Color Measurement Method and System for Dyed Polyester Fabrics Based on Computer Vision

Abstract

The present invention provides a method and system for measuring the color of dyed polyester fabrics based on computer vision, which relates to the field of image processing technology. By collecting temperature change data under current production conditions and setting a predetermined temperature range, and applying a uniform dynamic temperature change within this range, the present invention can accurately capture the thermal imaging characteristics of dyed polyester fabrics during the heating and cooling processes, and then extract key features such as temperature gradient and heat diffusion rate. This method not only improves the accuracy of color difference measurement, but also realizes a scientific evaluation of color performance through the combined analysis of calculating the humidity correction coefficient and the color difference deviation coefficient, effectively eliminating the interference of environmental factors and ensuring the accuracy of color measurement during the production process.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and specifically to a method and system for measuring the color of dyed polyester fabrics based on computer vision. Background Art

[0002] With the continuous progress of technology and the rapid development of the textile industry, the technology for measuring and controlling the color of dyed polyester fabrics has also been evolving continuously. The traditional dyeing process usually relies on manual experience and subjective judgment, lacking objective and accurate measurement means. In recent years, the development of computer vision technology has brought new opportunities for the color measurement of textiles. Automated color recognition systems based on image processing and machine learning have gradually been applied to the dyeing industry, which can provide fast and accurate color difference analysis by analyzing the color information in images. However, the existing technologies still face many challenges in practical applications, such as changes in the measurement environment, the influence of temperature and humidity on color performance, etc., resulting in limitations in the reliability and consistency of color difference measurement results.

[0003] In the prior art, the publication number is CN202310027735.2, and the name is a method for measuring the color of image-type dyed polyester fabrics. Under the standard D65 light source environment, the raw response values and predicted CIELAB values of an unknown polyester fabric sample are obtained. At the same time, the raw response values and true CIELAB values of M dyed polyester fabric samples are obtained, and P dyed polyester fabric samples with the highest similarity to the unknown polyester fabric sample are selected from them. The average value is calculated by taking the true CIELAB value of these samples and the predicted CIELAB value of the unknown polyester fabric sample together, and this average value is used as the true CIELAB value of the unknown polyester fabric sample. The method of the present invention has high precision and good stability.

[0004] The existing color measurement technologies for dyed polyester fabrics have poor adaptability in dynamic environments. Especially in the production process where temperature and humidity change frequently, traditional color measurement methods often cannot reflect the actual color performance of the fabric in real time. Specifically, temperature changes can cause significant visual differences in the color of dyed polyester fabrics, especially the thermal imaging characteristics during the heating and cooling processes, and these characteristics are often not fully considered. In addition, the existing technologies mainly focus on color measurement under static conditions and fail to effectively integrate the relationship between dynamic temperature changes and color performance. Therefore, in a changing production environment, the computer vision measurement results of the existing methods may have color measurement errors and cannot accurately evaluate the color consistency and quality of dyed polyester fabrics.

[0005] The above information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for measuring the color of dyed polyester fabrics based on computer vision to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for measuring the color of dyed polyester fabrics based on computer vision, the specific steps include:

[0009] Step S1: Collect the temperature change data and humidity change data of the dyed polyester fabric sample under the current production conditions, and set the temperature range determined by the temperature change data as the predetermined temperature interval; set the humidity range determined by the humidity change data as the predetermined humidity interval;

[0010] Step S2: Apply a uniform dynamic temperature change treatment to the dyed polyester fabric sample within the predetermined temperature interval, and capture the dynamic thermal imaging characteristics and RGB values of the dyed polyester fabric sample during the heating or cooling process in real time, and extract the key features corresponding to each heating or cooling process from these dynamic thermal imaging characteristics through computer vision technology. The key features include the average temperature gradient and the average heat diffusion rate;

[0011] Step S3: During the heating process, for each key feature, calculate the first measurement error rate representing the degree of visual measurement color difference error of the current heating process according to the visual measurement color difference error between the current, previous, and next heating processes of the dyed polyester fabric sample;

[0012] During the cooling process, for each key feature, calculate the second measurement error rate representing the degree of visual measurement color difference error of the current cooling process according to the visual measurement color difference error between the current, previous, and next cooling processes of the dyed polyester fabric sample;

[0013] Step S4: Divide the predetermined humidity interval into several continuous humidity sub-regions, collect the RGB value measurement error data of the dyed polyester fabric sample under each humidity sub-region, and analyze these RGB value measurement error data to generate a humidity correction coefficient, which is used to evaluate the influence degree of each humidity sub-region on the visual measurement color difference error of the dyed polyester fabric sample;

[0014] Step S5: Determine whether the dyed polyester fabric sample is in the heating process at the current moment. If it is in the heating process, combine and analyze the first measurement error rate and the humidity correction coefficient to generate the first measurement error calibration index;

[0015] If it is in the cooling process, combine and analyze the second measurement error rate and the humidity correction coefficient to generate the second measurement error calibration index;

[0016] Both the first measurement error calibration index and the second measurement error calibration index can be used to generate different levels of adjustment strategies for error calibration of the RGB values of the current dyed polyester fabric sample;

[0017] Step S6: Use computer vision technology to extract the RGB values of the current dyed polyester fabric sample, and perform error calibration on the RGB values of the current dyed polyester fabric sample according to different levels of adjustment strategies.

