.
  • embodiments of the present application also provide an illumination spectrum matching method, which is used to perform spectral matching on an illuminated object.
  • the method includes but is not limited to step S510 to step S540.
  • Step S510 Perform color sampling on the object to be matched to obtain the HSB information of the object
  • Step S520 Obtain the spectrum matching rule library established according to the color matching rules
  • Step S530 obtain the spectral formula library in any one of the methods in the embodiment of the first aspect of this application;
  • Step S540 Spectrum matching is performed on the HSB information according to the spectrum matching rule library and the spectrum formula library to determine the lighting spectrum formula uniquely corresponding to the HSB information.
  • step S510 in order to better obtain a unique, high-quality lighting spectrum formula that is consistent with the illuminated object, color sampling is performed on the object to be illuminated to obtain the HSB information of the illuminated object.
  • the main representation form of the color of the illuminated object is the HSB value.
  • different color parameter forms can be obtained due to different sampling equipment.
  • the HSB color model divides the HSB information to be matched into primary colors and determines the primary color information uniquely corresponding to the HSB information.
  • the color sampling process of the illuminated object is performed to obtain the color parameters of the illuminated object, and the obtained color parameters include distributed collected pixel values, RGB values, HSB values, etc., that is, the illuminated object is obtained through the sensor and the color picking rules.
  • HSB information of the illuminated object corresponding to the illuminated object, and the HSB information of the illuminated object is only one of the expression forms of the color of the illuminated object.
  • the color parameters of the illuminated object obtained through the sensor can be RGB (Red-Green-Blue, red-green-blue), HSL (Hue-Saturation-Lightness, hue-saturation-brightness), HSV (Hue -Saturation-Value (hue-saturation-brightness), YUV (brightness-chroma), RAW (data processed by the image sensor) and other different color models are displayed, and the different forms of color parameters obtained can be converted into equivalent HSB information of the illuminated object in the HSB color model, and then perform calculation and comparison processing on the HSB information of the illuminated object and the spectral formula library according to the spectral matching rule library, and obtain the unique lighting spectrum formula corresponding to the primary color information of the illuminated object and its unique The corresponding formula color block.
  • RGB Red-Green-Blue, red-green-blue
  • HSL Human-Saturation-Lightness, hue-saturation-brightness
  • HSV Hue -Saturation
  • sampling equipment for sampling the color parameters of the illuminated object includes radiation spectrometers and cmos equipment.
  • this application is not limited to the above-mentioned color sampling equipment.
  • Color sampling equipment with the same color sampling function is also applicable to this application. , which will not be described in detail here.
  • a spectrum matching rule library established based on color matching rules is obtained; and a spectrum recipe library in the lighting spectrum generation method of any one of the embodiments of the first aspect of the present application is obtained.
  • the color matching rules include the hue definition of the HSB color model, the preset primary color priority definition, and the preset judgment relational expression.
  • the hue definition of the HSB color model is the same as the hue definition in establishing the HSB color model.
  • the hue definition of the HSB color model means that the defined primary color information is calibrated as warm (ww), medium Any of the three types of sexual tones (nw) and cold tones (cw).
  • the three attributes of color are quantified, and the saturation S and brightness B are expressed as percentage values (0%-100%), and the chromaticity H is expressed as an angle (0°-360°).
  • the chromaticity In the range of H 0 values can overlap with 360 values and connect end to end, that is, 360° of chromaticity H is equivalent to 0°.
  • the preset base color priority is defined as 1 to 34, and is gradually reduced, and the base color information forms a mapping relationship with the formula color blocks in the spectral formula library.
  • the constructed spectral formula library package When there are 34 primary color ratios, the corresponding primary color number, hue classification, spectral formula library number, formula color block area number, and primary color priority are determined according to the HSB information data range of the object to be illuminated. Among them, S 1 to S 34 represent the primary color number.
  • P 1 to P 34 represent the corresponding spectral formula library numbers
  • G 1 to G 34 represent the formula color block area numbers mapped to the numbers in the spectral formula library numbers
  • the color classification includes warm colors (ww) and neutral colors (nw) , cool tone (cw) and natural tone (sw), determine the corresponding H value, B value and S value according to the obtained HSB information of the object, and then query the formula color patch area corresponding to the HSB information according to Figure 6, and then Obtain the illumination spectrum formula uniquely corresponding to the illuminated object according to the spectral formula library.
  • numbers 35 to 37 in FIG. 6 are used to represent numbers and tone classification information corresponding to warm tones (ww), neutral tones (nw), cold tones (cw) and natural tones (sw).
  • Step S521 First define the total color of the selected area as 1, extract the primary color proportions of the 34 primary colors set in the image recognition area, and count the proportional values of each primary color;
  • Step S522 Sort the content proportion according to the proportion value of each primary color to obtain the primary color C max with the largest proportion;
  • Step S523 Determine the judgment relational expression corresponding to the spectrum matching rule library based on the maximum primary color C max .
  • the maximum primary color C max is compared with a preset content threshold to determine the corresponding primary color number of the selected area in the spectral formula library. For example, when the content threshold is set to 70%, then when the maximum primary color C max is greater than or equal to 70%, match the corresponding primary color number of the primary color C max in the spectrum formula library, that is, match among P 1 to P 34 A; when the content threshold is set to 70%, then when the maximum primary color C max is less than 70%, then step S5231 and step S5232 are performed.
  • Step S5231 according to the descending sorting result of the content proportion sorting in step S522, obtain the first four groups of primary colors after arrangement, and set them to C 1 , C 2 , C 3 , and C 4 respectively, and set C 1 and C 2 , C 3 , and C 4 are set to 1; it should be noted that the number of primary colors selected from the arrangement results is not limited to four groups. When three groups of primary colors are selected, the corresponding settings are C 1 , C 2 , C 3 .
  • Step S5232 Classify the selected primary colors according to the calibrated warm tones (ww), neutral tones (nw), and cool tones (cw), and at the same time sort the primary color contents in the three tones in proportion, that is, define them respectively.
  • the corresponding contents of warm tones (ww), neutral tones (nw), and cool tones (cw) are expressed as C ww , C nw , and C cw , and the sum of C ww , C nw , and C cw is 1.
  • step S521 of some embodiments when the image recognition area has only two primary colors, the determination is based on the obtained difference between the primary color C max with the largest proportion and the primary color C min with the smallest proportion. Corresponding base color formula. Specifically, assuming that the lower threshold of the difference between C max and C min is 15% and the upper threshold of the difference between C max and C min is 25%, the following three situations 1 to 3 are included.
  • the color priority principle is adopted, that is, the spectral adaptation is selected to be the spectral formula corresponding to the color with higher primary color priority in the spectral formula library;
  • the content proportion is the priority principle, that is, the spectrum is selected to adapt to the spectrum formula corresponding to C max .
  • the upper limit threshold and the lower limit threshold of the difference between C max and C min are not specifically limited and can be adjusted according to needs.
  • step S521 of some embodiments when there are more than two primary colors in the image recognition area, the corresponding primary color is determined based on the obtained primary color C max with the largest proportion and the second primary color C 2 formula. Specifically, assuming that the lower threshold of the difference between C max and C 2 is 20% and the upper threshold of the difference between C max and C 2 is 30%, the following three situations from 4 to 6 are included.
  • the content proportion is the priority principle, that is, the spectrum is selected to adapt to the spectrum formula corresponding to C max ;
  • full spectrum matching rules include full spectrum matching rules for two primary colors and full spectrum matching rules for more than two primary colors.
  • rules for performing full spectrum matching of two primary colors include the following 7 to Five situations.
  • C max or C min is the primary color S 33
  • match the spectral formula of the primary color hue corresponding to the non-primary color S 33 for example, when C max is the primary color S 33 , C min is the primary color S 30 , and the primary color S 30 corresponds If the base color tone is cold tone (cw), then select the spectral adaptation corresponding to the spectrum formula of P cw .
  • the rules for performing full spectrum matching of more than two primary colors include the following and Two situations.
  • the spectral formula corresponding to the primary color hue of the primary color with the largest primary color content is selected for spectral adaptation. For example, when C ww >50%, select the spectral formula corresponding to P ww .
