A method for evaluating the similarity of fabric drape

Abstract

This invention discloses a method for evaluating the similarity of fabric drape, belonging to the field of quantitative technology for fabric drape similarity. The method includes: acquiring the two-dimensional projections of the three-dimensional drape shapes of two fabric samples to be compared onto the same horizontal reference plane, and extracting discrete point sequences on the two-dimensional projection contours; constructing a distance matrix based on the spacing between the discrete points on the two-dimensional projection contours of the two fabric samples; calculating the minimum and maximum cumulative distances of the discrete point sequences on the two-dimensional projection contours of the two fabric samples based on the distance matrix; calculating the drape similarity index of the two fabric samples based on the minimum and maximum cumulative distances; and quantitatively evaluating the drape similarity of the two fabric samples through the drape similarity index. This method overcomes the problem that traditional fabric drape evaluation relies solely on the drape coefficient and cannot distinguish differences in drape shape, thus providing a comprehensive and accurate quantification of fabric drape similarity.

Description

Technical Field

[0001] This invention relates to a method for evaluating the similarity of fabric drape, belonging to the field of quantitative technology for fabric drape similarity. Background Technology

[0002] Fabric drape refers to the curved surface shape of a fabric under gravity when naturally suspended. It directly affects the fabric's visual appeal and performance, and is a key factor influencing garment design, dynamic aesthetics, and wearing comfort. In textile and apparel product development, fabric selection, quality consistency control, and digital design and simulation, scientifically and objectively evaluating the similarity of different fabrics in drape performance has become a core technical issue connecting fabric physical properties with garment performance, influencing development efficiency and quality control. Accurately quantifying the similarity of fabric drape helps achieve precise digital matching and grading of fabric performance. For example, in the digital design stage of apparel, drape similarity can help designers quickly locate materials in a virtual fabric library that closely match the target drape effect, significantly improving design fidelity and development efficiency. In supply chain management, drape similarity evaluation methods can provide brands and suppliers with objective standards for drape consistency across batches and production locations, effectively reducing quality disputes and production delays caused by differences in fabric performance. Furthermore, in terms of process optimization and cost control, quantitative analysis of the impact of different finishing processes on drape can guide adjustments to process parameters, achieving dual optimization of quality and cost. Therefore, establishing a scientific method for evaluating fabric drape similarity not only provides objective basis for apparel brands in fabric selection, process parameter optimization, and finished product quality control, but also promotes the industry's transformation from experience-based to data-driven models, holding significant industrial importance for improving product quality, reducing development costs, and enhancing batch stability.

[0003] Currently, traditional fabric drape evaluation mainly relies on the drape coefficient, which quantifies the degree of drape by calculating the ratio of the projected area of ​​the draped fabric to the area of ​​the original sample. While this method is simple to operate and has clear physical meaning, it can only compare the drape coefficient values ​​when judging the similarity of drape between two fabrics. Since the drape coefficient is only a single scalar, its comparison results only reflect the similarity of the drape "quantity" and cannot cover the geometric differences in the drape "morphology." Therefore, fabric drape similarity evaluation based on the drape coefficient has significant limitations. In practical applications, fabrics with similar or even identical drape coefficients often exhibit significant differences in their drape morphology (such as the number, distribution, depth, and curvature changes of folds). For example, one fabric may form a few deep and wide folds, while another may present multiple fine and shallow folds. The two are visually and tactilely distinct, but traditional methods, relying solely on projected area calculations, cannot distinguish such differences, easily leading to misjudgments of similarity. Therefore, although the drape coefficient method is simple to operate, it is too coarse for evaluating the drape similarity of fabrics with high precision and fine granularity. It is difficult to meet the needs of the modern textile and apparel industry for accurate comparison in digital material selection, quality consistency control, and product development, and it also restricts the scientific decision-making of subsequent process optimization and supply chain management. Therefore, there is an urgent need to establish a method that can comprehensively characterize and quantify drape similarity. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the similarity of fabric drape, which can overcome the problem that traditional fabric drape evaluation relies only on the drape coefficient and cannot distinguish differences in drape shape, and can comprehensively and accurately quantify the similarity of fabric drape.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a method for evaluating the similarity of fabric drape, comprising:

[0007] Obtain the two-dimensional projection of the three-dimensional drape shape of the two fabric samples to be compared onto the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection profile.

[0008] A distance matrix is ​​constructed based on the spacing between discrete points on the two-dimensional projected contours of the two fabric samples;

[0009] The minimum and maximum cumulative distances of discrete point sequences on the two-dimensional projected profiles of the two fabric samples are calculated based on the distance matrix.

[0010] The drape similarity index of the two fabric samples is calculated based on the minimum and maximum cumulative distances, and the drape similarity of the two fabric samples is quantitatively evaluated through the drape similarity index.

[0011] In conjunction with the first aspect, further, the three-dimensional drape morphology of the two fabric samples to be compared is obtained as a two-dimensional projection onto the same horizontal reference plane, and the discrete point sequence on the two-dimensional projection profile is extracted, including:

[0012] Place the fabric sample on the drape tester, define the center of the fabric sample as the origin of the coordinate system, and define the plane containing the circular area of ​​the fabric sample fixed by the clamping plate of the drape tester as the horizontal reference plane. shaft and The orthogonal plane of the axes;

[0013] A top view of the fabric sample in its natural hanging state is captured by a camera fixed directly above the origin of the coordinate system, and a two-dimensional projection of the fabric sample on the horizontal reference plane is obtained.