[0018] A color measurement system for dyed polyester fabric based on computer vision, the system is used to execute the color measurement method for dyed polyester fabric based on computer vision, including:

[0019] Predetermined interval setting module: used to collect the temperature change data and humidity change data of the dyed polyester fabric sample under the current production conditions, and set the temperature range determined by the temperature change data as the predetermined temperature interval; set the humidity range determined by the humidity change data as the predetermined humidity interval;

[0020] Key feature extraction module: used to apply uniform dynamic temperature change processing to the dyed polyester fabric sample within the predetermined temperature interval, and capture the dynamic thermal imaging characteristics and RGB values of the dyed polyester fabric sample in the process of heating or cooling in real time, and extract the key features corresponding to each heating or cooling process from these dynamic thermal imaging characteristics through computer vision technology, and the key features include the average temperature gradient and the average heat diffusion rate;

[0021] Measurement error rate calculation module: used to calculate the first measurement error rate representing the degree of visual measurement color difference error of the current heating process for each key feature during the heating process according to the visual measurement color difference error between the current, previous and next heating processes of the dyed polyester fabric sample;

[0022] During the cooling process, for each key feature, calculate the second measurement error rate representing the degree of visual measurement color difference error of the current cooling process according to the visual measurement color difference error between the current, previous and next cooling processes of the dyed polyester fabric sample;

[0023] Humidity correction coefficient generation module: used to divide the predetermined humidity interval into several continuous humidity sub-zones, collect the RGB value measurement error data of the dyed polyester fabric sample under each humidity sub-zone, and analyze these RGB value measurement error data to generate a humidity correction coefficient, and the humidity correction coefficient is used to evaluate the influence degree of each humidity sub-zone on the visual measurement color difference error of the dyed polyester fabric sample;

[0024] Measurement error calibration index generation module: used to determine whether the dyed polyester fabric sample is in a heating process at the current moment. If it is in a heating process, it combines and analyzes the first measurement error rate and the humidity correction coefficient to generate the first measurement error calibration index;

[0025] If it is in a cooling process, it combines and analyzes the second measurement error rate and the humidity correction coefficient to generate the second measurement error calibration index;

[0026] Both the first measurement error calibration index and the second measurement error calibration index can be used to generate different-level adjustment strategies for error calibration of the RGB value of the current dyed polyester fabric sample;

[0027] Error calibration module: used to extract the RGB value of the current dyed polyester fabric sample by using computer vision technology, and perform error calibration on the RGB value of the current dyed polyester fabric sample according to different-level adjustment strategies.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: By collecting the temperature change data under the current production conditions and setting a predetermined temperature range, and applying a uniform dynamic temperature change within this range, the thermal imaging characteristics of the dyed polyester fabric during the heating and cooling processes can be accurately captured, and then key features such as temperature gradient and heat diffusion rate can be extracted; This method not only improves the accuracy of color difference measurement, but also realizes the scientific evaluation of color performance through the combined analysis of calculating the humidity correction coefficient and the color difference deviation coefficient, effectively eliminates the interference of environmental factors, and ensures the accuracy of color measurement during the production process. Description of the Drawings

[0029] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0030] Figure 2 It is a block diagram of the overall system module of the present invention. Detailed Embodiments

[0031] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the following further details the present invention in combination with specific embodiments.

[0032] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those 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 denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0033] Embodiment 1:

[0034] Please refer to Figure 1 , the present invention provides a technical solution:

[0035] A method for measuring the color of dyed polyester fabrics based on computer vision, the specific steps include:

[0036] Step S1: Collect the temperature change data and humidity change data of the dyed polyester fabric sample under the current production conditions, and set the temperature range determined by the temperature change data as the predetermined temperature interval; set the humidity range determined by the humidity change data as the predetermined humidity interval;

[0037] Step S2: Apply a uniform dynamic temperature change treatment to the dyed polyester fabric sample within the predetermined temperature interval, and capture the dynamic thermal imaging characteristics and RGB values of the dyed polyester fabric sample during the heating or cooling process in real time, and extract the key features corresponding to each heating or cooling process from these dynamic thermal imaging characteristics through computer vision technology. The key features include the average temperature gradient and the average heat diffusion rate;

[0038] Step S3: During the heating process, for each key feature, calculate the first measurement error rate representing the degree of visual measurement color difference error of the current heating process according to the visual measurement color difference error between the current, previous and next heating processes of the dyed polyester fabric sample.

[0039] The steps for determining the visual measurement color difference error are as follows:

[0040] 1.11) Determine the standard color:

[0041] Reference standards: Standard colors usually come from authoritative color databases or color swatches, such as Pantone, Munsell, or CIELAB standard color swatches; these standards provide precise color definitions for comparison with samples.

[0042] Laboratory measurements: Standard colors are obtained through laboratory measurements, using calibrated color difference meters and other equipment to measure the target color under controlled conditions.

[0043] 1.12) Calculation of visual measurement color difference error:

[0044] Color difference calculation: Compare the color data extracted from the dyed polyester fabric samples through computer vision technology with the standard color data. The specific comparison process is as follows:

[0045] Determine the start time t0 and end time t1 of the heating or cooling process. Computer vision technology extracts color data at t0 and t1 respectively, and calculates the difference between the color data extracted at these two times to obtain ΔRGB i =(ΔR i , ΔG i , ΔB i );

[0046] Use the CIELAB color space to quantify the color difference by calculating the ΔE value, and represent the RGB value measurement error corresponding to the i-th heating process as ΔRGB i =(ΔR i , ΔG i , ΔB i ); Represent the RGB value measurement error corresponding to the j-th cooling process as ΔRGB j =(ΔR j , ΔG j , ΔB j );

[0047] Data analysis: Analyze the color difference data to judge the matching degree between the sample color and the standard color.

[0048] During the cooling process, for each key feature, calculate the second measurement error rate representing the degree of visual measurement color difference error of the current cooling process according to the visual measurement color difference errors between the current, previous, and subsequent cooling processes of the dyed polyester fabric sample.

[0049] Step S4: Divide the predetermined humidity range into several continuous humidity sub-zones, collect the RGB value measurement error data of the dyed polyester fabric sample under each humidity sub-zone, and analyze these RGB value measurement error data to generate a humidity correction coefficient, which is used to evaluate the influence degree of each humidity sub-zone on the visual measurement color difference error of the dyed polyester fabric sample;

[0050] Step S5: Determine whether the dyed polyester fabric sample is in a heating process at the current moment. If it is in a heating process, combine and analyze the first measurement error rate and the humidity correction coefficient to generate a first measurement error calibration index;

[0051] If it is in a cooling process, combine and analyze the second measurement error rate and the humidity correction coefficient to generate a second measurement error calibration index;

[0052] The first measurement error calibration index and the second measurement error calibration index can evaluate the degree of color measurement error of the dyed polyester fabric sample according to the current key features and humidity partition of the dyed polyester fabric sample;

[0053] Both the first measurement error calibration index and the second measurement error calibration index can be used to generate different-level adjustment strategies for error calibration of the RGB values of the current dyed polyester fabric sample;

[0054] Step S6: Use computer vision technology to extract the RGB values of the current dyed polyester fabric sample, and perform error calibration on the RGB values of the current dyed polyester fabric sample according to different-level adjustment strategies.

