WO2025050993A1 - Leg shape intelligent identification method and apparatus, electronic device, and storage medium - Google Patents

Abstract

A leg shape intelligent recognition method and apparatus, an electronic device, and a storage medium, relating to the technical fields of human body measurement, garment fitness, and computer application. The method comprises: acquiring a key feature parameter value of a leg object to be identified (S102); and inputting the key feature parameter value into a trained support vector machine classification model to obtain a target leg shape category to which said leg object belongs and a circumference size category under the target leg shape category (S104).

Description

Leg shape intelligent recognition method, device, electronic equipment and storage medium

CROSS-REFERENCE TO RELATED APPLICATIONS

The present disclosure claims priority to Chinese patent application numbered 202311139982.8 filed on September 5, 2023, entitled “Leg shape intelligent identification method, device, electronic device and storage medium”, the entire contents of which are incorporated herein by reference.

Technical Field

The present invention relates to the field of human body measurement, clothing fit and computer application technology, and more particularly to a method, device, electronic device and storage medium for intelligently identifying leg shapes.

Background Art

The fit of clothing affects user satisfaction and wearing perception. Effective identification of user body shape and size helps product designers create a tailored clothing structure and form to meet individual body shape characteristics and wearing fitness requirements. However, due to the diversity of human body shapes, traditional size classification methods can no longer meet consumer needs. The rise of clothing personalization and customization requires accurate judgment and classification of user body shapes.

In the related art, the user's overall body shape and upper limb shape are usually classified. This classification and recognition method does not take the leg shape into consideration, resulting in a low classification accuracy.

It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field.

Summary of the invention

The embodiments of the present disclosure provide a leg shape intelligent recognition method, a leg shape intelligent recognition device, an electronic device and a storage medium. The method can quickly and accurately identify the target leg shape category to which the leg object to be identified belongs and the circumference size category under the target leg shape category, thereby saving design time for determining product size and shape based on leg shape analysis and improving the fit and design efficiency of leg clothing design.

Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

An embodiment of the present disclosure provides a method for intelligently identifying leg shapes, comprising: obtaining key feature parameter values of a leg object to be identified; inputting the key feature parameter values into a trained support vector machine classification model to obtain a target leg shape category to which the leg object to be identified belongs and a circumference size category under the target leg shape category.

In an exemplary embodiment, before inputting the key feature parameters into the trained support vector machine classification model, the method also includes: obtaining multiple basic feature parameter values of each leg object among a plurality of leg objects; determining the leg type category label and the circumference size category label to which each leg object belongs; using the multiple basic feature parameter values, the leg type category label and the circumference size category label of each leg object among the plurality of leg objects as training data, and training the support vector machine classification model to be trained according to the training data to obtain the trained support vector machine classification model.

In an exemplary embodiment, a support vector machine classification model to be trained is trained according to the training data to obtain the trained support vector machine classification model, including: dividing the training data into a training set and a test set according to different preset ratios; for each preset ratio, using the training set under the preset ratio to train the support vector machine classification model to obtain a candidate support vector machine classification model under the preset ratio; using the test set under the preset ratio to test the candidate support vector machine classification model to obtain the recognition accuracy under the preset ratio; and determining the candidate support vector machine classification model with the highest recognition accuracy as the trained support vector machine classification model.

In an exemplary embodiment, the method also includes: using a grid search algorithm to optimize the penalty factor and kernel parameters in the trained support vector machine classification model to obtain a first target penalty factor and a first target kernel parameter, and the first target penalty factor and the first target kernel parameter are used to identify leg type categories; using a particle swarm optimization algorithm to optimize the penalty factor and kernel parameters in the trained support vector machine classification model to obtain a second target penalty factor and a second target kernel parameter, and the second target penalty factor and the second target kernel parameter are used to identify circumference size categories.

In an exemplary embodiment, the multiple basic feature parameter values include the circumference and height of the thinnest part of the ankle, the circumference and height of the widest part of the calf, the circumference and height of the lower end of the knee, the circumference and height of the patellar protrusion, and the circumference and height of the thigh root; wherein, determining the leg type category label and circumference size category label to which each leg object belongs includes: determining the femoral slope of each leg object according to the circumference and height of the thigh root and the circumference and height of the patellar protrusion of each leg object; determining the knee slope of each leg object according to the circumference and height of the patellar protrusion and the circumference and height of the lower end of the knee of each leg object; determining the calf lateral convex angle of each leg object according to the circumference and height of the thinnest part of the ankle, the circumference and height of the widest part of the calf, and the circumference and height of the lower end of the knee of each leg object; clustering the multiple leg objects according to the femoral slope, knee slope and calf lateral convex angle of each leg object to obtain the leg type category label to which each leg object belongs.

In an exemplary embodiment, determining the leg type category label and the circumference size category label to which each leg object belongs includes: for each leg type category label, determining the leg type category label according to the thinnest part of the ankle of each leg object belonging to the leg type category label. The circumference of the widest part of the calf, the circumference of the lower knee, and the circumference of the thigh root are used to determine multiple circumference size category labels under the leg shape category label, and determine the circumference size category label to which each leg object belongs under the leg shape category label.

In an exemplary embodiment, after determining the leg shape category label to which each leg object belongs, the method further includes: grouping multiple leg shape category labels according to a Pearson correlation analysis algorithm to obtain multiple groups of leg shape categories; wherein each group of leg shape categories includes at least two leg shape category labels, and the leg shape category labels included in each group of leg shape categories have correlations; for each group of leg shape categories, according to the correlations between the leg shape category labels in the group of leg shape categories, a basic leg shape category label is determined from the multiple leg shape category labels in the group of leg shape categories, and a conversion relationship between the basic leg shape category label and other leg shape category labels in the group of leg shape categories is determined; wherein the conversion relationship is used to realize parameter or parameter value conversion of the basic leg shape category label to obtain other leg shape category labels in the group of leg shape categories.

In an exemplary embodiment, before obtaining the key feature parameter values of the leg object to be identified, the method also includes: determining multiple key feature parameters from multiple basic feature parameters to obtain key feature parameter values corresponding to each key feature parameter of the leg object to be identified; wherein, determining multiple key feature parameters from multiple basic feature parameters includes: obtaining basic feature parameter values corresponding to multiple basic feature parameters of each leg object in multiple leg objects; determining multiple basic feature parameter sets according to the multiple basic feature parameters based on a support vector machine recursive feature elimination algorithm and an empirical method; for each basic feature parameter set, inputting the basic feature parameter values corresponding to each leg object into a trained support vector machine classification model according to the basic feature parameters included in the basic feature parameter set to obtain the recognition accuracy corresponding to each basic feature parameter set; and determining the basic feature parameters included in the basic feature parameter set with the highest recognition accuracy as the multiple key feature parameters.

In an exemplary embodiment, the key characteristic parameter values include the circumference of the thinnest part of the ankle, the circumference of the widest part of the calf, the circumference of the lower end of the knee, the circumference of the patellar protrusion, the circumference of the middle thigh, the circumference of the thigh root, the height of the lower end of the knee and the height of the thigh root.

In an exemplary embodiment, the method further includes: recommending target clothing for a target object corresponding to the leg object to be identified according to the target leg shape category and circumference size category to which the leg object to be identified belongs.

An embodiment of the present disclosure provides an intelligent leg shape recognition device, comprising: an acquisition module, used to obtain key feature parameter values of a leg object to be identified; an acquisition module, used to input the key feature parameter values into a trained support vector machine classification model to obtain a target leg shape category to which the leg object to be identified belongs and a circumference size category under the target leg shape category.