[0014] Image processing software is used to denoise, convert grayscale, and binarize the two-dimensional projection to obtain a binary image;

[0015] Edge detection is performed on the binary image using a contour extraction algorithm to obtain a two-dimensional projected contour, and then a sequence of discrete points on the two-dimensional projected contour is extracted.

[0016] In conjunction with the first aspect, further, the three-dimensional drape morphology of the two fabric samples to be compared is obtained as a two-dimensional projection onto the same horizontal reference plane, and the discrete point sequence on the two-dimensional projection profile is extracted, including:

[0017] Place the fabric sample on the drape tester, define the center of the fabric sample as the origin of the coordinate system, and define the plane containing the circular area of ​​the fabric sample fixed by the clamping plate of the drape tester as the horizontal reference plane. shaft and The orthogonal plane of the axis, the normal axis of the horizontal reference plane is defined as The axis is defined as the drape direction of the fabric sample. The positive direction of the axis;

[0018] A 3D scanner was used to perform multi-angle 3D scanning on the fabric sample to obtain the initial natural drape 3D mesh model of the fabric sample.

[0019] The initial natural hanging 3D mesh model was denoised, hole-filling and non-manifold edge-cleaning were performed by reverse engineering to obtain the hanging 3D mesh model of the fabric sample.

[0020] The coordinates of all grid vertices of the suspended three-dimensional mesh model are extracted by reverse engineering and projected onto the horizontal reference plane to obtain the two-dimensional projection point set of the fabric sample on the horizontal reference plane.

[0021] The α-shape algorithm is used to calculate the envelope of the two-dimensional projection point set to obtain the two-dimensional projection contour, and the discrete point sequence on the two-dimensional projection contour is extracted.

[0022] In conjunction with the first aspect, further, the three-dimensional drape morphology of the two fabric samples to be compared is obtained as a two-dimensional projection onto the same horizontal reference plane, and the discrete point sequence on the two-dimensional projection profile is extracted, including:

[0023] A virtual drape tester model is created in 3D modeling software and imported into fabric simulation software. A virtual fabric sample is placed on the virtual drape tester model to simulate drape, thus obtaining a virtual drape fabric model.

[0024] The center of the virtual fabric sample is defined as the origin of the coordinate system, and the plane containing the circular area of ​​the virtual fabric sample fixed by the clamping plate of the virtual suspension tester model is defined as the horizontal reference plane. shaft and The orthogonal plane of the axis, the normal axis of the horizontal reference plane is defined as The axis is defined as the drape direction of the virtual fabric sample. The positive direction of the axis;

[0025] Import the virtual draped fabric model into the 3D data processing software to obtain the initial natural draped 3D model of the virtual fabric sample.

[0026] The initial natural hanging 3D mesh model was denoised, hole-filling, and non-manifold edge-cleaning were performed using reverse engineering techniques to obtain a hanging 3D mesh model of the virtual fabric sample.

[0027] The coordinates of all grid vertices of the suspended three-dimensional mesh model are extracted by reverse engineering and projected onto the horizontal reference plane to obtain the two-dimensional projection point set of the virtual fabric sample on the horizontal reference plane.

[0028] The α-shape algorithm is used to calculate the envelope of the two-dimensional projection point set to obtain the two-dimensional projection contour, and the discrete point sequence on the two-dimensional projection contour is extracted.

[0029] In conjunction with the first aspect, furthermore, the discrete point sequences on the two-dimensional projected contours of the two fabric samples are respectively and ,in, , … … These respectively represent the 1st, 2nd, ..., ...th elements on the two-dimensional projected contour of the first fabric sample. … discrete points, , … … The first, second, ..., numbers on the two-dimensional projected contour of the second fabric sample are respectively represented as the first, second, ... … There are discrete points, and the distance matrix is... , The Line number Column elements express and The spacing, ,in, , They represent of Axis coordinates Axis coordinates , They represent of Axis coordinates Axis coordinates.

[0030] In conjunction with the first aspect, further, calculating the minimum and maximum cumulative distances of the discrete point sequences on the two-dimensional projected profiles of the two fabric samples based on the distance matrix includes:

[0031] Construct the minimum cumulative distance matrix based on the distance matrix. , The Line number Column elements Indicates starting position Corresponding discrete point pairs Starting from the beginning, and following the path constraint of moving only to the right, down, or down-right each time, traverse to the current position. Corresponding discrete point pairs The minimum cumulative distance among all paths, ,in, , , They represent The Line number Column element, first Line number Column element, first Line number Column elements, express , , The minimum value in, with the initial condition being ,in, , They represent , The element in the first row and first column, The Line number Column elements This is the minimum cumulative distance between discrete point sequences on the two-dimensional projected contours of two fabric samples. ;

[0032] Construct the maximum cumulative distance matrix based on the distance matrix. , The Line number Column elements Indicates starting position Corresponding discrete point pairs Starting from the beginning, and following the path constraint of moving only to the right, down, or down-right each time, traverse to the current position. Corresponding discrete point pairs The maximum cumulative distance among all paths. ,in, , , They represent The Line number Column element, first Line number Column element, first Line number Column elements, express , , The maximum value in, with the initial condition being ,in, express The element in the first row and first column, The Line number Column elements This refers to the maximum cumulative distance of the discrete point sequence on the two-dimensional projected contours of the two fabric samples. .

