Evaluation method of pineapple leaf fiber fabric based on multispectral image

Abstract

The present invention provides a pineapple leaf fiber fabric evaluation method based on multispectral images, which relates to the field of technical fabric evaluation. The present invention places the pineapple fiber fabric under a black background and a white background, respectively captures image data, analyzes the image data, generates grayscale, analyzes the light transmittance of the fabric through grayscale difference, and generates a light transmittance index, and analyzes the grayscale to generate a mean grayscale and a deviation grayscale, which reflect the overall light and dark deviation degree of the fabric and indirectly reflect the flatness of the fabric. At the same time, the fabric is divided into nine areas, and standardized color value processing is performed on each area. The color deviation of the fabric in each area is analyzed in a color space to generate regional color difference. Finally, a comprehensive analysis is performed to generate a fabric evaluation index for evaluating the fabric grade, and finally, the fabric quality evaluation grade is output. No manual visual observation is required, and the evaluation is fast and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of fabric evaluation, and in particular to a method for evaluating pineapple leaf fiber fabric based on multispectral images. Background Art

[0002] Pineapple leaf fiber fabric, also known as "pineapple fiber," is a natural fiber material extracted from the leaves of the pineapple plant. Pineapple leaves are rich in cellulose, and through specific processing techniques such as boiling, soaking, and spinning, high-quality fibers can be effectively extracted. This fabric has excellent breathability and moisture absorption, making it very popular for summer clothing and household items. In addition, pineapple leaf fiber fabric is strong and wear-resistant, meeting the needs of daily wear and use. Its natural properties give it advantages in terms of environmental protection and align with the concept of sustainable development. Pineapple leaf fiber also has excellent antibacterial properties, effectively inhibiting the growth of bacteria. However, the processing of pineapple leaf fiber fabric can lead to uneven coating and dyeing during the processing process, resulting in inconsistent color purity and weave uniformity in the finished fabric.

[0003] Therefore, after pineapple leaf fiber fabrics are produced, they need to be manually inspected by the naked eye for color and density. However, the error assessment standards based on manual inspection are relatively broad and prone to omissions, making it impossible to quickly and accurately assess the quality grade of the fabric.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a method for evaluating pineapple leaf fiber fabrics based on multispectral images to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The evaluation method of pineapple leaf fiber fabric based on multispectral image includes the following steps:

[0008] S1. Using a camera to photograph a flat fabric against a black background and a white background, respectively, to obtain image data. Using Canva software to remove non-fabric background from the image data, the grayscale values ​​of pixels in the background-removed image data are obtained.

[0009] S2. Perform correlation analysis on the grayscale values ​​to generate grayscale parameters, wherein the grayscale parameters include average grayscale and deviation grayscale. The average grayscale is used to reflect the grayscale average value of the image data, and the deviation grayscale is used to reflect the grayscale deviation degree of the image data.

[0010] S3. Divide the fabric evenly into nine regions, perform correlation analysis on each region of the fabric, generate a color mean, and perform correlation analysis on the color mean to generate a color standard value.

[0011] S4. Perform a correlation analysis on the color standard values ​​to generate regional color differences, perform a correlation analysis on the regional color differences and grayscale parameters under a black background to generate a fabric color cast evaluation index MPZ, which is used to reflect the color purity of the fabric;

[0012] S5. performing correlation analysis on the grayscale parameters under the black background and the white background to generate a light transmittance index, where the light transmittance index is used to reflect the light transmittance performance of the fabric;

[0013] S6. Perform a correlation analysis on the fabric color cast evaluation index MPZ and the light transmittance index TGZ to generate a fabric evaluation index MGZ. Compare the fabric evaluation index MGZ with a threshold value to output a fabric evaluation grade.

[0014] Furthermore, the fabric is spread out flat, with the bottom background being black and white. The fabric is photographed separately to obtain image data. The Canva software is used to detect and identify the outline background in the image data using the Candy algorithm. The detected outline background is removed using the Gaussian blur algorithm. The pixels of the image data after background removal are numbered, with the total number of pixels being N. Correlation analysis is performed on the pixels of the image data to generate grayscale.

