CN111104740A - Clothing pressure measuring method and system based on virtual human model - Google Patents
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Abstract
The invention discloses a method and a system for measuring clothing pressure based on a virtual mannequin, wherein the method comprises the following steps: constructing a virtual mannequin with the same size as the entity mannequin, and deriving a color chart of the virtual clothing pressure condition; processing the color map, and obtaining a virtual garment pressure value of a specific part according to a map mapping function; acquiring a real garment pressure value of the entity mannequin; carrying out correlation analysis on the real garment pressure value and the virtual garment pressure value and verifying that the real garment pressure value and the virtual garment pressure value are in a linear change relationship; carrying out regression analysis on the real garment pressure value and the virtual garment pressure value, and establishing a regression relation equation of the real garment pressure value and the virtual garment pressure value; and acquiring the parameters of the virtual clothes to be detected and the parameters of the virtual human model, and outputting a clothes pressure measurement value. According to the invention, by establishing the virtual human model, the garment pressure corresponding to the garment parameters can be measured without actually putting on or taking off the garment, so that the detection efficiency is greatly improved.
Description
Technical Field
The invention relates to the field of pressure garment testing, in particular to a garment pressure measuring method and system based on a virtual human model.
Background
Garment pressure refers to the force of the garment acting on the surface of the human body. The pressure generated by the tight binding of the garment, i.e. the bundling pressure, is mainly studied at present, for example, western early bustier, japanese waist band, korean skirt waist, and modern elastic band, elastic briefs, etc. The factors influencing the garment pressure are mainly as follows: the clothing style is as follows: elastic garment compression decreases as its width margin increases; fabric elasticity: for the same skin style, the fabric with good elasticity has smaller pressure effect than the fabric with poor elasticity; human body shape: the body surface curved surfaces of different human bodies are different, so the stress conditions are also different; for the same person, the curved surface conditions of different parts are different, and the stress conditions of different parts are also different. This depends mainly on the magnitude of the curvature of the point of force.
The current methods for measuring the garment pressure are based on the actual wearing and taking off of the garment, for example, the dynamic garment pressure in the wearing state is measured by installing an air bag between the part to be measured of the human body and the garment. Patent CN201811505689 discloses an intelligent pressure test mannequin for simulating softness of a human body, and the contact pressure value between sports equipment of a mannequin main body is measured through a muscle layer and a skin layer for simulating softness of the human body.
The 3D clothing CAD virtual technology is a new technology in the clothing production industry, but is still not mature at present, for example, the main use scene of the 3D Runaway software is only to display the approximate situation of clothing pressure on a virtual human model after the clothing is virtually sewed, so as to be used for judging the fit degree of the model, the approximate situation of the clothing pressure can be represented only by the color depth, the obtained clothing pressure information is very fuzzy and perceptual, and the clothing pressure information can not be used for production reality at all.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the clothes pressure measuring method and system based on the virtual human model.
The purpose of the invention is realized by the following technical scheme:
the invention provides a garment pressure measuring method based on a virtual human model, which comprises the following steps:
constructing a virtual mannequin with the same size as the entity mannequin, and outputting a color chart of the virtual clothing pressing condition;
processing the color map, and obtaining a virtual garment pressure value of a specific part according to a map mapping function;
acquiring a real garment pressure value of the entity mannequin;
carrying out correlation analysis on the real garment pressure value and the virtual garment pressure value and verifying that the real garment pressure value and the virtual garment pressure value are in a linear change relationship;
carrying out regression analysis on the real garment pressure value and the virtual garment pressure value, and establishing a regression relation equation of the real garment pressure value and the virtual garment pressure value;
and acquiring the parameters of the virtual clothes to be detected and the parameters of the virtual human model, and outputting a clothes pressure measurement value.
As a preferred technical scheme, the entity mannequin adopts a real person or an intelligent pressure test mannequin simulating the softness of a human body.
As a preferred technical solution, the real garment pressure measurement portion of the solid mannequin comprises: lower abdomen, thigh base, crotch, thigh lower, knee, ankle, hip.
As a preferable technical scheme, the obtaining of the real garment pressure value of the real human model adopts an air bag type garment pressure measuring method to obtain the real garment pressure value of the real human model, an air bag with the thickness less than 3mm is arranged on a part to be measured, and the dynamic garment pressure in a wearing state is measured through the resistance change of a semiconductor pressure sensor connected with the air bag.