The present disclosure provides an electronic device, comprising: at least one processor; a storage terminal device for storing At least one program, when the at least one program is executed by at least one processor, enables the at least one processor to implement any of the above-mentioned leg shape intelligent recognition methods.

An embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements any of the above-mentioned leg shape intelligent recognition methods when executed by a processor.

An embodiment of the present disclosure provides a computer program product, including a computer program, which implements any of the above-mentioned leg shape intelligent recognition methods when executed by a processor.

The leg shape intelligent recognition method provided by the embodiment of the present disclosure obtains the key feature parameter values of the leg object to be identified, and inputs the key feature parameter values into the trained support vector machine classification model. It can quickly and accurately identify the target leg shape category and the circumference size category under the target leg shape category to which the leg object to be identified belongs, thereby saving the design time for determining the product size and shape based on leg shape analysis, and improving the fit and design efficiency of leg clothing design.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

Fig. 1 is a flow chart showing a method for intelligently identifying leg shapes according to an exemplary embodiment.

FIG. 2 is a schematic diagram of an interface for introducing key characteristic parameter measurement according to an example.

FIG. 3 is a schematic diagram of an interface for inputting key feature parameter values according to an example.

FIG. 4 is a schematic diagram of an interface showing target leg shape categories and circumference size categories according to an example.

FIG. 5 is a diagram showing an interface of clothing recommendation according to an example.

FIG. 6 is a schematic diagram showing some basic characteristic parameters according to an example.

Fig. 7 is a flow chart showing another method for intelligently identifying leg shapes according to an exemplary embodiment.

FIG. 8 is a schematic diagram showing a thigh slope, a knee slope, and a calf lateral convex angle according to an example.

FIG. 9 is a diagram showing a distribution diagram of leg type classification of a leg object according to an example.

FIG. 10 is a diagram showing a size classification distribution diagram of a leg object according to an example.

FIG. 11 is a schematic diagram of a reference atlas of 12 types of leg types according to an example.

FIG. 12 is a schematic diagram showing 12 types of leg types according to an example.

FIG. 13 is a schematic diagram of a reference map corresponding to eight circumference size categories under a type of leg type according to an example.

FIG. 14 is a schematic diagram of a leg corresponding to eight circumference size categories under a type of leg shape according to an example.

FIG. 15 is a schematic diagram of leg circumference data corresponding to eight circumference size categories under a type of leg shape according to an example.

FIG16 is a schematic diagram showing predicted values and true values of different leg types using the GS-SVM algorithm according to an example.

FIG. 17 is a schematic diagram showing the accuracy of prediction results of different leg sizes using the PSO-SVM algorithm according to an example.

FIG18 is a schematic diagram of the overall process of a leg shape intelligent recognition method and system according to an example.

Fig. 19 is a block diagram of a device for intelligently identifying leg shapes according to an exemplary embodiment.

FIG. 20 is a schematic diagram showing the structure of an electronic device suitable for implementing the exemplary embodiments of the present disclosure according to an exemplary embodiment.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

The features, structures or characteristics described in the present disclosure may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

The accompanying drawings are only schematic diagrams of the present disclosure, and the same reference numerals in the drawings represent the same or similar parts, so their repeated description will be omitted. Some block diagrams shown in the accompanying drawings do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in at least one hardware module or integrated circuit, or implemented in different networks and/or processor devices and/or microcontroller devices.

The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and steps, nor do they necessarily have to be executed in the order described. For example, some steps can be decomposed, and some steps can be combined or partially combined. The actual execution order may change according to the actual situation.

In addition, in the description of the present disclosure, the terms "a", "an", "the", "said" and "at least one" are used to indicate the presence of at least one element or component; the terms "comprising", "including" and "having" are used to express an open-ended inclusion and mean that additional elements or components may exist in addition to the listed elements or components; the terms "first", "second" and "third" etc. are used merely as labels and are not intended to limit the quantity of their objects.

Below, each step of the leg shape intelligent recognition method in the exemplary embodiment of the present disclosure will be described in more detail with reference to the accompanying drawings and embodiments.

Fig. 1 is a flow chart showing a method for intelligently identifying leg shapes according to an exemplary embodiment.

As shown in FIG. 1 , the method provided by the embodiment of the present disclosure may include the following steps.

In step S102, the key characteristic parameter values of the leg object to be identified are obtained.

In the disclosed embodiment, the leg object to be identified may be the user's leg, the key feature parameter value may be the parameter value corresponding to the key feature parameter, and the key feature parameter may be determined from a plurality of basic feature parameters.

In the disclosed embodiment, the user may input key characteristic parameters of his/her legs through an application (APP) of a terminal device, or may obtain the key characteristic parameters of the user's legs by scanning through the application of the terminal device.

In an exemplary embodiment, the key characteristic parameter values include the circumference of the thinnest part of the ankle (indicated by B), the circumference of the widest part of the calf (indicated by C), the circumference of the lower end of the knee (indicated by D), the circumference of the patellar protrusion (indicated by E), the circumference of the middle thigh (indicated by F), the circumference of the base of the thigh (5 cm below the crotch, indicated by G), the height of the lower end of the knee and the height of the base of the thigh.

FIG. 2 is a schematic diagram of an interface for introducing key characteristic parameter measurement according to an example.

Referring to FIG. 2 , before the user manually inputs or automatically scans the key characteristic parameters, the user may be prompted with the specific position of the leg corresponding to the key characteristic parameters, so as to facilitate the user to accurately measure the key characteristic parameters.

FIG. 3 is a schematic diagram of an interface for inputting key feature parameter values according to an example.

3 , the user may manually input or use automatic scanning to obtain the D-point height, G-point height, B-point circumference, C-point circumference, D-point circumference, E-point circumference, F-point circumference and G-point circumference of the user's leg.

In step S104, the key feature parameter values are input into the trained support vector machine classification model to obtain the target leg type category to which the leg object to be identified belongs and the circumference size category under the target leg type category.

In the disclosed embodiment, the key feature parameter values are input into the trained support vector machine classification model, which can automatically identify the target leg shape category and the circumference size category under the target leg shape category to which the leg object to be identified belongs.

In the embodiment of the present disclosure, the leg type category is obtained based on the leg morphology classification of multiple leg objects in the database. There may be multiple leg type categories. In the following example, 12 leg type categories (category 1 to category 12) are used as an example, but the present disclosure is not limited to this.

In the embodiment of the present disclosure, each leg shape category can correspond to multiple circumference size categories, and the circumference size categories are obtained based on the circumference classification of multiple parts of multiple leg objects in a database. In the following examples, it is taken as an example that each leg shape category can correspond to 8 circumference size categories (code I to code VIII), but the present disclosure is not limited to this.

FIG. 4 is a schematic diagram of an interface showing target leg shape categories and circumference size categories according to an example.

Referring to Figure 4, after the user manually inputs or uses automatic scanning to obtain the D-point height, G-point height, B-point circumference, C-point circumference, D-point circumference, E-point circumference, F-point circumference and G-point circumference of the user's leg, the target leg shape category (for example, Category 1) to which the user's leg belongs and the circumference size category under the target leg shape category (for example, size III) can be automatically identified and displayed on the interface.

In an exemplary embodiment, the method may further include: recommending target clothing for a target object corresponding to the leg object to be identified according to the target leg shape category and circumference size category to which the leg object to be identified belongs.