[0033] In conjunction with the first aspect, the formula for calculating the drape similarity index of the two fabric samples is as follows:

[0034] ;

[0035] in, This index represents the similarity of the drape of two fabric samples. , These represent the minimum cumulative distance and the maximum cumulative distance of the discrete point sequence on the two-dimensional projected contours of the two fabric samples, respectively.

[0036] like If the drape of the two fabric samples is similar, then the similarity is determined to be highly similar.

[0037] like If the drape of the two fabric samples is found to be moderately similar, then the similarity is determined to be moderate.

[0038] like If the drape similarity of the two fabric samples is low, then the similarity is determined to be low.

[0039] like If the drape of the two fabric samples is not similar, then they are determined to be dissimilar.

[0040] In a second aspect, the present invention provides a fabric drape similarity evaluation device, comprising:

[0041] The discrete point extraction module is used to obtain the two-dimensional projection of the three-dimensional drape shape of the two fabric samples to be compared on the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection contour.

[0042] The distance matrix construction module is used to construct a distance matrix based on the spacing between discrete points on the two-dimensional projected contours of two fabric samples.

[0043] The cumulative distance calculation module is used to calculate the minimum and maximum cumulative distances of discrete point sequences on the two-dimensional projected contours of two fabric samples based on the distance matrix.

[0044] The similarity assessment module is used to calculate the drape similarity index of two fabric samples based on the minimum and maximum cumulative distances, and to quantitatively assess the drape similarity of the two fabric samples through the drape similarity index.

[0045] Thirdly, the present invention provides a computer device, comprising:

[0046] Storage medium used to store computer programs;

[0047] A processor for executing the computer program to implement the fabric drape similarity evaluation method of the first aspect.

[0048] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the fabric drape similarity evaluation method described in the first aspect.

[0049] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the fabric drape similarity evaluation method described in the first aspect.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] The fabric drape similarity evaluation method provided by this invention measures the similarity of fabric drape by aligning discrete point sequences of drape projection contours. This method overcomes the limitations of traditional drape coefficients, which only reflect the ratio of projected areas and cannot distinguish differences in drape morphology. It can more comprehensively and accurately reflect the differences in fabric drape morphology and has good inclusiveness for different contour lengths and local deformations. Furthermore, in the process of developing and optimizing virtual fabrics, it can also provide objective and quantifiable evaluation criteria for drape matching between simulated and target fabrics, effectively assisting in digital fabric design and performance optimization. Attached Figure Description

[0052] Figure 1 This is a flowchart of the fabric drape similarity evaluation method provided in the embodiments of the present invention;

[0053] Figure 2 This is a schematic diagram of the extraction of the two-dimensional projection contour of a real fabric sample provided in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the extraction of the two-dimensional projection contour of the virtual fabric sample provided in the embodiment of the present invention, wherein (a) corresponds to the virtual fabric sample V1 and (b) corresponds to the virtual fabric sample V2.

[0055] Figure 4 This is a schematic diagram of the alignment results of discrete point sequences on the two-dimensional projected contours of two fabric samples provided in the embodiment of the present invention, wherein (a) corresponds to the alignment results of sequence A and sequence B1, and (b) corresponds to the alignment results of sequence A and sequence B2. Detailed Implementation

[0056] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0057] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. Unless otherwise specified, embodiments of the present invention and the technical features thereof can be combined with each other.

[0058] This invention provides a method for evaluating the similarity of fabric drape, comprising:

[0059] Obtain the two-dimensional projection of the three-dimensional drape shape of the two fabric samples to be compared onto the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection profile.

[0060] A distance matrix is ​​constructed based on the spacing between discrete points on the two-dimensional projected contours of the two fabric samples;

[0061] The minimum and maximum cumulative distances of discrete point sequences on the two-dimensional projected profiles of the two fabric samples are calculated based on the distance matrix.

[0062] The drape similarity index of the two fabric samples is calculated based on the minimum and maximum cumulative distances, and the drape similarity of the two fabric samples is quantitatively evaluated through the drape similarity index.

[0063] The fabric drape similarity evaluation method provided in this invention measures the similarity of fabric drape by aligning discrete point sequences of drape projection contours. This method overcomes the limitations of traditional drape coefficients, which only reflect the ratio of projected areas and cannot distinguish differences in drape morphology. It can more comprehensively and accurately reflect the differences in fabric drape morphology and has good inclusiveness for different contour lengths and local deformations, effectively improving the accuracy and discriminativeness of fabric drape performance evaluation.

[0064] Figure 1 This is a flowchart illustrating a fabric drape similarity evaluation method according to an embodiment of the present invention. This flowchart merely shows the logical sequence of the method in this embodiment; however, different methods may be used without conflict. Figure 1 Complete the steps shown or described in the order indicated.