[0055] Further explanation, the acquisition of the predetermined temperature range and the predetermined humidity range specifically includes:

[0056] Arrange multiple temperature sensors on the surface of the dyed polyester fabric sample to ensure that the sensors are evenly distributed and cover the entire sample area;

[0057] Start the temperature sensors and record the real-time temperature change data of the dyed polyester fabric sample under the current production conditions;

[0058] Monitor the collected temperature data in real time and record the temperature change curve;

[0059] Analyze the temperature change curve, determine its maximum and minimum values, and set a predetermined temperature range according to these values;

[0060] And set the predetermined temperature range as [T init , T final , where T init , T final are the minimum temperature and the maximum temperature in the temperature change curve respectively;

[0061] Record the real-time humidity change data of the dyed polyester fabric sample under the current production conditions; and record the humidity change curve;

[0062] Analyze the humidity change curve, determine its maximum and minimum values, and set the predetermined humidity range as [SD init , SDfinal , where SD init , SD final are respectively the minimum humidity and the maximum humidity in the humidity change curve.

[0063] Furthermore, key features are extracted from the dynamic thermal imaging characteristics, specifically including:

[0064] 1.1) For the dynamic temperature change processing:

[0065] Based on a predetermined temperature range [T init , T final , the heating rate during the heating process is defined as CSv;

[0066]

[0067] The cooling rate during the cooling process is defined as CJv;

[0068]

[0069] where ΔT1 and ΔT2 are respectively the temperature change values during the heating process and the cooling process in unit time t; in this embodiment, the unit time t is in minutes;

[0070] 1.11) Mark the number of times of each heating process to form a heating process number sequence set {1, 2,..., i,..., n}, where i represents the index of the i-th heating process, and the number of times of each heating process corresponds to the corresponding heating interval, and n represents the total number of heating processes; and when i = 1,

[0071] T1 represents the temperature value after the first heating process; and set T n = T init + n × ΔT1 = T final ;

[0072] 1.12) Mark each cooling process to form a cooling process number sequence set {1, 2,..., j,..., m}, where j represents the index of the j-th cooling process, and the number of times of each cooling process corresponds to the corresponding cooling interval, and m represents the total number of cooling processes; and when j = 1,

[0073] T1 represents the temperature value after the first cooling process; and set T m = T final - m × ΔT2 = T init ;

[0074] 1.2) For the dynamic thermal imaging characteristics capture:

[0075] Define the acquisition frequency of thermal imaging as f. During the heating or cooling process, capture the thermal imaging of the dyed polyester fabric sample at the acquisition frequency f.

[0076] In this embodiment, the thermal imaging acquisition frequency f = 30 frames per second.

[0077] 1.3) For the key feature extraction corresponding to each heating or cooling process:

[0078] Perform denoising processing on the captured thermal imaging by median filtering.

[0079] I filtered (x,y) = median(I(x + i′,y + j′)), i′,j′ ∈ {-1,0,1}

[0080] Where, I(x,y) is the intensity value of the original thermal imaging at the pixel position (x,y); the pixel position (x,y) represents the pixel at the coordinate (x,y).

[0081] I filtered (x,y) is the intensity value of the filtered thermal imaging at the pixel position (x,y).

[0082] median is the median operation, calculating the median of the pixel values in the neighborhood of the central pixel.

[0083] i′ and j′ respectively represent the pixel offsets moving left and right and up and down from the current pixel position (x,y), and form a 3x3 window to perform median filtering.

[0084] For the calculation of the average temperature gradient: Determine the start time t0 and end time t1 of each heating or cooling process, and calculate the temperature gradients at these two moments and find the average; specifically including:

[0085] For each moment t0 and t1, calculate the temperature gradient respectively:

[0086]

[0087] Where, is the temperature gradient vector of the pixel at the coordinate (x,y) at the moment t0.

[0088] is the temperature gradient vector of the pixel at the coordinate (x,y) at the moment t1.

[0089] respectively represent the partial derivatives of temperature with respect to the x and y coordinates.

[0090] Average the temperature gradients at t0 and t1 moments:

[0091]

[0092] Consider the global average temperature gradient to calculate the average temperature gradient in space:

[0093]

[0094] For the calculation of the average heat diffusion rate: Determine the temperature change at two moments t0 and t1;

[0095] For each moment t0 and t1 of the heating or cooling process, calculate the Laplacian operator of the temperature:

[0096]

[0097] Based on the Laplacian operator, calculate the heat diffusion rate at (x, y) and find the average:

[0098]

[0099] where RKv ( x, y ) is the heat diffusion rate at the coordinate (x, y), are the Laplacian operators of the corresponding temperatures at moments t0 and t1 respectively, representing the sum of the second-order derivatives of the temperature in space; α is the thermal diffusion coefficient; represents the physical properties of the material; M and N are the width and height of the thermal image respectively;

[0100] Calculate the global average heat diffusion rate to comprehensively consider all points in space. The calculation formula is as follows:

[0101]

[0102] is the average heat diffusion rate;

[0103] Through the above steps, the average temperature gradient and the average heat diffusion rate can be calculated for the thermal property evaluation of materials or structures under different temperature change conditions.

