Sock dyeing uniformity judgment method and system and storage medium

Abstract

The invention relates to the technical field of sock dyeing identification, in particular to a sock dyeing uniformity judgment method and system and a storage medium. Randomly sampling to obtain an initial sock sample; performing region division and shooting on the initial sock sample to obtain an initial sample image set; constructing a color difference recognition model to recognize the initial sample image set to obtain initial difference data; performing a stretching test on the initial sock sample to obtain a stretched sock sample and a stretched sample image set; identifying the stretching sample image set to obtain stretching difference data; performing a motion test on the stretched sock sample to obtain a motion sample image set; identifying the motion sample image set to obtain motion difference data; and constructing a dyeing uniformity identification model, and identifying the initial difference data, the stretching difference data and the motion difference data to obtain a sock dyeing uniformity coefficient. According to the method, the dyeing uniformity of the socks is accurately graded through the dyeing uniformity coefficient of the socks.

Description

Technical Field

[0001] The present invention relates to the technical field of sock dyeing identification, and in particular to a sock dyeing uniformity determination method, system and storage medium. Background Art

[0002] Socks are a kind of clothing worn on the feet, usually made of soft fabric materials. They can reduce the direct friction between shoes and feet, protect feet from injuries, absorb sweat from feet, and reduce the risk of bacterial growth. They have gradually become a must-have item in daily life. With the continuous development of sock production technology, their types have become increasingly complex and diverse, including short socks, mid-tube socks, long socks, and various functional socks to meet the differentiated needs of consumers.

[0003] In the production of socks, the dyeing process is an important component; due to factors such as raw materials, dyeing process, equipment performance, operating factors and external environment, the dyeing uniformity of socks will be affected to a certain extent, making the actual dyeing effect of socks inconsistent with the color distribution in the design plan, resulting in uneven dyeing. This will not only affect the quality of the product, but may also have a negative effect on consumer experience and brand image.

[0004] Therefore, it is necessary to study the dyeing uniformity of socks products in order to strengthen the quality control of socks production and improve dyeing uniformity and product quality.

[0005] With the development of image recognition technology, it has gradually been applied to the identification of dyeing uniformity of socks. By acquiring images of sock products and performing image recognition, the dyeing uniformity is measured to improve efficiency. However, image recognition technology focuses on identifying the color distribution on the surface of socks in the initial state, and does not take into account the changes in fiber density and fiber arrangement caused by the stretching of the fabric when the socks are worn, resulting in inaccurate recognition accuracy.

[0006] To this end, a method, system and storage medium for determining uniformity of sock dyeing are proposed. Summary of the invention

[0007] The purpose of the present invention is to provide a method, system and storage medium for determining the uniformity of sock dyeing; randomly sampling to obtain an initial sock sample; dividing the initial sock sample into regions and photographing the initial sock sample to obtain an initial sample image set; constructing a color difference recognition model to recognize the initial sample image set to obtain initial difference data; performing a stretching test on the initial sock sample to obtain a stretched sock sample and a stretched sample image set; recognizing the stretched sample image set to obtain stretched difference data; performing a motion test on the stretched sock sample to obtain a motion sample image set; recognizing the motion sample image set to obtain motion difference data; constructing a dyeing uniformity recognition model to recognize the initial difference data, the stretching difference data and the motion difference data to obtain a sock dyeing uniformity coefficient; and realizing accurate rating of the sock dyeing uniformity through the sock dyeing uniformity coefficient.

[0008] To achieve the above object, the present invention provides the following technical solutions: A method for determining the uniformity of sock dyeing, comprising: S10. Collect socks product data to obtain knitting process data and standard color distribution data; obtain initial socks samples through random sampling; S20. Divide the initial sock sample into multiple functional areas according to the knitting process data, including a sole area, a toe area, an instep area, and a sock opening area; photograph the functional areas to obtain an initial sample image set; S30. Constructing a color difference recognition model to recognize the initial sample image set, segmenting and detecting the image of the functional area through the standard color distribution data, and obtaining the initial difference data; S40. Determine stretch data according to the size data of the initial sock sample, perform a stretch test on the initial sock sample according to the stretch data, and obtain a stretched sock sample; photograph the functional area of ​​the stretched sock sample to obtain a stretched sample image set; S50. Recognize the stretched sample image set through the color difference recognition model to obtain the stretched difference data; S60. Screening users according to the size data of the initial sock sample to determine the first user; performing a sports test on the first user and the stretch sock sample to obtain a sports sock sample; photographing the functional area of ​​the sports sock sample to obtain a sports sample image set; S70. Recognize the motion sample image set through the color difference recognition model to obtain motion difference data; S80. Construct a dyeing uniformity recognition model, identify the initial difference data, stretching difference data and movement difference data of the same functional area, and obtain the color anomaly coefficient; then obtain the socks dyeing uniformity coefficient through color distribution anomaly coefficient recognition, and perform grade assessment.