[0015]

[0016] in, is the grayscale value of the i-th pixel of the image data, is the red component of the i-th pixel of the image data, is the green component of the i-th pixel of the image data, is the blue component of the i-th pixel of the image data.

[0017] Furthermore, the grayscale values ​​are subjected to correlation analysis to generate the average grayscale value. and deviation grayscale , based on the formula:

[0018]

[0019] Average grayscale Used to reflect the average grayscale value and deviation grayscale of fabric image data It is used to reflect the overall grayscale deviation of the fabric image data, that is, to reflect whether the overall brightness of the fabric is uniform.

[0020] Furthermore, the color mean includes a red mean R, a green mean G, and a blue mean B, wherein the red mean R is the mean of the red components of all pixels in the current area, the green mean G is the mean of the green components of all pixels in the current area, and the blue mean B is the mean of the blue components of all pixels in the current area. A correlation analysis is performed on the color mean to generate a color standard value, which includes X, Y, and Z, and is based on the following formula:

[0021]

[0022] in, , , , , , , 、 、 is the normalized color value, 、 、 is the color value after gamma correction, X, Y, and Z are the color values ​​after standardization, and the color output is unified.

[0023] Furthermore, correlation analysis is performed on the color standard values ​​to generate regional chromaticity values, which include lightness L, redness and greenness a, and yellowness and blueness b, based on the following formula:

[0024]

[0025] in, , , lightness L is used to reflect the color brightness of the area, redness a is used to reflect the redness and greenness of the area, and yellowness b is used to reflect the yellowness and blueness of the area;

[0026] Perform correlation analysis on regional chromaticity values ​​to generate regional color differences , based on the formula:

[0027]

[0028] in, 、 、 The brightness, redness, greenness, and yellowness of the jth fabric area in the fabric, j is used to index the area, They are the average values ​​of brightness, redness, greenness, yellowness and blueness of the nine regions, and regional color difference Used to reflect the degree of color deviation between different areas of the fabric;

[0029] The correlation analysis of regional color difference and grayscale parameters under black background is performed to generate the fabric color cast evaluation index MPZ based on the formula:

[0030]

[0031] Among them, the fabric color deviation evaluation index MPZ is used to reflect the overall color deviation of the fabric.

[0032] Furthermore, the average grayscale under the black background , Deviation Grayscale and the average grayscale on a white background , Deviation Grayscale Correlation analysis is performed to generate the light transmittance index TGZ, based on the formula:

[0033]

[0034] The light transmittance index TGZ is used to reflect the light transmittance of fabrics.

[0035] Furthermore, a correlation analysis is performed on the fabric color cast evaluation index MPZ and the light transmittance index TGZ to generate the fabric evaluation index MGZ, based on the following formula:

[0036]

[0037] The fabric evaluation index MGZ is used to assess the fabric grade. To compare, when When the fabric quality assessment grade is level 2, the fabric has poor color purity, low density, high light transmittance, and has color cast or dirt; when When the fabric quality assessment grade is level one, the fabric has higher color purity, higher density and lower light transmittance.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention places pineapple fiber fabric under a black background and a white background and respectively captures image data, analyzes the image data to generate grayscale, analyzes the light transmittance of the fabric through grayscale difference, and generates a light transmittance index, and analyzes the grayscale to generate a mean grayscale and a deviation grayscale, which reflect the overall light and dark deviation degree of the fabric and indirectly reflect the flatness of the fabric. At the same time, the fabric is divided into nine areas, and standardized color value processing is performed on each area. The color deviation of the fabric in each area is analyzed in the color space to generate regional color difference. Finally, a comprehensive analysis is performed to generate a fabric evaluation index for evaluating the fabric grade, and finally the fabric quality evaluation grade is output. No manual visual observation is required, and the evaluation is fast and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the overall method of the present invention. DETAILED DESCRIPTION

[0041] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0042] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0043] Example:

[0044] See also Figure 1 The pineapple leaf fiber fabric of the present invention is made based on the following method:

[0045] 1. Sizing: The sizing agent formula is: starch, polyvinyl alcohol (PVA), acrylate, the mass ratio is 6:3:1, the sizing concentration is 8-10%, the sizing temperature is 90-95℃, and the moisture regain of the yarn after sizing is controlled at 6-8%.