[0104] Define the RGB value measurement error corresponding to the i-th heating process of the dyed polyester fabric sample as ΔRGB i =(ΔR i , ΔG i , ΔB i ); And represent ΔRGB i and the corresponding key features in a mapping manner as:

[0105]

[0106] where f i,R , f i,G , f i,BThey are functions that map the corresponding features to each component of ΔRGB i respectively;

[0107] Define the RGB value measurement error corresponding to the j-th cooling process of the dyed polyester fabric sample as ΔRGB j =(ΔR j , ΔG j , ΔB j ), and represent ΔRGB j and the corresponding key features in a mapping manner as:

[0108]

[0109] where f j,R , f j,G , f j,B are functions that map the corresponding features to each component of ΔRGB j respectively;

[0110] Furthermore, the first measurement error rate and the second measurement error rate are specifically described as follows:

[0111] Define the first measurement error rate corresponding to the i-th heating process as The calculation formula is as follows:

[0112]

[0113] where, ΔRGB i-1 and ΔRGB i+1 are the RGB value measurement errors corresponding to the previous heating process and the subsequent heating process respectively; respectively represent the measurement error rates corresponding to the changes in R, G, and B values during the i-th heating process;

[0114] Adopt the Sigmoid function to limit the output value of within the range (0, 1), and the specific form is:

[0115]

[0116] where is the adjusted first measurement error rate;

[0117] The closer the value is to 1, the higher the change rate of the RGB measurement error in the i-th heating process;

[0118] The closer the value is to 0, the lower the change rate of the RGB measurement error in the i-th heating process;

[0119] Define the second measurement error rate corresponding to the j-th cooling process as The calculation formula is as follows:

[0120]

[0121] Where, ΔRGB j-1 and ΔRGB j+1 are the RGB value measurement errors corresponding to the previous cooling process and the subsequent cooling process respectively; respectively represent the measurement error rates corresponding to the changes in R, G, and B values during the j-th cooling process;

[0122] Using the Sigmoid function, the output value is limited within the range (0, 1), and the specific form is:

[0123]

[0124] Where is the adjusted second measurement error rate;

[0125] The closer the value is to 1, the higher the change rate of the RGB measurement error in the j-th cooling process;

[0126] The closer the value is to 0, the lower the change rate of the RGB measurement error in the j-th cooling process.

[0127] Furthermore, the predetermined humidity range is divided into several consecutive humidity sub-regions, the RGB value measurement error data of the dyed polyester fabric samples are collected under each humidity sub-region, and the RGB value measurement error data are analyzed to generate a humidity correction coefficient, which is used to evaluate the influence degree of each humidity sub-region on the visual measurement color difference error of the dyed polyester fabric samples, specifically including:

[0128] The predetermined humidity range [SD init , SD final is divided into H consecutive humidity sub-regions of the same length, and these humidity sub-regions are denoted as {1, 2,..., h,..., H}, where h represents the index of the h-th humidity sub-region and H is the total number of humidity sub-regions;

[0129] When h = 1, the initial value of this humidity sub-region is SD init ; when h = H, the maximum value of this humidity sub-region is D final

[0130] The number of times of collecting the RGB value measurement error data within each humidity sub-region is denoted as {1, 2,..., u,..., U}, where u represents the index of the u-th RGB value measurement error collection times and U is the total number of collections;

[0131] Define the humidity correction coefficient for the h-th humidity zone as HC h , and the calculation formula is as follows:

[0132]

[0133] Set HC h to have a valid value range of (0, 1);

[0134] When HC h gets closer to 1, it indicates that the change rate of RGB measurement error in the h-th humidity zone is greater;

[0135] When HC h gets closer to 0, it indicates that the change rate of RGB measurement error in the h-th humidity zone is smaller.

[0136] Further explanation, the acquisition of the first measurement error calibration index and the second measurement error calibration index specifically includes:

[0137] Compare the temperature of the dyed polyester fabric sample at the current moment with the temperature at the previous moment. If the temperature value increases, it is a heating process, and match the temperature at the current moment with each temperature change interval corresponding to the heating process number sequence set {1, 2,..., i,..., n} to determine the index i of the heating process number; the previous moment is determined by the time length corresponding to each heating process or cooling process;

[0138] If the temperature value decreases, it is a cooling process, and match the temperature value at the current moment with each temperature change interval corresponding to the cooling process number sequence set {1, 2,..., j,..., m} to determine the index j of the cooling process number;

[0139] During the i-th heating process, define the first measurement error calibration index as ΔEc i , and the calculation formula is as follows:

[0140]

[0141] where a1 and a2 are the weight coefficients of the corresponding parameters, and a1 + a2 = 1, and the value ranges of a1 and a2 are both in the interval (0, 1);

[0142] μ1 is the first adjustment factor, 0.11 ≤ μ1 ≤ 0.65, and μ1 is used to ensure that the valid value range of ΔEc i is in the interval (0, 1);

[0143] Set and HC h 's reference judgment thresholds to be q1 i and q2 h ; and q1 iand q2 h The value ranges are both set to (0.1, 0.9); set ΔEc i The effective value range of is (0, 1);

[0144] In the i-th heating process, when the first measurement error calibration index of the dyed polyester fabric sample exceeds 30% of the total RGB value measurement error of the current heating process, the value at this time is taken as the value of q1 i value;

[0145] In the h-th humidity zone, when the RGB value measurement error of the dyed polyester fabric sample exceeds 15%, the HC h value at this time is taken as the value of q2 h value;

[0146] When ΔEc i ≥0.5, and HC h ≥q2 h At this time, both a1 and a2 are set to 0.5, a1 = a2, indicating that and HC h have equal importance. At this time and HC h both have a great influence on the color measurement error of the dyed polyester fabric sample. Under the key features and humidity zone h corresponding to the current i-th heating process, a primary adjustment strategy is carried out on the RGB value of the dyed polyester fabric sample;

[0147] When ΔEc i ≥0.5, and and HC h There is any one less than the corresponding reference judgment threshold, then the parameter weight less than the corresponding reference judgment threshold is set to qz1′, and 0 < qz1′ < 0.5; the specific value of qz1′ is determined by the expert group through experimental data; at this time or HC h has a great influence on the color measurement error of the dyed polyester fabric sample. Under the key features and humidity zone h corresponding to the current i-th heating process, a secondary adjustment strategy is carried out on the RGB value of the dyed polyester fabric sample;

[0148] When ΔEc i <0.5, and and HC h both are less than the corresponding reference judgment threshold, both a1 and a2 are set to 0.5, a1 = a2, indicating that and HC h have equal importance. At this time and HC hBoth have little impact on the color measurement error of the dyed polyester fabric sample. Under the key features and humidity zone h corresponding to the current i-th heating process, a three-level adjustment strategy is performed on the RGB values of the dyed polyester fabric sample;