In the embodiments of the present disclosure, in addition to recommending target clothing for the target object corresponding to the leg object to be identified based on the target leg shape category and circumference size category to which the leg object to be identified belongs, the function, style, color, size, price, quantity, applicable scenarios (scope) and related product and service information (for example, ordering and delivery method and time, etc.) of the target clothing can also be recommended to connect users, leg shapes, clothing, smart manufacturing, product selection and services. The present disclosure does not impose any restrictions on this.

The target garment may be functional pressure/tight-fitting garment or other types of garment, which is not limited in the present disclosure.

In the disclosed embodiment, the user can enter the starting interface of the APP or network platform and select the product interaction terminal or the smart manufacturing interaction terminal; the starting interface of the APP or network platform prompts the user to enter key feature parameters, or the system scans and automatically extracts key feature parameters, and enters the recognition page after the user confirms. The system dynamically displays the leg shape and size recognition process and enters the recognition result.

In the disclosed embodiment, the user can enter the user-product interaction terminal; the user-product interaction terminal can recommend relevant functional clothing to the user based on the body size recognition results; the user can also select relevant clothing products based on his or her own body size characteristics, for example, the user can select suitable clothing for himself or herself based on the above-mentioned automatically identified leg shape category and size category.

In the embodiment of the present disclosure, after the target leg type category of the leg object to be identified and the circumference size category under the target leg type category are identified, the leg object to be identified can be further classified according to the target leg type category of the leg object to be identified and the circumference size category under the target leg type category. The circumference size categories below recommend suitable clothing, such as functional compression and tights, making it easy for users to quickly choose suitable clothing.

FIG. 5 is a diagram showing an interface of clothing recommendation according to an example.

For example, referring to Figure 5, after identifying that the target leg shape category of the leg object to be identified is Category 1 and the circumference size category under the target leg shape category is code III, compression socks with moderate elasticity and suitable size pressure of pressure level III can be recommended to the user. In addition, compression socks of appropriate length (for example, medium/long) can be recommended to the user based on the length of the leg object to be identified.

In the disclosed embodiment, the user can also enter the user and intelligent manufacturing interaction terminal, that is, the user's body size feature data is further transmitted to the intelligent manufacturing system. The intelligent manufacturing system intelligently weaves suitable functional clothing for the user based on the body size recognition results, relevant manufacturing processes and parameter ratios to quickly respond to user needs.

In the disclosed embodiment, the system recognition result provides a basis for users to quickly select body-fitting clothing (especially functional pressure/tight-fitting clothing), and also provides guidance on shape and size for product design and large-scale manufacturing.

In an exemplary embodiment, before obtaining the key feature parameter value of the leg object to be identified, the method may further include: determining multiple key feature parameters from multiple basic feature parameters to obtain key feature parameter values corresponding to each key feature parameter of the leg object to be identified.

Specifically, which key feature parameters of the leg object to be identified are obtained can be determined according to multiple basic feature parameters of each leg object of multiple leg objects in the database.

The specific process of determining multiple key characteristic parameters from multiple basic characteristic parameters is described below.

In an exemplary embodiment, multiple key feature parameters are determined from multiple basic feature parameters, including: obtaining basic feature parameter values corresponding to multiple basic feature parameters of each leg object in a plurality of leg objects; determining multiple basic feature parameter sets according to the multiple basic feature parameters based on a support vector machine recursive feature elimination algorithm and an empirical method; for each basic feature parameter set, inputting the basic feature parameter values corresponding to each leg object into a trained support vector machine classification model according to the basic feature parameters included in the basic feature parameter set to obtain the recognition accuracy corresponding to each basic feature parameter set; and determining the basic feature parameters included in the basic feature parameter set with the highest recognition accuracy as multiple key feature parameters.

In the embodiment of the present disclosure, multiple users can be selected, and basic characteristic parameter values corresponding to multiple basic characteristic parameters of the legs of each user can be collected by actual measurement or by using a digital three-dimensional human body scanning system. For example, 480 users and 36 basic characteristic parameters of the legs of each user are selected, where the 36 basic characteristic parameters can be: the depth, height, width, circumference and cross-sectional area of seven parts: the thinnest part of the ankle (B), the ankle arm (B1), the widest part of the calf (C), the lower end of the knee (D), the patellar protrusion (E), the middle of the thigh (F), and the root of the thigh (G), as well as the side of the calf (B). The outer convex angle is shown in Figure 7 and Table 1.

FIG. 6 is a schematic diagram showing some basic characteristic parameters according to an example.

Referring to Figure 6, cG represents the circumference of the thigh root, cF represents the circumference of the middle thigh, cE represents the circumference of the patellar protrusion, cD represents the circumference of the lower end of the knee, cC represents the circumference of the widest part of the calf, cB1 represents the circumference of the ankle arm, and cB represents the circumference of the thinnest part of the ankle. hG represents the height of the thigh root, hF represents the height of the middle thigh, hE represents the height of the patellar protrusion, hD represents the height of the lower end of the knee, hC represents the height of the widest part of the calf, hB1 represents the height of the ankle arm, and hB represents the height of the thinnest part of the ankle.

Table 1

In the disclosed embodiments, multiple basic feature parameters can be arranged and combined based on a support vector machine-recursive feature elimination (SVM-RFE) algorithm and an empirical method to obtain multiple basic feature parameter sets, each of which includes some basic feature parameters; each group of basic feature parameter sets is respectively input into an SVM algorithm model to obtain the recognition accuracy corresponding to each group of basic feature parameter sets, and the basic feature parameters in the basic feature parameter set with the highest recognition accuracy and the least number of parameters in the basic feature parameter set are used as key feature parameters.

Specifically, the 36 basic feature parameter values of each of the above 480 leg objects can be input into the SVM-RFE algorithm, and the 36 basic feature parameters of each of the 480 leg objects can be sorted according to the contribution (i.e., importance) to the leg type classification through the SVM-RFE algorithm; refer to Table 2, which shows the sorting results of the first 15 sets composed of different basic measurement parameters.

Table 2

Then, these 15 groups can be input into the SVM algorithm model for leg shape recognition, and 15 leg shape recognition rates can be obtained respectively; the group with the highest recognition rate is determined, and the basic measurement parameters contained in this group are used as a set of key feature parameters. This set of key feature parameters is used to perform systematic leg shape and size recognition.

In the embodiment of the present disclosure, the SVM-RFE algorithm can be used to reduce the number of multiple basic feature parameters and retain the most critical key feature parameters. While ensuring the recognition accuracy, the amount of data acquisition and data processing can be reduced, thereby improving the recognition efficiency.

Specifically, according to industry practice experience, the above 36 basic measurement parameters can be arranged and combined to obtain 39 sets (as shown in Table 3), and these 39 sets are respectively input into the SVM algorithm for leg shape recognition to obtain 39 leg shape recognition rates. Then, a set with the highest recognition rate is determined, and the basic measurement parameters contained in the set are used as a set of key feature parameters.

Table 3

In the embodiment of the present disclosure, the number of multiple basic feature parameters can be reduced through empirical methods, and the most critical key feature parameters can be retained. While ensuring the recognition accuracy, the amount of data acquisition and data processing can be reduced, thereby improving the recognition efficiency.

In the embodiment of the present disclosure, after determining the basic feature parameter set based on the SVM-RFE algorithm and the empirical method respectively, the recognition rates corresponding to the basic feature parameter set determined based on the SVM-RFE algorithm and the empirical method can be compared, and the basic feature parameters in the basic feature parameter set with a higher recognition rate can be selected as the key feature parameters.