As a preferred technical solution, the method for performing the correlation analysis between the real garment pressure value and the virtual garment pressure value adopts any one or more of a rank correlation test, a KENDALL correlation test, a pearson correlation test, or a typical correlation analysis method.
As a preferable technical solution, the typical correlation analysis method performs a corresponding analysis on each part of the real garment pressure measurement and a measurement value of each part of the virtual mannequin.
As a preferred technical solution, the analyzing the correlation between the real garment pressure value and the virtual garment pressure value and verifying that the correlation is a linear variation relationship, specifically, the analyzing method is any one of a t-test method, an F-test method or a correlation coefficient test method.
As a preferred technical scheme, the establishing of the regression relation equation between the real garment pressure value and the virtual garment pressure value specifically comprises:
Y＝μ(x)+ε
wherein x represents the independent variable virtual garment pressure value, Y represents the dependent variable real garment pressure value, and epsilon is a random error which is the sum of various factors influencing Y except the independent variable.
The invention also provides a clothing pressure measuring system based on the virtual mannequin, which is characterized by comprising the following components:
the system comprises a virtual human model construction module, a color map processing module, a real garment pressure value acquisition module, a correlation analysis verification module, a regression analysis module and a garment pressure measured value output module;
the virtual human model building module is used for building a virtual human model with the same size as the physical human model and deriving a color map of the virtual garment pressure condition;
the color map processing module is used for processing the color map and obtaining a virtual garment pressure value of a specific part according to a map mapping function;
the real clothing pressure value acquisition module is used for acquiring a real clothing pressure value of the entity mannequin;
the correlation analysis and verification module is used for carrying out correlation analysis on the real garment pressure value and the virtual garment pressure value and verifying that the real garment pressure value and the virtual garment pressure value are in a linear change relationship;
the regression analysis module is used for carrying out regression analysis on the real clothing pressure value and the virtual clothing pressure value and establishing a regression relation equation between the real clothing pressure value and the virtual clothing pressure value;
the clothing pressure measured value output module is used for acquiring the virtual clothing parameters to be detected and the virtual human model parameters and outputting clothing pressure measured values.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the fuzzy pressure distribution condition is quantized into an accurate numerical value which can be applied to industrial production design, and the testing method has small calculated amount and low complexity.
2. According to the invention, by establishing the virtual human model, the garment pressure corresponding to the garment parameters can be measured without actually putting on or taking off the garment, so that the detection efficiency is greatly improved.
Drawings
Fig. 1 is a flowchart of a method for measuring garment pressure based on a virtual human model according to the present embodiment;
FIG. 2(a), FIG. 2(b), and FIG. 2(c) are schematic diagrams of the front, side, and back of the measurement site of the present embodiment, respectively
FIG. 3 is a first exemplary variable coordinate scattergram according to the present embodiment;
FIG. 4 is a third exemplary variable coordinate scattergram according to the present embodiment;
FIG. 5 is a color compression chart of the DRaways virtual garment of this embodiment 3;
fig. 6 is a virtual garment pressure value display interface in the garment pressure measuring system based on the virtual human model according to the embodiment;
fig. 7 is a dialog box enlargement interface of the virtual clothing pressure value display interface colors according to the embodiment.
Detailed Description
For better understanding of the technical solutions of the present invention, the following detailed description is provided for the embodiments of the present invention with reference to the accompanying drawings, but the embodiments of the present invention are not limited thereto.
Examples
As shown in fig. 1, the present embodiment provides a method for measuring garment pressure based on a virtual human model, including the following steps:
s1, constructing a virtual mannequin with the same size as the entity mannequin, and deriving a color chart of the virtual clothing pressure condition;
s2, processing the color map, and obtaining a virtual garment pressure value of a specific part according to the map mapping function;
constructing a palette constructing function in the palette class, and initializing two variables in the function, wherein the two variables define the foreground color and the background color of the palette;
grabbing numerical values: firstly, different colors of a picture form an image number by three sets of RGB (yellow, green and red) of colors expressed by a computer, a mouse obtains a specific RGB value of the image number, then according to a virtual clothing pressure value corresponding to the RGB value defined by the picture (the value can be captured in a picture derived by a third-party program), a virtual clothing pressure value of a specific part can be expressed by converting the RGB value and the virtual clothing pressure according to a proportion, and then a virtual clothing pressure value closer to the real clothing pressure is calculated by a formula Y of 9.4024+ 4.4852X;
s3, acquiring the real garment pressure value of the entity mannequin;
the entity human model is a real person, and the measuring part comprises:
as shown in fig. 2(a), the measurement site is front:
① lower abdomen the lower abdomen is formed by the accumulation of abdominal fat, where garment compression is very important in relation to the comfort of the garment after wearing it is also large due to the thick subcutaneous fat.