[0065] The fabric drape similarity evaluation method provided in this embodiment of the invention can be applied to a terminal and can be executed by a fabric drape similarity evaluation device. This device can be implemented by software and / or hardware and can be integrated into the terminal, such as any tablet computer or computer device with communication function.

[0066] In one possible embodiment, based on a two-dimensional image processing method, the three-dimensional drape shapes of the two fabric samples to be compared are obtained as two-dimensional projections on the same horizontal reference plane, and the discrete point sequence on the two-dimensional projection contour is extracted. Specifically, this includes the following steps:

[0067] Step 1: Place the fabric sample on the drape tester. Define the center of the fabric sample as the origin of the coordinate system, and define the plane containing the circular area of ​​the fabric sample fixed by the clamping plate of the drape tester as the horizontal reference plane. shaft and The orthogonal plane of the axes;

[0068] Step 2: Take a top view of the fabric sample in its natural hanging state by taking a vertically downward photo with a camera fixed directly above the origin of the coordinate system, and obtain a two-dimensional projection of the fabric sample on the horizontal reference plane.

[0069] Step 3: Use image processing software to denoise, grayscale, and binarize the 2D projection to obtain a binary image;

[0070] Step 4: Perform edge detection on the binary image through a contour extraction algorithm to obtain a two-dimensional projection contour, and extract the discrete point sequence on the two-dimensional projection contour.

[0071] In a possible embodiment, based on a three-dimensional scanning method, obtaining the two-dimensional projections of the three-dimensional draping morphologies of two fabric specimens to be compared on the same horizontal reference plane, and extracting the discrete point sequence on the two-dimensional projection contour specifically includes the following steps:

[0072] Step 1: Place the fabric specimen on a draping tester, define the center of the fabric specimen as the coordinate origin, define the plane of the circular area where the fabric specimen is fixed by the clamping disk of the draping tester as the horizontal reference plane, that is, the orthogonal plane of the axis and the axis, define the normal axis of the horizontal reference plane as the axis, and define the draping direction of the fabric specimen as the positive direction of the

[0073] Step 2: Use a three-dimensional scanner to perform multi-angle three-dimensional scanning on the fabric specimen to obtain the initial natural draping three-dimensional mesh model of the fabric specimen.

[0074] Specifically, a circular fabric specimen with a radius of 120 mm cut within 100 mm from the cloth edge is conditioned in a standard environment (temperature of 18°C to 22°C, relative humidity of 61% to 69%), and reflective marking points with a diameter of 6 mm to 10 mm are evenly pasted on its surface, and the distance between each reflective marking point is 30 mm to 50 mm. The fabric specimen is fixed on the clamping disk of the draping tester through the positioning hole (with a diameter of 1 mm) at the center of the circle, and the diameter of the clamping disk is 120 mm to ensure that the fabric specimen drapes naturally. Use a three-dimensional scanner to perform multi-angle three-dimensional scanning on the fabric specimen placed on the draping tester to obtain the three-dimensional point cloud data of the fabric specimen in the initial natural draping state, and through the three-dimensional scanning data processing software supporting the three-dimensional scanner, reconstruct and encapsulate the three-dimensional point cloud data into the initial natural draping three-dimensional mesh model of the fabric specimen.

[0075] Step 3: Denoise, fill holes, and clean non-manifold edges of the initial natural draping three-dimensional mesh model through reverse engineering technology to obtain the draping three-dimensional mesh model of the fabric specimen.

[0076] Step 4: Extract the coordinates of all mesh vertices of the draping three-dimensional mesh model through reverse engineering technology and project them onto the horizontal reference plane to obtain the two-dimensional projection point set of the fabric specimen on the horizontal reference plane.

[0077] Step 5: Use the α-shape algorithm to perform envelope calculation on the two-dimensional projection point set to obtain the two-dimensional projection contour, and extract the discrete point sequence on the two-dimensional projection contour.

[0078] Specifically, the α parameter is adaptively selected based on the density of the two-dimensional projection point set. All α-shaped edges that meet the conditions are connected in sequence to form a closed polygonal outline. The outermost and longest closed edge is retained, which is the two-dimensional projection outline of the fabric drape model.

[0079] In one possible embodiment, based on a 3D modeling method, the 3D drape shape of the two fabric samples to be compared is obtained as a 2D projection onto the same horizontal reference plane, and the discrete point sequence on the 2D projection contour is extracted. Specifically, this includes the following steps:

[0080] Step 1: Create a virtual drape tester model in 3D modeling software, import the virtual drape tester model into fabric simulation software, place the virtual fabric sample on the virtual drape tester model to perform drape simulation, and obtain a virtual drape fabric model.

[0081] Specifically, referencing the structure and principle of a real drape tester, a virtual drape tester model is created in 3D modeling software. The virtual drape tester model also includes a supporting frustum and a clamping plate, with the diameter of the clamping plate set to 120mm. The diameter of the virtual fabric sample is 240mm. The virtual drape tester model is imported into fabric simulation software, and the virtual fabric sample is placed on the virtual drape tester model for drape simulation. The virtual fabric sample is adjusted according to the fabric property parameters measured in the experiment until the drape shape of the virtual fabric sample is completely stable, thus obtaining the virtual drape fabric model.