Specifically, referring to Table 4, when 8 empirically selected feature parameters are used from the 36 basic human body measurement parameters extracted based on the SVM-RFE algorithm and the industrial experience method, the recognition rate is the highest (66%). Therefore, these 8 key feature parameters, including 6 circumferences (cB, cC, cD, cE, cE, cF, cG) and 2 heights (hD and hG) are determined as key characteristic parameters suitable for leg shape and size recognition. In practical applications, as the database data increases/expands, the key characteristic parameters and their quantities can be adjusted or optimized accordingly, and the recognition rate can also be further improved, but the present disclosure is not limited thereto.

Table 4

Fig. 7 is a flow chart showing another method for intelligently identifying leg shapes according to an exemplary embodiment.

In the disclosed embodiment, the embodiment of FIG7 shows the training process of the support vector machine classification model; before step S102 of the leg shape intelligent recognition method shown in FIG1 , the leg shape intelligent recognition method shown in FIG7 may further include the following steps.

In step S702, a plurality of basic characteristic parameter values of each leg object among a plurality of leg objects are obtained.

In the disclosed embodiment, multiple users may be selected, and basic characteristic parameter values corresponding to multiple basic characteristic parameters of the legs of each user may be collected by actual measurement or by using a digital three-dimensional human body scanning system. For example, 480 users and 36 basic characteristic parameters of the legs of each user are selected, wherein the 36 basic characteristic parameters may be: the depth, height, width, circumference and cross-sectional area of seven parts, namely, the thinnest part of the ankle (B), the ankle arm (B1), the widest part of the calf (C), the lower end of the knee (D), the patellar protrusion (E), the middle of the thigh (F), and the root of the thigh (G), as well as the lateral convex angle of the calf, as shown in FIG7 and the above-mentioned Table 1.

In step S704, the leg shape category label and the circumference size category label to which each leg object belongs are determined.

In the disclosed embodiment, multiple leg objects can be clustered based on obtaining multiple basic feature parameter values of each leg object in the multiple leg objects to obtain the leg type category label to which each leg object belongs.

In an exemplary embodiment, the plurality of basic characteristic parameter values include the circumference and height of the thinnest part of the ankle, the circumference and height of the widest part of the calf, the circumference and height of the lower end of the knee, the circumference and height of the patellar protrusion, and the circumference of the thigh root. and height; wherein, determining the leg type category label and circumference size category label to which each leg object belongs, including: determining the femoral slope of each leg object according to the circumference and height of the thigh root of each leg object, and the circumference and height of the patellar protrusion; determining the knee slope of each leg object according to the circumference and height of the patellar protrusion of each leg object, and the circumference and height of the lower end of the knee; determining the calf lateral convex angle of each leg object according to the circumference and height of the thinnest part of the ankle of each leg object, the circumference and height of the widest part of the calf, and the circumference and height of the lower end of the knee; clustering multiple leg objects according to the femoral slope, knee slope and calf lateral convex angle of each leg object to obtain the leg type category label to which each leg object belongs.

FIG. 8 is a schematic diagram showing a thigh slope, a knee slope, and a calf lateral convex angle according to an example.

Specifically, referring to FIG. 8 , the thigh slope G _GE of each leg object may be determined according to the following formula.

Among them, cG represents the circumference of the thigh root, cE represents the circumference of the patellar protrusion, hG represents the height of the thigh root, and hE represents the height of the patellar protrusion.

Specifically, referring to FIG. 8 , the knee slope G _ED of each leg object may be determined according to the following formula.

Among them, cD represents the circumference of the lower end of the knee, and hD represents the height of the lower end of the knee.

Specifically, referring to FIG. 8 , the calf side convex angle cos∠BCD of each leg object may be determined according to the following formula.

Among them, BC, BD and CD can be determined according to the following formula.

Among them, cC represents the circumference of the widest part of the calf, cB represents the circumference of the thinnest part of the ankle, hC represents the height of the widest part of the calf, and hB represents the height of the thinnest part of the ankle.

In the disclosed embodiment, after the thigh slope, knee slope and calf lateral convex angle are calculated for each leg object, different clustering algorithms (such as fuzzy C-means, K-means, K-medoids, Mini Batch K-means) can be used to cluster the multiple leg objects, and an algorithm with the highest classification efficiency (such as K-means) is determined as the target clustering algorithm, and the target clustering algorithm is used to obtain the leg type category label to which each leg object belongs.

For example, the K-means mean clustering method can be used to cluster 480 groups of leg objects into 12 different leg types (1 to 12 categories) based on the calf lateral convex angle cos∠BCD, knee slope GED and thigh slope GGE; the proportions of these 12 different leg types in the leg object database are: 4.375%, 8.958%, 2.292%, 4.375%, 8.333%, 10%, 9.792%, 16.875%, 6.042%, 4.792%, 12.917% and 11.25%.

FIG9 is a distribution diagram of leg type classification of a leg object according to an example. Referring to FIG9 , it can be seen that the sample size under each leg type category is normally distributed, indicating that the classification effect of the embodiment of the present disclosure on the leg object is good.

In the disclosed embodiment, after classifying multiple leg objects to obtain the leg type category label of each leg object, a multiple-percentile approach can be used for each leg type category label, and each type of leg type can be further coded according to the circumference of the four basic parameters of the leg (B, C, D, G) and the height of the patellar protrusion (E) and the thigh root (G), thereby generating 8 different levels of circumference size categories for each type of leg type from small to large.

In an exemplary embodiment, the leg shape category label and the circumference size category label to which each leg object belongs are determined, including: for each leg shape category label, based on the circumference of the thinnest part of the ankle, the circumference of the widest part of the calf, the circumference below the knee, and the circumference of the thigh of each leg object belonging to the leg shape category label, multiple circumference size category labels under the leg shape category label are determined, and the circumference size category label to which each leg object belongs under the leg shape category label is determined.

For example, according to the multi-percentile method and the basic characteristic parameters of each leg object under each leg type category, the leg objects under each leg type category can be further divided into the following 8 circumference size intervals (each circumference size interval can be regarded as a circumference size category): 0%~17%, 17%~25%, 25%~33%, 33%~50%, 50%~67%, 67%~75%, 75%~83%, 83%~100%.

In the disclosed embodiment, the circumference size category label to which each leg object belongs under the leg shape category label is determined. After identifying the user's leg shape, the leg objects can be further divided into target circumference size categories to facilitate the production of fitted lower limb clothing products.

FIG10 is a size classification distribution diagram of a leg object according to an example. Referring to FIG10 , it can be seen that the sample size under each size type is normally distributed, indicating that the data collected by the embodiment of the present disclosure is reasonable and representative, and the size classification effect for the leg object is good.

In the disclosed embodiment, for each leg shape category label, multiple leg length size category labels under the leg shape category label can also be determined based on the height of the patellar protrusion and the height of the thigh root of each leg object belonging to the leg shape category label, and the leg length size category label to which each leg object belongs under the leg shape category label can be determined.

For example, based on the multi-percentile method and the basic characteristic parameters of each leg object in each leg type category, each leg object in each leg type category can be further divided into the following three leg length size intervals (each leg length size interval can be regarded as a leg length size category): 0% to 33% too short, 33% to 75% too medium, and 75% to 100% too long).

In the disclosed embodiment, the leg length size category label to which each leg object belongs under the leg shape category label is determined. After identifying the user's leg shape and circumference size, the leg object can be further divided into "short type", "standard type" or "long type" to facilitate the production of fitted lower limb clothing products.