② the root of thigh, here the joint movement point, the combination of the bone characteristics and muscle fat, etc., determines that the point is the test point for the clothes pressure of the lower body trousers.
③ crotch part, the pressure on the garment under the crotch part should be relatively large because of the fat accumulation on the inner side of the thigh, and here, the starting point of the two legs of the trousers are separated, and the size of the crosspiece is the size of the circumference of the root of the leg and the boundary of the free area, so the pressure on the garment plays an important role in research.
④ thigh, the quadriceps muscle of thigh is the developed muscle of human body, since the test clothes are close-fitting pants, the belly of the thigh part is tight, and the clothes pressure reaches a large value.
⑤ lower thigh part, the pressure of the trousers at the lower part will gradually decrease as judged by perceptual knowledge.
⑥ the knee is the joint between the thigh and the calf.
⑦ ankle this point is the reference point for measuring the length of the trousers.
As shown in fig. 2(b), test points are arranged along the side seam of the pants at the side of the measurement position, and the positions of the test points correspond to the front test position;
as shown in fig. 2(c), 2-7 points on the back of the measuring part correspond to the front test points, and 2, 4, 5, 6 and 7 test points are laid along the back center line of the pants;
buttock salient point: the gluteus maximus attached to the pelvis and the fat form a remarkable protruding part of a human body, which is a main reason for the female body to be in an S shape, and the garment pressure plays an important role in research, can reflect the fit degree of the pants and also can represent the comfort degree of the pants;
the real garment pressure value of the entity model is obtained through an air bag type garment pressure measuring method, an air bag with the thickness less than 3mm is placed on a part to be measured, the dynamic garment pressure in the wearing state is measured through the resistance change of a semiconductor pressure sensor connected with the air bag, and an air bag test garment pressure measuring instrument selects an AMI3037 AMI7062 model and has the technical indexes: measuring range of the sensor: 0-35kPa, accuracy +/-1.0 kPa, specification
Main cell measurement point: 10 points; the measuring range is 0-34 kPa; the output voltage is 0-3.4V; the precision is +/-0.2-0.45 kPa;
the garment pressures measured at each part of the front middle seam, the side seam and the rear middle seam of the grey cloth close-fitting pants/close-fitting pants and the transverse elastic cotton cloth close-fitting pants/close-fitting pants (negative looseness) in about 1 minute are taken as modes to represent the garment pressure of a certain part, and because the modes are the most frequent numbers, the modes can represent the garment pressure of a certain part to a great extent, and 80 real garment pressure data are obtained.
Through the measurement, a grey cloth front middle part garment pressure value table compared with a skin plate type is obtained, and is shown in the following table 1:
table 1 white gray fabric is compared with the front middle garment pressure value table unit of the fit panel type: v
The data of table 1 were processed as follows to give table 2 below:
(1) the mode of pressing and taking the real clothes: the method comprises the steps of performing summary processing on measured garment pressures of white gray fabric attached trousers/attached trousers and transverse elastic cotton fabric attached trousers/attached trousers (negative looseness amount) in front, middle and rear positions (positions corresponding to buttocks) for 1 minute, and determining to take a mode to represent the garment pressure of a certain position through analyzing parameters on an upper table, wherein the mode is the most frequent number, so that the mode can represent the garment pressure of a certain position to a great extent, and 80 real garment pressure data are obtained.
(2) Virtual garment pressing: and extracting corresponding 80 virtual clothing pressure values by utilizing clothing pressure data software.
(3) Unifying units: converting the real garment pressure unit into the unit g/cm consistent with the virtual garment pressure2. The unit conversion is performed according to the following formula.