  • the embodiment of this application obtains the color parameters of the illuminated object, divides the obtained basic color data into units, converts the color data of each unit into HSB information, and at the same time performs judgment processing through the spectral matching rule library, combined with the figure
  • the HSB information, spectral formula library, formula color block area and other information set in 6 are finally obtained. Quality lighting spectrum formula.
  • the object being illuminated may be a single color or a complex color.
  • the sampling method is similar to dividing a color image of the object into a grid, and dividing the color of each area in the image into relatively small ones. grid, and then perform statistical processing on each grid color to obtain its corresponding HSB information, and then perform spectral matching on the HSB information of the illuminated object through the established spectral recipe library and spectral matching rule library to determine the illuminated object Unique, high-quality lighting spectrum formula.
  • the formula color block area corresponding to the spectral formula library uses the coordinate value of the CIE color coordinate area range.
  • the corresponding relationship between the spectral formula library and the formula color block area is constructed when the formula library is expressed as the color coordinate xy center point combined with the MacAdam ellipse as the range delimiting the formula library color block, where the MacAdam ellipse
  • the representation includes the x center point, y center point, deflection angle, major axis, and minor axis of the corresponding color block area.
  • This application can perform spectral matching on the HSB information of the illuminated object through the established spectral formula library and spectral matching rule library to determine the unique and high-quality lighting spectrum formula of the illuminated object.
  • the spectral matching rule base includes matching of single-color objects and mixed-color objects.
  • step S540 spectrum matching is performed on the HSB information according to the spectrum matching rule library and the spectrum formula library to determine the illumination spectrum formula uniquely corresponding to the HSB information.
  • the HSB information of the illuminated object and the spectral formula library are calculated and compared according to the spectral matching rule library, and the unique corresponding primary color information of the HSB information is determined.
  • the unique corresponding primary color information is determined according to the spectral formula library.
  • the lighting spectrum formula determines the formula color block that uniquely corresponds to the base color information corresponding to the HSB information to be matched, based on the mapping relationship between the base color information and the formula color block.
  • This application can perform spectral matching on the HSB information of the illuminated object through the established spectral formula library and spectral matching rule library to determine the unique and high-quality lighting spectrum formula of the illuminated object.
  • Embodiments of the present application also provide an illumination spectrum generation device for executing the illumination spectrum generation method of the above embodiment.
  • the device includes a primary color information acquisition module, a sample set construction module, a first parameter acquisition module, a second parameter acquisition module, Similarity fitting module and spectral recipe generation module.
  • the primary color information acquisition module is used to obtain the preset HSB color model, and divide the HSB information to be matched by primary colors through the HSB color model to determine the unique primary color information corresponding to the HSB information;
  • the sample set construction module is used to obtain the primary color information based on the HSB color model Corresponding multiple color samples, and the multiple color samples constitute a color sample set;
  • the first parameter acquisition module is used to collect sample parameters corresponding to the color sample set through the color sampling device, wherein the sample parameters include the corresponding color sample set
  • the second parameter acquisition module is used to obtain the second spectral power distribution function under the fitted light source and the preset regression function threshold;
  • the similarity fitting module is used to use the regression function to calculate the first spectral power
  • the distribution function and the second spectral power distribution function are subjected to similarity fitting processing to obtain a similarity value;
  • the spectral recipe generation module is used to judge the similarity value based on the regression
  • An illumination spectrum generating device in an embodiment of the present application is used to perform the illumination spectrum generating method in the above embodiment.
  • the specific processing process is the same as the illumination spectrum generating method in the above embodiment, and will not be described again here.
  • An embodiment of the present application also provides an illumination spectrum matching device for executing the illumination spectrum matching method of the above embodiment,
  • the device includes an HSB information acquisition module, a matching rule library acquisition module, a spectrum formula library acquisition module and a spectrum matching module.
  • the HSB information acquisition module is used to sample the color of the object to be matched and obtain the HSB information of the object;
  • the matching rule library acquisition module is used to obtain the spectral matching rule library established according to the color matching rules;
  • the spectral recipe library acquisition module is used to Obtain the spectral formula library in the illumination spectrum generation method of any one of the embodiments of the first aspect of the present application;
  • the spectral matching module is used to perform spectral matching on the HSB information according to the spectral matching rule library and the spectral formula library to determine the unique correspondence of the HSB information illumination spectrum formula.
  • An illumination spectrum matching device in an embodiment of the present application is used to perform the illumination spectrum matching method in the above embodiment.
  • the specific processing process is the same as the illumination spectrum matching method in the above embodiment, and will not be described again here.
  • Embodiments of the present application also provide a computer device.
  • the computer device includes a memory and a processor, wherein a program is stored in the memory.
  • the processor is used to execute any one of the embodiments of the first aspect of the present application.
  • the computer device includes: a processor 810, a memory 820, an input/output interface 830, a communication interface 840, and a bus 850.
  • the processor 810 can be implemented by a general CPU (Central Process in Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., for Execute relevant procedures to implement the technical solutions provided by the embodiments of this application;
  • the memory 820 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device or RAM (Random Access Memory).
  • the memory 820 can store operating systems and other application programs.
  • the relevant program codes are stored in the memory 820 and called by the processor 810 to execute the implementation of this application.
  • Input/output interface 830 used to implement information input and output
  • Communication interface 840 is used to realize communication interaction between this device and other devices. Communication can be achieved through wired methods (such as USB, network cables, etc.) or wireless methods (such as mobile network, WIFI, Bluetooth, etc.); and bus 850, transmit information between various components of the device (such as processor 810, memory 820, input/output interface 830, and communication interface 840);
  • the processor 810, the memory 820, the input/output interface 830 and the communication interface 840 implement communication connections between each other within the device through the bus 850.
  • Embodiments of the present application also provide a computer-readable storage medium.
  • the computer-readable storage medium stores a computer program.
  • the computer program When the computer program is executed by the computer, the computer is used to perform the illumination spectrum according to any one of the embodiments of the first aspect of the present application.
  • a generation method or an illumination spectrum matching method as in any one of the embodiments of the second aspect of the present application.
  • memory can be used to store non-transitory software programs and non-transitory computer executable programs.
  • the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device.
  • the memory may optionally include memory located remotely from the processor, and the remote memory may be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separate, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • At least one (item) refers to one or more, and “plurality” refers to two or more.
  • “And/or” is used to describe the relationship between associated objects, indicating that there can be three relationships. For example, “A and/or B” can mean: only A exists, only B exists, and A and B exist simultaneously. , where A and B can be singular or plural. The character “/” generally indicates that the related objects are in an "or” relationship. “At least one of the following” or similar expressions thereof refers to any combination of these items, including any combination of a single item (items) or a plurality of items (items).
  • At least one of a, b or c can mean: a, b, c, "a and b", “a and c", “b and c", or "a and b and c” ”, where a, b, c can be single or multiple.
  • the disclosed devices and methods can be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or can be integrated into another system, or some features can be ignored, or not implemented.
  • the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application can be integrated into one processing unit, each unit can exist physically alone, or two or more units can be integrated into one unit.
  • the above integrated units can be It can be implemented in the form of hardware or in the form of software functional units.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. that can store programs. medium.

