A system and method for providing and visualizing textile information

Abstract

The present disclosure relates to a system and method for providing and visualizing textile information. The method for determining the damage level of a textile comprises: receiving an image of at least a portion of the textile; receiving information of a fabric type of the at least a portion of the textile; analyzing the image by a machine learning method to identify the fabric properties of the at least a portion of the textile; determining a severity value associated with the identified fabric properties by a machine learning method based on the received image, the identified fabric properties and the fabric type; and determining the damage level of the textile based on the determined severity value.

Description

Technical Field

[0001] The present disclosure relates to the field of computer image recognition, and in particular to a system and method for providing textile information and visualizing the same using a machine learning method. Background Art

[0002] Consumers around the world clean and care for their textiles, such as clothing, by using a variety of washing methods and products. Most current washing machines offer a variety of washing modes to suit different types of clothing. There are also a large number of washing products available on the market for consumers to choose from. This creates a certain difficulty for consumers, as they find it difficult to determine which type of product and how to apply it to best clean and protect their clothing from such a large number of washing products. Furthermore, this problem is further complicated by the fact that consumers' clothing has a wide variety of weaves and materials.

[0003] Traditionally, consumers consult at a laundry or retail counter in a shopping mall or supermarket, where a counter consultant may identify the type and problem of the customer's clothing and provide a solution, which is then communicated to the consumer for discussion. Finally, the consultant will recommend appropriate care products and care methods for the user to choose.

[0004] However, such consultations are highly subjective. The type and number of defects and potential problems identified can vary from consultant to consultant, even for the same item of clothing. Consultation results are more likely to vary over time, and even the same consultant may reach different conclusions from one consultation to another. It can be difficult for a consultant to communicate the defects she sees to a customer, and the trial-and-error process of testing recommendations is time-consuming and tedious.

[0005] Therefore, there is a need for an improved system and method for analyzing and visualizing information related to textiles and recommending care strategies and products. Summary of the invention

[0006] The present disclosure provides a novel system and method for analyzing textile-related information and recommending care strategies and products, as well as visualizing the same.

[0007] According to a first aspect of the present disclosure, a method for determining a damage level of a textile is provided, comprising: receiving an image of at least a portion of the textile; receiving information on the fabric type of the at least a portion of the textile; analyzing the image by a machine learning method to identify fabric properties of the at least a portion of the textile; determining a severity value associated with the identified fabric properties by a machine learning method based on the received image, the identified fabric properties and the fabric type; and determining the damage level of the textile based on the determined severity value.

[0008] The method according to the first aspect also includes determining the risk type and level of the textile based on the fabric properties and fabric type; determining the estimated usage age of the textile based on the fabric properties, fabric type and damage level; providing recommended care strategies based on the damage level of the textile and the risk type and level; providing recommended care products based on the recommended care strategies; generating simulated care results after caring for the textile using multiple care strategies and care products; and providing options for users to purchase the care products.

[0009] According to a second aspect of the present disclosure, a method for determining a condition of a textile is provided, comprising: receiving a digital image of at least a portion of the textile; electronically analyzing the received digital image using a machine learning method to identify fabric properties of the at least a portion of the textile, the fabric properties indicating a textile condition of the textile; and determining the textile condition of the textile in the analyzed digital image based on the identified fabric properties.

[0010] According to a third aspect of the present disclosure, a method for providing textile care recommendations is provided, comprising: receiving an image of at least a portion of the textile; analyzing the image using a machine learning method to identify fabric properties of the at least a portion of the textile, the fabric properties indicating a textile condition of the textile; determining the textile condition of the textile in the analyzed digital image based on the fabric properties; and recommending a textile care strategy for caring for the textile condition.

[0011] According to a fourth aspect of the present disclosure, a method for visualizing textile information is provided, comprising: displaying a first option to receive an image of at least a portion of the textile from a user; displaying a second option to receive information about the fabric type of the at least a portion of the textile from the user; analyzing the image by a machine learning method to identify fabric properties of the at least a portion of the textile; determining a damage level of the textile by a machine learning method based on the received image, the fabric properties and the fabric type; and displaying the damage level of the textile.

[0012] The method according to the fourth aspect also includes: determining and displaying the risk type and level of the textile based on the fabric properties and fabric type; determining and displaying the usage age of the textile based on the fabric properties, fabric type and damage level; displaying a third option to receive user input about personal preferences; displaying recommended care strategies based on the damage level of the textile and the risk type and level; displaying recommended care products based on the recommended care strategies; displaying simulated care results after caring for the textile using multiple care strategies and care products; and displaying a fourth option to enable the user to purchase the care product.

[0013] According to a fifth aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; and a memory on which computer executable instructions are stored, and when the computer executable instructions are executed by the one or more processors, the one or more processors execute any aspect of the aforementioned method.

[0014] According to a sixth aspect of the present disclosure, a non-transitory computer-readable medium is provided, on which computer-executable instructions are stored, and when the computer-executable instructions are executed by one or more processors, the one or more processors execute according to any aspect of the aforementioned method.

[0015] Other features and advantages of the present invention will become more apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0017] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0018] Figure 1 A general architecture diagram for providing textile information according to an exemplary embodiment of the present invention is shown.

[0019] Figure 2 A diagram of a computing environment for providing textile information according to an exemplary embodiment of the present invention is shown.

[0020] Figure 3A A flow chart for determining a damage level of a textile according to an exemplary embodiment of the present invention is shown.

[0021] Figure 3B A flow chart for providing other textile information according to an exemplary embodiment of the present invention is shown.

[0022] Figure 4A schematic diagram showing a method for determining a damage level of a textile according to an exemplary embodiment of the present invention is shown.

[0023] Figure 5 A schematic diagram of a convolutional neural network model according to an exemplary embodiment of the present invention is shown.

[0024] Fig. 6A A flow chart of a method for two-dimensionally visualizing textile information according to an exemplary embodiment of the present invention is shown.

[0025] Figure 6B A flow chart of a method for two-dimensionally visualizing textile information according to another exemplary embodiment of the present invention is shown.

[0026] Figures 7A to 7F A diagram of a user interface for two-dimensionally visualizing textile information according to an exemplary embodiment of the present invention is shown.

[0027] Figure 8 A flow chart for determining a textile condition of a textile according to an exemplary embodiment of the present invention is shown.

