Intelligent lamp mirror for image preprocessing

Abstract

The invention relates to the technical field of intelligent lamp mirrors, in particular to an intelligent lamp mirror for image preprocessing, the intelligent lamp mirror comprises a cabinet body, a main control module, a camera module, a projection module and a mirror body arranged on the cabinet body, the camera module is used for collecting shape data of a user wearing clothes; the main control module is used for storing a three-dimensional model of clothes unfolded in a standard state, performing three-dimensional modeling on the body of a user based on the shape and wearing data of the user, comparing the clothes data with the three-dimensional modeling of the user, scoring the current image of the user and outputting an image improvement suggestion; and the projection module is used for projecting the corresponding score data around the mirror image of the user in the mirror body. According to the intelligent lamp mirror for image preprocessing, personalized image optimization service is provided for a user through high-precision three-dimensional modeling, a scientific scoring algorithm and an intuitive mirror surface projection technology.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent light mirrors, and in particular to an intelligent light mirror for image preprocessing. Background Art

[0002] With the rapid development of e-commerce and virtual fitting technology, consumers are increasingly demanding personalized shopping experiences. Traditional fitting methods rely on physical fitting rooms, which have problems such as low efficiency and poor experience. In recent years, virtual fitting technology has provided users with a more convenient fitting experience by combining computer vision, 3D modeling, and artificial intelligence algorithms. However, existing virtual fitting systems still have the following problems: unintuitive user experience, limited clothing matching effects, and lack of personalized scoring. Summary of the invention

[0003] In order to overcome the shortcomings and deficiencies in the prior art, the purpose of the present invention is to provide an intelligent light mirror for image preprocessing, which provides users with personalized image optimization services through high-precision three-dimensional modeling, scientific scoring algorithm and intuitive mirror projection technology.

[0004] The present invention is achieved through the following technical solutions: In a first aspect, the present invention discloses an intelligent light mirror for image preprocessing, comprising a cabinet, a main control module, a camera module, a projection module and a mirror body installed in the cabinet, wherein the camera module is used to collect body data of a user after wearing clothes; The main control module is used to store the three-dimensional model of the clothing unfolded in a standard state and to perform three-dimensional modeling of the user's body based on the user's body shape and wearing data, and to compare the clothing data with the user's three-dimensional modeling, score the user's current image and output image improvement suggestions; The projection module is used to project the corresponding scoring data around the user's mirror image in the mirror body.

[0005] In a second aspect, the present invention further discloses an image preprocessing method for an intelligent light mirror, the method comprising the following steps: S100. The camera module collects the body data of the user wearing clothes, and sends the data to the main control module, which uses the data to construct a three-dimensional model of the user; S200. The main control module obtains the three-dimensional model of the clothing stored in the server, and compares the clothing data with the three-dimensional modeling of the user; S300. The main control module scores the user's current image and outputs image improvement suggestions according to the set algorithm; S400. The projection module projects the corresponding scoring data around the user's image in the mirror body.

[0006] In combination with the second aspect, further, in step S100, point cloud data is acquired by a depth camera, and point cloud registration is performed by an ICP algorithm to generate a three-dimensional model of the user; The modeling function of the user's three-dimensional model is:

[0007] in, and They are Direction and Direction B-spline basis functions, For the control point.

[0008] In combination with the second aspect, further, in step S200, the main control module is used to pre-process the body image of the user currently wearing the clothes collected by the camera, extract clothing features, identify and classify the clothes, and match the three-dimensional data of the clothes; Combined with the second aspect, further, the formula for clothing image preprocessing is:

[0009] in, is the preprocessed image data, is the standard deviation of the Gaussian kernel; Extract the key features of clothing from the preprocessed image and use convolutional neural network to extract features:

[0010] in are network parameters, is the convolution operation, is the activation function; and SIFT is used to extract local features; Based on the extracted features, use machine learning or deep learning models to classify clothing and identify the type of clothing the user is currently wearing; According to the identified clothing type, the corresponding clothing 3D data is retrieved from the shopping platform and matched with the user's 3D model. The formula for 3D data matching is:

[0011] in, is the rotation matrix, is the translation vector, are the corresponding points of the user model and the clothing model respectively.