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Abstract

本申请提供照明光谱生成方法、光谱匹配方法及装置、设备、介质，属于照明技术领域。其中，照明光谱生成方法包括：通过预设的HSB色彩模型对待匹配的HSB信息进行基色划分，确定唯一对应的基色信息；将基色信息的多个颜色样品构成色彩样品集；采集色彩样品集对应的样品参数，得到第一光谱功率分布函数；获取拟合光源下的第二光谱功率分布函数和预设的回归函数阈值；对第一光谱功率分布函数和第二光谱功率分布函数进行相似度拟合处理，得到相似度数值；对相似度数值进行相似度判断，以确定基色信息唯一对应的照明光谱配方。本申请通过建立的光谱配方库和光谱匹配规则库对HSB信息进行光谱匹配，以确定被照物唯一的、高品质的照明光谱配方。

Description

照明光谱生成方法、光谱匹配方法及装置、设备、介质 技术领域
本申请涉及照明技术领域，尤其涉及照明光谱生成方法、光谱匹配方法及装置、设备、介质。
背景技术
在实际的照明场景中，不同的被照物选择不同的照明光谱，才能让整个照明环境更加真实，从而将被照物体更好地表现出来，让整个光环境更加符合人们的期望。例如，在服装领域的照明应用中，男性服装建议采用3500K色温的照明光谱照，女性服装建议采用3000K色温偏白的照明光谱，而对于童装则建议采用3200K的照明光谱。此外，在超市区域的照明应用中，牛肉区域应采用1800K色温的照明光谱，蔬菜瓜果等区域应采用3500K偏红的照明光盘光谱，从而将被照物体更好地表现出来。
然而，目前很多超市、商场因为自身水平的局限性或灯光设计师不够专业，使得不同颜色的服装或肉类统一采用固定的照明光谱配方，从而导致客户完成购买后，在家中看到的物品颜色与在超市或商场的颜色存在很大色差，不仅不能还原被照物的本色、突出商品的价值，反而降低了客户的购买欲望。
发明内容
本申请实施例的主要目的在于提出照明光谱生成方法、光谱匹配方法及装置、设备、介质，能够通过所建立的光谱配方库和光谱匹配规则库对被照物的HSB信息进行光谱匹配，以确定被照物唯一的、高品质的照明光谱配方。
为实现上述目的，本申请实施例的第一方面提出了照明光谱生成方法，包括：
获取预设的HSB色彩模型，并通过所述HSB色彩模型对待匹配的HSB信息进行基色划分，确定所述HSB信息唯一对应的基色信息；
根据所述HSB色彩模型得到所述基色信息对应的多个颜色样品，并将所述多个颜色样品构成色彩样品集；
通过颜色采样装置采集所述色彩样品集对应的样品参数，其中，所述样品参数包括所述色彩样品集对应的第一光谱功率分布函数；
获取拟合光源下的第二光谱功率分布函数和预设的回归函数阈值；
利用回归函数对所述第一光谱功率分布函数和所述第二光谱功率分布函数进行相似度拟合处理，得到相似度数值；
根据所述回归函数阈值对所述相似度数值进行相似度判断，以从预设的光谱配方库中确定所述基色信息唯一对应的照明光谱配方。
在一些实施例，所述方法还包括：
获取所述光谱配方库中基色信息与配方色块的映射关系；
根据所述基色信息与配方色块的映射关系，确定所述HSB信息的所述基色信息唯一对应的配方色块。
在一些实施例中，所述利用回归函数对所述第一光谱功率分布函数和所述第二光谱功率分布函数进行相似度拟合处理，得到相似度数值，包括：
获取所述第一光谱功率分布函数和其对应的第一色度信息；
获取所述第二光谱功率分布函数和其对应的第二色度信息；
对所述第一色度信息和所述第二色度信息进行色度判断处理，得到色度比较结果；
建立所述第一光谱功率分布函数和所述第二光谱功率分布函数之间的函数关系，得到所述拟合光源的拟合配比信息；
根据所述色度比较结果调整所述函数关系，以更新所述拟合配比信息；
利用回归函数对所述第一光谱功率分布函数和所述第二光谱功率分布函数进行相似度拟合处理，得到相似度数值。
在一些实施例，所述根据所述回归函数阈值对所述相似度数值进行相似度判断，以从预设的光谱配方库中确定所述基色信息唯一对应的照明光谱配方，包括：
对所述回归函数阈值和所述相似度数值进行相似度判断；
当所述相似度数值大于所述回归函数阈值，根据所述拟合配比信息从预设的光谱配方库中确定所述基色信息唯一对应的照明光谱配方。
在一些实施例，所述根据所述HSB色彩模型得到所述基色信息对应的多个颜色样品，并将所述多个颜色样品构成色彩样品集，包括：
根据所述HSB色彩模型对所述基色信息进行色调划分，得到所述基色信息唯一对应的基色色调，所述基色色调包括冷色调、中性色调和暖色调中的任一种；
根据所述基色信息对应的所述基色色调，制作所述基色信息对应的多个颜色样品，其中，每个颜色样品包含不同的材质和不同的反射系数；
将所述多个颜色样品构成色彩样品集。
本申请实施例的第二方面提出了照明光谱匹配方法，包括：
对待匹配的被照物进行颜色取样，得到所述被照物的HSB信息；
获取基于颜色匹配规则建立的光谱匹配规则库；
获取本申请第一方面实施例任一项所述的方法的光谱配方库；
根据所述光谱匹配规则库和所述光谱配方库对所述HSB信息进行光谱匹配，以确定所述HSB信息唯一对应的照明光谱配方。
本申请实施例的第三方面提出了一种照明光谱生成装置，包括：
基色信息获取模块，用于获取预设的HSB色彩模型，并通过所述HSB色彩模型对待匹配的HSB信息进行基色划分，确定所述HSB信息唯一对应的基色信息；
样品集构建模块，用于根据所述HSB色彩模型得到所述基色信息对应的多个颜色样品， 并将所述多个颜色样品构成色彩样品集；
第一参数获取模块，用于通过颜色采样装置采集所述色彩样品集对应的样品参数，其中，所述样品参数包括所述色彩样品集对应的第一光谱功率分布函数；
第二参数获取模块，用于获取拟合光源下的第二光谱功率分布函数和预设的回归函数阈值；
相似度拟合模块，用于利用回归函数对所述第一光谱功率分布函数和所述第二光谱功率分布函数进行相似度拟合处理，得到相似度数值；
光谱配方生成模块，用于根据所述回归函数阈值对所述相似度数值进行相似度判断，以从预设的光谱配方库中确定所述基色信息唯一对应的照明光谱配方。
本申请实施例的第四方面提出了一种照明光谱匹配装置，包括：
HSB信息获取模块，用于对待匹配的被照物进行颜色取样，得到所述被照物的HSB信息；
匹配规则库获取模块，用于获取根据颜色匹配规则建立的光谱匹配规则库；
光谱配方库获取模块，用于获取本申请第一方面实施例任一项所述的方法中的光谱配方库；
光谱匹配模块，用于根据所述光谱匹配规则库和所述光谱配方库对所述HSB信息进行光谱匹配，以确定所述HSB信息唯一对应的照明光谱配方。
本申请实施例的第五方面提出了计算机设备，所述计算机设备包括存储器和处理器，其中，所述存储器中存储有计算机程序，所述计算机程序被所述处理器执行时所述处理器用于执行：
如本申请第一方面实施例任一项所述的照明光谱生成方法；或
如本申请第二方面实施例任一项所述的照明光谱匹配方法。
本申请实施例的第六方面提出了计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，在所述计算机程序被计算机执行时，所述计算机用于执行：
如本申请第一方面实施例任一项所述的照明光谱生成方法；或
如本申请第二方面实施例任一项所述的照明光谱匹配方法。
本申请实施例提出的照明光谱生成方法、光谱匹配方法及装置、设备、介质，通过获取预设的HSB色彩模型，并通过HSB色彩模型对待匹配的HSB信息进行基色划分，确定HSB信息唯一对应的基色信息。根据HSB色彩模型得到基色信息对应的多个颜色样品，并将多个颜色样品构成色彩样品集。之后，通过颜色采样装置采集色彩样品集对应的样品参数，其中，样品参数包括所述色彩样品集对应的第一光谱功率分布函数。获取拟合光源下的第二光谱功率分布函数和预设的回归函数阈值，利用回归函数对第一光谱功率分布函数和第二光谱功率分布函数进行相似度拟合处理，得到相似度数值。根据回归函数阈值对相似度数值进行相似度判断，以从预设的光谱配方库中确定基色信息唯一对应的照明光谱配方。本申请能够通过所建立的光谱配方库和光谱匹配规则库对被照物的HSB信息进行光谱匹配，以确定被照物唯 一的、高品质的照明光谱配方，从而根据该照明光谱配方将被照物更好地表现出来。