[0028] Fig. 9 A flow chart of a recommended textile care strategy according to an exemplary embodiment of the present invention is shown.

[0029] Fig.10 An exemplary configuration diagram of a computing device in which an embodiment of the present invention may be implemented is shown. DETAILED DESCRIPTION

[0030] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Details and functions that are not essential to the present invention are omitted so as not to obscure the understanding of the present invention.

[0031] Please note that like reference numbers and letters refer to like items in the figures, so once an item is defined in one figure, it does not necessarily need to be discussed in subsequent figures.

[0032] In the present disclosure, the terms “first”, “second”, etc. are used merely to distinguish between elements or steps, but are not intended to indicate a time sequence, priority, or importance.

[0033] Combine the following Figure 1 The general inventive concept is described. Figure 1is a diagram showing an overall framework for providing textile information according to an exemplary embodiment of the present invention. The textiles herein may include raw fabrics and various final products made from the raw fabrics, such as clothing, clothing accessories, home textiles, decorative fabric products, gloves, fabric toys, etc. However, the scope of the present invention is not limited thereto, but may be extended to products formed from any fabric and capable of being washed.

[0034] like Figure 1 As shown, the system receives an image 101 of at least a portion of a textile from a user. The image 101 may be previously stored by the user or taken by the user in real time. The image 101 may be a macro image or other image that can reflect the details of the textile. The user may take a macro image of the textile through a macro lens built into a portable device or an external macro lens connected to the portable device.

[0035] After receiving the image 101, the system analyzes it through a pre-established fabric property prediction model 102 to obtain the fabric property 103 of the textile. The fabric property can be a weave type, gloss, elasticity, or a combination thereof. For ease of description, the weave type is described below as an example of a fabric property, but those skilled in the art will understand that the inventive concept can also be applied to the analysis of other fabric properties or a combination of multiple fabric types. The weave type is related to the structure of the textile, and the pattern of the specific weave type can indicate the textile condition and / or damage level of the textile.

[0036] The weave type 103 may include, for example, twill, plain, knitted, and satin. The weave type prediction model 102 may be obtained by training a convolutional neural network (CNN) using a training sample set including a large number of textile images. Figure 5 The CNN model is further described.

[0037] The system also receives input 104 from the user regarding the fabric type of the textile, i.e., the material type or fabric type. The material type may include one or more of cotton, TENCELTM, regenerated fiber, polyester fiber, lyocell, nylon, high-content polyester, low-content polyester, modal, wool, cashmere, rayon, acrylic, viscose, artificial cotton, and silk fabrics. The silk fabric may include one or more of natural silk fabrics, artificial silk fabrics, and silk.

[0038] The system uses a damage level prediction model 105 to analyze the image 101 according to the weave type 103 and the material type 104 to obtain a damage level 106 of the textile. The damage level 106 can be displayed in a statistical graph, text, a word cloud graph superimposed on the textile image, or any combination thereof. The damage level prediction model 105 may include a plurality of convolutional neural network models, each of which corresponds to a combination of at least one weave type from the plurality of weave types and at least one material type from the plurality of material types. Figure 3A and Figure 4 This step is further described.

[0039] Alternatively or further, the system may also determine the risk type and level 107 of the textile according to the weaving type 103 and the material type 104. The risk type and level 107 may be determined by searching a database 111 storing the weaving type, the material type and the corresponding risk type and level. The risk type may include one or more of fuzzing, pilling, deformation, fading, wrinkling, shrinkage, odor and static electricity. The risk level may also be displayed in a statistical graph, text, a word cloud graph superimposed on the textile image, or any combination thereof.

[0040] Alternatively or further, the system can also infer the age 113 of the textile based on the weave type 103, material type 104 and damage level 106. The age 113 can be determined by searching a database 111 storing weave types, material types, damage levels and corresponding ages.

[0041] Alternatively or further, the system may recommend a care strategy 108 based on the damage level 106 and the risk type and level 107. The care strategy 108 may be determined by searching a database 111 storing damage levels, risk types and levels, and corresponding care strategies. The care strategy may include, for example, what water temperature to use, what washing mode to use, etc. to care for the clothing.

[0042] Alternatively or further, the system may recommend care products 109 according to the care strategy 108. The care products 109 may be determined by searching a database 111 storing care strategies and corresponding care products. The care products may include what brand of detergent and / or softener, and so on.

[0043] In addition, the recommended care strategies 108 and care products 109 may also be combined with the personal preferences 110 input by the user, such as which detergent the user is more accustomed to using.

[0044] Alternatively or further, the system can generate simulated care results 112 of the textile obtained after washing with different care strategies and products. For example, the system can generate simulated care results 112 for one or more of the default care strategy and care product, the care strategy and care product selected by the user, and the recommended care strategy and recommended care product.

[0045] It should be recognized that Figure 1 It is illustrative and is not intended to limit the embodiments of the present disclosure. Other variations, modifications, and alternatives will be apparent to those of ordinary skill in the art.

[0046] Figure 2 A computing environment diagram of a system 20 for providing textile information according to an exemplary embodiment of the present invention is shown. The system 20 may include a mobile device 201, a remote server 202, a training device 203, and a database 204, which are coupled to each other via a network 205. The network 205 may be embodied as a wide area network (such as a mobile phone network, a public switched telephone network, a satellite network, the Internet, etc.), a local area network (such as Wi-Fi, Wi-Max, ZigBee, etc.), or a remote server 202. TM 、Bluetooth TM etc.) and / or other forms of networking capabilities.

[0047] The mobile device 201 may be a mobile phone, a tablet computer, a laptop computer, a personal digital assistant and / or other computing devices configured to capture, store and / or transmit images such as digital photos. Therefore, the mobile device 201 may include an image capture device such as a digital camera and / or may be configured to receive images from other devices. The mobile device 201 may include a display. The display may be configured to provide one or more user interfaces to the user 200, and the user interface may include multiple interface elements, and the user 200 may interact with the interface elements, etc. For example, the user 200 may use the mobile device 201 to take a photo of the textile and upload or store the image, and input material information about the textile. The mobile device 201 may output status information about the textile to the user and recommend care strategies and products, etc.

[0048] The remote server 202 may be configured to analyze the textile images and material information received from the mobile device 201 via the network 205 to determine the textile damage level, risk type and level, and recommend care strategies and care products. The remote server 202 may also be configured to create and train a convolutional neural network (CNN).

