Extraction and comparison method of fabric colors

Abstract

The invention discloses a fabric color extraction and comparison method, which comprises the following steps of: S1, installing and opening a program by using a mobile phone, and turning on a power supply of handheld equipment; s2, automatically searching a handheld device near a link by the program; s3, selecting a colorimetric function, aligning a handheld device with the source fabric, and pressing an acquisition button; s4, the handheld device collects spectral data and transmits the spectral data to the mobile phone through Bluetooth; s5, the mobile phone processes the spectral data, calculates an RGB color value according to a spectral color library, and feeds back and displays the RGB color value on a mobile phone screen; s6, the handheld device is aligned with the target fabric and presses an acquisition button; s7, collecting data again, and feeding back and displaying the data on the screen of the mobile phone; step S8, comparing the two times of data; and S9, feeding back a conclusion on a screen, and realizing quantified color data and preview, so that a method for accurately comparing the colors of the fabric with reference significance is generated, a practitioner obtains a reliable result by using the scheme, the working efficiency is improved, and the working value is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of clothing production, and in particular to a method for extracting and comparing fabric colors. Background Art

[0002] Fabric selection is a crucial step in garment design and production, and the most important aspect of this selection is the contrast and confirmation of fabric colors. Traditionally, designers relied on experience or laboratory equipment for this process, which has drawbacks such as lighting effects, operational lag, and lack of portability. Existing technical solutions emphasize the importance of designers while ignoring the accuracy of the results. Color extraction and contrast should be regarded as the technical embodiment of basic professions to ensure the controllability of the clothing design process: Comparison with the naked eye: experienced professionals with normal vision can judge whether the color difference meets the requirements through observation and comparison. This requires a high level of color recognition and perception ability of the human eye, which is easily affected by light sources and the observer's psychological and physiological factors. It cannot be accurately described or measured, and can only be a vague judgment. It varies from person to person, is inaccurate, and there is no standard answer. Gray card tools are used to determine whether the color difference level meets the requirements. They are mainly used for color fastness evaluation. Although they provide a grade reference, they are essentially compared with the naked eye and are also affected by light sources, psychological and physiological factors of the observer. Colorimeter is a professional colorimeter used in the laboratory to sample and compare the standard sample and test sample to accurately give the color difference value. Generally, this kind of equipment is cumbersome to use and usually requires multiple steps. Setting complex parameters (light source, field of view angle, color gamut space, color difference formula, etc.) is prone to errors and has high requirements for operators. It is also expensive, not portable, and has a low cost performance.

[0003] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0004] In view of the problems in the related art, the present invention proposes a method for extracting and comparing fabric colors to overcome the above technical problems existing in the existing related art.

[0005] To this end, the specific technical solutions adopted in the present invention are as follows: A method for extracting and comparing fabric colors comprises the following steps: Step S1: Use your mobile phone to install and open this program, and turn on the power of the handheld device; Step S2: This program automatically searches for and links to nearby handheld devices; Step S3: Select the colorimetric function, aim the handheld device at the source fabric and press the capture button; Step S4: The handheld device collects spectral data and transmits it to the mobile phone via Bluetooth; Step S5: The mobile phone processes the spectral data, calculates the RGB color value by comparing it with the spectral color library, and displays the feedback on the mobile phone screen; Step S6: Aim the handheld device at the target fabric and press the capture button; Step S7: collect data again and display the feedback on the mobile phone screen; Step S8: Compare the two data and use the CMC formula to calculate the color difference of the left, center, and right positions of the same sampling area, and take the average color difference value as a reference for measurement stability; Step S9: Feedback the conclusion on the screen. If ΔE≤0.1, it is a match. A natural language evaluation is given according to the color difference level, such as reddish, dark, etc.