附图说明
图1是本申请实施例提供的照明光谱生成方法的流程图；
图2是本申请另一实施例提供的照明光谱生成方法的流程图；
图3是图1中的步骤S150的流程图；
图4是图1中的步骤S120的流程图；
图5是本申请实施例提供的照明光谱匹配方法的流程图；
图6是本申请实施例提供的HSB信息数据范围与光谱配方库和配方色块信息的对应信息的详细示意图；
图7是本申请实施例提供的光谱配方库和对应的配方色块区域在CI E色坐标范围下的详细示意图；
图8是本申请实施例提供的计算机设备的硬件结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
需要说明的是，虽然在装置示意图中进行了功能模块划分，在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于装置中的模块划分，或流程图中的顺序执行所示出或描述的步骤。说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本文中所使用的术语只是为了描述本申请实施例的目的，不是旨在限制本申请。
HSB(Hue-Saturation-Brightness)，表示一种色彩模式，即色度(H)、饱和度(S)、亮度(B)的色彩模式，以人类对颜色的感觉为基础，描述了颜色的三种基本特性。HSB色彩模式采用颜色的三属性来表示，即将颜色三属性进行量化，将饱和度S和亮度B以百分比值(0％-100％)表示，色度以角度(0°-360°)表示。饱和度S代表色彩的纯度，饱和度为零时即为灰色。白、黑和其他灰度色彩都没有饱和度，饱和度越大时颜色越纯。亮度B是指色彩的明亮度，亮度为零时即为黑色，亮度最大是色彩最鲜明的状态。
在实际的照明场景中，不同的被照物选择不同的照明光谱，才能让整个照明环境更加真实，从而将被照物体更好地表现出来，让整个光环境更加符合人们的期望。例如，在服装领域的照明应用中，男性服装建议采用3500K色温的照明光谱照，女性服装建议采用3000K色温偏白的照明光谱，而对于童装则建议采用3200K的照明光谱。此外，在超市区域的照明应用中，牛肉区域应采用1800K色温的照明光谱，蔬菜瓜果等区域应采用3500K偏红的照明光 盘光谱，从而将被照物体更好地表现出来。
然而，目前很多超市、商场因为自身水平的局限性或灯光设计师不够专业，使得不同颜色的服装或肉类统一采用固定的照明光谱配方，从而导致客户完成购买后，在家中看到的物品颜色与在超市或商场的颜色存在很大色差，不仅不能还原被照物的本色、突出商品的价值，反而降低了客户的购买欲望。
基于此，本申请实施例的主要目的在于提出照明光谱生成方法、光谱匹配方法及装置、设备、介质，能够通过建立光谱配方库和光谱匹配规则库，高效、准确地确定不同颜色被照物的照明光谱配方，从而根据该照明光谱配方将被照物更好地表现出来。
参照图1，根据本申请实施例第一方面实施例的照明光谱生成方法，包括但不限于步骤S110至步骤S160。
S110，获取预设的HSB色彩模型，并通过HSB色彩模型对待匹配的HSB信息进行基色划分，确定HSB信息唯一对应的基色信息；
S120，根据HSB色彩模型得到基色信息对应的多个颜色样品，并将多个颜色样品构成色彩样品集；
S130，通过颜色采样装置采集色彩样品集对应的样品参数，其中，样品参数包括色彩样品集对应的第一光谱功率分布函数；
S140，获取拟合光源下的第二光谱功率分布函数和预设的回归函数阈值；
S150，利用回归函数对第一光谱功率分布函数和第二光谱功率分布函数进行相似度拟合处理，得到相似度数值；
S160，根据回归函数阈值对相似度数值进行相似度判断，以从预设的光谱配方库中确定基色信息唯一对应的照明光谱配方。
在步骤S110中，获取预设的HSB色彩模型，并通过HSB色彩模型定义待匹配的HSB信息，具体是，根据HSB色彩模式建立预设的HSB色彩模型，根据HSB色彩模型将待匹配的被照物的颜色划定对应的范围，即用于定义目标颜色的HSB信息，则确定了HSB信息唯一对应的基色信息，记为基色s。
需要说明的是，对待匹配的被照物进行颜色取样时，由于采样设备的不同，可以得到不同的颜色参数形式，但是通过将不同的颜色参数行书转化成等同的HSB信息的形式，则可以通过HSB色彩模型对待匹配的HSB信息进行基色划分，确定HSB信息唯一对应的基色s。具体地，当通过传感器等采样设备获取的颜色参数时，该颜色参数形式包括以RGB(Red-Green-Blue，红－绿－蓝)、HSL(Hue-Saturation-Lightness，色相－饱和度－亮度)、HSV(Hue-Saturation-Value，色调－饱和度－亮度)、YUV(明亮度－色度)、RAW(图像传感器所处理数据)等不同的色彩模型形式表现出来的数值形式，则可以将获取的不同形式的颜色参数转化为等同于HSB色彩模式的对应的HSB信息。此外，颜色参数还包括基色物体反射光谱分布函数、像素值、RGB数值、HSB数值等。
在步骤S120中，为了更准确、全面地获取目标颜色对应的样品参数，根据HSB色彩模 型得到基色信息对应的多个颜色样品，并将多个颜色样品构成色彩样品集。
在步骤S130中，通过颜色采样装置采集色彩样品集对应的样品参数，其中，样品参数包括色彩样品集对应的第一光谱功率分布函数fs。具体地，颜色采样装置包括辐射光谱仪、积分球(暗室)、标准光源以及采样软件，需要说明的是，本申请并不局限于上述颜色采样设备，具有同样颜色采样功能的颜色采样设备同样适用于本申请，在此不再赘述。
需要说明的是，通过颜色采样装置采集色彩样品集对应的样品参数，具体是，将每一种基色s对应的多个颜色样品放进例如R98反射系数的积分球内后，放置标准光源、积分球和辐射光谱仪，且积分球连接辐射光谱仪，同时光谱辐射仪需要采用挡板隔开，从而避免标准光源的光线直接进入探头。其中，为了避免造成光线的强反射，积分球需要尽量增大，且标准光源是不直接照射颜色样品，而是与该颜色样品成15度角照射。
在步骤S140至步骤S160中，获取拟合光源下的第二光谱功率分布函数fc和预设的回归函数阈值，并利用回归函数对第一光谱功率分布函数和第二光谱功率分布函数进行相似度拟合处理，即通过建立第一光谱功率分布函数fs与第二光谱功率分布函数fc之间的函数关系，求解该函数关系式，得到相似度数值。之后，根据预设的回归函数阈值对相似度数值进行相似度判断，以从预设的光谱配方库中确定基色信息唯一对应的照明光谱配方。需要说明的是，从预设的光谱配方库中确定基色信息唯一对应的照明光谱配方，建立了基色信息与光谱配方库中对应照明光谱配方的映射关系，即根据获取的多个照明光谱配方构建了光谱配方库。
需要说明的是，确定了待匹配的HSB信息唯一对应的基色s后，根据HSB信息中的色度(H)、饱和度(S)和亮度(B)将每种基色s进行色度划分，色度具体划分为冷色调(cw)、中性色调(nw)和暖色调(ww)中的任一种，即基色s具有唯一对应的基色色调。之后，根据定义的冷色调(cw)、中性色调(nw)和暖色调(ww)，定义了符合三种色调的标准光源，分别为A光源、U35光源和D50光源。为了最大限度地消除单一光源与基色色调不同步而引起的偏色误差，建立如表1所示的不同色调光源的对应的光源权重，根据结合不同基色s的基色色调在不同标准光源下的光源权重比例，获取到基色s最准确的反射光谱曲线函数fr。为了更有效地进行光谱拟合，将获取的反射光谱曲线函数fr的曲线波长在380-780nm范围内进行截取，并对截取后的曲线进行归一化处理，得到第一光谱功率分布函数fs的曲线。
表1
