CN110879914B - ANFIS-based trousers virtual fit evaluation method - Google Patents
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Abstract
The invention discloses an ANFIS-based trousers virtual fit evaluation method, which comprises the following steps of: s1: collecting a real try-on data set of the trousers, and dividing the data set into two types of fit and non-fit; s2: collecting a key pressure parameter data set in the virtual try-on of the trousers; s3, the component is based on a trousers virtual fitting fit evaluation model of the self-adaptive nerve fuzzy inference system, and training is carried out on the trousers real fitting data set obtained in the step S1 and the trousers virtual fitting key pressure parameter data set obtained in the step S2; s4: and predicting a group of new trousers key pressure parameters through the trained trousers virtual fit evaluation model, and outputting the new trousers key pressure parameters as fit or uncombined. Compared with other evaluation methods, the method has high accuracy and low measurement error value.
Description
Technical Field
The invention belongs to the field of computer vision research, and particularly relates to a trousers virtual fit evaluation method based on a self-adaptive neuro-fuzzy inference system (ANFIS).
Background
With the continuous acceleration of life pace, online shopping has become a main method for modern people to consume. With rapid development of machine learning and artificial intelligence technology, three-dimensional (hereinafter, abbreviated as 3D) virtual try-in technology is also gradually maturing. How to establish an effective clothes fit prediction method, whether a piece of clothes is suitable for the customer can be rapidly and accurately predicted, the life quality can be effectively improved for users, and the production cost can be effectively reduced for service institutions.
Currently, with the advent of the artificial intelligence era, artificial neural networks and fuzzy inference systems are applied successively to various research fields. The neural network is established based on the structure and the function of the simulated brain neural network, and can learn external information and make corresponding processing decisions; the fuzzy reasoning comprises more uncertain factors compared with inductive reasoning and deductive reasoning, and the uncertain information of various parameters provided by a user exists for virtual try-on of clothing on line.
Disclosure of Invention
In view of the above, the present invention provides a trousers virtual fit evaluation method based on an Adaptive neural Network-based Fuzzy Inference System (ANFIS for short). According to the method, the real trousers fitting data set is used as output data, the trousers fitting key pressure parameter data set collected in the virtual fitting process is used as input data, an ANFIS-based trousers fitting virtual fitting fit evaluation system is trained, and fit evaluation is carried out on a group of new trousers fitting key pressure parameters which are not learned.
The invention solves the problems by the following technical means: an ANFIS-based pant virtual fit assessment method comprising the steps of:
s1: collecting a real try-on data set of the trousers, and dividing the data set into two types of fit and non-fit;
s2: collecting a key pressure parameter data set in the virtual try-on of the trousers;
s3, the component is based on a trousers virtual fitting fit evaluation model of the self-adaptive nerve fuzzy inference system, and training is carried out on the trousers real fitting data set obtained in the step S1 and the trousers virtual fitting key pressure parameter data set obtained in the step S2;
s4: and predicting a group of new trousers key pressure parameters through the trained trousers virtual fit evaluation model, and outputting the new trousers key pressure parameters as fit or uncombined.
The step S1 specifically comprises the following steps: the method comprises the steps of collecting real try-on data sets of the trousers by a try-on wearer, dividing the real try-on data sets of the trousers into two types of fit and fit-out, and distinguishing the two types of fit and fit-out by marking the real try-on data sets as 1 and 0 respectively.
The specific method of the step S2 is as follows: a pressure collector is arranged on a template of each pair of trousers used in the virtual try-on of the trousers, and the pressure parameters of 20 different parts are collected.
The specific method of the step S3 is as follows: and training the evaluation model by taking the real fit data set of the trousers collected in the step S1 as output data of the evaluation model and the key pressure parameter data set of the virtual fit of the trousers collected in the step S2 as input data of the evaluation model.