[0006] As a further solution of the present invention, the processing steps for processing the spectral data in step S5 are: Data acquisition: Acquire spectral data, which can be performed by spectrometer equipment. Spectral data usually uses wavelength and light intensity as the main parameters; Pretreatment; Calibration: Perform instrument calibration to eliminate errors and drifts introduced by the instrument; Denoising: remove possible noise to improve data quality; Interpolation: For unevenly sampled data, interpolation may be required to obtain uniform data points; Baseline correction: If there is baseline drift, baseline correction is performed to restore the true characteristics of the spectrum; Feature extraction; Peak detection: Identifying and locating peaks in a spectrum, which is helpful for analyzing specific compounds or features; Integration region: For a specific spectral region, it may be necessary to calculate the integral value or other statistical features; Data analysis; Quantitative analysis: Use calibration curves or other methods to correlate spectral data with the concentration or content of the target substance; Qualitative analysis: Classify, identify or authenticate samples based on spectral characteristics; Pattern recognition: Use machine learning or pattern recognition techniques to perform classification, clustering, or regression analysis on spectral data; Interpretation of results; Spectrogramming: Displaying spectral data in a graphical form, usually as a graph or bar chart of wavelength versus light intensity; Result interpretation: explaining the characteristics, composition or other information of the sample based on the analysis results; Reporting and Application; Result reporting: Write reports or generate data documents to present the analysis results to relevant personnel; Application: The analysis results can be used in practical applications, such as color calibration and comparison in the field of textile printing and dyeing.

[0007] As a further solution of the present invention, the calculation formula of RGB in step S5 is: R=∫S(λ)·r(λ)dλ G = ∫ S (λ) · g (λ) dλ B = ∫ S (λ) · b (λ) dλ in: R, G, and B represent the intensity values of the red, green, and blue channels respectively; S(λ) is the spectral distribution, which represents the intensity of light at different wavelengths (λ); r(λ), g(λ), and b(λ) are the response curves of the red, green, and blue channels, respectively.

[0008] As a further solution of the present invention, the CMC formula in step S8 is: x' / K2a2+y' / K2b2=1 (2) Where: x'=Δxcosθ+Δysinθ y'=-Δxsinθ+Δycosθ; Δx and Δy represent the errors relative to the target coordinate values x and y, g11, g12, and g22 represent the coefficients determined by each target value, K is the color tolerance, and the standard color grade of fabrics is divided into five levels: Level 5: Color difference requirement is 0, tolerance requirement is 0.2; Level 4-5: color difference requirement is 0.8, tolerance requirement is ±0.2; Level 4: color difference requirement is 1.7, tolerance requirement is ±0.3; Level 3-4: color difference requirement is 2.5, tolerance requirement is ±0.35; Level 3: color difference requirement is 3.4, tolerance requirement is ±0.4; Level 2-3: color difference requirement is 4.8, tolerance requirement is ±0.5; Level 2: color difference requirement is 6.8, tolerance requirement is ±0.6; Level 1-2: color difference requirement is 9.6, tolerance requirement is ±0.7; Level 1: The color difference requirement is 13.6 and the tolerance requirement is ±1.0.

[0009] As a further solution of the present invention, the handheld device in step S4 is cylindrical, with a body about the size of a lipstick, and the device uses a D / 8° integrating sphere + full-band LED light source to collect SCI (including specular reflected light) spectral data.

[0010] Beneficial effects of the present invention This solution utilizes color extraction, spectral color library, color difference comparison and other technologies to achieve quantitative color data and preview, thereby producing a reference-worthy method for accurate fabric color comparison. Practitioners use this solution to obtain reliable results, improve work efficiency and enhance work value. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 The present invention is a flowchart of a method for extracting and comparing fabric colors. DETAILED DESCRIPTION

[0012] According to an embodiment of the present invention, a method for extracting and comparing fabric colors is provided.

[0013] A method for extracting and comparing fabric colors according to an embodiment of the present invention includes the following steps: Step S1: Use your mobile phone to install and open this program, and turn on the power of the handheld device; Step S2: This program automatically searches for and links to nearby handheld devices; Step S3: Select the colorimetric function, aim the handheld device at the source fabric and press the capture button; Step S4: The handheld device collects spectral data and transmits it to the mobile phone via Bluetooth; Step S5: The mobile phone processes the spectral data, calculates the RGB color value by comparing it with the spectral color library, and displays the feedback on the mobile phone screen; Step S6: Aim the handheld device at the target fabric and press the capture button; Step S7: collect data again and display the feedback on the mobile phone screen; Step S8: Compare the two data and use the CMC formula to calculate the color difference of the left, center, and right positions of the same sampling area, and take the average color difference value as a reference for measurement stability; Step S9: Feedback the conclusion on the screen. If ΔE≤0.1, it is a match. A natural language evaluation is given according to the color difference level, such as reddish, dark, etc.