需要说明的是，拟合光源可以为单色光、复合光以及单色光与复合光的任意组合，拟合光源可以为能独立控制且包含两种以上光源，且拟合光源的主波长为350nm至780nm之间。此外，拟合光源至少含有一组复合白光，复合白光的色温范围为1000K—10000K，且复合白光的主波长为350nm至780nm，对应的Ra显色指数为0至100之间的任意值。同时，拟合 光源的形态及排列可以包括交错、穿插、嵌入及无规律自由分布中的任意一种，拟合光源的封装形式可以为SMD、COB等其他形式的任意封装，或任意封装相结合的形式。
需要说明的是，该光谱配方库可以覆盖适用于包括单种色和多种色在内的所有被照物的HSB信息。
在一些实施例中，如图2所示，本申请实施例的照明光谱生成方法还包括但不限于步骤S210和步骤S220。
步骤S210，获取光谱配方库中基色信息与配方色块的映射关系；
步骤S220，根据基色信息与配方色块的映射关系，确定HSB信息对应的基色信息唯一对应的配方色块。
具体地，在一些实施例中，获取光谱配方库中基色信息与配方色块的映射关系，其中，基色信息与光谱配方库中对应照明光谱配方存在映射关系，光谱配方库中的每一种照明光谱配方以配方色块的形式表现，即对应的HSB信息与配方色块之间存在映射关系。根据基色信息与配方色块的映射关系，确定待匹配的HSB信息对应的基色信息唯一对应的配方色块。
需要说明的是，对光谱配方库进行色块划分处理，得到每个照明光谱配方对应的配方色块，配方色块唯一对应于基色信息。具体地，对得到的光谱配方库进行色块划分处理，即采用CIE色坐标xy中心点结合麦克亚当椭圆作为划定光谱配方库色块的范围，得到每个照明光谱配方对应的配方色块，则待匹配的HSB信息与光谱配方库中的配方色块构成映射关系，进而得到待匹配的被照物的颜色与光谱配方库中配方色块的对应关系。
在一些实施例中，如图3所示，步骤S150具体包括但不限于步骤S310至步骤S360。
步骤S310，获取第一光谱功率分布函数和其对应的第一色度信息；
步骤S320，获取第二光谱功率分布函数和其对应的第二色度信息；
步骤S330，对第一色度信息和第二色度信息进行色度判断处理，得到色度比较结果；
步骤S340，建立第一光谱功率分布函数和第二光谱功率分布函数之间的函数关系，得到拟合光源的拟合配比信息；
步骤S350，根据色度比较结果调整所述函数关系，以更新拟合配比信息；
步骤S360，利用回归函数对第一光谱功率分布函数和第二光谱功率分布函数进行相似度拟合处理，得到相似度数值。
在步骤S310至步骤S330中，获取第一光谱功率分布函数fs和其对应的第一色度信息，获取第二光谱功率分布函数fc对应的第二色度信息。具体是，根据参考标准GB-T 7921-2008、GB5820-2003等均匀色空间和色差公式，求出第一光谱功率分布函数fs的相关色温CCT、色坐标、色域指数Rg、保真度指数Rf等第一色度信息，根据IES TM-30-18Method for Evaluating Light Source Color Rendition(光源颜色再现的评价方法)求出第二光谱功率分布函数fc和其对应的第二色度信息。因此，对获取的第一色度信息和第二色度信息进行色度判断处理，得到色度比较结果。
在步骤S340中，建立第一光谱功率分布函数fs和第二光谱功率分布函数fc之间的函数 关系，得到拟合光源的拟合配比信息。具体地，假设有n组进行光谱拟合的独立拟合光源(n为正整数)，通过辐射光谱仪测试每一组拟合光源的光源光谱功率分布f1、f2...fn，则第二光谱功率分布函数fc用于表示n组拟合光源拟合后的光谱功率分布函数。具体方式为，获取第一光谱功率分布函数fs的多组典型峰值，即假设有z个典型峰值(z为正整数)，令fs1、fs2...fsz分别表示第1个到第z个典型峰值的数值，如公式(1)所示，通过求解公式(1)中的多组关系式得到z组数值cs11、cs12...cs1n，cs21、cs22...cs2n，直至csz1、csz2...cszn，之后，分别求解z组数值cs11、cs12...cs1n，cs21、cs22...cs2n，直至csz1、csz2...cszn中的每一组数值的平均值，从而得到表示n组独立拟合光源的光通占比值c1、c2....cn，再结合公式(2)，建立第一光谱功率分布函数fs和第二光谱功率分布函数fc之间的函数关系，从而得到拟合光源的拟合配比信息。

fs≈fc＝c1*f1+c2*f2+c3*f3...+cn*fn   (2)
在步骤S350至步骤S360中，根据色度比较结果调整上述函数关系，以更新拟合配比信息，并利用回归函数对第一光谱功率分布函数fs和第二光谱功率分布函数fc进行相似度拟合处理，得到相似度数值。具体地，当第一色度信息和第二色度信息的色度比较结果为相差较大，则需要调整进行光谱拟合的拟合光源中典型峰值z的基数，以更新拟合光源对应的拟合配比信息。例如，当第一色度信息和第二色度信息的色度比较结果为相差较大，且所得到的相似度数值小于等于预设的回归函数阈值时，则需要将典型峰值z的基数增大，即增加典型峰值z的个数，直到相似度数值大于预设的回归函数阈值为止。然而，当典型峰值z的基数已经变得非常大，则需要通过增加光谱拟合中独立拟合光源的基数n，直到相似度数值大于预设的回归函数阈值为止。
在一些实施例中，步骤S160具体包括：对回归函数阈值和相似度数值进行相似度判断，当相似度数值大于回归函数阈值，根据拟合配比信息从预设的光谱配方库中确定基色信息唯一对应的照明光谱配方。
具体地，通过建立第一光谱功率分布函数fs与第二光谱功率分布函数fc之间的函数关系，求解该函数关系式，得到相似度数值。为了更好地验证第一光谱功率分布函数fs与第二光谱功率分布函数fc之间的相似性，对回归函数阈值和相似度数值进行相似度判断，当相似度数值大于回归函数阈值，根据拟合配比信息从预设的光谱配方库中确定基色信息唯一对应的照明光谱配方。例如，假设预设的回归函数阈值为0.95，则当相似度数值大于预设的回归函数阈值0.95，且色度比较结果表示第一色度信息和第二色度信息小于预设的色度比较阈值，则根据第二光谱功率分布函数fc得到的拟合配比信息得到基色信息唯一对应的照明光谱配 方。
需要说明的是，第一光谱功率分布函数fs和第二光谱功率分布函数fc进行相似度拟合处理所采用的回归函数包括CORREL(相关系数函数)和RSQ(相关系数的平方函数)，即得CORREL对应的数值R和RSQ对应的数值R2。因此，当相似度数值大于预设的回归函数阈值，即CORREL对应的数值R和RSQ对应的数值R2满足公式(3)，则根据拟合配比信息得到基色信息唯一对应的照明光谱配方。
需要说明的是，在实际应用中，为了得到更准确地确定符合基色s对应的照明光谱配方，当相似度数值大于预设的回归函数阈值0.95，可以同时结合关于基色信息与光谱配方库匹配的实验数据和大数据统计下大众的颜色偏好程度，得到符合待匹配的基色信息唯一对应的照明光谱配方。
在一些实施例中，如图4所示，步骤S120具体包括但不限于步骤S410至步骤S430。
步骤S410，根据HSB色彩模型对基色信息进行色调划分，得到基色信息唯一对应的基色色调，基色色调包括冷色调、中性色调和暖色调中的任一种；
步骤S420，根据基色信息对应的基色色调，制作基色信息对应的多个颜色样品，其中，每个颜色样品包含不同的材质和不同的反射系数；
步骤S430，将多个颜色样品构成色彩样品集。
在步骤S410中，为了更准确地获取颜色参数中的反射光谱数据，根据HSB色彩模型对每个基色信息进行色调划分。具体地，对待匹配的HSB信息定义了唯一对应的基色信息后，根据基色信息中对应的色相，将每种基色信息划分为冷色调(cw)、中性色调(nw)和暖色调(ww)中的任一种，即基色信息具有唯一对应的基色色调，即待匹配的HSB信息具有唯一对应的基色色调。
在步骤S420至步骤S430中，为了获得更准确、更完备且更符合实际照明应用的色彩样品集的颜色参数，则根据基色信息对应的基色色调制作采用不同的材质和不同的反射系数的多个颜色样品。具体地，制作颜色样品的材质包括皮质、纤维、棉麻、丝绸等，则根据不同材质选取的反射系数也包括很多种类，从而可以获取基色信息对应的多个颜色样品。最后，根据多个颜色样品构成了色彩样品集，从而能够获取基色信息更准确和全面的样品参数。本申请能够通过所建立的光谱配方库和光谱匹配规则库对被照物的HSB信息进行光谱匹配，以确定被照物唯一的、高品质的照明光谱配方，从而根据该照明光谱配方将被照物更好地表现出来。