[0049] The training device 203 may be coupled to the network 205 to facilitate the training of the CNN. The training device 203 may have multiple CPUs and / or GPUs to assist in training the CNN. For example, a trainer may provide one or more digital images of a textile to the CNN via the training device 203. The trainer may also provide information and other instructions to inform the CNN which assessments are correct and which assessments are incorrect. Based on the input from the trainer, the CNN may automatically adjust its own parameters.

[0050] The database 204 can be coupled to the network 205 and provide the data required by the remote server 202 to perform relevant calculations. For example, the database 204 can store data related to fabric properties, material types, damage levels, risk types and levels, care strategies and care products, etc. The database can be implemented using various database technologies known in the art. The remote server 202 can access the database 204 as needed to perform relevant calculations.

[0051] It should be understood that the computing environment here is only an example. Those skilled in the art can add more devices or delete some devices as needed, and can modify the functions and configurations of some devices.

[0052] Combine the following Figure 3A and 3B A method of providing textile information according to an exemplary embodiment of the present invention is described.

[0053] refer to Figure 3A In step S301, the system receives an image of at least a portion of the textile. As described above, the image may be previously stored by the user or taken by the user in real time. The user may take a photo of the main part of the textile or a damaged part. The image may be a macro image or other image that can reflect the details of the textile. The user may take a macro image of the textile through a macro lens built into the portable device or an external macro lens connected to the portable device.

[0054] In step S302, the system receives information about the fabric type, i.e., material type, of the textile. The user can enter the material type of the textile by manually entering the material type or by checking the material type option provided on the mobile device. As mentioned above, the material type may include one or more of cotton, tencel, recycled fiber, polyester fiber, lyocell, nylon, high-content polyester, low-content polyester, modal, wool, cashmere, rayon, acrylic, viscose, artificial cotton, and silk fabrics. It should be understood that the material type is not limited to 15 types, but may include other material types currently known or to be developed in the future. When the textile is formed by a variety of material types, the user can enter a variety of materials at the same time, or select a main material to enter. For example, if the cotton content in a piece of clothing accounts for 80% and modal accounts for 20%, the user can enter cotton as the material type of the clothing, or can enter cotton and modal as the material type.

[0055] In step S303, the system analyzes the textile image by a machine learning method to identify fabric properties of the textile.

[0056] Machine learning methods may include deep learning methods. As known to those skilled in the art, various deep learning models for computer vision recognition technology have been proposed. For example, convolutional neural network (CNN), regional convolutional neural network (R-CNN), fast regional convolutional neural network (fast R-CNN), You Only Look Once (YOLO, you only need to look once), Single Shot MultiBox Detector (SSD), etc. The present invention is described using CNN as an example. It should be understood that the concept of the present invention can be practiced using other known or future developed deep learning models.

[0057] In this step, the image is analyzed by a pre-established fabric property prediction model to obtain the fabric properties of the textile. For example, if the fabric property is a weave type, the weave type may include four types such as twill, plain, knitted, and satin. It should be understood that the weave type is not limited to four, but may include other weave types currently known or developed in the future. The fabric property prediction model can be obtained by training a CNN using a training sample set including a large number (e.g., thousands) of textile images.

[0058] In step S304, the system determines the severity value of the textile by a machine learning method based on the textile image, the identified fabric properties and the information of the material type.

[0059] Reference below Figure 4 This step is described in more detail. Figure 4As shown, the damage level of the textile can be determined by the severity prediction model 402. The severity prediction model 402 may include multiple CNN models, namely CNN model 1, CNN model 2...CNN model N. In an embodiment where the fabric attribute is a weaving type, each CNN model corresponds to a combination of at least one weaving type in a plurality of weaving types and at least one material type in a plurality of material types. For example, for 4 weaving types and 15 material types, if the weaving type and material type of the textile are both selected as a single type, a total of 60 combinations may exist, such as cotton + twill, cotton + plain weave, polyester fiber + twill... etc. Therefore, there may be 60 CNN models. Further, a CNN model may be constructed for a textile composed of a composite material formed by a plurality of materials and a plurality of weaving types. For example, a CNN model for cotton + modal + plain weave may be created. In addition, in order to reduce the difficulty of calculation, CNN models for relatively rare combinations, such as a combination of cotton + satin, may be omitted. Therefore, the number of CNN models is not limited to 60, but more or less. Images of a plurality of textiles formed by corresponding weaving types and corresponding material types and having different severity values ​​are used to train each CNN model. In practice, the CNN models can be trained using images of textiles captured after multiple rounds of machine washing. The damage level of the textile will be different depending on the number of times it is machine washed. Therefore, images of the corresponding damage level can be obtained by machine washing the textile multiple times.

[0060] The system inputs the information of the identified weaving type and material type into the classifier 401. The classifier 401 determines which CNN model of the plurality of CNN models 402 should be used for prediction according to the received weaving type and material type. The corresponding CNN model is activated to receive the image 101 of the textile and analyze it to determine the severity value. The severity value can be, for example, 0 to N, where N is any integer greater than 0.

[0061] In step S305, the system determines the damage level of the textile according to the severity value. For example, a severity value of 0 may correspond to no damage, 1 may correspond to mild damage, 2 may correspond to moderate damage, and 3 may correspond to severe damage. It should be noted that the severity values ​​and damage levels of 0-3 here are only examples, and those skilled in the art can expect severity values ​​and damage levels of any granularity.

[0062] In addition to determining the damage level of the textile, the system may alternatively or further determine other information of the textile. Figure 3B Give a description.

[0063] refer to Figure 3BIn step S306, the system can also determine the risk type and level of the textile according to the weaving type and material type. As mentioned above, the risk type and level can be determined by searching a database storing the weaving type, material type and corresponding risk type and level. The risk type can include one or more of pilling, pilling, deformation, fading, wrinkling, shrinkage, odor and static electricity.

[0064] In step S307, the system can also infer the estimated age of the textile based on the weave type, material type and damage level. The age can be determined by searching a database that stores the weave type, material type, damage level and corresponding age. For example, the database can store data such as "cotton + plain weave + moderate damage: estimated age is 2 years". The system can obtain the estimated age of the textile by searching the corresponding entry in the database. It should be understood that the form of the data in the database is not limited to the example form described here, but can adopt various storage methods commonly used in databases, such as identifier mapping, etc.