[0009] The knitting process data includes three-dimensional shape data and textile structure data of the socks; the initial sock sample is divided into a sole area, a toe area, an instep area and a sock opening area according to the knitting process data; The initial sample image set includes a first sole area image, a first toe area image, a first instep area image and a first sock opening area image; the stretching sample image set includes a second sole area image, a second toe area image, a second instep area image and a second sock opening area image; the motion sample image set includes a third sole area image, a third toe area image, a third instep area image and a third sock opening area image.

[0010] The color difference recognition model includes a sock data acquisition layer, a sock image segmentation layer and a sock color recognition layer; The socks data acquisition layer is used to acquire standard color distribution data and a sample image set of socks, wherein the sample image set includes a sole area image, a toe area image, an instep area image, and a sock opening area image; The sock image segmentation layer segments the functional region images in the sample image set according to the standard color distribution data, and divides the sub-regions in the region image that belong to the same color distribution into one category, thereby obtaining a region image subset; The sock color recognition layer is used to identify the color distribution data in the regional image subset to obtain the regional subset color data; obtain the difference area according to the regional subset color data and the standard color distribution data; obtain the difference coefficient according to the data difference between the color distribution data of the difference area and the corresponding standard color distribution data; and output the difference area and the difference coefficient as the difference data.

[0011] The process of the tensile test is: Acquire the size data of the initial sock sample; determine the foot shape data of the user according to the size data, and use the foot shape data as stretching data; Determine foot mold parameters according to the stretching data, and obtain an experimental foot mold according to the foot mold parameters; wear the initial sock sample on the experimental foot mold to obtain a stretched sock sample; The stretched socks sample is photographed by a photographing device to obtain a stretched sample image set.

[0012] The process of the exercise test is as follows: Determine the user's foot shape data according to the size data; identify the user according to the foot shape data, and take the user who meets the foot shape data as the first user; The process of the first user wearing the stretch socks sample to exercise is identified, and the socks sample in exercise is used as a sports socks sample; the exercise type includes walking, running, jumping and spinning; Photographing the functional areas of the sports socks samples to obtain a sports sample image set; The motion sample image set includes a walking image subset, a running image subset, a jumping image subset and a rotating image subset; based on the identification of each image subset, walking difference data, running difference data, jumping difference data and rotation difference data are obtained; and then based on the comprehensive identification of the walking difference data, running difference data, jumping difference data and rotation difference data, motion difference data is obtained.

[0013] The dyeing uniformity recognition model includes a first difference recognition layer, a second difference recognition layer, a third difference recognition layer and a dyeing uniformity recognition layer; The first difference recognition layer is used to recognize the initial difference data of the functional area to obtain the initial difference area and the initial difference coefficient of the functional area; the initial difference coefficient is weighted according to the ratio of the area of ​​the initial difference area to the area of ​​the corresponding functional area to obtain the initial color abnormality coefficient of the functional area; The second difference recognition layer is used to recognize the stretch difference data of the functional area to obtain the stretch difference area and the stretch difference coefficient of the functional area; according to the ratio of the area of ​​the stretch difference area to the area of ​​the corresponding functional area, the stretch difference coefficient is weighted to obtain the stretch color abnormality coefficient of the functional area; The third difference recognition layer is used to recognize the motion difference data of the functional area to obtain the motion difference area and the motion difference coefficient of the functional area; according to the ratio of the area of ​​the motion difference area to the area of ​​the corresponding functional area, the motion difference coefficient is weighted to obtain the motion color abnormality coefficient of the functional area; The dyeing uniformity identification layer performs weighted identification according to the initial color abnormality coefficient, the stretching color abnormality coefficient and the motion color abnormality coefficient of each functional area to obtain the socks dyeing uniformity coefficient.

[0014] A socks dyeing uniformity determination system, comprising: The sample sampling module collects sock product data to obtain knitting process data and standard color distribution data; and obtains the initial sock sample through random sampling; The initial image acquisition module divides the initial sock sample into multiple functional areas according to the knitting process data, including the sole area, the toe area, the instep area and the sock opening area; and photographs the functional areas to obtain an initial sample image set; The initial image recognition module builds a color difference recognition model to recognize the initial sample image set, and segments and detects the image of the functional area through standard color distribution data to obtain initial difference data; A stretching image acquisition module determines stretching data according to the size data of the initial sock sample, performs a stretching test on the initial sock sample according to the stretching data, and obtains a stretching sock sample; photographs the functional area of ​​the stretching sock sample to obtain a stretching sample image set; A stretched image recognition module, which recognizes the stretched sample image set through a color difference recognition model to obtain stretched difference data; The motion image acquisition module screens users according to the size data of the initial sock sample to determine the first user; performs a motion test on the first user and the stretched sock sample to obtain a motion sock sample; and photographs the functional area of ​​the motion sock sample to obtain a motion sample image set; The motion image recognition module uses a color difference recognition model to identify the motion sample image set to obtain motion difference data; The dyeing grade assessment module builds a dyeing uniformity recognition model to identify the initial difference data, stretching difference data and movement difference data of the same functional area to obtain the color anomaly coefficient; then the socks dyeing uniformity coefficient is obtained through color distribution anomaly coefficient identification for grade assessment.