[0082] Step 2: Define the center of the virtual fabric sample as the origin of the coordinate system, and define the plane containing the circular area of ​​the virtual fabric sample fixed by the clamping plate of the virtual suspension tester model as the horizontal reference plane, i.e. shaft and The orthogonal plane of the axis, the normal axis of the horizontal reference plane is defined as The axis is defined as the drape direction of the virtual fabric sample. The positive direction of the axis;

[0083] Step 3: Import the virtual draped fabric model into the 3D data processing software to obtain the initial natural draped 3D model of the virtual fabric sample.

[0084] Step 4: Denoise, fill holes and clean up non-manifold edges of the initial natural draped 3D mesh model by reverse engineering to obtain the draped 3D mesh model of the virtual fabric sample.

[0085] Step 5: Extract the coordinates of all grid vertices of the suspended 3D mesh model using reverse engineering techniques and project them onto the horizontal reference plane to obtain the two-dimensional projection point set of the virtual fabric sample on the horizontal reference plane;

[0086] Step 6: Use the α-shape algorithm to calculate the envelope of the two-dimensional projection point set, obtain the two-dimensional projection contour, and extract the discrete point sequence on the two-dimensional projection contour.

[0087] Specifically, the α parameter is adaptively selected based on the density of the two-dimensional projection point set. All α-shaped edges that meet the conditions are connected in sequence to form a closed polygonal outline. The outermost and longest closed edge is retained, which is the two-dimensional projection outline of the fabric drape model.

[0088] In this embodiment, the discrete point sequences on the two-dimensional projected contours of the two fabric samples are respectively and ,in, , … … These respectively represent the 1st, 2nd, ..., ...th elements on the two-dimensional projected contour of the first fabric sample. … discrete points, This represents the total number of discrete points on the two-dimensional projected profile of the first fabric sample. To meet integers, , … … The first, second, ..., numbers on the two-dimensional projected contour of the second fabric sample are respectively represented as the first, second, ... … discrete points, This represents the total number of discrete points on the two-dimensional projected profile of the second fabric sample. To meet integers, the distance matrix is , The Line number Column elements express and The spacing, ,in, , They represent of Axis coordinates Axis coordinates , They represent of Axis coordinates Axis coordinates.

[0089] In this embodiment, calculating the minimum and maximum cumulative distances of discrete point sequences on the two-dimensional projected contours of two fabric samples based on the distance matrix specifically includes the following steps:

[0090] Step 1: Construct the minimum cumulative distance matrix based on the distance matrix , The Line number Column elements Indicates starting position Corresponding discrete point pairs Starting from the beginning, and following the path constraint of moving only to the right, down, or down-right each time, traverse to the current position. Corresponding discrete point pairs The minimum cumulative distance among all paths, ,in, , , They represent The Line number Column element, first Line number Column element, first Line number Column elements, express , , The minimum value in, with the initial condition being ,in, , They represent , The element in the first row and first column, The Line number Column elements This is the minimum cumulative distance between discrete point sequences on the two-dimensional projected contours of two fabric samples. ;

[0091] Step 2: Construct the maximum cumulative distance matrix based on the distance matrix. , The Line number Column elements Indicates starting position Corresponding discrete point pairs Starting from the beginning, and following the path constraint of moving only to the right, down, or down-right each time, traverse to the current position. Corresponding discrete point pairs The maximum cumulative distance among all paths. ,in, , , They represent The Line number Column element, first Line number Column element, first Line number Column elements, express , , The maximum value in, with the initial condition being ,in, express The element in the first row and first column, The Line number Column elements This refers to the maximum cumulative distance of the discrete point sequence on the two-dimensional projected contours of the two fabric samples. .

[0092] In this embodiment, the formula for calculating the drape similarity index of the two fabric samples is as follows:

[0093] ;

[0094] in, This index represents the similarity of the drape of two fabric samples. The value range of is [0,1]. , These represent the minimum cumulative distance and the maximum cumulative distance of the discrete point sequence on the two-dimensional projected contours of the two fabric samples, respectively.

[0095] like If the drape of the two fabric samples is similar, then the similarity is determined to be highly similar.

[0096] like If the drape of the two fabric samples is found to be moderately similar, then the similarity is determined to be moderate.

[0097] like If the drape similarity of the two fabric samples is low, then the similarity is determined to be low.

[0098] like If the drape of the two fabric samples is not similar, then they are determined to be dissimilar.

[0099] In one possible embodiment, the fabric drape similarity evaluation method provided in this invention is used to evaluate the drape similarity between a given piece of real fabric and two virtual fabrics.

[0100] The real fabric is designated as sample R: a rayon blend fabric (20% polyester, 69% rayon, 11% nylon), with a weight of 136 g / m². 2 .

[0101] like Figure 2 As shown, the three-dimensional projection of the three-dimensional drape shape on the horizontal reference plane of the real fabric sample R is obtained by three-dimensional scanning method, and the discrete point sequence on the two-dimensional projection contour is extracted and denoted as sequence A.

[0102] The two virtual fabrics are designated as virtual fabric sample V1 and virtual fabric sample V2, respectively.

[0103] like Figure 3 As shown in (a) and (b), the two-dimensional projections of virtual fabric sample V1 and virtual fabric sample V2 on the horizontal reference plane are obtained by using a three-dimensional modeling method, and the discrete point sequences on the two-dimensional projection contours are extracted and denoted as sequence B1 and sequence B2.