[0014] In an optional embodiment, the processing step of the spectral data in step S5 is: Data acquisition: Acquire spectral data, which can be performed by spectrometer equipment. Spectral data usually uses wavelength and light intensity as the main parameters; Pretreatment; Calibration: Perform instrument calibration to eliminate errors and drifts introduced by the instrument; Denoising: remove possible noise to improve data quality; Interpolation: For unevenly sampled data, interpolation may be required to obtain uniform data points; Baseline correction: If there is baseline drift, baseline correction is performed to restore the true characteristics of the spectrum; Feature extraction; Peak detection: Identifying and locating peaks in a spectrum, which is helpful for analyzing specific compounds or features; Integration region: For a specific spectral region, it may be necessary to calculate the integral value or other statistical features; Data analysis; Quantitative analysis: Use calibration curves or other methods to correlate spectral data with the concentration or content of the target substance; Qualitative analysis: Classify, identify or authenticate samples based on spectral characteristics; Pattern recognition: Use machine learning or pattern recognition techniques to perform classification, clustering, or regression analysis on spectral data; Interpretation of results; Spectrogramming: Displaying spectral data in a graphical form, usually as a graph or bar chart of wavelength versus light intensity; Result interpretation: explaining the characteristics, composition or other information of the sample based on the analysis results; Reporting and Application; Result reporting: Write reports or generate data documents to present the analysis results to relevant personnel; Application: The analysis results can be used in practical applications, such as color calibration and comparison in the field of textile printing and dyeing.

[0015] In this embodiment: processing spectral data generally involves multiple steps, which may vary depending on the specific application and data type. Disclosed in the present invention are general steps for processing spectral data under normal circumstances, and it is not a strictly linear process. Usually, cross-talk and adjustment are performed according to specific circumstances. Processing spectral data requires combining knowledge and technology in related fields to ensure the accuracy and reliability of the analysis results.

[0016] In an optional embodiment, the calculation formula of RGB in step S5 is: R=∫S(λ)·r(λ)dλ G = ∫ S (λ) · g (λ) dλ B = ∫ S (λ) · b (λ) dλ in: R, G, and B represent the intensity values of the red, green, and blue channels respectively; S(λ) is the spectral distribution, which represents the intensity of light at different wavelengths (λ); r(λ), g(λ), and b(λ) are the response curves of the red, green, and blue channels, respectively.

[0017] In an optional embodiment, the CMC formula in step S8 is: x' / K2a2+y' / K2b2=1 (2) Where: x'=Δxcosθ+Δysinθ y'=-Δxsinθ+Δycosθ; Δx and Δy represent the errors relative to the target coordinate values x and y, g11, g12, and g22 represent the coefficients determined by each target value, K is the color tolerance, and the standard color grade of fabrics is divided into five levels: Level 5: Color difference requirement is 0, tolerance requirement is 0.2; Level 4-5: color difference requirement is 0.8, tolerance requirement is ±0.2; Level 4: color difference requirement is 1.7, tolerance requirement is ±0.3; Level 3-4: color difference requirement is 2.5, tolerance requirement is ±0.35; Level 3: color difference requirement is 3.4, tolerance requirement is ±0.4; Level 2-3: color difference requirement is 4.8, tolerance requirement is ±0.5; Level 2: color difference requirement is 6.8, tolerance requirement is ±0.6; Level 1-2: color difference requirement is 9.6, tolerance requirement is ±0.7; Level 1: The color difference requirement is 13.6 and the tolerance requirement is ±1.0.

[0018] In an optional embodiment, the handheld device in step S4 is cylindrical, with a body about the size of a lipstick, and the device uses a D / 8° integrating sphere + a full-band LED light source to collect spectral data of SCI (including specular reflected light).

[0019] The present invention is applicable to: Textile printing and dyeing color difference detection: In the textile printing and dyeing process, all links that require color difference detection include color difference between batches, cylinder difference, head and tail of the same batch, left and right color difference, etc. Color difference detection for digital printing: helps digital printers adjust colors and detect color differences on printed products (the equipment is used for automatic color calibration using the RIP software developed by our company); Brands purchase batch colors: Brands usually confirm the color difference acceptance standards and test the batch colors of fabrics provided by suppliers; Designer and colorist tools: It can be used as a color picking tool for designers to help them collect colors. It can also be used as a colorist tool to provide a reference for color difference and color cast, helping colorists to adjust colors better and faster.