[0046] 2. Warping: Arrange the yarns into warp sheets according to the process requirements and wind them onto the warp beam. Warping speed: 300-400m / min, warp tension: 15-20cN / strand, warp beam winding density: 0.5-0.6g / cm³. This ensures even warp arrangement and consistent tension to avoid problems such as twisting and breakage.

[0047] 3. Thread the warp yarns into the healds and reed according to the requirements of the plain weave. Heald density: 10-12 yarns / cm, reed gauge: 60-80, threading method: straight threading. Carefully check the threading to avoid missing or incorrect threads.

[0048] 4. Weaving and Loom Selection: Use a rapier loom or air-jet loom, preferably a rapier loom to suit the characteristics of pineapple leaf fiber. Loom speed: 400-500 r / min, warp tension: 20-25 cN / strand, weft tension: 15-20 cN / strand, opening time: 300°-320°, weft insertion time: 80°-100°, beating-up force: 200-250 N, warp density: 50-60 strands / cm, weft density: 40-50 strands / cm, fabric weave: 1 / 1 plain weave. Control the warp and weft tension during weaving to avoid problems such as yarn breakage and weft shrinkage.

[0049] 5. Post-treatment, Desizing: Desize the woven fabric to remove the sizing agent. Use amylase for desizing at 60-70°C for 30-40 minutes. Dyeing: Use reactive dyes at 60°C for 30 minutes. Rinse after dyeing to remove loose color. Setting: Heat-set the fabric using a setting machine at 150-160°C for 30-40 seconds. Use a cationic modified silicone softener for softening to improve the fabric's feel and comfort.

[0050] The pineapple leaf fiber fabric of the present invention is dyed in a pure color.

[0051] The present invention provides a technical solution:

[0052] The evaluation method for pineapple leaf fiber fabric based on multispectral images is based on acquiring data through multispectral image capture and performing averaging on the data to reduce the error value of capturing under a single spectrum. The specific steps include:

[0053] Step 1: Using a multispectral camera, photograph a flat piece of fabric under different spectra against a black and white background, respectively, to obtain averaged image data. The non-fabric background is removed from the image data using Canva software, and the grayscale values ​​of the pixels in the background-removed image data are obtained. The camera resolution is 1920*1080.

[0054] The fabric is spread out flat with a black and white background at the bottom. The fabric is photographed separately to obtain image data. The Canva software uses the Candy algorithm to detect and identify the outline background in the image data. The detected outline background is removed using the Gaussian blur algorithm. The pixels of the image data after background removal are numbered, with the total number of pixels being N. Correlation analysis is performed on the pixels of the image data to generate grayscale.

[0055]

[0056] in, is the grayscale value of the i-th pixel of the image data, is the red component of the i-th pixel of the image data, is the green component of the i-th pixel of the image data, is the blue component of the i-th pixel in the image data. The grayscale value can reflect the brightness of the pixel in the image data. The larger the grayscale value, the brighter it is.

[0057] Step 2: Perform correlation analysis on the grayscale values ​​to generate grayscale parameters. The grayscale parameters include average grayscale and deviation grayscale. The average grayscale is used to reflect the grayscale average value of the image data, and the deviation grayscale is used to reflect the grayscale deviation degree of the image data.

[0058] When there is a difference in the color of the image data, the gray value will have a corresponding difference, so the gray value is correlated and the average gray value is generated. and deviation grayscale , based on the formula:

[0059]

[0060] Average grayscale Used to reflect the average grayscale value and deviation grayscale of fabric image data It is used to reflect the deviation degree of the overall grayscale of the fabric image data, that is, to reflect whether the overall brightness of the fabric is uniform. It can also reflect whether the overall color of the fabric is consistent from the side. The deviation grayscale The lower the value, the more consistent the color.