In the embodiment of the present disclosure, after multiple leg objects are divided into different leg categories (for example, 12 categories) and further divided into different circumference size categories (for example, 8 categories), the leg category to which each leg object belongs can be used as a leg category label, and the circumference size category to which each leg object belongs can be used as a circumference size category label to construct a human leg database, thereby providing a reference basis (benchmark) for subsequent intelligent recognition of the legs, and providing a data source for the leg intelligent recognition (for example, SVM) model for training and testing recognition algorithms.

FIG. 11 is a schematic diagram of a reference atlas of 12 types of leg shapes according to an example, and FIG. 12 is a schematic diagram of 12 types of leg shapes according to an example.

In the disclosed embodiment, the benchmark maps of leg types shown in Figures 11 and 12 are determined based on the leg sample data of the cluster centers of each type of leg types, and the benchmark maps are used to show the differences in basic morphology of the 12 determined types of leg types; referring to Figure 11, the horizontal and vertical axes represent the circumference and height of the 12 types of leg types, respectively, the negative part of the horizontal axis represents 1/2 of the overall circumference of the leg, and the positive part of the horizontal axis represents the other 1/2 of the overall circumference of the leg. The positive and negative coordinates are mirror-symmetrical, which facilitates the complete expression of the overall circumference of the leg, and different broken lines represent the circumference changes corresponding to the changes in height of different leg types; the schematic diagram of the 12 types of leg types in Figure 12 is drawn based on the circumference and height data of the cluster centers of each leg type determined in Figure 12, and the morphological differences between different leg types can be vividly seen from Figure 12.

FIG13 is a schematic diagram of a reference spectrum corresponding to eight circumference size categories under a type of leg shape according to an example, and FIG14 is a schematic diagram of a leg corresponding to eight circumference size categories under a type of leg shape according to an example.

Taking leg type 1 among the above 12 leg types as an example, Figures 14 and 15 show the 8 leg size contours and leg size examples (size 1 to size 8) included in leg type 1, which progress from small to large and can be used as a reference body shape map for the body shape intelligent recognition system.

Referring to Figure 13, the broken lines 1301, 1302, 1303, 1304, 1305, 1306, 1307, and 1308 in the figure respectively represent the reference maps of 8 circumference size categories (size 1 to size 8) from thin to thick corresponding to leg type 1; the broken line 1309 represents the leg data of the cluster center corresponding to leg type 1, and the leg shape of leg type 1 can be intuitively outlined based on the data of the broken line 1309, such as the 8 leg size contour examples in Figure 14; the broken line 1310 represents the middle size of the 8 circumference sizes of leg type 1, so as to intuitively distinguish the degree to which the size of the measured sample is too large or too small for a specific leg type.

In actual use, as the amount of data in the human leg database increases, is expanded/updated and adjusted, the number of leg type classifications, classification algorithms and specific size ranges can be dynamically updated or optimized accordingly.

FIG. 15 is a schematic diagram of leg circumference data corresponding to eight circumference size categories under a type of leg shape according to an example.

Still taking leg type 1 as an example, FIG. 15 shows the corresponding foot size of each size type from size 1 to size 8 corresponding to leg type 1. The circumference range includes the circumference of the thinnest part of the ankle (cB), the circumference of the widest part of the calf (cC), the circumference below the knee (cD) and the circumference of the thigh root (cG).

In an exemplary embodiment, after determining the leg type category label to which each leg object belongs, the method may further include: grouping multiple leg type category labels according to a Pearson correlation analysis algorithm to obtain multiple groups of leg type categories; wherein each group of leg type categories includes at least two leg type category labels, and the leg type category labels included in each group of leg type categories are correlated; for each group of leg type categories, according to the correlation between the leg type category labels in the group of leg type categories, a basic leg type category label is determined from the multiple leg type category labels in the group of leg type categories, and a conversion relationship between the basic leg type category label and other leg type category labels in the group of leg type categories is determined; wherein the conversion relationship is used to realize parameter or parameter value conversion of the basic leg type category label to obtain other leg type category labels in the group of leg type categories.

In the disclosed embodiment, after the leg types are classified into 12 categories, the 12 categories of leg types can also be grouped.

Specifically, at the intelligent manufacturing interactive end, the system can further group the predetermined 12 types of leg types according to the correlation coefficients based on the Pearson correlation analysis algorithm. The leg types in each group have the highest correlation. In other words, as long as a small number of parameters or parameter values are updated or modified locally on the leg types in the group, a certain leg type in the group can be quickly converted to other leg types, thereby saving trial fitting time and improving output efficiency.

Specifically, Table 5 is used as an example to illustrate that the predetermined 12 types of leg types are divided into 3 groups with high correlation according to the Pearson correlation analysis algorithm, namely:

Group A: includes leg type 4, leg type 2 and leg type 3, among which leg type 4 is the basic leg type (because leg type 4 has a high correlation coefficient with the other two leg types in the group, and the same applies below).

Group B: includes leg type 10, leg type 1, leg type 5 and leg type 9, among which leg type 10 is the basic leg type.

Group C: includes leg type 12, leg type 6, leg type 7, leg type 8 and leg type 11, among which leg type 12 is the basic leg type.

The leg type determined as the basic leg type in the above groups has a high correlation with each leg type within the group.

The circumference of body shape parameters B, C, D, and G are used as the key adjustment dimensions, and the basic leg shape of each group can be quickly adjusted to other leg shapes in the group. As shown in Table 3, for example, + represents an increase of 1 cm in circumference, ++ represents an increase of 2 cm in circumference, +++ represents an increase of 3-4 cm in circumference, - represents a decrease of 2 cm in circumference, and - represents a decrease of 3-4 cm in circumference.

Table 5 Intelligent manufacturing end leg type rapid conversion

In step S706, multiple basic feature parameter values, leg shape category labels and circumference size category labels of each leg object in the multiple leg objects are used as training data, and the support vector machine classification model to be trained is trained according to the training data to obtain a trained support vector machine classification model.

In the disclosed embodiment, the leg type category label of each leg object determined as above can be used as a training label to train the support vector machine classification model to be trained. The trained support vector machine classification model thus obtained can automatically identify the leg type category of the leg type to be identified.

In the disclosed embodiment, the circumference size category label of each leg object determined as above can be used as a training label to train the support vector machine classification model to be trained. The trained support vector machine classification model thus obtained can automatically identify the circumference size category of the leg type to be identified.

Specifically, the above 480 leg objects and 36 basic feature parameters of each user can be used as training data. According to the leg type category label of each leg object determined above, the training data can be divided into a training set and a test set, and normalized to [0,1]. The training set is used to train the SVM classification model, the penalty factor c in the preset parameters of the SVM can be set to 145, and the kernel parameter g can be set to 0.1. The test set is used to test the SVM classification model to identify leg type and size.

In an exemplary embodiment, a support vector machine classification model to be trained is trained according to training data to obtain a trained support vector machine classification model, including: dividing the training data into a training set and a test set according to different preset ratios; for each preset ratio, using the training set under the preset ratio to train the support vector machine classification model to obtain a candidate support vector machine classification model under the preset ratio; using the test set under the preset ratio to test the candidate support vector machine classification model to obtain the recognition accuracy under the preset ratio; and determining the candidate support vector machine classification model with the highest recognition accuracy as the trained support vector machine classification model.

In the embodiments of the present disclosure, the preset ratio may include, but is not limited to, 1:1, 2:1, 3:1 and 7:3, and each preset ratio is used to divide the training data into a training set and a test set. Table 4 shows the recognition accuracy (the ratio of the number of correctly recognized samples to the total number of samples) corresponding to each preset ratio. Referring to Table 6, when the ratio of the training set to the test set is 2:1, the recognition efficiency is higher.