[0020] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for extracting and comparing fabric colors, characterized in that: The following steps are involved: Step S1: Use your mobile phone to install and open this program, and turn on the power of the handheld device; Step S2: This program automatically searches for and links to nearby handheld devices; Step S3: Select the colorimetric function, aim the handheld device at the source fabric and press the capture button; Step S4: The handheld device collects spectral data and transmits it to the mobile phone via Bluetooth; Step S5: The mobile phone processes the spectral data, calculates the RGB color value by comparing it with the spectral color library, and displays the feedback on the mobile phone screen; Step S6: Aim the handheld device at the target fabric and press the capture button; Step S7: collect data again and display the feedback on the mobile phone screen; Step S8: Compare the two data and use the CMC formula to calculate the color difference of the left, center, and right positions of the same sampling area, and take the average color difference value as a reference for measurement stability; Step S9: Feedback the conclusion on the screen. If ΔE≤0.1, it is a match. A natural language evaluation is given according to the color difference level, such as reddish, dark, etc.

2. The method for extracting and comparing fabric colors according to claim 1, wherein: The processing steps for spectral data processing in step S5 are: Data acquisition: Acquire spectral data, which can be performed by spectrometer equipment. Spectral data usually uses wavelength and light intensity as the main parameters; Pretreatment; Calibration: Perform instrument calibration to eliminate errors and drifts introduced by the instrument; Denoising: remove possible noise to improve data quality; Interpolation: For unevenly sampled data, interpolation may be required to obtain uniform data points; Baseline correction: If there is baseline drift, baseline correction is performed to restore the true characteristics of the spectrum; Feature extraction; Peak detection: Identifying and locating peaks in a spectrum, which is helpful for analyzing specific compounds or features; Integration region: For a specific spectral region, it may be necessary to calculate the integral value or other statistical features; Data analysis; Quantitative analysis: Use calibration curves or other methods to correlate spectral data with the concentration or content of the target substance; Qualitative analysis: Classify, identify or authenticate samples based on spectral characteristics; Pattern recognition: Use machine learning or pattern recognition techniques to perform classification, clustering, or regression analysis on spectral data; Interpretation of results; Spectrogramming: Displaying spectral data in a graphical form, usually as a graph or bar chart of wavelength versus light intensity; Result interpretation: explaining the characteristics, composition or other information of the sample based on the analysis results; Reporting and Application; Result reporting: Write reports or generate data documents to present the analysis results to relevant personnel; Application: The analysis results can be used in practical applications, such as color calibration and comparison in the field of textile printing and dyeing.

3. The method for extracting and comparing fabric colors according to claim 1, wherein: The calculation formula of RGB in step S5 is: R=∫S(λ)·r(λ)dλ G = ∫ S (λ) · g (λ) dλ B = ∫ S (λ) · b (λ) dλ in: R, G, and B represent the intensity values of the red, green, and blue channels respectively; S(λ) is the spectral distribution, which represents the intensity of light at different wavelengths (λ); r(λ), g(λ), and b(λ) are the response curves of the red, green, and blue channels, respectively.

4. The method for extracting and comparing fabric colors according to claim 1, wherein: The CMC formula in step S8 is: x' / K2a2+y' / K2b2=1 (2) Where: x'=Δxcosθ+Δysinθ y'=-Δxsinθ+Δycosθ; Δx and Δy represent the errors relative to the target coordinate values x and y, g11, g12, and g22 represent the coefficients determined by each target value, K is the color tolerance, and the standard color grade of fabrics is divided into five levels: Level 5: Color difference requirement is 0, tolerance requirement is 0.2; Level 4-5: color difference requirement is 0.8, tolerance requirement is ±0.2; Level 4: color difference requirement is 1.7, tolerance requirement is ±0.3; Level 3-4: color difference requirement is 2.5, tolerance requirement is ±0.35; Level 3: color difference requirement is 3.4, tolerance requirement is ±0.4; Level 2-3: color difference requirement is 4.8, tolerance requirement is ±0.5; Level 2: color difference requirement is 6.8, tolerance requirement is ±0.6; Level 1-2: color difference requirement is 9.6, tolerance requirement is ±0.7; Level 1: The color difference requirement is 13.6 and the tolerance requirement is ±1.

0.

5. The method for extracting and comparing fabric colors according to claim 1, wherein: In step S4, the handheld device is cylindrical, with a body about the size of a lipstick, and uses a D / 8° integrating sphere + full-band LED light source to collect spectral data of SCI (including specular reflection light).

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