[0104] The minimum and maximum cumulative distances between sequence A and sequence B1 are calculated using the cumulative distance algorithm provided in this embodiment. The alignment results of sequence A and sequence B1 are as follows: Figure 4 As shown in (a).

[0105] The minimum and maximum cumulative distances between sequence A and sequence B2 are calculated using the cumulative distance algorithm provided in this embodiment. The alignment results of sequence A and sequence B2 are as follows: Figure 4 As shown in (b).

[0106] The drape similarity index algorithm provided in this embodiment is used to calculate the drape similarity index between the real fabric sample R and the virtual fabric sample V1 and the virtual fabric sample V2, respectively. The calculation results are shown in Table 1.

[0107] Table 1: Calculation results of drape similarity index between real fabric sample R and virtual fabric samples V1 and V2

[0108] .

[0109] As shown in Table 1, the drape similarity index between the virtual fabric sample V1 and the real fabric sample R is 0.9222, which is highly similar. This indicates that the drape profile of the virtual fabric sample V1 is highly consistent with that of the real fabric sample R in terms of key features such as overall shape, wrinkle distribution, and edge fluctuation. Compared with the virtual fabric sample V2 (drape similarity index of 0.6562), it can better reproduce the drape behavior of the real fabric.

[0110] In one possible embodiment, the fabric drape similarity evaluation method provided in this embodiment of the invention is used to evaluate the drape similarity of two fabrics of different materials.

[0111] The two fabrics of different materials are denoted as:

[0112] Fabric sample M: ​​Ramie-cotton blended fabric (50% ramie, 50% cotton), weight 67 g / m². 2 ;

[0113] Fabric sample N: Linen-rayon blend fabric (46% linen, 54% rayon), weight 138 g / m² 2 .

[0114] For each type of fabric sample, three independent samples (circular in size with a diameter of 240 mm) were taken for repeatability testing, and the sample numbers were M1, M2, M3 and N1, N2, N3, respectively. The drape coefficients of each sample for both types of fabrics are shown in Table 2.

[0115] Table 2: Drape coefficients of fabric samples M and N

[0116] .

[0117] Since the drape shape obtained from each independent sample is not exactly the same, this embodiment performs a cyclic comparison for each sample of fabric sample M and fabric sample N, where M×N represents the comparison between each sample of fabric sample M and each sample of fabric sample N. The calculation results are shown in Tables 3 and 4.

[0118] Table 3: Calculation results of drape similarity index between fabric sample M and fabric sample N for each sample.

[0119] .

[0120] As shown in Table 3, when evaluating the drape morphology of fabrics using the drape similarity index, pairwise comparisons can be made between three repeated experimental samples of the same fabric, and the drape similarity index can be calculated for each sample. The average of the three paired drape similarity indices is then used as the comprehensive characterization index of the fabric's drape morphology. Specifically, for fabric sample M, the pairwise paired drape similarity indices for the three repeated experiments were 0.9218, 0.8366, and 0.9173, with a mean of 0.8919; for fabric sample N, the pairwise paired drape similarity indices for the three repeated experiments were 0.8181, 0.9283, and 0.8342, with a mean of 0.8602. This indicates that although there are some random fluctuations in the drape pattern in a single experiment, the drape similarity index among repeated experiments of the same fabric remains at a high level (>0.8). This further shows that the drape pattern of the same fabric maintains a high degree of consistency in repeated experiments. Therefore, it is recommended that the average value of the drape similarity index of three repeated experiments be used as an effective basis for characterizing the drape pattern of the fabric.

[0121] Table 4: Calculation results of drape similarity index between fabric sample M and fabric sample N in cyclic comparisons

[0122] .

[0123] As shown in Table 4, to evaluate the drape similarity between fabric sample M and fabric sample N, three repeated experiments (M1, M2, M3) of fabric sample M were compared cyclically with three repeated experiments (N1, N2, N3) of fabric sample N, resulting in a total of 9 pairs. The drape similarity index of each pair was calculated, and the average of these 9 pairs was used as the comprehensive characterization of the drape similarity between fabric sample M and fabric sample N. The drape similarity indices of the 9 pairs between fabric sample M and fabric sample N ranged from 0.4765 to 0.7586, with a mean of 0.6554, which is considered a moderate level of similarity, reflecting differences in the drape and pleat patterns of the two fabrics.

[0124] It is worth noting that although the drape similarity index analysis showed morphological differences between fabric sample M and fabric sample N, the independent samples t-test of the drape coefficients of fabric samples M and N in Table 2 showed that the difference did not reach statistical significance (p=0.374>0.05). This comparison indicates that the traditional drape coefficient has limited sensitivity in distinguishing fabric drape morphology, while the drape similarity evaluation method based on the drape similarity index can more comprehensively and precisely reflect the overall similarity of the two fabrics in drape morphology through multiple comparisons, thus providing a more effective quantitative basis for the differentiated evaluation of fabric drape performance.

[0125] The fabric drape similarity evaluation method provided in this invention effectively overcomes the shortcomings of traditional methods in characterizing drape morphology details through innovative contour feature extraction and sequence alignment strategies, providing a powerful technical tool for digital evaluation of fabric drape performance, product development, quality control, and standard setting.