[0065] In step S308, the system can recommend a care strategy based on the damage level and risk type and level. The care strategy may include, for example, what water temperature should be used, what washing mode should be used, etc. to care for clothing. The care strategy can be determined by searching a database that stores damage levels, risk types and levels, and corresponding care strategies. For example, the database may store data such as "Silk + Plain Weave + Mild Damage: The care strategy is cold water washing to better protect the color of the fabric. Choose a laundry bag for machine washing, and choose the quick wash mode to keep the fabric in shape after multiple washes. Using softener can make clothes have a better wearing experience, flowing and stylish without sticking to the body." The system can obtain the recommended care strategy for textiles by searching for corresponding entries in the database. It should be noted that this care strategy is only an example. Those skilled in the art can provide more specific or simpler care strategy recommendations or use different expressions based on the present invention.

[0066] In step S309, the system recommends care products according to the care strategy. The care products may include what brand of detergent and / or softener, etc. The care products may be determined by searching a database storing care strategies and corresponding care products. For example, the database may store "cold water washing + quick wash mode: care products are The system can obtain the recommended care products for textiles by searching the corresponding entries in the database. It should be noted that the care products are only examples. Those skilled in the art can provide other suitable care products based on the concept of the present invention.

[0067] In addition, the recommended care strategies and care products can also be combined with the personal preferences input by the user, such as which detergent the user is more accustomed to using.

[0068] In step S310, the system may generate simulated care results of the textile obtained after washing using different care strategies and products. For example, the system may generate simulated care results for one or more of a default care strategy and care product, a user-selected care strategy and care product, and a recommended care strategy and recommended care product.

[0069] It should be noted that Figure 3A and 3B Some steps in the embodiment are not necessarily performed in the order shown in the figure, but they can be performed simultaneously, in different orders or in an overlapping manner. In addition, those skilled in the art can add some steps or omit some steps as needed.

[0070] Figure 5 A schematic diagram of a convolutional neural network model according to an exemplary embodiment of the present invention is shown.

[0071] As known to those skilled in the art, a convolutional neural network (CNN) is a feedforward type artificial neural network, which generally includes an input layer 501, multiple convolutional layers 502-1, 502-2 ... (hereinafter collectively referred to as 502), multiple pooling layers 503-1, 503-2 ... (hereinafter collectively referred to as 503), multiple fully connected layers 504 and an output layer 505. The input layer 501 receives an input image. The convolutional layer 502 implements the inner product operation of the pixels of the input image and the convolution kernel. The number and size of the convolution kernel can be set according to the specific application. The pooling layer 503 can reduce the size of the feature map generated by the convolution layer. Common pooling methods include maximum pooling, average pooling, etc. The fully connected layer 504 can integrate the features in the image feature map that has passed through multiple convolutional layers and pooling layers for subsequent use in image classification. The output layer 505 outputs the result of image classification. For example, if the damage level is specified to be 0 to 3, the output layer outputs one of 0 to 3.

[0072] Under the guidance of the inventive concept, those skilled in the art can train the CNN model using a training sample set containing a large number of textile images to obtain a trained CNN model with specific parameters for use in the system according to an embodiment of the present invention.

[0073] Another aspect of the present invention relates to visualizing textile information. For example, the method of the present invention can be implemented as an executable program on a personal computer, an application on a mobile smart device, and / or a small program running in other applications on the mobile smart device. Fig. 6A , 6BThe present embodiment mainly focuses on how to visualize the information of textiles. For those features that are the same or similar to the corresponding features in the previous text, the various aspects described in the previous text will also apply to the method and system of this embodiment, so their detailed description will be omitted. Fig. 6A , 6B The method flow chart of 7A to 7F and the user interface (UI) diagrams of 7A to 7F describe the method of visualization in a two-dimensional format, but those skilled in the art will appreciate that the present invention may include visualization in a three-dimensional format.

[0074] refer to Fig. 6A In step S601, the system displays a first option to receive an image of at least a portion of a textile from a user. Fig. 7A As shown, an icon 701 is displayed on the display screen of the mobile device, and the user can take a photo of the textile or select a previously taken photo from an album by clicking the icon.

[0075] In step S602, the system displays a second option to receive information about the fabric type, i.e., the material type, of the textile from the user. Figure 7B As shown, the interface element 702 on the display screen prompts the user to input the material information of the textile, and provides multiple material types for the user to select. The user can enter the material type by checking the corresponding checkbox. It should be understood that this is only an example of inputting the material type. Those skilled in the art can also adopt other ways to input the material type, for example, the system can display a text box for the user to manually input the material type.

[0076] In step S603, the system analyzes the image using a pre-built textile fabric property prediction model to identify the fabric properties of the textile. Figure 3A and Figure 5 The method described here performs this step. The identified fabric attributes may not necessarily be displayed on the display screen, but may also be displayed on the display screen for user confirmation.

[0077] In step S604, the system determines the damage level of the textile by machine learning method based on the information of the image, fabric properties and fabric type. Figure 3A and Figure 4 This step was performed as described.

[0078] In step S605, the system displays the damage level of the textile. Figure 7CAs shown, an interface element 703 is displayed on the display screen of the mobile device, indicating that the damage level of the textile is mild. Those skilled in the art should understand that the manner of displaying the damage level is not limited to text, but can be in the form of statistical graphics (such as a bar chart, etc.), text (such as: undamaged, mild, moderate, severe, etc.), digital percentage, word cloud graphics superimposed on the textile image, or any combination thereof.

[0079] In addition to displaying the damage level of the textile, the system may alternatively or further display other information of the textile. Figure 6B Give a description.

[0080] In step S606, the system determines and displays the risk type and level of the textile according to the information of fabric properties and material type. Figure 7C As shown, an interface element 704 is displayed on the display screen of the mobile device, which indicates the risk type and level of the textile. In this example, the risks displayed are fuzzing, pilling, shrinkage, odor, and static electricity. The corresponding risk levels are two stars, two stars, one star, two stars, and two stars. Those skilled in the art should understand that the method of displaying risk types and levels is not limited to Figure 7C Instead, the display may be in the form of statistical graphics, text, numerical percentages, word cloud graphics superimposed on the textile image, or any combination thereof.

[0081] In step S607, the system determines and displays the estimated age of the textile based on the fabric properties, material type information and damage level. The estimated age may not necessarily be displayed on the display screen, but may also be displayed on the display screen for user confirmation.