[0061] Step 3: Grayscale is one aspect of fabric. To evaluate fabric based on color differences, the fabric is evenly divided into nine regions. Correlation analysis is performed on each region to generate a color mean. The color mean is used to reflect the RGB three-channel mean of each region. Correlation analysis is performed on the color mean to generate a color standard value.

[0062] The color mean includes the red mean R, green mean G, and blue mean B. The red mean R is the mean of the red components of all pixels in the current area, the green mean G is the mean of the green components of all pixels in the current area, and the blue mean B is the mean of the blue components of all pixels in the current area. A correlation analysis is performed on the color mean to generate a color standard value. The color standard value includes X, Y, and Z. First, the RGB components are normalized to generate normalized values. 、 、 , and then perform gamma correction on the normalized value to generate 、 、 , and then through the contribution matrix 、 、 Processing is performed to generate color standard values ​​X, Y, and Z based on the following formula:

[0063]

[0064] in, , , , , , , 、 、 is the normalized color value, 、 、 The contribution matrix is ​​defined based on the CIE1931 color space. The processed color standard values ​​X, Y, and Z enable unified color management across all devices. X represents the red stimulus value, which is related to the wavelength of the red spectrum and its perception by the human eye. Y represents the lightness value, which generally corresponds to the human eye's perception of brightness; higher values ​​indicate brighter colors. Z represents the blue stimulus value, which is related to the wavelength of the blue spectrum and its perception by the human eye.

[0065] Step 4: Perform a correlation analysis on the color standard values ​​to generate regional color differences. Perform a correlation analysis on the regional color differences and grayscale parameters under a black background to generate a fabric color cast evaluation index MPZ. The fabric color cast evaluation index is used to reflect the color purity of the fabric.

[0066] Correlation analysis is performed on the color standard values ​​to generate regional chromaticity values, which include lightness L, redness and greenness a, and yellowness and blueness b. The formula is as follows:

[0067]

[0068] in, , ,function Used to convert relative brightness values ​​to perceived brightness, Used to distinguish between bright and dark areas. When x is greater than this value, cube root processing is used; when x is not greater than this value, linear processing is used, with x as the independent variable. Regional chromaticity values ​​are used to reflect the color conditions of the nine areas on the fabric, among which lightness L is used to reflect the color brightness of the area, redness a is used to reflect the red-green hue of the area, and yellow-blue b is used to reflect the yellow-blue hue of the area. By analyzing whether there are differences in color brightness, red-green hue, and yellow-blue hue between regions, it is determined whether the overall color of the fabric has color cast, and thus a correlation analysis is performed on the regional chromaticity values ​​to generate regional color differences. , based on the formula:

[0069]

[0070] in, 、 、 The brightness, redness, greenness, and yellowness of the jth fabric area in the fabric, j is used to index the area, They are the average values ​​of brightness, redness, greenness, yellowness and blueness of the nine regions, and regional color difference Used to reflect the degree of color deviation between different areas of the fabric, regional color difference The larger the value is, the higher the color deviation of the nine areas of the fabric is, that is, the more impure the color is;

[0071] Under a black background, the regional color difference and grayscale parameters under the black background are correlated and analyzed to generate the fabric color cast evaluation index MPZ. The formula is:

[0072]

[0073] Among them, the fabric color cast evaluation index MPZ is used to reflect the degree of deviation in the overall color and brightness of the fabric. The smaller the fabric color cast evaluation index, the smaller the deviation in fabric color and fabric brightness, that is, the smoother the fabric, the purer the color, and the better the quality.

[0074] Step 5: performing correlation analysis on the grayscale parameters under the black background and the white background to generate a light transmittance index, which is used to reflect the light transmittance performance of the fabric;

[0075] Among them, in the image data, under the black background of the pineapple leaf fiber fabric, the translucent part is black, and its three-color values ​​are 0, 0, 0. Under the white background, the translucent part is white, and the three-color values ​​of white are 255, 255, 255. Based on this difference, the greater the difference in brightness of the corresponding pixels in the black image data and the white image data, the more pixels in the translucent part.