[0149] Among them, the adjustment amplitudes of the first-level adjustment strategy, the second-level adjustment strategy, and the third-level adjustment strategy decrease in sequence;

[0150] During the j-th cooling process, the second measurement error calibration index is defined as ΔEc j , and the calculation formula is as follows:

[0151]

[0152] Among them, b1 and b2 are the weight coefficients of the corresponding parameters respectively, and b1 + b2 = 1, and the value ranges of b1 and b2 are both in the interval (0, 1);

[0153] μ2 is the second adjustment factor, 0.12 ≤ μ2 ≤ 0.71, and μ2 is used to ensure that the effective value range of ΔEc j is in the interval (0, 1); In this embodiment, μ2 = 0.2 is set;

[0154] Set The reference judgment thresholds of are q1 j respectively; and q1 j The value ranges are both set to (0. 1, 0.9); Set the effective value range of ΔEc j to be (0, 1);

[0155] During the j-th cooling process, when the RGB value measurement error corresponding to the color difference deviation degree of the dyed polyester fabric sample exceeds 30%, the value at this time is used as the value of q1 j ;

[0156] When ΔEc j ≥ 0.5, and HC h ≥ q2 h , b1 and b2 are both set to 0.5, b1 = b2, indicating that and HC h are equally important. At this time and HC h both have a large impact on the color measurement error of the dyed polyester fabric sample. Under the key features and humidity zone h corresponding to the current j-th cooling process, a four-level adjustment strategy is performed on the RGB values of the dyed polyester fabric sample;

[0157] When ΔEc j ≥ 0.5, and and HC hIf any one of them is less than the corresponding reference judgment threshold, the parameter weight less than the corresponding reference judgment threshold is set to qz2′, and 0 < qz2′ < 0.5; the specific value of qz2′ is determined by the expert group based on experimental data; at this time or HC h It has a great impact on the color measurement error of the dyed polyester fabric sample. Under the key features and humidity zone h corresponding to the current j - th cooling process, a five - level adjustment strategy is carried out on the RGB values of the dyed polyester fabric sample;

[0158] When ΔEc j <0.5, and and HC h are both less than the corresponding reference judgment threshold, both b1 and b2 are set to 0.5, b1 = b2, indicating that and HC h have the same degree of importance. At this time and HC h both have little impact on the color measurement error of the dyed polyester fabric sample. Under the key features and humidity zone h corresponding to the current j - th cooling process, a six - level adjustment strategy is carried out on the RGB values of the dyed polyester fabric sample;

[0159] Among them, the adjustment amplitudes of the four - level adjustment strategy, five - level adjustment strategy and six - level adjustment strategy decrease in turn.

[0160] Furthermore, the computer vision technology is used to extract the RGB values of the current dyed polyester fabric sample, and the error calibration of the RGB values of the current dyed polyester fabric sample is carried out according to different levels of adjustment strategies, specifically including:

[0161] Use a high - resolution camera to collect images of the dyed polyester fabric sample;

[0162] Use computer vision algorithms to extract the initial RGB values (R init , G init , B init ) of the current dyed polyester fabric sample;

[0163] The computer vision algorithm adopts any one of image processing algorithms, edge detection algorithms, and color clustering algorithms;

[0164] Ensure that the RGB values are within the range of 0 to 255 and perform initial normalization processing;

[0165] Determine whether the current dyed polyester fabric sample is in a heating process or a cooling process, determine the key features corresponding to the heating process or the cooling process, and the humidity zone where the current dyed polyester fabric sample is located; Denote the current heating process or cooling process as i1 and j1 respectively, and i1 ∈ {1, 2, …, n}; j1 ∈ {1, 2, …, m}; Denote the current humidity zone as h1, and h1 ∈ {1, 2, …, H};

[0166] Define the humidity correction coefficient of the current h1-th humidity zone as HC h1 ;

[0167] In the i1-th heating process, define the first measurement error calibration index as ΔEc i1 ;

[0168] In the i1-th heating process, define the adjusted first measurement error rate as

[0169] In the j1-th cooling process, define the second measurement error calibration index as ΔEc j1 ;

[0170] In the j1-th cooling process, define the adjusted second measurement error rate as

[0171] Define the adjustment formula for the value of the initial RGB value (R init , G init , B init ) as follows:

[0172]

[0173] where (R adj , G adj , B adj ) is the fine-tuned RGB value; When ΔRGB ve > 0, it means that during the ve-th temperature change trend process, the RGB value measurement error is in an increasing trend; At this time, it is necessary to judge the size relationship between the RGB value corresponding to the RGB value measurement error and the initial RGB value (R init , G init , B init ), and according to the judgment result of the size relationship, fine-tune the initial RGB value (R init , G init , B init ); Refer to the following same judgment method for the judgment result of the size relationship:

[0174] η1, η2, η3 are correction factors respectively, and the value ranges of η1, η2, η3 are all (0, 1);

[0175] The ve-th temperature change trend process represents the i1-th heating process or the j1-th cooling process;

[0176] When ΔRGB ve <0, indicating that the RGB value measurement error is decreasing during the ve-th temperature change trend; at this time, if the RGB value measurement error corresponds to an RGB value greater than the initial RGB value (R init ,G init ,B init ) value, then reduce the initial RGB value (R init ,G init ,B init ) value; F adj is the fine-tuning factor; if the RGB value measurement error corresponds to an RGB value less than the initial RGB value (R init ,G init ,B init ) value, then through F adj To increase the initial RGB value (R init ,G init ,B init ) value;

[0177] Reduce the initial RGB value (R init ,G init ,B init ) value corresponding to F adj for

[0178] Increase the initial RGB value (R init ,G init ,B init ) value corresponding to F adj for

[0179] If ΔRGB ve =0, then F adj =1;

[0180] When ΔEc i1 ≥0.5, and HC h1 ≥q2 h1 When the first-level adjustment strategy corresponds to η1, η2, and η3,

[0181] When ΔEc i1 ≥0.5, and and HC h1 When any one of them is less than the corresponding benchmark judgment threshold, the η1, η2, and η3 corresponding to the secondary adjustment strategy are

[0182] When ΔEci1 <0.5, and and HC h1 when both are less than the corresponding reference judgment threshold, η1, η2, and η3 corresponding to the three-level adjustment strategy are respectively

[0183] When ΔEc j1 ≥0.5, and HC h1 ≥q2 h1 at this time, η1, η2, and η3 corresponding to the four-level adjustment strategy are respectively

[0184] When ΔEc j1 ≥0.5, and and HC h1 when any one of them is less than the corresponding reference judgment threshold, η1, η2, and η3 corresponding to the five-level adjustment strategy are respectively

[0185] When ΔEc j1 <0.5, and and HC h1 when both are less than the corresponding reference judgment threshold, η1, η2, and η3 corresponding to the six-level adjustment strategy are respectively

[0186] Ensure that the fine-tuned RGB values (R adj , G adj , B adj ) are still within the range of 0 to 255.