[0015] The knitting process data includes three-dimensional shape data and textile structure data of the socks; the initial sock sample is divided into a sole area, a toe area, an instep area and a sock opening area according to the knitting process data; The initial sample image set includes a first sole area image, a first toe area image, a first instep area image and a first sock opening area image; the stretching sample image set includes a second sole area image, a second toe area image, a second instep area image and a second sock opening area image; the motion sample image set includes a third sole area image, a third toe area image, a third instep area image and a third sock opening area image.

[0016] The color difference recognition model includes a sock data acquisition layer, a sock image segmentation layer and a sock color recognition layer; The socks data acquisition layer is used to acquire standard color distribution data and a sample image set of socks, wherein the sample image set includes a sole area image, a toe area image, an instep area image, and a sock opening area image; The sock image segmentation layer segments the functional region images in the sample image set according to the standard color distribution data, and divides the sub-regions in the region image that belong to the same color distribution into one category, thereby obtaining a region image subset; The sock color recognition layer is used to identify the color distribution data in the regional image subset to obtain the regional subset color data; obtain the difference area according to the regional subset color data and the standard color distribution data; obtain the difference coefficient according to the data difference between the color distribution data of the difference area and the corresponding standard color distribution data; and output the difference area and the difference coefficient as the difference data.

[0017] A storage medium stores a computer program, which, when executed by a processor, implements the method for determining the uniformity of sock dyeing.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention constructs a color difference recognition model to recognize the obtained sample image set; obtains images of each functional area in the sample image set, divides the functional area into sub-areas according to standard color distribution data, and classifies sub-areas with the same color distribution into one category to obtain regional image subsets; obtains difference areas and difference coefficients by identifying the difference between the color distribution in the regional image subset and the standard color distribution data, and accurately recognizes the uneven area in the initial state.

[0019] 2. The present invention determines the foot shape data of the user according to the size of the socks, and uses the foot shape data as stretching data to perform a stretching test on the initial sock sample to obtain a stretched sock sample and a stretched sample image set; then the user is screened according to the sock size data to determine the first user, and a motion test is performed based on the first user and the stretched sock sample to obtain a motion sample image set; the color distribution data of the socks in the stretched state and the motion state are accurately obtained.

[0020] 3. The present invention constructs a dyeing uniformity recognition model to recognize the initial difference data of each functional area, the stretching difference data of the stretching state, and the motion difference data of the motion state, and weights the difference coefficient by the ratio of the area of ​​the difference area to the area of ​​the corresponding functional area to obtain the initial color abnormality coefficient, the stretching color abnormality coefficient, and the motion color abnormality coefficient of each functional area; and then obtains the socks dyeing uniformity coefficient through recognition, so as to accurately measure the dyeing uniformity of the socks. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the process of a method for determining uniformity of sock dyeing of the present invention; Figure 2 It is a structural schematic diagram of the color difference recognition model of the present invention; Figure 3 It is a structural schematic diagram of the dyeing uniformity recognition model of the present invention; Figure 4 The figure is a schematic diagram of the structure of a socks dyeing uniformity determination system of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] When socks are worn, they will be stretched to a certain extent, and as the user's movement behavior and method change, the stretching amplitude and stretching state of the socks will also change; during the stretching process of the socks, the fiber density and fiber arrangement of the socks fabric will change, resulting in changes in the overall color distribution of the fabric; therefore, it is necessary to identify the color distribution of the socks during wearing.

[0024] To this end, a method, system and storage medium for determining uniformity of sock dyeing are proposed.

[0025] Embodiment 1 The present invention provides a method for determining the uniformity of sock dyeing, the process of which is as follows: Figure 1 As shown, including: S10. Collect socks product data to obtain knitting process data and standard color distribution data; obtain initial socks samples through random sampling.

[0026] S20. According to the knitting process data, the initial sock sample is divided into multiple functional areas, including a sole area, a toe area, an instep area and a sock opening area; the functional areas are photographed to obtain an initial sample image set.

[0027] Dividing the initial sock sample into a sole area, a toe area, an instep area and a sock opening area according to knitting process data; the knitting process data includes three-dimensional shape data and textile structure data of the socks; The initial sample image set includes a first sole area image, a first toe area image, a first instep area image, and a first sock opening area image.

[0028] The same style means that the knitting process data and the standard color distribution data of the socks are consistent; the textile structure data refers to the fabric structure formed by the combination of yarns. The standard color distribution data is obtained through the dyeing design scheme of the socks product.

[0029] The present invention obtains knitting process data of socks products, including three-dimensional shape data and textile structure data of socks; divides the socks into regions according to the knitting process data to obtain a sole region, a toe region, an instep region and a sock opening region; and performs differentiated recognition according to different regions of the socks to improve recognition accuracy.

[0030] S30. Construct a color difference recognition model to recognize the initial sample image set, segment and detect the images of the functional areas through standard color distribution data, and obtain initial difference data.