Table 6

In an exemplary embodiment, the method may also include: using a grid search algorithm to optimize the penalty factor and kernel parameters in the trained support vector machine classification model to obtain a first target penalty factor and a first target kernel parameter, and the first target penalty factor and the first target kernel parameter are used to identify the leg type category; using a particle swarm optimization algorithm to optimize the penalty factor and kernel parameters in the trained support vector machine classification model to obtain a second target penalty factor and a second target kernel parameter, and the second target penalty factor and the second target kernel parameter are used to identify the circumference size category.

In the disclosed embodiment, after obtaining the trained support vector machine classification model, the grid search algorithm (GS), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm can be further used to optimize the penalty factor c and kernel parameter g in the SVM algorithm used to identify leg shape and size, thereby improving the recognition accuracy.

Specifically, the GS algorithm and GA algorithm can be used to optimize the penalty factor c and kernel parameter g in the SVM algorithm respectively; the data set containing 8 key feature parameters and the corresponding leg type labels are input into the GS algorithm and GA algorithm respectively to determine the optimal parameters c and g respectively; then, the c and g values are respectively brought into the SVM algorithm for leg type recognition, and the leg type recognition rates are obtained respectively; by comparing the results of the GS algorithm and the GA algorithm, the algorithm with a higher recognition rate is adopted as the algorithm model for leg type recognition.

For example, the penalty factor c and kernel parameter g determined by the optimized GS-SVM (Grid Search-Support Vector Machine) algorithm are c=1024 and g=0.082469, respectively, and the corresponding leg recognition rate can reach 68.67%; the penalty factor c and kernel parameter g determined by the optimized GA-SVM algorithm are c=2000 and g=0.0097752, respectively, and the same recognition rate of 68.67% is obtained. Both algorithm environments use the determined 8 key feature number parameters, and the optimized training set and test set ratio is 2:1.

Since the recognition rate results of the GS algorithm and the GA algorithm are the same, and considering that the smaller the c value, the better the generalization performance of the model, the c value and g value obtained by the GS algorithm with a smaller c value are selected as the target c value and target g value in the SVM algorithm for leg shape recognition.

Similarly, after studying and comparing various artificial intelligence algorithms, when the PSO algorithm is used to optimize the SVM algorithm, the highest size recognition rate can be obtained (for example, using PSO-SVM (Particle Swarm Optimization-Support The values of penalty factor c and kernel parameter g determined by particle swarm optimization (PSO) algorithm are: c=27.1147 and g=7.879, respectively, and the recognition rate of leg size can reach 81.82%~100%); therefore, PSO algorithm is used for SVM model for size type recognition.

Specifically, the leg data including 8 key feature parameters and the corresponding size category labels are input into the PSO algorithm program to obtain the optimized parameters c and g, and then the optimized c and g parameter values are brought into the SVM algorithm for size type recognition.

FIG16 is a schematic diagram showing predicted values and true values of different leg shapes predicted using the GS-SVM algorithm according to an example, and FIG17 is a schematic diagram showing the accuracy of the predicted results of different leg sizes predicted using the PSO-SVM algorithm according to an example.

With reference to FIG. 16 and FIG. 17 , it can be seen that the intelligent leg shape recognition method provided by the embodiment of the present disclosure has high leg shape recognition accuracy and size recognition accuracy, is suitable for leg shape and size recognition, and can more conveniently perform clothing fit design and selection.

It should be noted that with the increase and expansion of the human leg database and the optimization of the classification algorithm, the recognition rate can continue to improve and is not limited to this range.

In the disclosed embodiments, the obtained leg shape recognition results and leg size recognition results can provide a digital body shape reference for subsequent industrial manufacturing, and can help produce clothing suitable for specific user bodies, especially functional pressure clothing or accessories, such as pressure stockings, tights, etc. This method can not only quickly determine and identify the user's leg shape, but also help to achieve low-cost personalized customization or balanced customized large-scale production, so as to effectively respond to users' needs for body-fitting clothing.

The leg shape intelligent recognition method provided by the embodiment of the present disclosure obtains the key feature parameter values of the leg object to be identified, and inputs the key feature parameter values into the trained support vector machine classification model. It can quickly and accurately identify the target leg shape category and the circumference size category under the target leg shape category to which the leg object to be identified belongs, thereby saving the design time for determining the product size and shape based on leg shape analysis, and improving the fit and design efficiency of leg clothing design.

In addition, the leg shape intelligent recognition method provided by the embodiment of the present disclosure can quickly digitize and manufacture low-cost clothing according to given body size and can provide efficient personalized services.

It should be noted that the intelligent body shape recognition method, device, electronic device and storage medium provided in the embodiments of the present disclosure are not limited to the recognition of lower limb leg shape and size, but can also be applied to the recognition of body shape and size of other parts of the body, for example, upper limbs, upper torso, whole body or part of the body, etc., without limitation.

The leg shape intelligent recognition method and system provided in the embodiments of the present disclosure combine artificial intelligence algorithms, industrial experience and algorithm optimization to construct an intelligent human body morphology (leg shape and size) recognition system, recognition method and application scenario. Using this method, 480 lower limbs (17,280 data) from 240 people aged 40-65 were tested in practice. The recognition rates of leg shape and size can reach 68.67% and 100% respectively; after further verification with 160 lower limbs from 80 people aged 18 to 25 (5760 data), the recognition rates of leg shape and size can reach 76.25% and 80.63% respectively. With the increase and expansion of the human body shape database and the optimization of the classification algorithm, the recognition rate can be continuously improved, not limited to this range.

FIG18 is a schematic diagram of the overall process of a leg shape intelligent recognition method and system according to an example.

In the disclosed embodiment, referring to FIG. 18 , key feature selection can be performed from 36 basic parameters of human body measurement by SVM-RFE and empirical method, the selected parameters are input into the SVM classifier, the leg shape and size are identified by the SVM classifier, and it is judged whether the number of features is the least and the recognition rate is the highest. If so, the selected key feature parameters are determined; if not, the key feature parameter selection is performed again. In actual application, the user inputs the key feature parameters, and the key feature parameters input by the user are input into the artificial intelligence recognition system to identify the user's body shape and size, and the user's body shape and size recognition results are obtained. According to the product interactive terminal or smart manufacturing interactive terminal selected by the user, suitable clothing is recommended or woven for the user. Among them, the artificial intelligence recognition system is divided into a basic model and an optimization model. When the artificial intelligence recognition system is optimized, the key feature parameter data can be input into the SVM, and the GS algorithm/GA algorithm is used to optimize the SVM model to improve the leg shape recognition rate, and the PSO algorithm is used to optimize the SVM model to improve the leg size recognition rate. After each parameter adjustment, it is judged whether the recognition rate is the highest. When the recognition rate is not the highest, the parameters are adjusted continuously; when the recognition rate reaches the highest, the optimization is completed, and the optimized recognition system is obtained.

The specific process of each step in Figure 18 can be found in the text description of the above embodiment, and the present disclosure will not go into details here.

In the disclosed embodiment, the system recognition result obtained provides a digital body shape reference for subsequent industrial manufacturing, which is connected with the manufacturing program and database to produce clothing suitable for a specific user's body, especially functional pressure clothing or accessories, such as pressure socks, tights, etc., providing a new method for human body shape recognition and application. This method not only quickly determines and recognizes the user's body shape, but also helps to achieve low-cost personalized customization or balance customized mass production and cost, so as to effectively respond to the user's demand for body-fitting clothing.