[0076] Therefore, the average grayscale under the black background , Deviation Grayscale and the average grayscale on a white background , Deviation Grayscale Correlation analysis is performed to generate the light transmittance index TGZ, based on the formula:

[0077]

[0078] The light transmittance index (TGZ) reflects the light transmittance of a fabric. Against a black background, the average grayscale indicates the material's ability to absorb light. Higher grayscale values ​​indicate a greater ability to reflect or transmit light. Deviation grayscale reflects the dispersion of grayscale values. By summing the deviation grayscale values, the stability of grayscale values ​​under different backgrounds can be comprehensively reflected. Grayscale differences can be used to assess light transmittance. A higher transmittance index indicates greater light transmittance and lower fabric density. A large difference in average grayscale values ​​between black and white backgrounds indicates significant variations in overall image brightness, indicating significant variations in light transmittance. A better transmittance index indicates a more consistent grayscale distribution. Larger deviation grayscale differences indicate varying degrees of grayscale variation under different backgrounds, potentially interfering with light transmittance analysis under black and white backgrounds. Greater deviations lead to less accurate transmittance indexes. That is, the greater the average grayscale deviation under the black and white background, the better the transmittance and the larger the transmittance index TGZ. The greater the deviation grayscale under the black and white background, the higher the degree of interference with the transmittance evaluation. The better the transmittance, the more difficult it is to evaluate and the smaller the transmittance index TGZ.

[0079] Step 6: Perform a correlation analysis on the fabric color cast evaluation index MPZ and the light transmittance index TGZ to generate a fabric evaluation index MGZ. Compare the fabric evaluation index MGZ with the threshold and output the fabric evaluation grade.

[0080] The fabric color cast evaluation index MPZ and the light transmittance index TGZ are correlated and analyzed to generate the fabric evaluation index MGZ. The formula is:

[0081]

[0082] The influence of the light transmittance index TGZ on the fabric color cast evaluation index is determined by the light transmittance weight factor. When it is greater than 0, the transmittance index TGZ is positively correlated with the fabric color cast evaluation index. The larger the transmittance index TGZ, the larger the fabric evaluation index MGZ, and the worse the quality evaluation. The larger the fabric color cast evaluation index MPZ, the greater the fabric color deviation, the larger the fabric evaluation index MGZ, and the worse the quality evaluation. The fabric evaluation index MGZ is used to evaluate the fabric grade. is the transmittance weight factor, and the range is set to , used to evaluate the importance of fabric transmittance. When set to 0, it means that transmittance is not important for fabric evaluation. The larger the value, the more important transmittance is in quality evaluation. The threshold Used to define the quality grade limit of fabric evaluation, obtained by analyzing samples, and comparing the fabric evaluation index MGZ with the threshold To compare, when When the fabric quality assessment grade is level 2, the fabric has poor color purity, low density, high light transmittance, and has color cast or dirt; when When the fabric quality assessment grade is level one, the fabric has higher color purity, higher density and lower light transmittance.

[0083] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0084] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0085] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0086] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A pineapple leaf fiber fabric evaluation method based on multispectral images is characterized in that: The specific steps include: S1, pineapple leaf fiber fabric is laid on the background of pure black and white respectively, the fabric that is spread out is photographed by camera, and image data is obtained. The image data adopts Canva software to remove non-fabric background, and the grayscale value of pixel point in the image data after background removal is obtained; S2. Perform correlation analysis on the grayscale values ​​to generate grayscale parameters, wherein the grayscale parameters include average grayscale and deviation grayscale. The average grayscale is used to reflect the grayscale average value of the image data, and the deviation grayscale is used to reflect the grayscale deviation degree of the image data. S3. Under a black background, the fabric is evenly divided into nine areas. A correlation analysis is performed on each area of ​​the fabric to generate a color mean value. The color mean value is used to reflect the RGB three-channel mean value of each area of ​​the fabric. A correlation analysis is performed on the color mean value to generate a color standard value. S4. Perform a correlation analysis on the color standard values ​​to generate regional color differences, perform a correlation analysis on the regional color differences and grayscale parameters under a black background to generate a fabric color cast evaluation index MPZ, which is used to reflect the color purity of the fabric; S5. performing correlation analysis on the grayscale parameters under the black background and the white background to generate a light transmittance index, where the light transmittance index is used to reflect the light transmittance performance of the fabric; S6. Perform a correlation analysis on the fabric color cast evaluation index MPZ and the light transmittance index TGZ to generate a fabric evaluation index MGZ, compare the fabric evaluation index MGZ with a threshold, and output a fabric evaluation grade. The fabric color cast evaluation index MPZ and the light transmittance index TGZ are correlated and analyzed to generate the fabric evaluation index MGZ. The formula is: is the transmittance weight factor, and the range is set to The fabric evaluation index MGZ is used to evaluate the fabric grade. The fabric evaluation index MGZ is compared with the threshold To compare, when When the fabric quality assessment grade is level 2, the fabric has poor color purity, low density, high light transmittance, and has color cast or dirt; when When the fabric quality assessment grade is level one, the fabric has higher color purity, higher density and lower light transmittance.