Further, the adaptive neural fuzzy inference system of S3 includes 5 layers, respectively:
the first layer is a blurring layer, a membership function is generated for each input, and the membership function can be a gaussian function, a trigonometric function or a bell-shaped function, and the gaussian function formula is as follows:
in the method, in the process of the invention,is the output of the first layer, the input variable of x, μ Xi Is the ith membership function of x, { c i ,δ i -parameters in a gaussian function;
the second layer is a rule layer, the output of the second layer is the product of the algebraic coefficients of all input signals, the output of each node represents the excitation intensity of one rule, and the calculation formula is as follows:
in the method, in the process of the invention,is the output of the second layer, mu Xi (x),μ Yi (y) is the output of the first layer;
the third layer is a normalization layer, the excitation intensity of each rule is normalized, and the calculation formula is as follows:
in the method, in the process of the invention,is the output of the third layer, omega i Is the excitation intensity of the ith rule;
the fourth layer is a deblurring layer, and a weight value of each node is calculated, wherein the calculation formula is as follows:
in the method, in the process of the invention,is the output of the fourth layer,/->Is the output of the third layer, { p, q, r } is called the conclusion parameter set, automatically generated by the ANFIS network;
the fifth layer is an output layer, the output of the deblurring layer is calculated and combined to be used as a total output, and the calculation formula is as follows:
in the method, in the process of the invention,the output of the fifth layer, i.e. the total output of all input signals.
The ANFIS-based trousers virtual fit evaluation method has the following beneficial effects: the invention discloses an ANFIS-based trousers virtual fit evaluation method, which comprises the following steps of: s1: collecting a real try-on data set of the trousers, and dividing the data set into two types of fit and non-fit; s2: collecting a key pressure parameter data set in the virtual try-on of the trousers; s3, the component is based on a trousers virtual fitting fit evaluation model of the self-adaptive nerve fuzzy inference system, and training is carried out on the trousers real fitting data set obtained in the step S1 and the trousers virtual fitting key pressure parameter data set obtained in the step S2; s4: and predicting a group of new trousers key pressure parameters through the trained trousers virtual fit evaluation model, and outputting the new trousers key pressure parameters as fit or uncombined. Compared with other evaluation methods, the method has high accuracy and low measurement error value.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for evaluating fit of a pants virtual fit based on ANFIS according to the present invention;
FIG. 2 is a block diagram of an ANFIS used in the present invention;
FIG. 3 is a three-dimensional body suit pressure measurement point used in the present invention;
FIG. 4 is a graph showing the evaluation results of the embodiment of the present invention.
Detailed Description
In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
The present invention will be described in detail with reference to the accompanying drawings and examples.
The method of the present invention is implemented using Matlab R2018b programming in the following examples. The experimental platform is mainly configured as follows: the operating system is Windows10, the CPU is Intel Core i7 5600U, and the RAM is 8G.
Example 1
An ANFIS-based pant virtual fit assessment method comprising the steps of:
s1: collecting a real try-on data set of the trousers, and dividing the data set into two types of fit and non-fit;
s2: collecting a key pressure parameter data set in the virtual try-on of the trousers;
s3, the component is based on a trousers virtual fitting fit evaluation model of the self-adaptive nerve fuzzy inference system, and training is carried out on the trousers real fitting data set obtained in the step S1 and the trousers virtual fitting key pressure parameter data set obtained in the step S2;
s4: and predicting a group of new trousers key pressure parameters through the trained trousers virtual fit evaluation model, and outputting the new trousers key pressure parameters as fit or uncombined.
The step S1 specifically comprises the following steps: the method comprises the steps of collecting real try-on data sets of the trousers by a try-on wearer, dividing the real try-on data sets of the trousers into two types of fit and fit-out, and distinguishing the two types of fit and fit-out by marking the real try-on data sets as 1 and 0 respectively.
The specific method of the step S2 is as follows: the method comprises the steps of arranging pressure collectors at 20 different positions of each pair of trousers templates used in virtual try-on of trousers, mainly distributing the 20 measuring points at four parts of the waist, the buttock, the crotch and the thigh of a person, wherein the four parts have the greatest influence on the fit and the comfort of the trousers, for any pair of measured trousers, the positions of the 20 measuring points are fixed, as shown in fig. 3, the size of a mannequin used in virtual try-on is adjusted to be the same as that of a try-on person, each pair of trousers used in virtual try-on of trousers is sequentially worn on the mannequin, the virtual trousers pressure values are measured according to the positions of the pressure collectors, and the pressure parameters of 20 different parts are collected.