[0012] In combination with the second aspect, further, in step S300, key features are extracted from the three-dimensional model of the user. , and the 3D model of the clothing Comparing them respectively, the scoring formula for the user's current image is:

[0013] in, is the Lagrange multiplier, For labels, is the kernel function, For bias.

[0014] In combination with the second aspect, further, in step S400, a mirror projection transformation is used to project the image next to the user's projection in the mirror body.

[0015] In a third aspect, the present invention further discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the image preprocessing method of the smart light mirror as described above.

[0016] In a fourth aspect, the present invention further discloses a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the image preprocessing method of the smart light mirror as described above.

[0017] Beneficial effects of the present invention: The present invention provides an intelligent light mirror, processing method, electronic device and storage medium for image preprocessing. By setting a main control module, a camera module and a projection module, the camera module collects the body data of the user after wearing clothes, the main control module compares the clothing data with the three-dimensional modeling of the user, and the projection module projects the scoring data output by the main control module around the image of the user in the mirror body, thereby forming intuitive scoring and image suggestions, and providing personalized image optimization services for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0019] Figure 1 A flowchart of the steps of the image preprocessing method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0021] With the rapid development of e-commerce and virtual fitting technology, consumers are increasingly demanding personalized shopping experiences. Traditional fitting methods rely on physical fitting rooms, which have problems such as low efficiency and poor experience. In recent years, virtual fitting technology has provided users with a more convenient fitting experience by combining computer vision, 3D modeling, and artificial intelligence algorithms. However, existing virtual fitting systems still have the following problems: unintuitive user experience, limited clothing matching effects, and lack of personalized scoring.

[0022] In order to solve the above problems, this embodiment discloses an intelligent light mirror for image preprocessing, which includes a cabinet, a main control module, a camera module, a projection module and a mirror body installed in the cabinet, wherein the camera module is used to collect the body data of the user after wearing clothes; The main control module is used to store the three-dimensional model of the clothing unfolded in a standard state and to perform three-dimensional modeling of the user's body based on the user's body shape and wearing data, and to compare the clothing data with the user's three-dimensional modeling, score the user's current image and output image improvement suggestions; The projection module is used to project the corresponding scoring data around the user's mirror image in the mirror body.

[0023] The smart light mirror of the present application is provided with a main control module, a camera module and a projection module. The camera module collects the body data of the user after wearing clothes, the main control module compares the clothing data with the three-dimensional modeling of the user, and the projection module projects the scoring data output by the main control module around the user's image in the mirror body, thereby forming intuitive scoring and image suggestions, and providing users with personalized image optimization services.

[0024] In addition, some embodiments of the present application also provide an image preprocessing method for a smart light mirror, the method comprising the following steps: S100. The camera module collects the body data of the user wearing clothes, and sends the data to the main control module, which uses the data to construct a three-dimensional model of the user; S200. The main control module obtains the three-dimensional model of the clothing stored in the server, and compares the clothing data with the three-dimensional modeling of the user; S300. The main control module scores the user's current image and outputs image improvement suggestions according to the set algorithm; S400. The projection module projects the corresponding scoring data around the user's image in the mirror body. Preferably, the projector uses mirror projection transformation to project the data next to the user's projection in the mirror body.

[0025] In step S100, point cloud data is acquired by a depth camera, and point cloud registration is performed by an ICP algorithm to generate a three-dimensional model of the user. It should be noted that the three-dimensional model of the user in this embodiment is a three-dimensional model of the user wearing clothes, which can reflect the body characteristics of the user wearing clothes, and is convenient for comparison with the three-dimensional model of the clothes unfolded in a standard state; The modeling function of the user's three-dimensional model is:

[0026] in, and They are Direction and Direction B-spline basis functions, For the control point.