True garment pressure measured by the instrument-10 true garment pressure (unit kpa)
Real garment pressure (unit kpa) · 10 · 2 ═ real garment pressure (unit g/cm)2)
Table 2 white gray fabric is compared with the transition table unit of the front middle garment pressure value of the veneer type: v
The resulting table of real garment pressures and virtual garment pressures is shown in table 3 below:
TABLE 3 table of real garment pressure and virtual garment pressure values
S4, analyzing the correlation between the real clothing pressure value and the virtual clothing pressure value and verifying that the real clothing pressure value and the virtual clothing pressure value are in a linear change relationship;
in the embodiment, a Pearson correlation test method is adopted to be matched with a typical correlation analysis method to verify the correlation, so that the test reliability is improved;
s4.1, performing correlation coefficient calculation and test by a Pear.test (X, Y) function in the R language by a Pearson correlation test method, inputting a real garment pressure value X and a virtual garment pressure value Y in a table 3, and outputting a correlation coefficient of 0.7912356; wherein the P value is also 2.2e-16< <0.05, and the original assumption is rejected, the variable X is considered to be positively correlated with the variable Y, namely the real clothing pressure and the virtual clothing pressure are positively correlated, and the calculation interval is (0.69 and 0.86) from the interval, and the X and the Y are also shown to be greatly positively correlated.
S4.2 canonical correlation analysis
(1) Principle of typical correlation analysis: in the above analysis methods, the real garment pressure and the virtual garment pressure are regarded as two groups of data corresponding to each other one by one, and the correlation between the real garment pressure and the virtual garment pressure is analyzed. Next, we subdivide these two sets of data, and divide the data into six sets, each set including two pairs (real and virtual pressure) of data, and introduce new parameters for typical correlation analysis, based on the heel, knee, mid-thigh (crotch cannot be subjected to typical correlation analysis because data is missing at a lateral position), mid-thigh circumference, front-middle/back-middle, and six positions.
Canonical correlation analysis (canonical correlation analysis) is a statistical method for analyzing the degree of correlation between two random variables, which can effectively explain the correlation between two random variables and another variable. Taking this data as an example, the real garment presses the heel, the knee cap, the middle of the thighThe garment pressure at six locations of the thigh circumference, anterior-medial/posterior-medial is (X)1,X2,...X6) Corresponding virtual garment pressure is (Y)1,Y2,...Y6)。
In general, assume that there are two random variables X1,X2,...X6And Y1,Y2,...YqThe correlation relationship is researched, and when p ═ q ═ 1, the correlation relationship between two variables X and Y is common; when p > 1 and q > 1, a method similar to principal component analysis is used to find the linear combination U of the group 1 variables and the linear combination V of the group 2 variables, i.e.
U＝a1X1+a2X2+...+apXp，
V＝b1Y1+b2Y2+...+bqYq，
The problem of studying the correlation between two sets of variables is then converted into a problem of studying the correlation between two sets of variables, and the respective coefficients a, b can be adjusted appropriately to maximize the correlation between the variables U and V, which is called a correlation canonical correlation, and an analysis method based on this principle is called canonical correlation analysis. In the embodiment, p is 6, q is 6, and the correlation problem of two groups of variables is converted into the correlation problem of two variables by using a typical correlation analysis method, wherein the number of samples is 12.
Let X ═ X1,X2,...,Xp)T,Y＝(Y1,Y2,...,Yq)TFor random vectors, using a linear combination of X and YTX and bTCorrelation between Y to investigate the correlation between X and Y, and hopefully find a and b, ρ (a)TX,bTY) is maximal.
By the definition of the correlation coefficient(s),
for any of α and c, d, there are
ρ(α(aTX)+β,c(bTY)+d)＝ρ(aTX,bTY)。
The above equation illustrates a that maximizes the correlation coefficientTX and bTY is not exclusive. Thus, in synthesizing variables, one can define
var(aTX)＝1,var(bTY)＝1
Let X ═ X1,X2,...,Xp)T,T＝(Y1,Y2,...,Yp)TRandom vector of dimension p + q
The mean value of (1) is 0, and the covariance matrix is positive. If a exists1＝(a11,a12,...,a1p)TAnd b1＝(b11,b12,...,b1q)TSo that
Is a problem of constraint
maxρ(aTX,bTY)
s.t.var(aTX)＝1
var(bTY)＝1
The maximum value of the objective function, then called,
the first pair (set) of typical variables (variables) for X, Y, called the correlation coefficient ρ (U) between them1,V1) Is the first typical correlation coefficient (canonicalcorrelation).