[0031] The color difference recognition model is constructed based on a deep neural network model, and its structure is as follows: Figure 2 As shown, it includes a socks data acquisition layer, a socks image segmentation layer and a socks color recognition layer; The socks data acquisition layer is used to acquire standard color distribution data and a sample image set of socks, wherein the sample image set includes a sole area image, a toe area image, an instep area image, and a sock opening area image; The sock image segmentation layer segments the functional region images in the sample image set according to the standard color distribution data, and divides the sub-regions in the region image that belong to the same color distribution into one category, thereby obtaining a region image subset; The sock color recognition layer is used to identify the color distribution data in the regional image subset to obtain the regional subset color data; obtain the difference area according to the regional subset color data and the standard color distribution data; obtain the difference coefficient according to the data difference between the color distribution data of the difference area and the corresponding standard color distribution data; and output the difference area and the difference coefficient as the difference data.

[0032] The calculation formula of the coefficient of difference is: ; in, The coefficient of variation indicating the difference area; Pixels representing the difference area; , and Represents the color data of a pixel; , and Indicates the standard color distribution data corresponding to the pixel; it is RGB color model data.

[0033] The present invention constructs a color difference recognition model to recognize the obtained sample image set; obtains images of each functional area in the sample image set, divides the functional area into sub-areas according to standard color distribution data, and divides the sub-areas with the same color distribution into one category to obtain regional image subsets; and obtains the difference area and the difference coefficient by identifying the difference between the color distribution in the regional image subset and the standard color distribution data.

[0034] S40. Determine stretching data according to the size data of the initial sock sample, perform a stretching test on the initial sock sample according to the stretching data to obtain a stretched sock sample; photograph the functional area of ​​the stretched sock sample to obtain a stretched sample image set.

[0035] The process of the tensile test is: Acquire the size data of the initial sock sample; determine the foot shape data of the user according to the size data, and use the foot shape data as stretching data; Determine foot mold parameters according to the stretching data, and obtain an experimental foot mold according to the foot mold parameters; wear the initial sock sample on the experimental foot mold to obtain a stretched sock sample; The stretched socks sample is photographed by a photographing device to obtain a stretched sample image set.

[0036] The present invention determines the foot shape data of the user according to the size data of the socks, and uses the foot shape data as the stretching data; determines the matching foot mold parameters according to the stretching data to obtain the experimental foot mold; performs a stretching test on the initial sock sample according to the experimental foot mold, and photographs to obtain a stretching sample image set, so as to accurately obtain the color distribution data of the socks when worn.

[0037] S50. Recognize the stretched sample image set through the color difference recognition model to obtain stretched difference data.

[0038] S60. Screening users according to the size data of the initial sock sample to determine the first user; performing a sports test on the first user and the stretch sock sample to obtain a sports sock sample; photographing the functional area of ​​the sports sock sample to obtain a sports sample image set; The process of the exercise test is as follows: Determine the user's foot shape data according to the size data; identify the user according to the foot shape data, and take the user who meets the foot shape data as the first user; Identify the process of the first user wearing the stretch socks sample to exercise, and use the socks sample in exercise as a sports socks sample; wherein the exercise types include walking, running, jumping and spinning; The functional areas of the sports socks samples are photographed to obtain a sports sample image set.

[0039] The present invention determines the user's foot shape data according to the sock size data, screens the user group according to the foot shape data, and obtains the first user; identifies the process of the first user wearing a stretch sock sample to exercise, and photographs to obtain a set of motion sample images, so as to accurately obtain the color distribution data of the socks during movement.

[0040] S70. Identify the motion sample image set through the color difference recognition model to obtain motion difference data; The motion sample image set includes a walking image subset, a running image subset, a jumping image subset and a rotating image subset; based on the identification of each image subset, walking difference data, running difference data, jumping difference data and rotation difference data are obtained; and then based on the comprehensive identification of the walking difference data, running difference data, jumping difference data and rotation difference data, motion difference data is obtained.

[0041] S80. Construct a dyeing uniformity recognition model, identify the initial difference data, stretching difference data and movement difference data of the same functional area, and obtain the color anomaly coefficient; then obtain the socks dyeing uniformity coefficient through color distribution anomaly coefficient recognition, and perform grade assessment.

[0042] The dyeing uniformity recognition model is constructed based on a deep neural network model, and its structure is as follows: Figure 3 As shown, it includes a first difference recognition layer, a second difference recognition layer, a third difference recognition layer and a dyed uniform recognition layer; The first difference recognition layer is used to recognize the initial difference data of the functional area to obtain the initial difference area and the initial difference coefficient of the functional area; the initial difference coefficient is weighted according to the ratio of the area of ​​the initial difference area to the area of ​​the corresponding functional area to obtain the initial color abnormality coefficient of the functional area; The second difference recognition layer is used to recognize the stretch difference data of the functional area to obtain the stretch difference area and the stretch difference coefficient of the functional area; according to the ratio of the area of ​​the stretch difference area to the area of ​​the corresponding functional area, the stretch difference coefficient is weighted to obtain the stretch color abnormality coefficient of the functional area; The third difference recognition layer is used to recognize the motion difference data of the functional area to obtain the motion difference area and the motion difference coefficient of the functional area; according to the ratio of the area of ​​the motion difference area to the area of ​​the corresponding functional area, the motion difference coefficient is weighted to obtain the motion color abnormality coefficient of the functional area; The dyeing uniformity identification layer performs weighted identification according to the initial color abnormality coefficient, the stretching color abnormality coefficient and the motion color abnormality coefficient of each functional area to obtain the socks dyeing uniformity coefficient.