[0027] In this embodiment, the camera module is preferably one or more of a camera, a depth camera or a laser scanner. The camera and the laser scanner are responsible for collecting the user's wearing data, and the depth camera is responsible for collecting the user's body data.

[0028] Among them, the depth value is obtained through the depth camera , generate a depth image ; Then, the depth image is converted into three-dimensional point cloud data, and the formula for point cloud generation is: .in, is the intrinsic parameter matrix of the depth camera, is the pixel coordinate; then multiple frames of point cloud data are registered and fused to generate complete 3D point cloud data. Based on the fused point cloud data, Poisson surface reconstruction Generate a smooth 3D model where is an implicit function, is the normal vector of the point cloud.

[0029] The output of point cloud registration and fusion is the fused point cloud data, which is the input of 3D modeling. The fused point cloud data contains complete 3D information and provides a basis for subsequent Poisson surface reconstruction or B-spline surface fitting.

[0030] In this embodiment, the B-spline basis functions are a set of piecewise polynomial functions used to define the shape of the B-spline curve or surface. They have local support, that is, each basis function is non-zero only in a local interval.

[0031] B-spline basis functions The recursive definition of is as follows: 1. Basis function of order 1:

[0032] 2. The order is The basis functions are:

[0033] in, are the node values ​​in the node vector, is the order of the spline.

[0034] It should be noted that The recursive definition of The same, I will not repeat it here.

[0035] Control points are points used to define the shape of a B-spline curve or surface. They affect the shape of the curve or surface by weighting the B-spline basis functions.

[0036] B-spline surface fitting process: 1. Determine the node vector: Node vector The support interval of the basis function is defined; 2. Calculate basis functions: Calculate B-spline basis functions based on node vectors and spline order and ; 3. Determine the control points: Determine the control points based on the point cloud data using the least squares method or other optimization methods. ; 4. Generate surface: Generate B-spline surface through weighted basis functions and control points .

[0037] In step S200, the main control module pre-processes the image of the clothing currently worn by the user captured by the camera, extracts clothing features, identifies and classifies clothing, and performs three-dimensional clothing data matching.

[0038] Furthermore, the formula for clothing image preprocessing is:

[0039] in, is the preprocessed image data, is the standard deviation of the Gaussian kernel.

[0040] In addition, use histogram equalization to enhance the image contrast:

[0041] in, is the input grayscale, is the output grayscale, Grayscale The number of pixels, is the total number of pixels.

[0042] Extract key features of clothing from preprocessed images, such as color, texture, shape, etc. Use Convolutional Neural Network (CNN) to extract features: ,in are network parameters, is the convolution operation, is the activation function; and SIFT is used to extract local features.

[0043] Based on the extracted features, machine learning or deep learning models are used to classify clothing and identify the type of clothing the user is currently wearing. Based on the identified clothing type, the corresponding three-dimensional clothing data is retrieved from the shopping platform and matched with the user's three-dimensional model.

[0044] The formula for three-dimensional data matching is:

[0045] in, is the rotation matrix, is the translation vector, are the corresponding points of the user model and the clothing model respectively.

[0046] Furthermore, in step S300, key features are extracted from the user's three-dimensional model. , and the 3D model of the clothing The comparisons were made respectively, among which, and It can include height, chest circumference, waist circumference, hip circumference, body index, length, shoulder width, sleeve length, etc.

[0047] After obtaining the above features, the scoring formula for the user's current image is:

[0048] in, is the Lagrange multiplier, For labels, is the kernel function, For bias.

[0049] Generate personalized improvement suggestions based on the scoring results, specifically setting a threshold , if the score is below the threshold , then generate improvement suggestions, which are generated by statistics and analysis of the data set of the large model.

[0050] The smart light mirror provided in the above-mentioned embodiment of the present application and the image preprocessing method provided in the embodiment of the present application are based on the same application concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0051] Some embodiments of the present application also provide an electronic device, comprising: a processor, a memory, a bus and a communication interface, wherein the processor, the communication interface and the memory are connected via a bus; the memory stores a computer program that can be run on the processor, and when the processor runs the computer program, it executes the method provided in any of the aforementioned embodiments of the present application.