If a existsk＝(ak1,ak2,...,akp)TAnd bk＝(bk1,bk2,...,bkq)TSuch that:
(a)
and k-1 above are not related to typical variables;
(b)
(c)
and
the correlation coefficient is the largest.
Then call
The k-th pair (group) of typical variables of X, Y are called the correlation coefficient rho (U) between themk,Vk) Is a typical correlation coefficient for the k (k 2, 3.., min { p, q }).
(2) Typical correlation analysis of real and virtual garment pressure R software: and analyzing the correlation between the real garment pressure and the virtual garment pressure by using a typical correlation coefficient, wherein the garment pressure is divided into 6 groups of a heel, a knee upper part, a thigh middle part, a thigh root circumference and a front middle part/back middle part for corresponding analysis. The specific program language and the test result are shown in the appendix:
(3) typical correlation analysis results of real and virtual garment pressure R software: where cor is a typical correlation coefficient, it is known that the typical correlation coefficient of the real garment pressure and the virtual garment pressure is 1, that is, very typical correlation, xcoef is a coefficient corresponding to data X, also called typical loads (canonical loads) with respect to data X, that is, a transpose of a sample typical variable U coefficient matrix a; ycoef is the coefficient corresponding to data Y, also called the typical payload on data Y, i.e. the transpose of the sample typical variable V coefficient matrix B; $ xcenter is the center of data X, i.e., the sample mean of data X
$ ycenter is the data Y center, i.e., the sample mean of data Y
Since the data has been normalized, the sample mean calculated here is 0.
For real and virtual garment pressure data, the mathematical meaning corresponding to the calculation result is:
wherein
i is 1,2,3 is normalized data, and the corresponding correlation coefficient is:
ρ(U1,V1)＝0.796,ρ(U2,V2)＝0.201,ρ(U3,V3)＝0.0726
it can be seen that the coefficients are not unique, but any multiple thereof.
The number of samples is calculated according to the score under the representative variables below. Since U equals AX and V equals BY, the R program to calculate the score is:
U<-as.matrix(test[,1:6])％*％ca$xcoef
>V<-as.matrix(test[,7:12])％*％ca$ycoef
the above formula is to calculate the number of samples based on the scores under typical variables, and the present embodiment is drawn with the associated variable U1,V1And U3, V3A data scatter plot which is a coordinate, as shown in fig. 3, in which the coordinate scatter plot represents a first typical variable; as shown in fig. 4, the dotted points of the coordinates in the figure represent the third exemplary variable;
as can be seen from the two graphs, the points in the coordinate scattergram as the first typical variable are all on a straight line (the corresponding cor typical correlation coefficient is 1.0000), while the points in the coordinate scattergram as the third typical variable are relatively dispersed (the corresponding cor typical correlation coefficient is 0.9608820), but are still substantially near a straight line, so that the typical correlation between the real garment pressure and the virtual garment pressure can be determined to be very significant.
The correlation between the numerical values of the real garment pressure and the virtual garment pressure is detected to be consistent by the correlation detection method, so that the real garment pressure and the virtual garment pressure have very obvious correlation, the next operation can be carried out, the two groups of numbers are subjected to linear regression, the linear relation between the two groups of numbers is analyzed, a linear regression relation is obtained, and the significance effect of the relation is detected.
In the embodiment, the correlation coefficient test method verifies that the two are in a linear variation relationship:
s4.3, order β1Denotes the rate of change of E (Y) linearly with X, if β10 then E (Y) does not change linearly with X, only when β1Not equal to 0, E (Y) varies linearly with X, and only then does the univariate linear regression equation make sense. Thus, the hypothesis is tested as
H0:β1＝0,H1:β1≠0
Note the book
Let R be the sample correlation coefficient, and for a given significance level α, look up the correlation coefficient threshold table to obtain Rα(n-2), the rejection field of the test is
|R|＞rα(n-2)
When rejecting H0The linear regression equation is considered significant.
(2) Linear regression analysis of real and virtual garment pressure R software: the program language and the result of the univariate linear regression relationship of the real garment pressure and the virtual garment pressure are analyzed by using R software and are shown in an appendix.
(3) The linear regression analysis results of the real and virtual garment pressure R software are as follows: the formula of the corresponding regression model is listed in the first part (call) of the calculation: lm (format ═ y to 1+ x). Listed in the second step (Residuals:) are the minimum-28.954, 1/4 quantile-7.091, the median 3.825, 3/4 quantile 3.825 and the maximum 51.108 points of the residual.