[0126] This invention provides a fabric drape similarity evaluation device, comprising:

[0127] The discrete point extraction module is used to obtain the two-dimensional projection of the three-dimensional drape shape of the two fabric samples to be compared on the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection contour.

[0128] The distance matrix construction module is used to construct a distance matrix based on the spacing between discrete points on the two-dimensional projected contours of two fabric samples.

[0129] The cumulative distance calculation module is used to calculate the minimum and maximum cumulative distances of discrete point sequences on the two-dimensional projected contours of two fabric samples based on the distance matrix.

[0130] The similarity assessment module is used to calculate the drape similarity index of two fabric samples based on the minimum and maximum cumulative distances, and to quantitatively assess the drape similarity of the two fabric samples through the drape similarity index.

[0131] The fabric drape similarity evaluation device provided in this embodiment of the invention can execute the fabric drape similarity evaluation method provided in this embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0132] This invention provides a computer device, comprising:

[0133] Storage medium used to store computer programs;

[0134] A processor is used to execute a computer program to implement the fabric drape similarity evaluation method provided in the embodiments of the present invention.

[0135] This invention provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the fabric drape similarity evaluation method provided in this invention.

[0136] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the fabric drape similarity evaluation method provided in this embodiment of the invention.

[0137] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating the similarity of fabric drape, characterized in that, include: Obtain the two-dimensional projection of the three-dimensional drape shape of the two fabric samples to be compared onto the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection profile. A distance matrix is ​​constructed based on the spacing between discrete points on the two-dimensional projected contours of the two fabric samples; The minimum and maximum cumulative distances of discrete point sequences on the two-dimensional projected profiles of the two fabric samples are calculated based on the distance matrix, including: Construct the minimum cumulative distance matrix based on the distance matrix. , The Line number Column elements Indicates starting position Corresponding discrete point pairs Starting from the beginning, and following the path constraint of moving only to the right, down, or down-right each time, traverse to the current position. Corresponding discrete point pairs The minimum cumulative distance among all paths, ,in, Distance matrix The Line number Column elements, , , They represent The Line number Column element, first Line number Column element, first Line number Column elements, express , , The minimum value in, with the initial condition being ,in, , They represent , The element in the first row and first column, The Line number Column elements This is the minimum cumulative distance between discrete point sequences on the two-dimensional projected contours of two fabric samples. ; Construct the maximum cumulative distance matrix based on the distance matrix. , The Line number Column elements Indicates starting position Corresponding discrete point pairs Starting from the beginning, and following the path constraint of moving only to the right, down, or down-right each time, traverse to the current position. Corresponding discrete point pairs The maximum cumulative distance among all paths. ,in, , , They represent The Line number Column element, first Line number Column element, first Line number Column elements, express , , The maximum value in, with the initial condition being ,in, express The element in the first row and first column, The Line number Column elements This refers to the maximum cumulative distance of the discrete point sequence on the two-dimensional projected contours of the two fabric samples. ; The drape similarity index of the two fabric samples is calculated based on the minimum and maximum cumulative distances, and the drape similarity of the two fabric samples is quantitatively evaluated through the drape similarity index.

2. The fabric drape similarity evaluation method according to claim 1, characterized in that, Obtain the two-dimensional projections of the three-dimensional drape morphology of the two fabric samples to be compared onto the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection profile, including: Place the fabric sample on the drape tester, define the center of the fabric sample as the origin of the coordinate system, and define the plane containing the circular area of ​​the fabric sample fixed by the clamping plate of the drape tester as the horizontal reference plane. shaft and The orthogonal plane of the axes; A top view of the fabric sample in its natural hanging state is captured by a camera fixed directly above the origin of the coordinate system, and a two-dimensional projection of the fabric sample on the horizontal reference plane is obtained. Image processing software is used to denoise, convert grayscale, and binarize the two-dimensional projection to obtain a binary image; Edge detection is performed on the binary image using a contour extraction algorithm to obtain a two-dimensional projected contour, and then a sequence of discrete points on the two-dimensional projected contour is extracted.

3. The fabric drape similarity evaluation method according to claim 1, characterized in that, Obtain the two-dimensional projections of the three-dimensional drape morphology of the two fabric samples to be compared onto the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection profile, including: Place the fabric sample on the drape tester, define the center of the fabric sample as the origin of the coordinate system, and define the plane containing the circular area of ​​the fabric sample fixed by the clamping plate of the drape tester as the horizontal reference plane. shaft and The orthogonal plane of the axis, the normal axis of the horizontal reference plane is defined as The axis is defined as the drape direction of the fabric sample. The positive direction of the axis; A 3D scanner was used to perform multi-angle 3D scanning on the fabric sample to obtain the initial natural drape 3D mesh model of the fabric sample. The initial natural hanging 3D mesh model was denoised, hole-filling and non-manifold edge-cleaning were performed by reverse engineering to obtain the hanging 3D mesh model of the fabric sample. The coordinates of all grid vertices of the suspended three-dimensional mesh model are extracted by reverse engineering and projected onto the horizontal reference plane to obtain the two-dimensional projection point set of the fabric sample on the horizontal reference plane. The α-shape algorithm is used to calculate the envelope of the two-dimensional projection point set to obtain the two-dimensional projection contour, and the discrete point sequence on the two-dimensional projection contour is extracted.