It should also be understood that the above is only to help those skilled in the art better understand the embodiments of the present disclosure, and is not intended to limit the scope of the embodiments of the present disclosure. Based on the above examples given, those skilled in the art can obviously make various equivalent modifications or changes. For example, some steps in the above method may not be necessary, or some new steps may be added. Or a combination of any two or any multiple embodiments of the above. Such modifications, changes or combined solutions also fall within the scope of the embodiments of the present disclosure.

It should also be understood that the above description of the embodiments of the present disclosure focuses on emphasizing the differences between the various embodiments, and the same or similar points that are not mentioned can be referenced to each other. For the sake of brevity, they will not be repeated here.

It should also be understood that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present disclosure.

It should also be understood that in the various embodiments of the present disclosure, unless otherwise specified or there is a logical conflict, the terms and/or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

The above text describes in detail the examples of the leg shape intelligent recognition method and virtual fitting method provided by the present disclosure. It is understandable that in order to realize the above functions, the computer device includes hardware structures and/or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

The following are embodiments of the device disclosed herein, which can be used to execute the method embodiments disclosed herein. For details not disclosed in the device embodiments disclosed herein, please refer to the method embodiments disclosed herein.

Fig. 19 is a block diagram of a device for intelligently identifying leg shapes according to an exemplary embodiment.

As shown in FIG. 19 , the leg shape intelligent recognition device 1900 may include an acquisition module 1902 and an obtaining module 1904 .

Among them, the acquisition module 1902 is used to obtain the key feature parameter values of the leg object to be identified; the acquisition module 1904 is used to input the key feature parameter values into the trained support vector machine classification model to obtain the target leg type category to which the leg object to be identified belongs and the circumference size category under the target leg type category.

In an exemplary embodiment, the acquisition module 1902 is also used to: obtain multiple basic feature parameter values of each leg object among multiple leg objects; determine the leg shape category label and the circumference size category label to which each leg object belongs; use the multiple basic feature parameter values, the leg shape category label and the circumference size category label of each leg object among the multiple leg objects as training data, and train the support vector machine classification model to be trained according to the training data to obtain the trained support vector machine classification model.

In an exemplary embodiment, the acquisition module 1904 is used to: divide the training data into a training set and a test set according to different preset ratios; for each preset ratio, use the training set under the preset ratio to train the support vector machine classification model to be trained to obtain a candidate support vector machine classification model under the preset ratio; use the test set under the preset ratio to test the candidate support vector machine classification model to obtain the recognition accuracy under the preset ratio; and determine the candidate support vector machine classification model with the highest recognition accuracy as the trained support vector machine classification model.

In an exemplary embodiment, the obtaining module 1904 is further used to: use a grid search algorithm to optimize the penalty factor and kernel parameters in the trained support vector machine classification model to obtain a first target penalty factor and a first target kernel parameter. Parameters, the first target penalty factor and the first target kernel parameter are used to identify the leg type category; the penalty factor and the kernel parameter in the trained support vector machine classification model are optimized using a particle swarm optimization algorithm to obtain a second target penalty factor and a second target kernel parameter, and the second target penalty factor and the second target kernel parameter are used to identify the circumference size category.

In an exemplary embodiment, the multiple basic feature parameter values include the circumference and height of the thinnest part of the ankle, the circumference and height of the widest part of the calf, the circumference and height of the lower end of the knee, the circumference and height of the patellar protrusion, and the circumference and height of the thigh root; wherein the acquisition module 1904 is also used to: determine the femoral slope of each leg object according to the circumference and height of the thigh root and the circumference and height of the patellar protrusion of each leg object; determine the knee slope of each leg object according to the circumference and height of the patellar protrusion and the circumference and height of the lower end of the knee of each leg object; determine the calf lateral convex angle of each leg object according to the circumference and height of the thinnest part of the ankle, the circumference and height of the widest part of the calf, and the circumference and height of the lower end of the knee of each leg object; cluster the multiple leg objects according to the femoral slope, knee slope and calf lateral convex angle of each leg object to obtain the leg type category label to which each leg object belongs.

In an exemplary embodiment, the acquisition module 1904 is also used to: for each leg type category label, determine multiple circumference size category labels under the leg type category label based on the circumference of the thinnest part of the ankle, the circumference of the widest part of the calf, the circumference below the knee, and the circumference of the thigh root of each leg object belonging to the leg type category label, and determine the circumference size category label to which each leg object belongs under the leg type category label.

In an exemplary embodiment, the acquisition module 1904 is also used to: group multiple leg shape category labels according to the Pearson correlation analysis algorithm to obtain multiple groups of leg shape categories; wherein each group of leg shape categories includes at least two leg shape category labels, and the leg shape category labels included in each group of leg shape categories are correlated; for each group of leg shape categories, according to the correlation between the leg shape category labels in the group of leg shape categories, determine a basic leg shape category label from the multiple leg shape category labels in the group of leg shape categories, and determine a conversion relationship between the basic leg shape category label and other leg shape category labels in the group of leg shape categories; wherein the conversion relationship is used to realize parameter or parameter value conversion of the basic leg shape category label to obtain other leg shape category labels in the group of leg shape categories.

In an exemplary embodiment, the acquisition module 1904 is further used to: determine multiple key feature parameters from multiple basic feature parameters to obtain key feature parameter values corresponding to each key feature parameter of the leg object to be identified; wherein, determining multiple key feature parameters from multiple basic feature parameters includes: obtaining basic feature parameter values corresponding to multiple basic feature parameters of each leg object in multiple leg objects; determining multiple basic feature parameter sets according to the multiple basic feature parameters based on a support vector machine recursive feature elimination algorithm and an empirical method; for each basic feature parameter set, according to the basic feature parameters included in the basic feature parameter set, inputting the basic feature parameter values corresponding to each leg object into the trained support vector machine classification model to obtain the recognition accuracy corresponding to each basic feature parameter set; The basic feature parameters included in the basic feature parameter set with the highest recognition accuracy are determined as the multiple key feature parameters.

In an exemplary embodiment, the key characteristic parameter values include the circumference of the thinnest part of the ankle, the circumference of the widest part of the calf, the circumference of the lower end of the knee, the circumference of the patellar protrusion, the circumference of the middle thigh, the circumference of the thigh root, the height of the lower end of the knee and the height of the thigh root.

In an exemplary embodiment, the device further includes: a recommendation module for recommending target clothing for a target object corresponding to the leg object to be identified according to the target leg shape category and circumference size category to which the leg object to be identified belongs.

It should be noted that the block diagrams shown in the above figures are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and/or processor terminal devices and/or microcontroller terminal devices.

Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

Fig. 20 is a schematic diagram showing a structure of an electronic device suitable for implementing an exemplary embodiment of the present disclosure according to an exemplary embodiment. It should be noted that the electronic device shown in Fig. 20 is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present disclosure.

As shown in FIG. 20 , the electronic device 2000 includes a central processing unit (CPU) 2001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 2002 or a program loaded from a storage part 2008 into a random access memory (RAM) 2003. In the RAM 2003, various programs and data required for the operation of the system 2000 are also stored. The CPU 2001, the ROM 2002, and the RAM 2003 are connected to each other through a bus 2004. An input/output (I/O) interface 2005 is also connected to the bus 2004.