In the third part of the calculation (Coefficients:), Estimate represents the regression equation parameters, i.e.
(standard deviation) denotes the standard deviation of the regression parameter, i.e.
value is t value, i.e.:
pr (> | T |) represents a P value, i.e., a probability value P { T > | TzhiAnd l. There are also notations where ". x" indicates extreme prominence, ". x" indicates highly prominent, ". x" indicates prominence, ". j" indicates less prominence, and no notation is not as prominent. The data inspection result shows that the expression is extremely obvious, namely the inspection shows that the regression relation between the real clothes pressure and the virtual clothes pressure is obvious.
In the fourth part of the calculation, the Residual standard error represents the standard deviation of the Residual error, i.e. the error value
The degree of freedom is n-2. Multiple R-Squared is the square of the correlation coefficient, i.e.
The R-Squared test shows that the regression relationship between the real clothes pressure and the virtual clothes pressure is obvious, and the correlation test is passed.
F-static denotes the F statistic, i.e. by correlation test
The degree of freedom of the data in this paper is (1, 78), and the result obtained by F test is 130.2, i.e.
F≥F0.99(1,78)
In the inspection rejection region with the significance level of 99%, the inspection shows that the regression relationship between the real and the virtual clothes pressure is significant.
P-value is the value of P, i.e., the probability value P { F > | FzhiL, by correlation test. From the result of the calculationIt can be seen that the regression equation passes the check of the regression parameters and the check of the regression equation.
S5, carrying out regression analysis on the real clothing pressure value and the virtual clothing pressure value, establishing a regression relation equation of the real clothing pressure value and the virtual clothing pressure value, and writing the equation into software;
the equation for establishing the regression relationship between the real clothing pressure value and the virtual clothing pressure value is as follows:
Y＝μ(x)+ε
wherein x represents an independent variable virtual garment pressure value; y represents the dependent variable real garment pressure value; ε is the random error, which is the sum of the various factors that have an effect on Y except for the independent variable; and substituting the real garment pressure value and the virtual garment pressure value to obtain a regression equation of 9.4024+ 4.4852X.
And S6, importing the parameters of the virtual clothes to be detected and the parameters of the virtual human model to obtain the measured value of the clothes pressure.
The embodiment further provides a garment pressure measuring system based on a virtual mannequin, wherein the system is based on 3d roadways software, and the system comprises: the system comprises a virtual human model construction module, a color map processing module, a real garment pressure value acquisition module, a correlation analysis verification module, a regression analysis module and a garment pressure measured value output module;
in this embodiment, the virtual human model constructing module is configured to construct a virtual human model having the same size as the physical human model, and derive a color map of the virtual garment pressure;
in this embodiment, the color map processing module is configured to process the color map, and obtain a virtual garment pressure value of a specific location according to a map mapping function;
in this embodiment, the real garment pressure value obtaining module is configured to obtain a real garment pressure value of the physical model;
in this embodiment, the correlation analysis and verification module is configured to perform correlation analysis between the real garment pressure value and the virtual garment pressure value and verify that the two are in a linear variation relationship;
in this embodiment, the regression analysis module is configured to perform regression analysis on the real garment pressure value and the virtual garment pressure value, and establish a regression relationship equation between the real garment pressure value and the virtual garment pressure value;
in this embodiment, the clothing pressure measurement value output module is configured to obtain a virtual clothing parameter to be detected and a virtual human model parameter, and output a clothing pressure measurement value.
As shown in fig. 5, the virtual garment pressure module of 3DRaways displays the approximate distribution of garment pressures. The right side of the picture is a ruler, the ruler shows the range of the virtual clothing pressure (101.8g/cm 2-0.3 g/cm2), the ruler is red to blue from top to bottom, and the red gauge pressure is the largest (101.8 g/cm)2) Blue represents the minimum pressure (-0.3 g/cm)2) The middle color is gradually changed from red to blue, and the corresponding clothing pressure is from 101.8g/cm2～-0.3g/cm2But there is no specific numerical value. And the virtual clothes pressure of the trousers after the trousers are virtually worn corresponds to the colors of the ruler marks one by one.