[0043] The present invention constructs a dyeing uniformity recognition model, identifies the initial difference data of each functional area, the stretching difference data of the stretching state and the motion difference data of the motion state, and weights the difference coefficient by the ratio of the area of ​​the difference area to the area of ​​the corresponding functional area, so as to obtain the initial color abnormality coefficient, the stretching color abnormality coefficient and the motion color abnormality coefficient of each functional area; and then obtains the socks dyeing uniformity coefficient through recognition, so as to accurately measure the dyeing uniformity of the socks.

[0044] The present invention provides a storage medium, on which a computer program is stored for implementing the method for determining the uniformity of sock dyeing.

[0045] The present invention randomly samples sock products of the same style to obtain initial sock samples; divides and photographs the initial sock samples to obtain an initial sample image set; constructs a color difference recognition model to recognize the initial sample image set to obtain initial difference data; performs a stretching test on the initial sock samples to obtain a stretch sock sample and a stretch sample image set; recognizes the stretch sample image set to obtain stretch difference data; performs a motion test on the stretch sock samples to obtain a motion sample image set; recognizes the motion sample image set to obtain motion difference data; constructs a dyeing uniformity recognition model to recognize the initial difference data, the stretching difference data and the motion difference data to obtain a sock dyeing uniformity coefficient. The present invention realizes accurate rating of sock dyeing uniformity through the sock dyeing uniformity coefficient.

[0046] Embodiment 2 Socks processing factory R is engaged in the production and sale of socks, wherein the dyeing uniformity of socks is an important factor affecting the quality and sales volume of socks. In order to understand the dyeing uniformity of socks products so as to update the production technology, the socks processing factory R adopts a socks dyeing uniformity determination system described in the present invention for identification.

[0047] The structure of the socks dyeing uniformity determination system proposed by the present invention is as follows: Figure 4 As shown, it includes: a sample sampling module, an initial image acquisition module, an initial image recognition module, a stretched image acquisition module, a stretched image recognition module, a motion image acquisition module, a motion image recognition module and a dyeing grade assessment module.

[0048] The sample sampling module collects sock product data to obtain knitting process data and standard color distribution data; and obtains the initial sock sample through random sampling.

[0049] Sock processing factory R takes sock product A as the research object and randomly samples product A produced in the same batch with consistent size data to obtain initial sock samples.

[0050] The initial image acquisition module divides the initial sock sample into multiple functional areas according to the knitting process data, including the sole area, toe area, instep area and sock opening area; and photographs the functional areas to obtain an initial sample image set.

[0051] The same style refers to the consistency of the knitting process data and color distribution data of the socks; the textile structure data refers to the fabric structure formed by the combination of yarns.

[0052] The initial sock sample is divided into a sole area, a toe area, an instep area and a sock opening area according to the knitting process data; wherein the sole area refers to the bottom of the sock, covering the front half of the sole and the heel; the toe area refers to the front end of the sock, covering the toe area; the instep area refers to the middle part of the sock, covering the top of the foot; the sock opening area refers to the top of the sock, covering the calf and ankle.

[0053] The sole area, toe area, instep area and sock opening area of ​​the initial sock sample are photographed to obtain an initial sample image set; the initial sample image set includes a first sole area image, a first toe area image, a first instep area image and a first sock opening area image.

[0054] The initial image recognition module builds a color difference recognition model to identify the initial sample image set, and segments and detects the images of the functional areas through standard color distribution data to obtain initial difference data.

[0055] The color difference recognition model includes a sock data acquisition layer, a sock image segmentation layer and a sock color recognition layer; The socks data acquisition layer is used to acquire standard color distribution data and a sample image set of socks, wherein the sample image set includes a sole area image, a toe area image, an instep area image, and a sock opening area image; The sock image segmentation layer segments the functional region images in the sample image set according to the standard color distribution data, and divides the sub-regions in the region image that belong to the same color distribution into one category, thereby obtaining a region image subset; The sock color recognition layer is used to identify the color distribution data in the regional image subset to obtain the regional subset color data; obtain the difference area according to the regional subset color data and the standard color distribution data; obtain the difference coefficient according to the data difference between the color distribution data of the difference area and the corresponding standard color distribution data; and output the difference area and the difference coefficient as the difference data.

[0056] The present invention constructs a color difference recognition model to recognize the obtained sample image set; obtains images of each functional area in the sample image set, divides the functional area into sub-areas according to standard color distribution data, and divides the sub-areas with the same color distribution into one category to obtain regional image subsets; and obtains the difference area and the difference coefficient by identifying the difference between the color distribution in the regional image subset and the standard color distribution data.

[0057] The calculation formula of the coefficient of variation is: ; in, The coefficient of variation indicating the difference area; Pixels representing the difference area; , and Represents the color data of a pixel; , and Indicates the standard color distribution data corresponding to the pixel, which is the RGB color model data.