The specific method of the step S3 is as follows: and training the evaluation model by taking the real fit data set of the trousers collected in the step S1 as output data of the evaluation model and the key pressure parameter data set of the virtual fit of the trousers collected in the step S2 as input data of the evaluation model.
Specifically, the present invention uses a genfis2 function that uses subtractive clustering to generate a fuzzy logic inference system structure from data, where the vector radii is set to 0.3.
Further, the adaptive neural fuzzy inference system of S3 includes 5 layers, respectively:
the first layer is a blurring layer, a membership function is generated for each input, and the membership function can be a gaussian function, a trigonometric function or a bell-shaped function, and the gaussian function formula is as follows:
in the method, in the process of the invention,is the output of the first layer, the input variable of x, μ Xi Is the ith membership function of x, { c i ,δ i The number of the prior parameters is determined by the type and the number of the membership functions, which are parameters in the Gaussian function and are automatically generated by the ANFIS network and are called prior parameters.
The second layer is a rule layer, the output of the second layer is the product of the algebraic coefficients of all input signals, the output of each node represents the excitation intensity of one rule, and the calculation formula is as follows:
in the method, in the process of the invention,is the output of the second layer, mu Xi (x),μ Yi (y) is the output of the first layer;
the third layer is a normalization layer, the excitation intensity of each rule is normalized, and the calculation formula is as follows:
in the method, in the process of the invention,is the output of the third layer, omega i Is the excitation intensity of the ith rule;
the fourth layer is a deblurring layer, and a weight value of each node is calculated, wherein the calculation formula is as follows:
in the method, in the process of the invention,is the output of the fourth layer,/->Is the output of the third layer, { p, q, r } is called the conclusion parameter set, automatically generated by the ANFIS network;
the fifth layer is an output layer, the output of the deblurring layer is calculated and combined to be used as a total output, and the calculation formula is as follows:
in the method, in the process of the invention,the output of the fifth layer, i.e. the total output of all input signals.
Example 2
An application example of the model for the fit evaluation of the pants virtual fit based on the ANFIS constructed above is shown in fig. 4, and the specific steps are as follows:
s1: the customer provides the body type size data 160/84A of the customer, and adjusts the 3D human model according to the body type size data;
s2: after a customer selects trousers, selecting a corresponding trousers template from a trousers template database according to trousers selected by the customer, and fitting the template onto the 3D human body model in the step 1;
s3: measuring a pressure parameter of the pants template according to the three-dimensional human body pants pressure measurement points adopted in fig. 3; the specific method comprises the following steps: providing 20 pressure measurement points on the selected pant template, namely points F1-F15 and B1-B5 in FIG. 3, wherein F1, F2, F3, B1, B2 are provided on the waist of the person; f4 F5, F8, F9, F10, F11, F12, F13, F14, F15, B5 are provided at the thigh of the person, F6, F7 are provided at the crotch of the person, and B3, B4 are provided at the buttocks of the person. The reason is that these four parts have the greatest effect on the fit and comfort of the pants. The specific data collected were: f1 F2=10.16, f3=11.8, f4=5.9, f5=7.07, f6=15.85, f7=43.25, f8=8.08, f9=8.07, f10=12.2, f11=12.45, f12=11.91, f13=7.38, f14=6, f15=3.39, b1=13.98, b2=14.19, b3=9.99, b4=7.44, b5=14.16.