After 10 pictures are tested (including clothes pressure in different ranges of trousers, knitwear, evening dress, business suit and the like), the software processes picture information, the accuracy rate of the obtained virtual clothes pressure value reaches more than 95%, the sensitivity is very good, the requirement of extracting the virtual clothes pressure value can be met, and the software can be used for thesis research and industrial production design. As shown in fig. 6, the virtual clothing pressure value display interface is shown, when the mouse is moved to the location where the clothing pressure value needs to be displayed, the virtual clothing pressure value of the corresponding scale of the point is displayed in the presis of the color dialog box, and as shown in fig. 7, the clothing pressure value of the point is 48.34g/cm2。
The embodiment is based on the existing 3D roadways virtual garment pressure module, the fuzzy pressure distribution situation is quantized into an accurate numerical value which can be applied to industrial production design, and the testing method is small in calculation amount and low in complexity.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (9)
1. A clothes pressure measuring method based on a virtual human model is characterized by comprising the following steps:
constructing a virtual mannequin with the same size as the entity mannequin, and outputting a color chart of the virtual clothing pressing condition;
processing the color map, and obtaining a virtual garment pressure value of a specific part according to a map mapping function;
acquiring a real garment pressure value of the entity mannequin;
carrying out correlation analysis on the real garment pressure value and the virtual garment pressure value and verifying that the real garment pressure value and the virtual garment pressure value are in a linear change relationship;
carrying out regression analysis on the real garment pressure value and the virtual garment pressure value, and establishing a regression relation equation of the real garment pressure value and the virtual garment pressure value;
and acquiring the parameters of the virtual clothes to be detected and the parameters of the virtual human model, and outputting a clothes pressure measurement value.
2. The garment pressure measuring method according to claim 1, wherein the physical mannequin employs a real person or an intelligent pressure testing mannequin that simulates softness of a human body.
3. The garment pressure test method according to claim 1, wherein the actual garment pressure measurement site of the physical human model comprises: lower abdomen, thigh base, crotch, thigh lower, knee, ankle, hip.
4. The garment pressure test method according to claim 1, wherein the actual garment pressure value of the real human model is obtained by a gas-bag garment pressure measurement method, a gas bag with a thickness of less than 3mm is placed on the part to be measured, and the dynamic garment pressure in the wearing state is measured by the resistance change of a semiconductor pressure sensor connected with the gas bag.
5. The method of claim 1, wherein the correlation analysis of the real garment pressure value and the virtual garment pressure value is performed by one or more of rank correlation test, KENDALL correlation test, pearson correlation test, or canonical correlation analysis.
6. The garment pressure testing method according to claim 5, wherein the canonical correlation analysis analyzes correspondence between the measured values of the respective portions of the real garment pressure measurement and the measured values of the respective portions of the virtual mannequin.
7. The method according to claim 1, wherein the correlation between the real garment pressure value and the virtual garment pressure value is analyzed and verified as a linear variation relationship, and the analysis method is any one of a t-test method, an F-test method and a correlation coefficient test method.
8. The garment pressure testing method according to claim 1, wherein the establishing of the regression relation equation between the real garment pressure value and the virtual garment pressure value is specifically:
Y＝μ(x)+ε
wherein x represents the independent variable virtual garment pressure value, Y represents the dependent variable real garment pressure value, and epsilon is a random error which is the sum of various factors influencing Y except the independent variable.
9. A clothing pressure measurement system based on virtual human model, characterized by comprising: the system comprises a virtual human model construction module, a color map processing module, a real garment pressure value acquisition module, a correlation analysis verification module, a regression analysis module and a garment pressure measured value output module;
the virtual human model building module is used for building a virtual human model with the same size as the physical human model and deriving a color map of the virtual garment pressure condition;
the color map processing module is used for processing the color map and obtaining a virtual garment pressure value of a specific part according to a map mapping function;
the real clothing pressure value acquisition module is used for acquiring a real clothing pressure value of the entity mannequin;
the correlation analysis and verification module is used for carrying out correlation analysis on the real garment pressure value and the virtual garment pressure value and verifying that the real garment pressure value and the virtual garment pressure value are in a linear change relationship;
the regression analysis module is used for carrying out regression analysis on the real clothing pressure value and the virtual clothing pressure value and establishing a regression relation equation between the real clothing pressure value and the virtual clothing pressure value;
the clothing pressure measured value output module is used for acquiring the virtual clothing parameters to be detected and the virtual human model parameters and outputting clothing pressure measured values.
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