[0187] Example 2:

[0188] Please refer to Figure 2 , a color measurement system for dyed polyester fabrics based on computer vision, which is used to execute the color measurement method for dyed polyester fabrics based on computer vision, including:

[0189] Predetermined interval setting module: used to collect the temperature change data and humidity change data of the dyed polyester fabric sample under the current production conditions, and set the temperature range determined by the temperature change data as the predetermined temperature interval; set the humidity range determined by the humidity change data as the predetermined humidity interval;

[0190] Key feature extraction module: used to apply uniform dynamic temperature change processing to the dyed polyester fabric sample within the predetermined temperature interval, and capture the dynamic thermal imaging characteristics and RGB values of the dyed polyester fabric sample during the heating or cooling process in real time, and extract the key features corresponding to each heating or cooling process from these dynamic thermal imaging characteristics through computer vision technology, and the key features include the average temperature gradient and the average heat diffusion rate;

[0191] Measurement error rate calculation module: used to calculate the first measurement error rate of the dyed polyester fabric sample between the current heating process and the previous and next heating processes for each key feature during the heating process;

[0192] During the cooling process, for each key feature, calculate the second measurement error rate of the dyed polyester fabric sample between the current cooling process and the previous and next cooling processes;

[0193] Humidity correction coefficient generation module: used to divide a predetermined humidity range into several continuous humidity sub - ranges, collect RGB value measurement error data of the dyed polyester fabric sample under each humidity sub - range, and analyze these RGB value measurement error data to generate a humidity correction coefficient, which is used to evaluate the influence degree of each humidity sub - range on the visual measurement color difference error of the dyed polyester fabric sample;

[0194] Measurement error calibration index generation module: used to determine whether the current moment of the dyed polyester fabric sample is in the heating process. If it is in the heating process, conduct a combined analysis of the first measurement error rate and the humidity correction coefficient to generate the first measurement error calibration index;

[0195] If it is in the cooling process, conduct a combined analysis of the second measurement error rate and the humidity correction coefficient to generate the second measurement error calibration index;

[0196] The first measurement error calibration index and the second measurement error calibration index can evaluate the color measurement error degree of the dyed polyester fabric sample according to the current key features and humidity sub - ranges of the dyed polyester fabric sample;

[0197] Both the first measurement error calibration index and the second measurement error calibration index can be used to generate different - level adjustment strategies for error calibration of the RGB values of the current dyed polyester fabric sample;

[0198] Error calibration module: used to extract the RGB values of the current dyed polyester fabric sample using computer vision technology and perform error calibration on the RGB values of the current dyed polyester fabric sample according to different - level adjustment strategies.

[0199] The above formulas are all dimensionless and take their numerical calculations. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by technicians in the field according to the actual situation.

[0200] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any 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 realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0201] 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. They may be located in one place or 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.

[0202] As described above, the specific implementation manners of the present application are only described, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all such changes or substitutions should be covered by the protection scope of the present application.

Claims

1. A color measurement method for dyed polyester fabric based on computer vision, characterized in that: The specific steps include: Step S1: collecting temperature change data and humidity change data of a dyed polyester fabric sample under current production conditions, and setting the temperature range determined by the temperature change data as a predetermined temperature interval; setting the humidity range determined by the humidity change data as a predetermined humidity interval; Step S2: Applying a uniform dynamic temperature change process to the dyed polyester fabric sample within a predetermined temperature range, and capturing the dynamic thermal imaging characteristics and RGB values of the dyed polyester fabric sample during the heating or cooling process in real time. Using computer vision technology, key features corresponding to each heating or cooling process are extracted from these dynamic thermal imaging characteristics. The key features include the average temperature gradient and the average thermal diffusion rate. Step S3: During the heating process, for each key feature, based on the visual measurement color difference errors of the dyed polyester fabric sample between the current, previous, and next heating processes, calculating a first measurement error rate indicating the degree of visual measurement color difference error in the current heating process; During the cooling process, for each key feature, calculating a second measurement error rate indicating the degree of visual measurement color difference error of the current cooling process based on visual measurement color difference errors of the dyed polyester fabric sample between the current, previous, and next cooling processes; Step S4: Dividing the predetermined humidity range into a plurality of continuous humidity zones, collecting RGB value measurement error data of the dyed polyester fabric sample in each humidity zone, and analyzing the RGB value measurement error data to generate a humidity correction coefficient. The humidity correction coefficient is used to assess the degree of influence of each humidity zone on the visual measurement color difference error of the dyed polyester fabric sample; Step S5: determining whether the dyed polyester fabric sample is in a heating process at the current moment; if it is in a heating process, combining and analyzing the first measurement error rate and the humidity correction coefficient to generate a first measurement error calibration index; If it is a cooling process, the second measurement error rate and the humidity correction coefficient are combined and analyzed to generate a second measurement error calibration index; The first measurement error calibration index and the second measurement error calibration index can both be used to generate different levels of adjustment strategies for error calibration of the RGB value of the current dyed polyester fabric sample; Step S6: extracting the RGB value of the current dyed polyester fabric sample using computer vision technology, and performing error calibration on the RGB value of the current dyed polyester fabric sample according to different levels of adjustment strategies.