[0082] In step S608, the system displays a third option to receive user input regarding personal preferences. Fig.7D As shown, an interface element 705 is displayed on the display screen of the mobile device, which indicates various personal preferences for the user to input. In this example, the system can display options about the user's gender, the most commonly used laundry products, and the most commonly used auxiliary agents for the user to select. Those skilled in the art should understand that the system can provide other options about personal preferences for the user to input. The system can also provide options so that the user can manually input relevant information.

[0083] In step S609, the system displays the recommended care strategy according to the damage level of the textile and the risk type and level. Alternatively or further, the system may also display the recommended care strategy according to the personal preference input by the user. Fig. 7EAs shown, the display screen displays a recommended care strategy, "Cold water washing to better protect the color of the fabric. Choose a laundry bag for machine washing and select the quick wash mode to keep the fabric in shape after multiple washes. Use softener to make clothes have a better wearing experience, flowing and stylish without sticking to the body." It should be noted that the expression and display methods of the care strategy are only examples. Those skilled in the art can provide more specific or simpler care strategy recommendations or use different display methods based on the present concept.

[0084] In step S610, the system displays recommended care products based on the recommended care strategy. Figure 7F As shown, the display screen displays an interface element 707 indicating a recommended care product. In this example, the care product is Natural laundry detergent (with natural rejuvenating essence, smooth and lint-free). The system can also display product images of recommended products to facilitate user identification and purchase. It should be noted that the display method of the care product is only an example. Those skilled in the art can use different display methods according to the present invention.

[0085] In step S611, the system displays the simulated care results after the textile is cared for using multiple care strategies and care products. The multiple care strategies and care products include one or more of the default care strategies and care products, the care strategies and care products selected by the user, and the recommended care strategies and recommended care products. Figure 7F As shown, an interface element 708 is displayed on the display screen, which indicates the simulated care results. In this example, the system displays the simulated care results after the textiles are cared for under ordinary washing methods and ordinary detergents (for example, the detergent selected by the user when entering personal preferences), and under the care strategies and products recommended by the system. The simulated care results are in the form of a radial graph, and each radial bar represents a possible risk. The farther from the center, the higher the corresponding risk. The dotted line and the bold solid line represent the simulation results of ordinary washing and recommended washing, respectively. It can be seen that ordinary washing methods will lead to a higher risk of pilling, pilling, static electricity, odor, shrinkage and wrinkles on textiles. It should be noted that Figure 7F The display method of the simulated care results shown is only an example. Those skilled in the art may use different display methods according to the present invention as long as they can distinguish different washing results. For example, different colors may be used to represent the results of ordinary washing and recommended washing, respectively, instead of different lines. Different shaded areas may also be used to represent the results of the two washings.

[0086] In step S612, the system displays a fourth option to enable the user to purchase the care product. Figure 7FAs shown, an interface element 709 is displayed on the display screen, which guides the user to purchase the recommended care products.

[0087] In addition to being able to analyze the condition of used textiles, the present invention can also be used to analyze the condition of new unused textiles and provide users with corresponding care recommendations. Figure 8 and Fig. 9 Give a description.

[0088] Figure 8 A flowchart for determining a textile condition of a textile according to another exemplary embodiment of the present invention is described. The textile of this embodiment may be a used textile or a new textile that has not been used. For those features that are the same or similar to the corresponding features in the previous text, the various aspects described in the previous text will also apply to the method and system of this embodiment, so their detailed description will be omitted.

[0089] In step S801 , the system receives a digital image of at least a portion of a textile.

[0090] In step S802, the system electronically analyzes the received digital image using a machine learning method in combination with a pre-established fabric attribute database to identify a fabric attribute of the at least a portion of the textile, the fabric attribute being able to indicate a textile condition of the textile. The fabric attribute may be a weave pattern, a fabric type, a sheen, an elasticity, or a combination thereof. The aforementioned reference may be used to identify the fabric attribute of the at least a portion of the textile. Figure 3A , Figure 4 , Figure 5 This step can be performed by a method. For example, the glossiness of the textile can be identified.

[0091] In step S803, the system determines the textile condition of the textile in the analyzed digital image based on the identified fabric properties. For example, the system can determine the textile condition based on the size of the glossiness, such as whether the textile is new or slightly damaged, etc. This step can be completed by a deep learning model, or by comparing images stored in a database to obtain the corresponding textile condition. The above has described an embodiment of determining the textile condition based on a deep learning model, which will not be repeated here. When obtaining the corresponding textile condition by comparing images stored in a database, one embodiment may be that a plurality of images of a plurality of textiles composed of specific fabric properties (e.g., weaving patterns) and specific material types and having different stages are pre-stored in the database, and each stage represents a different degree of damage to the specific fabric property (e.g., weaving pattern) and the specific material type. By comparing the image of the textile with the image in the database, the textile condition of the textile can be obtained.

[0092] Alternatively or additionally, the method further comprises step S804, in which the system assigns a severity level to the textile condition of the textile in the analyzed digital image. For example, the severity level may be determined by comparing the textile condition to a predetermined value associated with a set of images of the fabric property. The severity level of the textile condition may be a fabric damage value.

[0093] Fig. 9 A flowchart of a recommended textile care strategy according to another exemplary embodiment of the present invention is shown. The textile of this embodiment can be a used textile or a new textile that has not been used. For those features that are the same or similar to the corresponding features in the previous text, the various aspects described in the previous text will also apply to the method and system of this embodiment, so their detailed description will be omitted.

[0094] In step S901 , the system receives a digital image of at least a portion of a textile.

[0095] In step S902, the system analyzes the received digital image using a machine learning method, combined with a pre-established fabric property database, to identify fabric properties of at least a portion of the textile, which fabric properties can indicate a textile condition of the textile. The fabric property can be a weave pattern, a fabric type, a gloss, an elasticity, or a combination thereof. The above reference can be used to identify the fabric property of the at least a portion of the textile. Figure 3A , Figure 4 , Figure 5 This step can be performed by a method. For example, the glossiness of the textile can be identified.

[0096] In step S903, the system determines the textile condition of the textile in the analyzed digital image based on the identified fabric properties. For example, the system can determine the textile condition based on the glossiness, such as whether the textile is new or slightly damaged, etc. This step can be completed by a deep learning model, or by comparing images stored in a database to obtain corresponding textile conditions.