4. The fabric drape similarity evaluation method according to claim 1, characterized in that, Obtain the two-dimensional projections of the three-dimensional drape morphology of the two fabric samples to be compared onto the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection profile, including: A virtual drape tester model is created in 3D modeling software and imported into fabric simulation software. A virtual fabric sample is placed on the virtual drape tester model to simulate drape, thus obtaining a virtual drape fabric model. The center of the virtual fabric sample is defined as the origin of the coordinate system, and the plane containing the circular area of ​​the virtual fabric sample fixed by the clamping plate of the virtual suspension tester model is defined as the horizontal reference plane. shaft and The orthogonal plane of the axis, the normal axis of the horizontal reference plane is defined as The axis is defined as the drape direction of the virtual fabric sample. The positive direction of the axis; Import the virtual draped fabric model into the 3D data processing software to obtain the initial natural draped 3D model of the virtual fabric sample. The initial natural hanging 3D mesh model was denoised, hole-filling, and non-manifold edge-cleaning were performed using reverse engineering techniques to obtain a hanging 3D mesh model of the virtual fabric sample. The coordinates of all grid vertices of the suspended three-dimensional mesh model are extracted by reverse engineering and projected onto the horizontal reference plane to obtain the two-dimensional projection point set of the virtual fabric sample on the horizontal reference plane. The α-shape algorithm is used to calculate the envelope of the two-dimensional projection point set to obtain the two-dimensional projection contour, and the discrete point sequence on the two-dimensional projection contour is extracted.

5. The fabric drape similarity evaluation method according to claim 1, characterized in that, The discrete point sequences on the two-dimensional projected contours of the two fabric samples are as follows: and ,in, , … … These respectively represent the 1st, 2nd, ..., ...th elements on the two-dimensional projected contour of the first fabric sample. … discrete points, , … … The first, second, ..., numbers on the two-dimensional projected contour of the second fabric sample are respectively represented as the first, second, ... … There are discrete points, and the distance matrix is... , The Line number Column elements express and The spacing, ,in, , They represent of Axis coordinates Axis coordinates , They represent of Axis coordinates Axis coordinates.

6. The fabric drape similarity evaluation method according to claim 1, characterized in that, The formula for calculating the drape similarity index of two fabric samples is as follows: ； in, This index represents the similarity of the drape of two fabric samples. , These represent the minimum cumulative distance and the maximum cumulative distance of the discrete point sequence on the two-dimensional projected contours of the two fabric samples, respectively. like If the drape of the two fabric samples is similar, then the similarity is determined to be highly similar. like If the drape of the two fabric samples is found to be moderately similar, then the similarity is determined to be moderate. like If the drape similarity of the two fabric samples is low, then the similarity is determined to be low. like If the drape of the two fabric samples is not similar, then they are determined to be dissimilar.

7. A fabric drape similarity evaluation device, characterized in that, include: The discrete point extraction module is used to obtain the two-dimensional projection of the three-dimensional drape shape of the two fabric samples to be compared on the same horizontal reference plane, and extract the discrete point sequence on the two-dimensional projection contour. The distance matrix construction module is used to construct a distance matrix based on the spacing between discrete points on the two-dimensional projected contours of two fabric samples. The cumulative distance calculation module is used to calculate the minimum and maximum cumulative distances of discrete point sequences on the two-dimensional projected contours of two fabric samples based on the distance matrix, including: Construct the minimum cumulative distance matrix based on the distance matrix. , The Line number Column elements Indicates starting position Corresponding discrete point pairs Starting from the beginning, and following the path constraint of moving only to the right, down, or down-right each time, traverse to the current position. Corresponding discrete point pairs The minimum cumulative distance among all paths, ,in, Distance matrix The Line number Column elements, , , They represent The Line number Column element, first Line number Column element, first Line number Column elements, express , , The minimum value in, with the initial condition being ,in, , They represent , The element in the first row and first column, The Line number Column elements This is the minimum cumulative distance between discrete point sequences on the two-dimensional projected contours of two fabric samples. ; Construct the maximum cumulative distance matrix based on the distance matrix. , The Line number Column elements Indicates starting position Corresponding discrete point pairs Starting from the beginning, and following the path constraint of moving only to the right, down, or down-right each time, traverse to the current position. Corresponding discrete point pairs The maximum cumulative distance among all paths. ,in, , , They represent The Line number Column element, first Line number Column element, first Line number Column elements, express , , The maximum value in, with the initial condition being ,in, express The element in the first row and first column, The Line number Column elements This refers to the maximum cumulative distance of the discrete point sequence on the two-dimensional projected contours of the two fabric samples. ; The similarity assessment module is used to calculate the drape similarity index of two fabric samples based on the minimum and maximum cumulative distances, and to quantitatively assess the drape similarity of the two fabric samples through the drape similarity index.

8. A computer device, characterized in that, include: Storage medium used to store computer programs; A processor for executing the computer program to implement the fabric drape similarity evaluation method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the fabric drape similarity evaluation method according to any one of claims 1 to 6.

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