2. The computer vision-based color measurement method for dyed polyester fabric according to claim 1, characterized in that: The acquisition of the predetermined temperature range and the predetermined humidity range specifically includes: Record the real-time temperature change data of dyed polyester fabric samples under current production conditions; and record the temperature change curve; Analyze the temperature change curve, determine its maximum and minimum values, and set a predetermined temperature range based on these values; And set the predetermined temperature range as [T init , T final ], where T init , T final are the minimum and maximum temperature in the temperature change curve respectively; Record the real-time humidity change data of dyed polyester fabric samples under current production conditions; and record the humidity change curve; Analyze the humidity change curve, determine its maximum and minimum values, and set the predetermined humidity range as [SD init , SD final ], where SD init , SD final They are the minimum and maximum humidity values in the humidity change curve respectively.

3. The computer vision-based color measurement method for dyed polyester fabric according to claim 2, characterized in that: Extract key features from dynamic thermal imaging characteristics, including: 1.1) For dynamic temperature change processing: Based on the predetermined temperature range [T init , T final ], the heating rate of the heating process is defined as CSv; The cooling rate of the cooling process is defined as CJv; Among them, ΔT1 and ΔT2 are the temperature changes in the heating process and cooling process per unit time t respectively; 1.11) Mark the number of times each heating process occurs to form a heating process number sequence set {1, 2, ..., i, ..., n}; 1.12) Each cooling process is marked to form a cooling process number sequence set {1, 2, ..., j, ..., m}, where j represents the index of the jth cooling process and m represents the total number of cooling processes; 1.2) For dynamic thermal imaging feature capture: The acquisition frequency of thermal imaging is defined as f. During the heating or cooling process, thermal imaging of the dyed polyester fabric sample is captured at the acquisition frequency f. 1.3) Extract key features corresponding to each heating or cooling process: Perform median filtering to denoise the captured thermal images; During the heating or cooling process, the average temperature gradient is defined as The average thermal diffusion rate is defined as The measurement error of the RGB value of the dyed polyester fabric sample corresponding to the i-th heating process is defined as ΔRGB i =(ΔR i ,ΔG i ,ΔB i );and ΔRGB i The corresponding key features are expressed in a mapping manner as follows: where f i,R ,f i,G ,f i,B Map the corresponding features to ΔRGB i The function of each component in (ΔR i ,ΔG i ,ΔB i ) represents the measurement error of the intensity of each color channel in the RGB value during the i-th heating process; The measurement error of the RGB value of the dyed polyester fabric sample corresponding to the jth cooling process is defined as ΔRGB j =(ΔR j ,ΔG j ,ΔB j ), and ΔRGB j The corresponding key features are expressed in a mapping manner as follows: where f j,R ,f j,G ,f j,B Map the corresponding features to ΔRGB j The function of each component in (ΔR j ,ΔG j ,ΔB j ) represents the measurement error of the intensity of each color channel in the RGB value during the j-th cooling process.

4. The computer vision-based color measurement method for dyed polyester fabric according to claim 3, characterized in that: The first measurement error rate and the second measurement error rate specifically include: The first measurement error rate corresponding to the i-th heating process is defined as The calculation formula is as follows: Among them, ΔRGB i-1 and ΔRGB i+1 are the RGB value measurement errors corresponding to the previous heating process and the next heating process respectively; Respectively represent the measurement error rates of the changes in R, G, and B values during the i-th heating process; Using Sigmoid function, The output value is limited to the range (0,1), and the specific form is: in is the adjusted first measurement error rate; The closer the value is to 1, the higher the rate of change of the RGB measurement error in the i-th heating process; The closer the value is to 0, the lower the rate of change of the RGB measurement error in the i-th heating process; The second measurement error rate corresponding to the jth cooling process is defined as The calculation formula is as follows: Among them, ΔRGB j-1 and ΔRGB j+1 are the RGB value measurement errors corresponding to the previous cooling process and the next cooling process respectively; Respectively represent the measurement error rates of the changes in R, G, and B values during the jth cooling process; Using Sigmoid function, The output value is limited to the range (0,1), and the specific form is: in is the adjusted second measurement error rate; The closer the value is to 1, the higher the rate of change of the RGB measurement error in the jth cooling process; The closer the value is to 0, the lower the rate of change of the RGB measurement error in the j-th cooling process.

5. The computer vision-based color measurement method for dyed polyester fabric according to claim 4, characterized in that: The predetermined humidity range is divided into several continuous humidity zones. The RGB value measurement error data of the dyed polyester fabric sample in each humidity zone is collected. These RGB value measurement error data are analyzed to generate humidity correction coefficients. The humidity correction coefficients are used to evaluate the degree of influence of each humidity zone on the visual measurement color difference error of the dyed polyester fabric sample. Specifically, the humidity correction coefficients include: The preset humidity interval [SD init , SD final ] is divided into H consecutive humidity partitions of the same length, and these humidity partitions are recorded as {1, 2, ..., h, ..., H}, where h represents the index of the hth humidity partition and H is the total number of humidity partitions; The number of times the RGB value measurement error data is collected in each humidity partition is recorded as {1, 2, ..., u, ..., U}, where u represents the index of the u-th RGB value measurement error collection number, and U is the total number of collections; Define the humidity correction coefficient of the hth humidity zone as HC h , the calculation formula is as follows: Setting HC h The valid value range is (0,1); ΔRGB h,u It represents the RGB value measurement error of the hth humidity partition under the uth RGB value measurement error data; When HC h The closer it is to 1, the greater the change rate of the RGB measurement error of the hth humidity partition; When HC h The closer it is to 0, the smaller the change rate of the RGB measurement error of the hth humidity partition is.