[0097] In step S904, the system recommends a textile care strategy for caring for the textile condition. Figure 1 , 3B , Figure 4 , Figure 5 method to perform this step.

[0098] Optionally or additionally, although not shown, the method may also include the following steps: Figure 8The severity level assignment step. In this step, the system assigns a severity level to the textile condition of the textile in the analyzed digital image. The severity level can be determined, for example, by comparing the textile condition to a predetermined value associated with a set of images of the fabric property. The severity level of the textile condition can be a fabric damage value.

[0099] The system and method of the present invention analyzes the condition of textiles and provides corresponding care recommendations by applying deep learning technology, thereby improving the accuracy and objectivity of the analysis. In addition, the present invention can more intuitively present various information about textiles to users, thereby improving the user experience. In addition, by conveniently providing users with professional care recommendations, the sales efficiency of products can be improved and marketing costs can be reduced.

[0100] Fig.10 An exemplary configuration of a computing device 1000 that can implement an embodiment of the present invention is shown. The computing device 1000 is an example of a hardware device to which the above aspects of the present invention can be applied. The computing device 1000 can be any machine configured to perform processing and / or calculations. The computing device 1000 can be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal data assistant (PDA), a smart phone, a vehicle-mounted computer, or a combination thereof.

[0101] like Fig.10 As shown, computing device 1000 may include one or more elements that may be connected or communicated with bus 1002 via one or more interfaces. Bus 1002 may include, but is not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, etc. Computing device 1000 may include, for example, one or more processors 1004, one or more input devices 1006, and one or more output devices 1008. One or more processors 1004 may be any kind of processor, and may include, but is not limited to, one or more general-purpose processors or special-purpose processors (such as special-purpose processing chips). Input device 1006 may be any type of input device capable of inputting information to a computing device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a microphone, and / or a remote controller. Output device 1008 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer.

[0102] The computing device 1000 may also include or be connected to a non-transitory storage device 1014, which may be any non-transitory storage device that can implement data storage, and may include, but is not limited to, a disk drive, an optical storage device, a solid-state memory, a floppy disk, a flexible disk, a hard disk, a tape, or any other magnetic medium, a compressed disk or any other optical medium, a cache memory, and / or any other storage chip or module, and / or any other medium from which a computer can read data, instructions, and / or code. The computing device 1000 may also include a random access memory (RAM) 1010 and a read-only memory (ROM) 1012. The ROM 1012 may store programs, utilities, or processes to be executed in a non-volatile manner. The RAM 1010 may provide volatile data storage and store instructions related to the operation of the computing device 1000. The computing device 1000 may also include a network / bus interface 1016 coupled to a data link 1018. The network / bus interface 1016 may be any type of device or system capable of enabling communication with an external device and / or a network, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication device, and / or a chipset (such as a Bluetooth TM equipment, 802.11 equipment, WiFi equipment, WiMax equipment, cellular communication facilities, etc.).

[0103] Various aspects, embodiments, implementations or features of the foregoing embodiments may be used alone or in any combination. Various aspects of the foregoing embodiments may be implemented by software, hardware or a combination of hardware and software.

[0104] For example, the foregoing embodiments may be embodied as computer readable code on a computer readable medium. A computer readable medium is any data storage device that can store data that can thereafter be read by a computer system. Examples of computer readable media include read-only memory, random access memory, CD-ROM, DVD, magnetic tape, hard drive, solid state drive, and optical data storage device. The computer readable medium may also be distributed in a network coupled computer system so that the computer readable code is stored and executed in a distributed manner.

[0105] For example, the aforementioned embodiments may be in the form of hardware circuits. The hardware circuits may include any combination of combinational logic circuits, clock storage devices (such as floppy disks, flip-flops, latches, etc.), finite state machines, memories such as static random access memories or embedded dynamic random access memories, custom designed circuits, programmable logic arrays, etc.

[0106] Some examples of the present invention are shown below.

[0107] Example 1. A method for determining a damage level of a textile product, comprising:

[0108] receiving an image of at least a portion of the textile;

[0109] receiving information of a fabric type of the at least a portion of the textile;

[0110] analyzing the image by a machine learning method to identify fabric properties of the at least a portion of the textile;

[0111] determining, by a machine learning method, a severity value associated with the identified fabric attribute based on the received image, the identified fabric attribute, and the fabric type; and

[0112] A damage level of the textile product is determined based on the determined severity value.

[0113] Example 2. A method as described in Example 1, wherein the severity value of the textile is determined by a severity prediction model, the severity prediction model includes multiple convolutional neural network models, and each convolutional neural network model is configured to analyze an image of a textile formed by at least one fabric attribute among multiple fabric attributes and at least one fabric type among multiple fabric types.

[0114] Example 3. A method as described in Example 1 or Example 2, wherein each convolutional neural network model is trained using images of multiple textiles formed by at least one fabric attribute among multiple fabric attributes and at least one fabric type among multiple fabric types and having different severity values.

[0115] Example 4. A method as described in any one of Examples 1 to 3, wherein the images of the plurality of textiles having different severity values ​​are obtained by acquiring corresponding images of the plurality of textiles after machine washing the plurality of textiles for different numbers of times.

[0116] Example 5. The method as described in any one of Examples 1 to 4, further comprising:

[0117] The risk type and level of the textile product are determined based on the information of the fabric properties and fabric type.

[0118] Example 6. The method as described in any one of Examples 1 to 5, further comprising:

[0119] An estimated age of the textile product is determined based on the fabric attributes, fabric type, and damage level.

[0120] Example 7. The method according to Example 5, further comprising:

[0121] Based on the level of damage to the textile and the type and level of risk, a recommended care strategy is provided.

[0122] Example 8. The method as described in Example 7 further includes:

[0123] According to the recommended care strategy, recommended care products are provided.

[0124] Example 9. The method of Example 8, wherein providing the recommended care strategy or the recommended care product is further based on user input regarding personal preferences.

[0125] Example 10: The method as described in Example 8, further comprising:

[0126] Simulated care results after the textile is cared for using a variety of care strategies and care products are generated.

[0127] Example 11. A method as described in Example 10, wherein the multiple care strategies and care products include default care strategies and care products, user-selected care strategies and care products, and one or more of the recommended care strategies and recommended care products.