S4: inputting the pressure parameters acquired in the step S3 into a trained evaluation model ANFIS, training for 150 times, generating a fuzzy logic reasoning system structure genfis2 function from data by the ANFIS through subtractive clustering, wherein the vector radii takes a value of 0.3, the membership function is a Gaussian function, and the output is 1, namely the fit is recommended to purchase.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. An ANFIS-based pant virtual fit assessment method, comprising the steps of:
s1: collecting a real try-on data set of the trousers, and dividing the data set into two types of fit and non-fit;
s2: collecting a key pressure parameter data set in the virtual try-on of the trousers;
s3, the component is based on a trousers virtual fitting fit evaluation model of the self-adaptive nerve fuzzy inference system, and training is carried out on the trousers real fitting data set obtained in the step S1 and the trousers virtual fitting key pressure parameter data set obtained in the step S2;
s4: and predicting a group of new trousers key pressure parameters through a trained trousers virtual fitting performance evaluation model, and outputting the predicted trousers key pressure parameters as fitting or uncoupling, wherein the self-adaptive neural fuzzy reasoning system of the S3 comprises 5 layers, namely:
the first layer is a blurring layer, a membership function is generated for each input, and the membership function can be a gaussian function, a trigonometric function or a bell-shaped function, and the gaussian function formula is as follows:
in the method, in the process of the invention,is the output of the first layer, the input variable of x, μ Xi Is the ith membership function of x, { c i ，δ i -parameters in a gaussian function;
the second layer is a rule layer, the output of the second layer is the product of the algebraic coefficients of all input signals, the output of each node represents the excitation intensity of one rule, and the calculation formula is as follows:
in the method, in the process of the invention,is the output of the second layer, mu Xi (x)，μ Yi (y) is the output of the first layer;
the third layer is a normalization layer, the excitation intensity of each rule is normalized, and the calculation formula is as follows:
the fourth layer is a deblurring layer, and a weight value of each node is calculated, wherein the calculation formula is as follows:
in the method, in the process of the invention,is the output of the fourth layer,/->Is the output of the third layer, { p, q i y+r } is called a conclusion parameter set, automatically generated by the ANFIS network;
the fifth layer is an output layer, the output of the deblurring layer is calculated and combined to be used as a total output, and the calculation formula is as follows:
in the method, in the process of the invention,the output of the fifth layer, i.e. the total output of all input signals.
2. The method for evaluating fit of a pants virtual fit based on ANFIS according to claim 1, wherein the step S1 is specifically: the method comprises the steps of collecting real try-on data sets of the trousers by a try-on wearer, dividing the real try-on data sets of the trousers into two types of fit and fit-out, and distinguishing the two types of fit and fit-out by marking the real try-on data sets as 1 and 0 respectively.
3. The method for evaluating the fit of a pants-type virtual fit based on ANFIS according to claim 1, wherein the specific method in step S2 is as follows: a pressure collector is arranged on a template of each pair of trousers used in the virtual try-on of the trousers, and the pressure parameters of 20 different parts are collected.
4. The method for evaluating the fit of a pants virtual fit based on ANFIS according to claim 1, wherein the specific method in step S3 is as follows: and training the evaluation model by taking the real fit data set of the trousers collected in the step S1 as output data of the evaluation model and the key pressure parameter data set of the virtual fit of the trousers collected in the step S2 as input data of the evaluation model.
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| WO2002086814A1 (en) * | 2001-04-23 | 2002-10-31 | Hrl Laboratories, Llc | An on-line method for self-organized learning and extraction of fuzzy rules |
| CN105956780A (en) * | 2016-05-11 | 2016-09-21 | 东华大学 | Garment fitness evaluation method based on Bayesian discrimination theory |
| WO2017203262A2 (en) * | 2016-05-25 | 2017-11-30 | Metail Limited | Method and system for predicting garment attributes using deep learning |
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| WO2002086814A1 (en) * | 2001-04-23 | 2002-10-31 | Hrl Laboratories, Llc | An on-line method for self-organized learning and extraction of fuzzy rules |
| CN105956780A (en) * | 2016-05-11 | 2016-09-21 | 东华大学 | Garment fitness evaluation method based on Bayesian discrimination theory |
| WO2017203262A2 (en) * | 2016-05-25 | 2017-11-30 | Metail Limited | Method and system for predicting garment attributes using deep learning |
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