6. The computer vision-based color measurement method for dyed polyester fabric according to claim 5, characterized in that: Acquiring the first measurement error calibration index and the second measurement error calibration index, and performing error calibration on the RGB value of the current dyed polyester fabric sample, specifically includes: Compare the temperature of the dyed polyester fabric sample at the current moment with the temperature at the previous moment. If the temperature value increases, it is a heating process. Match the current temperature with the corresponding temperature change intervals in the heating process number sequence set {1, 2, ..., i, ..., n} to determine the index i of the heating process number. If the temperature value decreases, it is a cooling process. The current temperature value is matched with the corresponding temperature change intervals in the cooling process number sequence set {1, 2, ..., j, ..., m} to determine the index j of the cooling process number. In the i-th heating process, the first measurement error calibration index is defined as ΔEc i , the calculation formula is as follows: Where a1 and a2 are weight coefficients of the corresponding parameters, and a1+a2=1, and the value range of a1 and a2 is the interval (0,1); μ1 is the first adjustment factor, 0.11≤μ1≤0.65, μ1 is used to ensure ΔEc i The valid value range of is the interval (0,1); set up and HC h The benchmark judgment thresholds are q1 i and q2 h ; and q1 i and q2 h The value range is set to (0.1, 0.9); set ΔEc i The valid value range of is (0,1); When ΔEc i ≥0.5, and HC h ≥q2 h When a1 and a2 are both set to 0.5, a1=a2, indicating and HC h are equally important, then and HC h Both have a great influence on the color measurement error of the dyed polyester fabric sample. Under the key features and humidity partition h corresponding to the current i-th heating process, the RGB value of the dyed polyester fabric sample is adjusted in a first-level strategy; When ΔEci≥0.5, and and HC h When any one of them is less than the corresponding benchmark judgment threshold, or HC h The color measurement error of the dyed polyester fabric sample has a great influence. Under the key features and humidity partition h corresponding to the current i-th heating process, a secondary adjustment strategy is implemented for the RGB value of the dyed polyester fabric sample; When ΔEc i <0.5, and and HC h When both are less than the corresponding benchmark judgment threshold, a1 and a2 are both set to 0.5, a1=a2, indicating that and HC h are equally important, then and HC h The influence of both on the color measurement error of the dyed polyester fabric sample is small. Under the key features and humidity partition h corresponding to the current i-th heating process, the RGB value of the dyed polyester fabric sample is adjusted in three levels; In the jth cooling process, the second measurement error calibration index is defined as ΔEc j , the calculation formula is as follows: Where b1 and b2 are weight coefficients of the corresponding parameters, and b1+b2=1, and the value range of b1 and b2 is the interval (0,1); μ2 is the second adjustment factor, 0.12≤μ2≤0.71, μ2 is used to ensure ΔEc j The valid value range of is the interval (0,1); set up The benchmark judgment thresholds are q1 j ; and q1 j The value range is set to (0.1, 0.9); set ΔEc j The valid value range of is (0,1); When ΔEc j ≥0.5, and HC h ≥q2 h When b1 and b2 are both set to 0.5, b1=b2, indicating and HC h are equally important, then and HC h Both have a great influence on the color measurement error of the dyed polyester fabric sample. Under the key features and humidity partition h corresponding to the current j-th cooling process, a four-level adjustment strategy is performed on the RGB value of the dyed polyester fabric sample; When ΔEc j ≥0.5, and and HC h When any one of them is less than the corresponding benchmark judgment threshold, or HC h The color measurement error of the dyed polyester fabric sample has a great influence. Under the key features and humidity partition h corresponding to the current j-th cooling process, the RGB value of the dyed polyester fabric sample is adjusted in five levels; When ΔEc j <0.5, and and HC h When both are less than the corresponding benchmark judgment threshold, b1 and b2 are both set to 0.5, b1=b2, indicating that and HC h are equally important, then and HC h The color measurement error of the dyed polyester fabric sample is slightly affected. Under the key features and humidity partition h corresponding to the current j-th cooling process, the RGB value of the dyed polyester fabric sample is adjusted in six levels. The adjustment ranges of the first-level adjustment strategy, the second-level adjustment strategy and the third-level adjustment strategy decrease in sequence; The adjustment ranges of the fourth-level adjustment strategy, the fifth-level adjustment strategy and the sixth-level adjustment strategy decrease in turn.

7. A computer vision-based color measurement system for dyed polyester fabrics, characterized by: The system is used to perform the computer vision-based color measurement method for dyed polyester fabric according to any one of claims 1 to 6, comprising: Predetermined interval setting module: used to collect temperature change data and humidity change data of the dyed polyester fabric sample under the current production conditions, and set the temperature range determined by the temperature change data as the predetermined temperature interval; set the humidity range determined by the humidity change data as the predetermined humidity interval; Key feature extraction module: This module is used to apply uniform dynamic temperature change processing to the dyed polyester fabric sample within a predetermined temperature range, and capture the dynamic thermal imaging characteristics and RGB values of the dyed polyester fabric sample in real time during the heating or cooling process. The module then uses computer vision technology to extract the key features corresponding to each heating or cooling process from these dynamic thermal imaging characteristics. These key features include the average temperature gradient and the average thermal diffusion rate. a measurement error rate calculation module configured to calculate, for each key feature during the heating process, a first measurement error rate representing the degree of visual measurement color difference error in the current heating process based on the visual measurement color difference error of the dyed polyester fabric sample between the current, previous, and next heating processes; During the cooling process, for each key feature, calculating a second measurement error rate indicating the degree of visual measurement color difference error of the current cooling process based on visual measurement color difference errors of the dyed polyester fabric sample between the current, previous, and next cooling processes; Humidity correction coefficient generation module: used to divide the predetermined humidity range into several continuous humidity zones, collect the RGB value measurement error data of the dyed polyester fabric sample in each humidity zone, analyze these RGB value measurement error data, and generate humidity correction coefficients. The humidity correction coefficients are used to evaluate the degree of influence of each humidity zone on the visual measurement color difference error of the dyed polyester fabric sample; A measurement error calibration index generation module is used to determine whether the dyed polyester fabric sample is in a heating process at the current moment. If it is a heating process, the first measurement error rate and the humidity correction coefficient are combined and analyzed to generate a first measurement error calibration index. If it is a cooling process, the second measurement error rate and the humidity correction coefficient are combined and analyzed to generate a second measurement error calibration index; The first measurement error calibration index and the second measurement error calibration index can both be used to generate different levels of adjustment strategies for error calibration of the RGB value of the current dyed polyester fabric sample; Error calibration module: used to extract the RGB value of the current dyed polyester fabric sample using computer vision technology, and perform error calibration on the RGB value of the current dyed polyester fabric sample according to different levels of adjustment strategies.

Images