[0128] Example 12. A method as described in any one of Examples 1 to 11, wherein the image of the textile is a macro image, and the macro image is taken by a portable device through a built-in macro lens or an external macro lens connected to the portable device.

[0129] Example 13: The method of Example 8, further comprising:

[0130] An option is provided for the user to purchase the care product.

[0131] Example 14. The method of any one of Examples 1 to 13, wherein the fabric property is one of the group consisting of: weave type, sheen, elasticity, and combinations thereof.

[0132] Example 15. The method of Example 14, wherein the weave type includes one or more of twill, plain, knit, and satin.

[0133] Example 16. A method as described in any one of Examples 1 to 13, wherein the fabric type includes one or more of cotton, Tencel, regenerated fiber, polyester fiber, lyocell, nylon, high-content polyester, low-content polyester, modal, wool, cashmere, rayon, acrylic, viscose, artificial cotton, and silk fabrics.

[0134] Example 17. A method as described in Example 16, wherein the silk fabric comprises one or more of natural silk fabric, artificial silk fabric, and silk.

[0135] Example 18. A method as described in Example 5, wherein the risk types include one or more of pilling, pilling, deformation, fading, wrinkling, shrinkage, odor and static electricity.

[0136] Example 19. A method for determining a condition of a textile, comprising:

[0137] receiving a digital image of at least a portion of the textile;

[0138] electronically analyzing the received digital image using a machine learning method to identify a fabric property of the at least a portion of the textile that is indicative of a textile condition of the textile; and

[0139] A textile condition of the textile in the analyzed digital image is determined based on the identified textile properties.

[0140] Example 20. The method according to Example 19, further comprising:

[0141] A severity level is assigned to a textile condition of the textile in the analyzed digital image.

[0142] Example 21. The method according to Example 20, wherein the step of assigning a severity level comprises:

[0143] The textile condition is compared to a predetermined value associated with a set of images of the fabric property.

[0144] Example 22. The method of Example 21, wherein the severity level of the textile condition includes a fabric damage value.

[0145] Example 23. A method according to any one of Examples 19 to 22, wherein the fabric property is one of the group consisting of: weave pattern, fabric type, gloss, elasticity, and combinations thereof.

[0146] Example 24. A method for providing textile care recommendations, comprising:

[0147] receiving an image of at least a portion of the textile;

[0148] analyzing the image using a machine learning method to identify a fabric property of the at least a portion of the textile, the fabric property being indicative of a textile condition of the textile;

[0149] determining a textile condition of the textile in the analyzed digital image based on the textile property; and

[0150] Recommend textile care strategies for caring for the described textile conditions.

[0151] Example 25. The method according to Example 24, further comprising:

[0152] A severity level is assigned to a textile condition of the textile in the analyzed digital image.

[0153] Example 26. The method of Example 25, wherein the step of assigning a severity level comprises:

[0154] The textile condition is compared to a predetermined value associated with a set of images of the fabric property.

[0155] Example 27. The method of Example 26, wherein the severity level of the textile condition includes a fabric damage value.

[0156] Example 28. A method according to any one of Examples 24 to 27, wherein the fabric property is one of the group consisting of: weave pattern, fabric type, gloss, elasticity, and combinations thereof.

[0157] Example 29. A method for visualizing textile information, comprising:

[0158] displaying a first option to receive an image of at least a portion of the textile from a user;

[0159] displaying a second option to receive information from a user about a fabric type of the at least a portion of the textile product;

[0160] analyzing the image by a machine learning method to identify fabric properties of the at least a portion of the textile;

[0161] Determining the damage level of the textile product by a machine learning method based on the received image, the fabric property and the fabric type; and

[0162] Displays the degree of damage to the textile product.

[0163] Example 30: The method of Example 29, further comprising:

[0164] According to the information of the fabric properties and fabric type, the risk type and level of the textile product are determined and displayed.

[0165] Example 31. The method of Example 29, further comprising:

[0166] The estimated age of the textile product is determined and displayed based on the fabric attributes, fabric type, and damage level.

[0167] Example 32: The method of Example 30 or 31, further comprising:

[0168] Based on the damage level of the textile and the risk type and level, a recommended care strategy is displayed.

[0169] Example 33. The method of any one of Examples 29 to 32, further comprising:

[0170] According to the recommended care strategy, recommended care products are displayed.

[0171] Example 34. The method of Example 33 further includes:

[0172] Display a third option to receive user input regarding personal preferences,

[0173] The recommended care strategies or recommended care products displayed are further based on the personal preferences.

[0174] Example 35. The method of Example 33 or 34, further comprising:

[0175] The simulated care results after caring for the textile using various care strategies and care products are shown.

[0176] Example 36. A method as described in any one of Examples 33 to 35, wherein the multiple care strategies and care products include default care strategies and care products, user-selected care strategies and care products, and one or more of the recommended care strategies and recommended care products.

[0177] Example 37. The method of any one of Examples 33 to 36, further comprising:

[0178] A fourth option is displayed to enable the user to purchase the care product.

[0179] Example 38. The method of any one of Examples 29 to 37, wherein the fabric property is one of the group consisting of: weave type, sheen, elasticity, and combinations thereof.

[0180] Example 39. The method of Example 38, wherein the weave type includes one or more of twill, plain, knit, and satin.

[0181] Example 40. A method as described in any one of Examples 29 to 39, wherein displaying a second option includes displaying cotton, Tencel, recycled fiber, polyester, lyocell, nylon, high-content polyester, low-content polyester, modal, wool, cashmere, rayon, acrylic, viscose, artificial cotton, and silk fabrics for user selection.

[0182] Example 41. A method as described in Example 40, wherein the silk fabric includes one or more of natural silk fabric, artificial silk fabric, and silk.

[0183] Example 42. A method as described in any one of Examples 29 to 41, wherein the risk type includes one or more of fuzzing, pilling, deformation, fading, wrinkling, shrinkage, odor and static electricity.

[0184] Example 43. A method as described in any one of Examples 29 to 42, wherein displaying the damage level of the textile includes displaying the damage level of the textile in a statistical graphic, text, percentage, a word cloud graphic superimposed on an image of at least a portion of the textile, or any combination thereof.

[0185] Example 44. An electronic device comprising:

[0186] one or more processors; and

[0187] A memory having computer executable instructions stored thereon, which when executed by the one or more processors cause the one or more processors to perform the method according to any one of Examples 1-43.