[0058] According to the standard color distribution data of socks product A, the color distribution of each functional area is identified and divided into regions, and the classification results are shown in Table 1.

[0059] Table 1 Color distribution area division data table of socks product A

[0060] It can be seen from Table 1 that the sole area of ​​sock product A includes two color distributions, which are distributed in the first sub-area set of the sole and the second sub-area set of the sole respectively; the toe area of ​​sock product A is distributed with only one color; the instep area of ​​sock product A is distributed with three colors; and the sock opening area of ​​sock product A is distributed with one color.

[0061] The stretching image acquisition module determines the stretching data according to the size data of the initial sock sample, performs a stretching test on the initial sock sample according to the stretching data, and obtains the stretched sock sample; and photographs the functional area of ​​the stretched sock sample to obtain a stretching sample image set.

[0062] The tensile test process for sock product A is: Acquire the size data of the initial sock sample; determine the foot shape data of the user according to the size data, and use the foot shape data as stretching data; Determine foot mold parameters according to the stretching data, and obtain an experimental foot mold according to the foot mold parameters; wear the initial sock sample on the experimental foot mold to obtain a stretched sock sample; The stretched socks sample is photographed by a photographing device to obtain a stretched sample image set.

[0063] Among them, the division method of each functional area of ​​the initial sock sample is obtained, and the division boundary data is identified; after the stretched sock sample is obtained through the stretching test, there is a stretching change in the stretched sock sample relative to the initial sock sample; at this time, the second sample is divided according to the division boundary data of the first sample.

[0064] The stretched image recognition module recognizes the stretched sample image set through the color difference recognition model to obtain the stretched difference data.

[0065] The motion image acquisition module screens users according to the size data of the initial sock sample to determine the first user; performs a motion test on the first user and the stretched sock sample to obtain a motion sock sample; and photographs the functional area of ​​the motion sock sample to obtain a motion sample image set.

[0066] The process of sports testing for socks product A is as follows: Determine the user's foot shape data according to the size data; identify the user according to the foot shape data, and take the user who meets the foot shape data as the first user; The process of the first user wearing the stretch socks sample to exercise is identified, and the socks sample in exercise is used as a sports socks sample; the exercise type includes walking, running, jumping and spinning; The functional areas of the sports socks samples are photographed to obtain a sports sample image set.

[0067] An image of the sole area of ​​the sock is acquired by causing the first user to move on the transparent glass plate.

[0068] The motion image recognition module obtains motion difference data by recognizing the motion sample image set through the color difference recognition model.

[0069] The motion sample image set includes a walking image subset, a running image subset, a jumping image subset and a rotating image subset; based on the identification of each image subset, walking difference data, running difference data, jumping difference data and rotation difference data are obtained; and then based on the comprehensive identification of the walking difference data, running difference data, jumping difference data and rotation difference data, motion difference data is obtained.

[0070] Among them, comprehensive identification refers to setting weights according to different exercise types and performing weighted processing on difference data; the weights of different exercise types can be set according to the exercise time distribution of the first user in the exercise test.

[0071] The dyeing grade assessment module builds a dyeing uniformity recognition model to identify the initial difference data, stretching difference data and movement difference data of the same functional area to obtain the color anomaly coefficient; then the socks dyeing uniformity coefficient is obtained through color distribution anomaly coefficient identification for grade assessment.

[0072] The dyeing uniformity recognition model includes a first difference recognition layer, a second difference recognition layer, a third difference recognition layer and a dyeing uniformity recognition layer; The first difference recognition layer is used to recognize the initial difference data of the functional area to obtain the initial difference area and the initial difference coefficient of the functional area; the initial difference coefficient is weighted according to the ratio of the area of ​​the initial difference area to the area of ​​the corresponding functional area to obtain the initial color abnormality coefficient of the functional area; The second difference recognition layer is used to recognize the stretch difference data of the functional area to obtain the stretch difference area and the stretch difference coefficient of the functional area; according to the ratio of the area of ​​the stretch difference area to the area of ​​the corresponding functional area, the stretch difference coefficient is weighted to obtain the stretch color abnormality coefficient of the functional area; The third difference recognition layer is used to recognize the motion difference data of the functional area to obtain the motion difference area and the motion difference coefficient of the functional area; according to the ratio of the area of ​​the motion difference area to the area of ​​the corresponding functional area, the motion difference coefficient is weighted to obtain the motion color abnormality coefficient of the functional area; The dyeing uniformity identification layer performs weighted identification according to the initial color abnormality coefficient, the stretching color abnormality coefficient and the motion color abnormality coefficient of each functional area to obtain the socks dyeing uniformity coefficient.

[0073] In order to verify the recognition accuracy of the dyeing uniformity recognition model, a verification experiment was conducted. In the verification experiment, Model 1, Model 2, Model 3 and Model 4 were constructed.