参照图5，本申请实施例还提供照明光谱匹配方法，该照明光谱匹配方法用于对被照物进行光谱匹配，该方法包括但不限于步骤S510至步骤S540。
步骤S510，对待匹配的被照物进行颜色取样，得到被照物的HSB信息；
步骤S520，获取根据颜色匹配规则建立的光谱匹配规则库；
步骤S530，获取由本申请第一方面实施例中任一项的方法中的光谱配方库；
步骤S540，根据光谱匹配规则库和光谱配方库对HSB信息进行光谱匹配，以确定HSB信息唯一对应的照明光谱配方。
在步骤S510中，为了更好地获得唯一的、符合被照物且高品质的照明光谱配方，对待匹配的被照物进行颜色取样，得到被照物的HSB信息。具体地，由于光谱配方库是根据HSB色彩模型构建的，因此对被照物的颜色获取的主要表现形式为HSB数值。需要说明的是，对待匹配的被照物进行颜色取样时，由于采样设备的不同，可以得到不同的颜色参数形式，但是通过将不同的颜色参数行书转化成等同的HSB信息的形式，则可以通过HSB色彩模型对待匹配的HSB信息进行基色划分，确定HSB信息唯一对应的基色信息。此外，对被照物进行颜色取样处理得到被照物的颜色参数，且得到的颜色参数包括分布采集的像素值、RGB数值、HSB数值等，即将被照物通过传感器结合取色规则获取到被照物对应的被照物HSB信息，且被照物HSB信息仅为被照物颜色表现形式之一。
需要说明的是，被照物通过传感器获取的颜色参数可以为RGB(Red-Green-Blue，红－绿－蓝)、HSL(Hue-Saturation-Lightness，色相－饱和度－亮度)、HSV(Hue-Saturation-Value，色调－饱和度－亮度)、YUV(明亮度－色度)、RAW(图像传感器所处理数据)等不同的色彩模型表现出来，可以将获取的不同形式的颜色参数转化为等同的HSB色彩模式的被照物HSB信息，从而根据光谱匹配规则库对被照物的HSB信息和光谱配方库进行运算比对处理，得到被照物的基色信息唯一对应的照明光谱配方和其唯一对应的配方色块。
需要说明的是，对被照物的颜色参数进行采样的采样设备包括辐射光谱仪、cmos设备，但本申请并不局限于上述颜色采样设备，具有同样颜色采样功能的颜色采样设备同样适用于本申请，在此不再赘述。
在步骤S520和步骤S530中，获取根据颜色匹配规则建立的光谱匹配规则库；获取本申请第一方面实施例任一项的照明光谱生成方法中的光谱配方库。具体地，颜色匹配规则包括HSB色彩模型的色调定义、预设的基色优先级定义和预设的判定关系式。在建立光谱匹配规则库时，HSB色彩模型的色调定义与建立HSB色彩模型中色调定义为相同的定义方法，HSB色彩模型的色调定义表示将定义后的基色信息标定为暖色调(ww)、中性色调(nw)、冷色调(cw)三种中的任一种。
需要说明的是，预设的基色优先级定义及预设的判定关系式在建立过程中，会结合大数据统计颜色偏好度、实际案例、基色信息与光谱配方库匹配得到的实验数据相结合的方式，实现根据该实验数据、HSB色彩模型的色调定义、基色优先级定义及判定关系式构成的光谱匹配规则库。
需要说明的是，将颜色三属性进行量化，将饱和度S和亮度B以百分比值(0％-100％)表示，色度H以角度(0°-360°)表示，其中，在色度H的范围中，0数值可以360数值重合、首尾相接，即色度H的360°相当于0°。如图5所示，预设的基色优先级定义为1至34级，且逐级降低，而基色信息与光谱配方库中的配方色块构成映射关系，当构建的光谱配方库包 含34种基色比例时，根据被照物HSB信息数据范围确定对应的基色编号、色调分类、光谱配方库编号、配方色块区域编号、基色优先级，其中，S1至S34表示基色编号，P1至P34表示对应的光谱配方库编号，G1至G34表示与光谱配方库编号中编号对应映射的配方色块区域编号，色调分类包括暖色调(ww)、中性色调(nw)、冷色调(cw)和自然色调(sw)，根据得到的被照物的HSB信息确定对应的H值、B值和S值，从而根据图6查询该HSB信息对应的配方色块区域，进而根据光谱配方库得到被照物唯一对应的照明光谱配方。其中，图6中的编号35至编号37用于表示对暖色调(ww)、中性色调(nw)、冷色调(cw)和自然色调(sw)对应的编号和色调分类信息。
需要说明的是，具体的判断关系式的步骤包括但不限于步骤S521至步骤S523。
步骤S521，首先定义所选取区域的颜色总量为1，提取图像识别区域的设定的34种基色的基色比例，统计每种基色的比例数值；
步骤S522，根据每种基色的比例数值进行含量占比排序，得到其中占比含量最大的基色Cmax；
步骤S523，根据最大的基色Cmax确定光谱匹配规则库对应的判定关系式。
具体地，在一些实施例的步骤S523中，将最大的基色Cmax与预设的含量阈值进行比较，确定所选取区域在光谱配方库中对应的基色编号。例如，当含量阈值设置为70％时，则当最大的基色Cmax大于或等于70％时，则匹配基色Cmax在光谱配方库中对应的基色编号，即匹配P1至P34中的其中一种；当含量阈值设置为70％时，则当最大的基色Cmax小于70％时，则进行步骤S5231和步骤S5232。
步骤S5231，根据步骤S522中进行含量占比排序进行降序排列的结果，获取排列后的前四组基色，分别设定为C1、C2、C3、C4，且将C1、C2、C3、C4的总量设置为1；需要说明的是，从排列的结果中选取的基色的数量并不限定为四组，当选取三组基色时，则对应设定为C1、C2、C3。
步骤S5232，对选取的基色按照标定的暖色调(ww)、中性色调(nw)、冷色调(cw)进行色调归类，同时对三种色调中的基色含量进行占比排序，即分别定义暖色调(ww)、中性色调(nw)、冷色调(cw)对应的含量表示为Cww、Cnw、Ccw，且Cww、Cnw和Ccw三者的总和为1。
需要说明的是，在一些实施例的步骤S521中，当图像识别区域只有两种基色时，根据得到的占比含量最大的基色Cmax和占比含量最小的基色Cmin之间的差值确定对应的基色配方。具体地，假设Cmax和Cmin之间的差值的下限阈值为15％，Cmax和Cmin之间的差值的上限阈值为25％，则包括下述①至③三种情况。
①当满足Cmax-Cmin<15％时，分别统计Cww、Cnw和Ccw的基色含量占比，并执行两种基色全光谱匹配规则；
②当满足15％≤Cmax-Cmin<25％时，以颜色优先原则，即选择光谱适配为光谱配方库中基色优先级较高的颜色对应的光谱配方；
③当满足Cmax-Cmin≥25％时，以含量比重为优先原则，即选择光谱适配为对应Cmax的光谱配方。
其中，Cmax和Cmin之间的差值的上限阈值和下限阈值并不做具体限定，可以根据需求进行调整。
需要说明的是，在一些实施例的步骤S521中，当图像识别区域中超过两种基色时，根据得到的占比含量最大的基色Cmax和占比含量第二的基色C2确定对应的基色配方。具体地，假设Cmax和C2之间的差值的下限阈值为20％，Cmax和C2之间的差值的上限阈值为30％，则包括下述④至⑥三种情况。
④当满足Cmax-C2≥30％，以含量比重为优先原则，即选择光谱适配为对应Cmax的光谱配方；
⑤当满足20％≤Cmax-C2<30％时，当Cmax是基色S34，则选择光谱适配为对应Pcw的光谱配方；当Cmax不是基色S34，分别统计Cww、Cnw和Ccw的基色含量占比，并执行两种以上基色全光谱匹配规则；
⑥当满足Cmax-C2<20％时，当Cmax是基色S34，则选择光谱适配为对应Pcw的光谱配方；当Cmax不是基色S34，分别统计Cww、Cnw和Ccw的基色含量占比，并执行两种以上基色全光谱匹配规则。
需要说明的是，全光谱匹配规则包括两种基色全光谱匹配规则和两种以上基色全光谱匹配规则。其中，执行两种基色全光谱匹配规则包括下述⑦至五种情况。