[0052] The memory may include high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0053] The bus may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. Among them, the memory is used to store the program, and the processor executes the program after receiving the execution instruction. The soft-pack battery cell recycling management method disclosed in any implementation of the above-mentioned embodiment of the present application can be applied to the processor, or implemented by the processor.

[0054] The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0055] The electronic device provided in the embodiment of the present application and the image preprocessing method provided in the embodiment of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0056] Some embodiments of the present application also provide a computer-readable storage medium corresponding to the image preprocessing method provided in the aforementioned embodiments, on which a computer program is stored. The computer-readable storage medium is a CD, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the image preprocessing method provided in any of the aforementioned embodiments.

[0057] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0058] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the soft-pack battery cell recycling management method provided in the embodiments of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0059] It should be noted that: in the above text, the term "comprises", "includes" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0060] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0061] The embodiments of the present application are described above in conjunction with the accompanying drawings, which are only specific implementation modes of the present application. However, the present application is not limited to the above-mentioned specific implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. An intelligent light mirror for image preprocessing, comprising a cabinet, a main control module, a camera module, a projection module and a mirror body installed in the cabinet, characterized in that: The camera module collects the body data of the user after wearing the clothes; The main control module is used to store the three-dimensional model of the clothing unfolded in a standard state and to perform three-dimensional modeling of the user's body based on the user's body shape and wearing data, and to compare the clothing data with the user's three-dimensional modeling, score the user's current image and output image improvement suggestions; The projection module is used to project the corresponding scoring data around the user's mirror image in the mirror body; The smart light mirror is also used to perform the following steps: S100. The camera module collects the body data of the user wearing clothes, and sends the data to the main control module, which uses the data to construct a three-dimensional model of the user; S200. The main control module obtains the three-dimensional model of the clothing stored in the server, and compares the clothing data with the three-dimensional modeling of the user; S300. The main control module scores the user's current image and outputs image improvement suggestions according to the set algorithm; S400. The projection module projects the corresponding scoring data around the user's image in the mirror body.

2. The intelligent light mirror for image preprocessing according to claim 1, characterized in that: In step S100, point cloud data is acquired through a depth camera, and point cloud registration is performed through an ICP algorithm to generate a three-dimensional model of the user; The modeling function of the user's three-dimensional model is: in, and They are Direction and Direction B-spline basis functions, For the control point.

3. The intelligent light mirror for image preprocessing according to claim 1, characterized in that: In step S200, the main control module is used to pre-process the body image of the user currently wearing clothes collected by the camera, extract clothing features, identify and classify clothes, and match clothing three-dimensional data.

4. The intelligent light mirror for image preprocessing according to claim 3, characterized in that: The formula for clothing image preprocessing is: in, is the preprocessed image data, is the standard deviation of the Gaussian kernel; Extract the key features of clothing from the preprocessed image and use convolutional neural network to extract features: in are network parameters, is the convolution operation, is the activation function; and SIFT is used to extract local features; Based on the extracted features, use machine learning or deep learning models to classify clothing and identify the type of clothing the user is currently wearing; According to the identified clothing type, the corresponding clothing 3D data is retrieved from the shopping platform and matched with the user's 3D model. The formula for 3D data matching is: in, is the rotation matrix, is the translation vector, are the corresponding points of the user model and the clothing model respectively.

5. The intelligent light mirror for image preprocessing according to claim 1, characterized in that: In step S300, key features are extracted from the user's 3D model , and the 3D model of the clothing Comparing them respectively, the scoring formula for the user's current image is: in, is the Lagrange multiplier, For labels, is the kernel function, For bias.

6. The intelligent light mirror for image preprocessing according to claim 1, characterized in that: In step S400, a mirror projection transformation is used to project the image next to the user's projection in the mirror body.

7. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps performed by the smart light mirror as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the steps performed by the smart light mirror as described in any one of claims 1-6.

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