[0188] Example 45. A non-transitory computer-readable medium having computer-executable instructions stored thereon, which, when executed by one or more processors, cause the one or more processors to perform the method according to any one of claims 1-43.

[0189] Although some specific embodiments of the present invention have been shown in detail by example, it will be appreciated by those skilled in the art that the above examples are intended to be illustrative only and do not limit the scope of the present invention. It should be appreciated that some steps in the aforementioned method are not necessarily performed in the order shown in the figure, but they can be performed simultaneously, in different orders or in an overlapping manner. In addition, those skilled in the art can add some steps or omit some steps as needed. Some components in the aforementioned system are not necessarily arranged according to the figure, and those skilled in the art can add some components or omit some components as needed. It will be appreciated by those skilled in the art that the above embodiments can be modified without departing from the scope and essence of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for determining textile information, comprising: receiving an image of at least a portion of the textile; receiving information of a fabric type of the at least a portion of the textile; analyzing the image by a machine learning method to identify fabric properties of the at least a portion of the textile; selecting a convolutional neural network (CNN) model from a plurality of CNN models based on the received fabric type and the identified fabric property, each of the plurality of CNN models corresponding to at least one fabric property of the plurality of fabric properties and at least one fabric type of the plurality of fabric types; determining, based on the received image, the identified fabric property, and the fabric type, a severity value associated with the identified fabric property by using the selected CNN model; determining a damage level of the textile product based on the determined severity value; Determining the risk type and level of the textile product based on the information of the fabric property and fabric type; providing a recommended care strategy based on the level of damage to the textile and the type and level of risk; as well as According to the recommended care strategy, recommended care products are provided.

2. The method of claim 1, wherein each convolutional neural network model is trained using images of a plurality of textiles formed by at least one fabric attribute among a plurality of fabric attributes and at least one fabric type among a plurality of fabric types and having different severity values. 3 . The method of claim 2 , wherein the images of the plurality of textiles having different severity values ​​are obtained by acquiring corresponding images of the plurality of textiles after machine washing the plurality of textiles for different numbers of times.

4. The method of claim 1, further comprising: An estimated age of the textile product is determined based on the fabric attributes, fabric type, and damage level.

5. The method of claim 1, wherein providing a recommended care strategy or a recommended care product is further based on user input regarding personal preferences.

6. The method of claim 1, further comprising: Simulated care results after the textile is cared for using a variety of care strategies and care products are generated.

7. The method of claim 6, wherein the plurality of care strategies and care products include one or more of a default care strategy and care product, a user-selected care strategy and care product, and the recommended care strategy and recommended care product. 8 . The method of claim 1 , wherein the image of the textile is a macro image, and the macro image is taken by a portable device through a built-in macro lens or an external macro lens connected to the portable device.

9. The method of claim 1, further comprising: An option is provided for the user to purchase the care product.

10. The method according to any one of claims 1 to 9, wherein the fabric property is one of the group consisting of: weave type, sheen, elasticity, and combinations thereof.

11. The method of claim 10, wherein the weave type comprises one or more of twill, plain, knit, and satin.

12. The method of any one of claims 1 to 9, wherein the fabric type comprises one or more of cotton, tencel, regenerated fiber, polyester, lyocell, nylon, high content polyester, low content polyester, modal, wool, cashmere, rayon, acrylic, viscose, rayon, and silk.

13. The method of claim 12, wherein the silk fabric comprises one or more of natural silk fabric, artificial silk fabric, and silk.

14. The method of claim 1, wherein the risk types include one or more of fuzzing, pilling, deformation, fading, wrinkling, shrinkage, odor, and static electricity.

15. A method for visualizing textile information, comprising: displaying a first option to receive an image of at least a portion of the textile from a user; displaying a second option to receive information from a user about a fabric type of the at least a portion of the textile product; analyzing the image by a machine learning method to identify fabric properties of the at least a portion of the textile; selecting a convolutional neural network (CNN) model from a plurality of CNN models based on the received fabric type and the identified fabric property, each of the plurality of CNN models corresponding to at least one fabric property of the plurality of fabric properties and at least one fabric type of the plurality of fabric types; Determining a damage level of the textile product by using the selected CNN model based on the received image, the fabric properties, and the fabric type; Display the degree of damage of the textile product; Determining and displaying the risk type and level of the textile product based on the information of the fabric property and fabric type; displaying a recommended care strategy based on the level of damage to the textile and the type and level of risk; as well as According to the recommended care strategy, recommended care products are displayed.

16. The method of claim 15, further comprising: The estimated age of the textile product is determined and displayed based on the fabric attributes, fabric type, and damage level.

17. The method of claim 15, further comprising: Display a third option to receive user input regarding personal preferences, The recommended care strategies or recommended care products displayed are further based on the personal preferences.

18. The method of claim 15, further comprising: The simulated care results after caring for the textile using various care strategies and care products are shown.

19. The method of claim 18, wherein the plurality of care strategies and care products include one or more of a default care strategy and care product, a user-selected care strategy and care product, and the recommended care strategy and recommended care product.

20. The method of claim 15, further comprising: A fourth option is displayed to enable the user to purchase the care product.

21. The method of any one of claims 15 to 20, wherein the fabric property is one of the group consisting of: weave type, sheen, elasticity, and combinations thereof.

22. The method of claim 21, wherein the weave type comprises one or more of twill, plain, knit, and satin.

23. The method of any one of claims 15 to 20, wherein displaying the second option comprises displaying cotton, tencel, recycled fiber, polyester, lyocell, nylon, high content polyester, low content polyester, modal, wool, cashmere, rayon, acrylic, viscose, artificial cotton, and silk fabrics for user selection.

24. The method of claim 23, wherein the silk fabric comprises one or more of natural silk fabric, artificial silk fabric, and silk.

25. The method according to any one of claims 15 to 20, wherein the risk types include one or more of fuzzing, pilling, deformation, fading, wrinkling, shrinking, odor and static electricity.

26. The method of any one of claims 15 to 20, wherein displaying the damage level of the textile comprises displaying the damage level of the textile in a statistical graphic, text, percentage, a word cloud graphic superimposed on an image of at least a portion of the textile, or any combination thereof.

27. An electronic device comprising: one or more processors; and A memory having computer executable instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1-26.

28. A non-transitory computer-readable medium having computer-executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform the method according to any one of claims 1-26.

Images