⑦当Cmax和Cmin均为暖色调(ww)，则选择光谱适配为对应Pww的光谱配方；
⑧当Cmax和Cmin均为中性色调(nw)，则选择光谱适配为对应Pnw的光谱配方；
⑨当Cmax和Cmin均为冷色调(cw)，则选择光谱适配为对应Pcw的光谱配方；
⑩当Cmax或Cmin为基色S33，则匹配其中非基色S33对应的基色色调的光谱配方，例如，当Cmax为基色S33，Cmin为基色S30，且基色S30对应的基色色调为冷色调(cw)，则选择光谱适配为对应Pcw的光谱配方。
当Cmax和Cmin未满足上述⑦至⑩的规则条件，则选择光谱适配为对应Psw的光谱配方。
其中，执行两种以上基色全光谱匹配规则包括下述和两种情况。
当Cww、Cnw、Ccw中的任意一个对应的基色含量占比超过50％时，则选择其中基色含量占比值最大的基色的基色色调对应的光谱配方进行光谱适配，例如，当Cww>50％，则选择光谱适配为对应Pww的光谱配方。
当Cww、Cnw、Ccw不符合上述中的规则条件，则选择光谱适配为对应Psw的光谱配方。需要说明的是，当Cww、Cnw、Ccw不满足上述①至的所有识别规则情况，则选择光谱适配为对应Psw的光谱配方。
本申请实施例通过获取被照物的颜色参数，将获取到的基础颜色数据，进行单元划分，将每一个单元的颜色数据，转化成HSB信息，同时通过光谱匹配规则库进行判定处理，结合图6中设定的HSB信息、光谱配方库、配方色块区域等信息，最终得到唯一符合被照物且高 品质的照明光谱配方。
在一具体的实施例中，被照物可能是单色和复杂色，取样会采用类似将一张被照物的颜色图像进行网格划分，将图像中每一个区域的色彩划分成比较小的网格，再对每一个网格颜色进行统计处理，得到其对应的HSB信息，然后通过所建立的光谱配方库和光谱匹配规则库对被照物的HSB信息进行光谱匹配，以确定被照物唯一的、高品质的照明光谱配方。
需要说明的是，如图7所示，为了更清楚地表现光谱配方库与配方色块区域之间的对应关系，光谱配方库对应的配方色块区域采用CIE色坐标区域范围的坐标值的表现形式，构建了当配方库表现形式为色坐标xy中心点结合麦克亚当椭圆表示作为划定配方库色块的范围时，光谱配方库与配方色块区域之间的对应关系，其中，麦克亚当椭圆表示中包含对应色块区域的x中心点、y中心点、区域的偏转角、长轴和短轴。本申请能够通过所建立的光谱配方库和光谱匹配规则库对被照物的HSB信息进行光谱匹配，以确定被照物唯一的、高品质的照明光谱配方。
需要说明的是，该光谱匹配规则库包含对单色物体和混合色物体的匹配。
在步骤S540中，根据光谱匹配规则库和光谱配方库对HSB信息进行光谱匹配，以确定HSB信息唯一对应的照明光谱配方。获取构建的光谱配方库后，根据光谱匹配规则库对被照物的HSB信息和光谱配方库进行运算比对处理，确定了HSB信息唯一对应的基色信息，根据光谱配方库确定基色信息唯一对应的照明光谱配方，根据基色信息与配方色块的映射关系，确定待匹配的HSB信息对应的基色信息唯一对应的配方色块。本申请能够通过所建立的光谱配方库和光谱匹配规则库对被照物的HSB信息进行光谱匹配，以确定被照物唯一的、高品质的照明光谱配方。
本申请实施例还提供一种照明光谱生成装置，用于执行上述实施例的照明光谱生成方法，该装置包括基色信息获取模块、样品集构建模块、第一参数获取模块、第二参数获取模块、相似度拟合模块和光谱配方生成模块。
基色信息获取模块用于获取预设的HSB色彩模型，并通过HSB色彩模型对待匹配的HSB信息进行基色划分，确定HSB信息唯一对应的基色信息；样品集构建模块用于根据HSB色彩模型得到基色信息对应的多个颜色样品，并将多个颜色样品构成色彩样品集；第一参数获取模块用于通过颜色采样装置采集色彩样品集对应的样品参数，其中，样品参数包括所述色彩样品集对应的第一光谱功率分布函数；第二参数获取模块用于获取拟合光源下的第二光谱功率分布函数和预设的回归函数阈值；相似度拟合模块，用于利用回归函数对第一光谱功率分布函数和第二光谱功率分布函数进行相似度拟合处理，得到相似度数值；光谱配方生成模块用于根据回归函数阈值对相似度数值进行相似度判断，以从预设的光谱配方库中确定基色信息唯一对应的照明光谱配方。
本申请实施例的一种照明光谱生成装置用于执行上述实施例中的照明光谱生成方法，其具体处理过程与上述实施例中的照明光谱生成方法相同，此处不再一一赘述。
本申请实施例还提供一种照明光谱匹配装置，用于执行上述实施例的照明光谱匹配方法， 该装置包括HSB信息获取模块、匹配规则库获取模块、光谱配方库获取模块和光谱匹配模块。
HSB信息获取模块用于对待匹配的被照物进行颜色取样，得到被照物的HSB信息；匹配规则库获取模块用于获取根据颜色匹配规则建立的光谱匹配规则库；光谱配方库获取模块用于获取本申请第一方面实施例中任一项的照明光谱生成方法中的光谱配方库；光谱匹配模块用于根据光谱匹配规则库和光谱配方库对HSB信息进行光谱匹配，以确定HSB信息唯一对应的照明光谱配方。本申请实施例的一种照明光谱匹配装置用于执行上述实施例中的照明光谱匹配方法，其具体处理过程与上述实施例中的照明光谱匹配方法相同，此处不再一一赘述。
本申请实施例还提供了计算机设备，该计算机设备包括存储器和处理器，其中，存储器中存储有程序，程序被处理器执行时处理器用于执行如本申请第一方面实施例中任一项的照明光谱生成方法或如本申请第二方面实施例中任一项的照明光谱匹配方法。
下面结合图8对计算机设备的硬件结构进行详细说明。该计算机设备包括：处理器810、存储器820、输入/输出接口830、通信接口840和总线850。
处理器810，可以采用通用的CPU(Central Process in Unit，中央处理器)、微处理器、应用专用集成电路(Application Specific Integrated Circuit，ASIC)，或者一个或多个集成电路等方式实现，用于执行相关程序，以实现本申请实施例所提供的技术方案；
存储器820，可以采用ROM(Read Only Memory，只读存储器)、静态存储设备、动态存储设备或者RAM(Random Access Memory，随机存取存储器)等形式实现。存储器820可以存储操作系统和其他应用程序，在通过软件或者固件来实现本说明书实施例所提供的技术方案时，相关的程序代码保存在存储器820中，并由处理器810来调用执行本申请实施例的照明光谱生成方法或者执行本申请实施例的照明光谱匹配方法；
输入/输出接口830，用于实现信息输入及输出；
通信接口840，用于实现本设备与其他设备的通信交互，可以通过有线方式(例如USB、网线等)实现通信，也可以通过无线方式(例如移动网络、WIFI、蓝牙等)实现通信；和总线850，在设备的各个组件(例如处理器810、存储器820、输入/输出接口830和通信接口840)之间传输信息；
其中处理器810、存储器820、输入/输出接口830和通信接口840通过总线850实现彼此之间在设备内部的通信连接。
本申请实施例还提供计算机可读存储介质，该计算机可读存储介质存储有计算机程序，在计算机程序被计算机执行时，计算机用于执行如本申请第一方面实施例中任一项的照明光谱生成方法或如本申请第二方面实施例中任一项的照明光谱匹配方法。
存储器作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序以及非暂态性计算机可执行程序。此外，存储器可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件，或其他非暂态固态存储器件。在一些实施方式中，存储器可选包括相对于处理器远程设置的存储器，这些远程存储器可以通过网络连接至该处理器。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
本申请实施例描述的实施例是为了更加清楚地说明本申请实施例的技术方案，并不构成对于本申请实施例提供的技术方案的限定，本领域技术人员可知，随着技术的演变和新应用场景的出现，本申请实施例提供的技术方案对于类似的技术问题，同样适用。