The following components are connected to the I/O interface 2005: an input section 2006 including a keyboard, a mouse, etc. (or a hand-touch digitizing screen, a desktop computer, or other display means or devices); an output section 2007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 2008 including a hard disk, etc.; and a communication section 2009 including a network interface card such as a LAN card, a modem, etc. The communication section 2009 performs communication processing via a network such as the Internet. A drive 2010 is also connected to the I/O interface 2005 as needed. A removable medium 2011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 2010 as needed, so that a computer program read therefrom is installed into the storage section 2008 as needed.

In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software (such as an application APP on a smartphone). For example, an embodiment of the present disclosure includes a computer program product, It includes a computer program carried on a computer readable medium, the computer program including a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 2009, and/or installed from a removable medium 2011. When the computer program is executed by the central processing unit (CPU) 2001, the above functions defined in the system of the present disclosure are executed.

It should be noted that the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes A sending unit, an acquiring unit, a determining unit, and a first processing unit. The names of these units do not limit the units themselves in some cases. For example, the sending unit can also be described as a "unit that sends a picture acquisition request to the connected server."

As another aspect, the present disclosure further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment. For example, the electronic device may implement the steps shown in Figure 1.

According to one aspect of the present disclosure, a computer program product or a computer program is provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in various optional implementations of the above-mentioned embodiments.

It should be understood that any number of elements in the drawings of the present disclosure is for illustration rather than limitation, and any naming is only for distinction rather than having any limiting meaning.

Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Industrial Applicability

The present disclosure is applicable to the field of artificial intelligence technology, and is used to solve the problem that the classification and recognition methods in related technologies do not take leg morphology into consideration, resulting in low classification accuracy, so as to achieve the effect of quickly and accurately identifying the target leg type category to which the leg object to be identified belongs and the circumference size category under the target leg type category.

Claims (13)

A leg shape intelligent recognition method, comprising: Obtain key feature parameter values of the leg object to be identified; The key feature parameter values are input into the trained support vector machine classification model to obtain the target leg type category to which the leg object to be identified belongs and the circumference size category under the target leg type category. The method according to claim 1, wherein, before inputting the key feature parameters into the trained support vector machine classification model, the method further comprises: Obtaining multiple basic feature parameter values of each leg object in the multiple leg objects; Determine the leg type category label and circumference size category label of each leg object; The multiple basic characteristic parameter values, the corresponding leg shape category labels and the circumference size category labels of each leg object in the multiple leg objects are used as training data, and the support vector machine classification model to be trained is trained according to the training data to obtain the trained support vector machine classification model. The method according to claim 2, wherein training the support vector machine classification model to be trained according to the training data to obtain the trained support vector machine classification model comprises: Dividing the training data into a training set and a test set according to different preset ratios; For each preset ratio, the support vector machine classification model to be trained is trained using the training set under the preset ratio to obtain a candidate support vector machine classification model under the preset ratio; the candidate support vector machine classification model is tested using the test set under the preset ratio to obtain the recognition accuracy under the preset ratio; The candidate support vector machine classification model with the highest recognition accuracy is determined as the trained support vector machine classification model. The method according to claim 2, further comprising: Using a grid search algorithm to optimize the penalty factor and kernel parameter in the trained support vector machine classification model to obtain a first target penalty factor and a first target kernel parameter, wherein the first target penalty factor and the first target kernel parameter are used to identify the leg type category; The penalty factor and kernel parameter in the trained support vector machine classification model are optimized using a particle swarm optimization algorithm to obtain a second target penalty factor and a second target kernel parameter, and the second target penalty factor and the second target kernel parameter are used to identify the circumference size category. The method according to claim 2, wherein the plurality of basic characteristic parameter values are 36 basic characteristic parameter values, and the 36 basic characteristic parameter values include the circumference and height of the thinnest part of the ankle, the circumference and height of the widest part of the calf, the circumference and height of the lower end of the knee, the circumference and height of the patellar protrusion, and the circumference and height of the thigh root; The leg type category label and the circumference size category label of each leg object are determined, including: Determine the femoral slope of each leg object according to the circumference and height of the thigh root and the circumference and height of the patellar prominence of each leg object; Determine the circumference and height of each leg object based on the circumference and height of the patellar protrusion and the circumference and height of the lower end of the knee. knee slope for each leg subject; Determine the lateral convex angle of the calf of each leg object according to the circumference and height of the thinnest part of the ankle, the circumference and height of the widest part of the calf, and the circumference and height of the lower end of the knee of each leg object; According to the thigh slope, knee slope and calf lateral convex angle of each leg object, clustering processing is performed on the multiple leg objects to obtain a leg type category label to which each leg object belongs. The method according to claim 5, wherein determining the leg type category label and the circumference size category label to which each leg object belongs comprises: For each leg type category label, multiple circumference size category labels under the leg type category label are determined based on the circumference of the thinnest part of the ankle, the circumference of the widest part of the calf, the circumference below the knee, and the circumference of the thigh root of each leg object belonging to the leg type category label, and the circumference size category label to which each leg object belongs under the leg type category label is determined. The method according to claim 2, wherein, after determining the leg type category label to which each leg object belongs, the method further comprises: Grouping multiple leg type category labels according to the Pearson correlation analysis algorithm to obtain multiple groups of leg type categories; wherein each group of leg type categories includes at least two leg type category labels, and the leg type category labels included in each group of leg type categories are correlated with each other; For each group of leg shape categories, based on the correlation between the leg shape category labels in the group of leg shape categories, a basic leg shape category label is determined from multiple leg shape category labels in the group of leg shape categories, and a conversion relationship between the basic leg shape category label and other leg shape category labels in the group of leg shape categories is determined; wherein the conversion relationship is used to realize parameter or parameter value conversion of the basic leg shape category label to obtain other leg shape category labels in the group of leg shape categories. The method according to claim 1, wherein, before obtaining the key characteristic parameter value of the leg object to be identified, the method further comprises: Determine a plurality of key feature parameters from the plurality of basic feature parameters to obtain key feature parameter values corresponding to the respective key feature parameters of the leg object to be identified; Among them, multiple key characteristic parameters are determined from multiple basic characteristic parameters, including: Obtaining basic feature parameter values corresponding to multiple basic feature parameters of each leg object in the multiple leg objects; Determine a plurality of basic feature parameter sets according to the plurality of basic feature parameters based on a support vector machine recursive feature elimination algorithm and an empirical method; For each basic feature parameter set, according to the basic feature parameters included in the basic feature parameter set, the basic feature parameter values corresponding to each leg object are input into the trained support vector machine classification model to obtain the recognition accuracy corresponding to each basic feature parameter set; The basic feature parameters included in the basic feature parameter set with the highest recognition accuracy are determined as the multiple key feature parameters. The method according to claim 1, wherein the key characteristic parameter values include the circumference of the thinnest part of the ankle, The circumference of the widest part of the calf, the circumference below the knee, the circumference of the patellar protrusion, the circumference of the middle thigh, the circumference of the thigh root, the height below the knee and the height of the thigh root. The method according to claim 1, further comprising: According to the target leg shape category and circumference size category to which the leg object to be identified belongs, target clothing is recommended for the target object corresponding to the leg object to be identified. A leg shape intelligent recognition device, comprising: An acquisition module, used for acquiring key characteristic parameter values of the leg object to be identified; An acquisition module is used to input the key feature parameter value into the trained support vector machine classification model to obtain the target leg type category to which the leg object to be identified belongs and the circumference size category under the target leg type category. An electronic device, comprising: at least one processor; A storage device, used to store at least one program, when the at least one program is executed by the at least one processor, enables the at least one processor to implement the method according to any one of claims 1 to 10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 10.