[0074] Among them, model one is a dyeing uniformity recognition model, which considers the initial state, stretching state and motion state, and recognizes through initial difference data, stretching difference data and motion difference data; model two is based on model one, does not consider the motion state, and recognizes through initial difference data and stretching difference data; model three is based on model one, does not consider the stretching state, and recognizes through initial difference data and motion difference data; model four is based on model one, does not consider the stretching state and motion state, and recognizes only through initial difference data; The same validation data set is used to identify the model one, model two, model three and model four, and their recognition accuracy is obtained. The recognition accuracy is obtained according to the data difference between the sock dyeing uniformity coefficient identified by the model and the dyeing uniformity label; the results of the validation experiment are shown in Table 2.

[0075] Table 2. Validation data of dyeing uniformity recognition model

[0076] From the data in Table 2, it can be seen that model 1 has the highest recognition accuracy.

[0077] After obtaining the sock dyeing uniformity coefficient of the sock product A sample, the sock dyeing uniformity coefficient is graded according to the set threshold value to obtain the dyeing uniformity grade data of the same batch of products.

[0078] The present invention obtains an initial sock sample by random sampling; divides and photographs the initial sock sample to obtain an initial sample image set; constructs a color difference recognition model to recognize the initial sample image set to obtain initial difference data; performs a stretching test on the initial sock sample to obtain a stretched sock sample and a stretched sample image set; recognizes the stretched sample image set to obtain stretching difference data; performs a motion test on the stretched sock sample to obtain a motion sample image set; recognizes the motion sample image set to obtain motion difference data; constructs a dyeing uniformity recognition model to recognize the initial difference data, the stretching difference data and the motion difference data to obtain a sock dyeing uniformity coefficient; and realizes accurate rating of sock dyeing uniformity.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for determining the uniformity of sock dyeing. It is characterized in that include: S10. Collect data on sock products to obtain knitting process data and standard color distribution data; Through random sampling, an initial sample of socks is obtained; S20. Divide the initial sock sample into multiple functional areas according to the knitting process data, including a sole area, a toe area, an instep area, and a sock opening area; photograph the functional areas to obtain an initial sample image set; S30. Constructing a color difference recognition model to recognize the initial sample image set, segmenting and detecting the image of the functional area through the standard color distribution data, and obtaining the initial difference data; S40. Determine stretch data according to the size data of the initial sock sample, perform a stretch test on the initial sock sample according to the stretch data, and obtain a stretched sock sample; photograph the functional area of ​​the stretched sock sample to obtain a stretched sample image set; S50. Recognize the stretched sample image set through the color difference recognition model to obtain the stretched difference data; S60. Screening users according to the size data of the initial sock sample to determine the first user; performing a sports test on the first user and the stretch sock sample to obtain a sports sock sample; photographing the functional area of ​​the sports sock sample to obtain a sports sample image set; S70. Recognize the motion sample image set through the color difference recognition model to obtain motion difference data; S80. Constructing a dyeing uniformity recognition model, identifying the initial difference data, stretching difference data and motion difference data of the same functional area, and obtaining a color anomaly coefficient; Then, the color distribution abnormality coefficient is identified to obtain the sock dyeing uniformity coefficient for grade assessment.

2. A method for determining the uniformity of sock dyeing according to claim 1, Features: The knitting process data includes three-dimensional shape data and textile structure data of the socks; the initial sock sample is divided into a sole area, a toe area, an instep area and a sock opening area according to the knitting process data; The initial sample image set includes a first sole area image, a first toe area image, a first instep area image and a first sock opening area image; the stretching sample image set includes a second sole area image, a second toe area image, a second instep area image and a second sock opening area image; the motion sample image set includes a third sole area image, a third toe area image, a third instep area image and a third sock opening area image.

3. A method for determining uniformity of sock dyeing according to claim 1, Features: The color difference recognition model includes a sock data acquisition layer, a sock image segmentation layer and a sock color recognition layer; The socks data acquisition layer is used to acquire standard color distribution data and a sample image set of socks, wherein the sample image set includes a sole area image, a toe area image, an instep area image, and a sock opening area image; The sock image segmentation layer segments the functional region images in the sample image set according to the standard color distribution data, and divides the sub-regions in the region image that belong to the same color distribution into one category, thereby obtaining a region image subset; The sock color recognition layer is used to identify the color distribution data in the regional image subset to obtain the regional subset color data; obtain the difference area according to the regional subset color data and the standard color distribution data; obtain the difference coefficient according to the data difference between the color distribution data of the difference area and the corresponding standard color distribution data; and output the difference area and the difference coefficient as the difference data.

4. A method for determining uniformity of sock dyeing according to claim 1, Features: The process of the tensile test is: Obtaining size data of the initial sock sample; Determining the user's foot shape data according to the size data, and using the foot shape data as stretching data; Determine foot mold parameters according to the stretching data, and obtain an experimental foot mold according to the foot mold parameters; wear the initial sock sample on the experimental foot mold to obtain a stretched sock sample; The stretched socks sample is photographed by a photographing device to obtain a stretched sample image set.