本领域技术人员可以理解的是，图1至图5中示出的技术方案并不构成对本申请实施例的限定，可以包括比图示更多或更少的步骤，或者组合某些步骤，或者不同的步骤。
以上所描述的装置实施例仅仅是示意性的，其中作为分离部件说明的单元可以是或者也可以不是物理上分开的，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统、设备中的功能模块/单元可以被实施为软件、固件、硬件及其适当的组合。
本申请的说明书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
应当理解，在本申请中，“至少一个(项)”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，用于描述关联对象的关联关系，表示可以存在三种关系，例如，“A和/或B”可以表示：只存在A，只存在B以及同时存在A和B三种情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b或c中的至少一项(个)，可以表示：a，b，c，“a和b”，“a和c”，“b和c”，或“a和b和c”，其中a，b，c可以是单个，也可以是多个。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以 采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括多指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory，简称ROM)、随机存取存储器(Random Access Memory，简称RAM)、磁碟或者光盘等各种可以存储程序的介质。
以上参照附图说明了本申请实施例的优选实施例，并非因此局限本申请实施例的权利范围。本领域技术人员不脱离本申请实施例的范围和实质内所作的任何修改、等同替换和改进，均应在本申请实施例的权利范围之内。

Claims (10)

  1. 照明光谱生成方法，其特征在于，包括：
    获取预设的HSB色彩模型，并通过所述HSB色彩模型对待匹配的HSB信息进行基色划分，确定所述HSB信息唯一对应的基色信息；
    根据所述HSB色彩模型得到所述基色信息对应的多个颜色样品，并将所述多个颜色样品构成色彩样品集；
    通过颜色采样装置采集所述色彩样品集对应的样品参数，其中，所述样品参数包括所述色彩样品集对应的第一光谱功率分布函数；
    获取拟合光源下的第二光谱功率分布函数和预设的回归函数阈值；
    利用回归函数对所述第一光谱功率分布函数和所述第二光谱功率分布函数进行相似度拟合处理，得到相似度数值；
    根据所述回归函数阈值对所述相似度数值进行相似度判断，以从预设的光谱配方库中确定所述基色信息唯一对应的照明光谱配方。
  2. 根据权利要求1所述的照明光谱生成方法，其特征在于，所述方法还包括：
    获取所述光谱配方库中基色信息与配方色块的映射关系；
    根据所述基色信息与配方色块的映射关系，确定所述HSB信息对应的所述基色信息唯一对应的配方色块。
  3. 根据权利要求1所述的照明光谱生成方法，其特征在于，所述利用回归函数对所述第一光谱功率分布函数和所述第二光谱功率分布函数进行相似度拟合处理，得到相似度数值，包括：
    获取所述第一光谱功率分布函数和其对应的第一色度信息；
    获取所述第二光谱功率分布函数和其对应的第二色度信息；
    对所述第一色度信息和所述第二色度信息进行色度判断处理，得到色度比较结果；
    建立所述第一光谱功率分布函数和所述第二光谱功率分布函数之间的函数关系，得到所述拟合光源的拟合配比信息；
    根据所述色度比较结果调整所述函数关系，以更新所述拟合配比信息；
    利用回归函数对所述第一光谱功率分布函数和所述第二光谱功率分布函数进行相似度拟合处理，得到相似度数值。
  4. 根据权利要求3所述的照明光谱生成方法，其特征在于，所述根据所述回归函数阈值对所述相似度数值进行相似度判断，以从预设的光谱配方库中确定所述基色信息唯一对应的照明光谱配方，包括：
    对所述回归函数阈值和所述相似度数值进行相似度判断，当所述相似度数值大于所述回归函数阈值，根据所述拟合配比信息从预设的光谱配方库中确定所述基色信息 唯一对应的照明光谱配方。
  5. 根据权利要求1至4任一项所述的照明光谱生成方法，其特征在于，所述根据所述HSB色彩模型得到所述基色信息对应的多个颜色样品，并将所述多个颜色样品构成色彩样品集，包括：
    根据所述HSB色彩模型对所述基色信息进行色调划分，得到所述基色信息唯一对应的基色色调，所述基色色调包括冷色调、中性色调和暖色调中的任一种；
    根据所述基色信息对应的所述基色色调，制作所述基色信息对应的多个颜色样品，其中，每个颜色样品包含不同的材质和不同的反射系数；
    将所述多个颜色样品构成色彩样品集。
  6. 照明光谱匹配方法，其特征在于，包括：
    对待匹配的被照物进行颜色取样，得到所述被照物的HSB信息；
    获取根据颜色匹配规则建立的光谱匹配规则库；
    获取权利要求1至5任一项所述的照明光谱生成方法中的光谱配方库；
    根据所述光谱匹配规则库和所述光谱配方库对所述HSB信息进行光谱匹配，以确定所述HSB信息唯一对应的照明光谱配方。
  7. 一种照明光谱生成装置，其特征在于，包括：
    基色信息获取模块，用于获取预设的HSB色彩模型，并通过所述HSB色彩模型对待匹配的HSB信息进行基色划分，确定所述HSB信息唯一对应的基色信息；
    样品集构建模块，用于根据所述HSB色彩模型得到所述基色信息对应的多个颜色样品，并将所述多个颜色样品构成色彩样品集；
    第一参数获取模块，用于通过颜色采样装置采集所述色彩样品集对应的样品参数，其中，所述样品参数包括所述色彩样品集对应的第一光谱功率分布函数；
    第二参数获取模块，用于获取拟合光源下的第二光谱功率分布函数和预设的回归函数阈值；
    相似度拟合模块，用于利用回归函数对所述第一光谱功率分布函数和所述第二光谱功率分布函数进行相似度拟合处理，得到相似度数值；
    光谱配方生成模块，用于根据所述回归函数阈值对所述相似度数值进行相似度判断，以从预设的光谱配方库中确定所述基色信息唯一对应的照明光谱配方。
  8. 一种照明光谱匹配装置，其特征在于，包括：
    HSB信息获取模块，用于对待匹配的被照物进行颜色取样，得到所述被照物的HSB信息；
    匹配规则库获取模块，用于获取根据颜色匹配规则建立的光谱匹配规则库；
    光谱配方库获取模块，用于获取权利要求1至5任一项所述的照明光谱生成方法中的光谱配方库；
    光谱匹配模块，用于根据所述光谱匹配规则库和所述光谱配方库对所述HSB信 息进行光谱匹配，以确定所述HSB信息唯一对应的照明光谱配方。
  9. 计算机设备，其特征在于，所述计算机设备包括存储器和处理器，其中，所述存储器中存储有计算机程序，所述计算机程序被所述处理器执行时所述处理器用于执行：
    如权利要求1至5中任一项所述的照明光谱生成方法；或
    如权利要求6中所述的照明光谱匹配方法。
  10. 计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机程序，在所述计算机程序被计算机执行时，所述计算机用于执行：
    如权利要求1至5中任一项所述的照明光谱生成方法；或
    如权利要求6中所述的照明光谱匹配方法。
PCT/CN2023/074902 2022-03-25 2023-02-08 照明光谱生成方法、光谱匹配方法及装置、设备、介质 WO2023179221A1 (zh)

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