2. The method for evaluating pineapple leaf fiber fabric based on multispectral image according to claim 1, wherein: The fabric is spread out flat with a black and white background at the bottom. The fabric is photographed separately to obtain image data. The Canva software uses the Candy algorithm to detect and identify the outline background in the image data. The detected outline background is removed using the Gaussian blur algorithm. The pixels of the image data after background removal are numbered, with the total number of pixels being N. Correlation analysis is performed on the pixels of the image data to generate grayscale. in, is the grayscale value of the i-th pixel of the image data, is the red component of the i-th pixel of the image data, is the green component of the i-th pixel of the image data, is the blue component of the i-th pixel of the image data, is the index of the pixel in the image data after background removal, and .

3. The method for evaluating pineapple leaf fiber fabric based on multispectral image according to claim 2, wherein: Perform correlation analysis on the grayscale values ​​to generate the average grayscale and deviation grayscale , based on the formula: Average grayscale Used to reflect the average grayscale value and deviation grayscale of fabric image data It is used to reflect the overall grayscale deviation of fabric image data and whether the overall brightness of the fabric is uniform.

4. The method for evaluating pineapple leaf fiber fabric based on multispectral imagery according to claim 3, wherein: The color mean includes the red mean R, the green mean G, and the blue mean B. The red mean R is the mean of the red components of all pixels in the current area, the green mean G is the mean of the green components of all pixels in the current area, and the blue mean B is the mean of the blue components of all pixels in the current area. A correlation analysis is performed on the color mean to generate a color standard value. The color standard value includes X, Y, and Z, and the formula based on it is: in, , , , , , , 、 、 is the normalized color value, 、 、 is the color value after gamma correction, and X, Y, and Z are the standardized color values ​​for unified color output.

5. The method for evaluating pineapple leaf fiber fabric based on multispectral image according to claim 4, wherein: Correlation analysis is performed on the color standard values ​​to generate regional chromaticity values, which include lightness L, redness and greenness a, and yellowness and blueness b. The formula is as follows: in, , , lightness L is used to reflect the color brightness of the area, redness a is used to reflect the redness and greenness of the area, and yellowness b is used to reflect the yellowness and blueness of the area; Perform correlation analysis on regional chromaticity values ​​to generate regional color differences , based on the formula: in, 、 、 are the brightness, redness, greenness, and yellowness of the jth region in the fabric, respectively. j is used to index the region. They are the average values ​​of brightness, redness, greenness, yellowness and blueness of the nine regions, and the regional color difference Used to reflect the degree of color deviation between different areas of the fabric; The correlation analysis of regional color difference and grayscale parameters under black background is performed to generate the fabric color cast evaluation index MPZ based on the formula: Among them, the fabric color deviation evaluation index MPZ is used to reflect the overall color deviation of the fabric.

6. The method for evaluating pineapple leaf fiber fabric based on multispectral image according to claim 5, wherein: Average grayscale against a black background , Deviation Grayscale and the average grayscale on a white background , Deviation Grayscale Correlation analysis is performed to generate the light transmittance index TGZ, based on the formula: The light transmittance index TGZ is used to reflect the light transmittance of fabrics.

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