5. A method for determining the uniformity of sock dyeing according to claim 1, Features: The process of the exercise test is as follows: Determine the user's foot shape data according to the size data; identify the user according to the foot shape data, and take the user who meets the foot shape data as the first user; The process of the first user wearing the stretch socks sample to exercise is identified, and the socks sample in exercise is used as a sports socks sample; the exercise type includes walking, running, jumping and spinning; Photographing the functional areas of the sports socks samples to obtain a sports sample image set; The motion sample image set includes a walking image subset, a running image subset, a jumping image subset and a rotating image subset; based on the identification of each image subset, walking difference data, running difference data, jumping difference data and rotation difference data are obtained; and then based on the comprehensive identification of the walking difference data, running difference data, jumping difference data and rotation difference data, motion difference data is obtained.

6. A method for determining the uniformity of sock dyeing according to claim 1, Features: The dyeing uniformity recognition model includes a first difference recognition layer, a second difference recognition layer, a third difference recognition layer and a dyeing uniformity recognition layer; The first difference recognition layer is used to recognize the initial difference data of the functional area to obtain the initial difference area and the initial difference coefficient of the functional area; the initial difference coefficient is weighted according to the ratio of the area of ​​the initial difference area to the area of ​​the corresponding functional area to obtain the initial color abnormality coefficient of the functional area; The second difference recognition layer is used to recognize the stretch difference data of the functional area to obtain the stretch difference area and the stretch difference coefficient of the functional area; according to the ratio of the area of ​​the stretch difference area to the area of ​​the corresponding functional area, the stretch difference coefficient is weighted to obtain the stretch color abnormality coefficient of the functional area; The third difference recognition layer is used to recognize the motion difference data of the functional area to obtain the motion difference area and the motion difference coefficient of the functional area; according to the ratio of the area of ​​the motion difference area to the area of ​​the corresponding functional area, the motion difference coefficient is weighted to obtain the motion color abnormality coefficient of the functional area; The dyeing uniformity identification layer performs weighted identification according to the initial color abnormality coefficient, the stretching color abnormality coefficient and the motion color abnormality coefficient of each functional area to obtain the socks dyeing uniformity coefficient.

7. A socks dyeing uniformity determination system, It is characterized in that include: The sample sampling module collects sock product data and obtains knitting process data and standard color distribution data; Through random sampling, an initial sample of socks is obtained; The initial image acquisition module divides the initial sock sample into multiple functional areas according to the knitting process data, including the sole area, the toe area, the instep area and the sock opening area; and photographs the functional areas to obtain an initial sample image set; The initial image recognition module builds a color difference recognition model to recognize the initial sample image set, and segments and detects the image of the functional area through standard color distribution data to obtain initial difference data; A stretching image acquisition module determines stretching data according to the size data of the initial sock sample, performs a stretching test on the initial sock sample according to the stretching data, and obtains a stretching sock sample; photographs the functional area of ​​the stretching sock sample to obtain a stretching sample image set; A stretched image recognition module, which recognizes the stretched sample image set through a color difference recognition model to obtain stretched difference data; The motion image acquisition module screens users according to the size data of the initial sock sample to determine the first user; performs a motion test on the first user and the stretched sock sample to obtain a motion sock sample; and photographs the functional area of ​​the motion sock sample to obtain a motion sample image set; The motion image recognition module uses a color difference recognition model to identify the motion sample image set to obtain motion difference data; The dyeing grade assessment module builds a dyeing uniformity recognition model to identify the initial difference data, stretching difference data and motion difference data of the same functional area to obtain the color anomaly coefficient; Then, the color distribution abnormality coefficient is identified to obtain the sock dyeing uniformity coefficient for grade assessment.

8. A socks dyeing uniformity determination system according to claim 7, Features: The knitting process data includes three-dimensional shape data and textile structure data of the socks; the initial sock sample is divided into a sole area, a toe area, an instep area and a sock opening area according to the knitting process data; The initial sample image set includes a first sole area image, a first toe area image, a first instep area image and a first sock opening area image; the stretching sample image set includes a second sole area image, a second toe area image, a second instep area image and a second sock opening area image; the motion sample image set includes a third sole area image, a third toe area image, a third instep area image and a third sock opening area image.

9. A socks dyeing uniformity determination system according to claim 7, Features: The color difference recognition model includes a sock data acquisition layer, a sock image segmentation layer and a sock color recognition layer; The socks data acquisition layer is used to acquire standard color distribution data and a sample image set of socks, wherein the sample image set includes a sole area image, a toe area image, an instep area image, and a sock opening area image; The sock image segmentation layer segments the functional region images in the sample image set according to the standard color distribution data, and divides the sub-regions in the region image that belong to the same color distribution into one category, thereby obtaining a region image subset; The sock color recognition layer is used to identify the color distribution data in the regional image subset to obtain the regional subset color data; obtain the difference area according to the regional subset color data and the standard color distribution data; obtain the difference coefficient according to the data difference between the color distribution data of the difference area and the corresponding standard color distribution data; and output the difference area and the difference coefficient as the difference data.

10. A storage medium, Features: The storage medium stores a computer program, which, when executed by a processor, implements a method for determining the uniformity of sock dyeing as claimed in any one of claims 1 to 6.

Images