Human body model measuring and typing method based on human body three-dimensional reconstruction

Abstract

The invention provides a human body model measuring and typing method based on human body three-dimensional reconstruction, which comprises the steps of S1, data acquisition, S2, data preprocessing, S3, model reconstruction, S4, model segmentation and S5, body key data measurement: using a matched model measuring tool to measure body key data. And S6, performing automatic body type classification, and performing clustering analysis on the extracted features by using a clustering algorithm. According to the three-dimensional scanning technology, a large amount of point cloud data can be obtained in an extremely short time, a highly vivid three-dimensional model is formed, the data acquisition precision is high, and fine features of an object can be captured. Therefore, the data acquisition efficiency is greatly improved, meanwhile, the data accuracy is ensured, and a solid foundation is provided for subsequent body type parting.

Description

Technical Field

[0001] The present invention relates to the technical field of human body model measurement, and in particular to a human body model measurement and typing method based on three-dimensional reconstruction of the human body. Background Art

[0002] There are two main methods for calculating body shape:

[0003] 1.BMI index method (Body Mass Index):

[0004] This is a body shape indicator commonly used in the medical field. The calculation method of BMI is weight (kg) divided by height (m) squared.

[0005] According to the World Health Organization's standards, a BMI value between 18.5 and 24.9 is considered normal weight; between 25 and 29.9 is considered overweight; and 30 and above is considered obese. However, different countries and regions may make adjustments based on specific circumstances.

[0006] It should be noted that although the BMI index is simple and easy to use, it cannot fully and accurately reflect a person's body shape and health status. Especially for people with larger muscle mass, the BMI value may be higher.

[0007] 2. Volume index method (t = w / (h*h)):

[0008] This is another method of calculating body size based on height and weight. Where t is body mass index, w is weight in kilograms, and h is height in meters.

[0009] Based on the value of body mass index, we can determine what type of weight it is: when t<18, it is underweight; 18≤t<25, it is normal weight; 25≤t<27, it is overweight; t≥27, it is obese.

[0010] Compared with BMI, the body index method takes into account the square relationship between height and weight and may be more accurate in some cases. Summary of the invention

[0011] The present invention aims to address the defects of the prior art and provide a human body model measurement and typing method for three-dimensional reconstruction of the human body.

[0012] The BMI index method and the body mass index method are both simple calculations based on height and weight. Although they can preliminarily judge a person's body shape, they cannot fully and accurately reflect a person's body shape and health status. Therefore, in medical evaluation, it is necessary to combine other indicators (such as waist circumference, body fat percentage, muscle mass, etc.) for comprehensive analysis. By further dividing the human body shape into H type, A type, T type, and X type,

[0013] 1) H type (straight type)

[0014] Features: The shoulders, waist, and hips are almost on the same straight line, the difference between waist and hip circumference is small, and the overall body shape is straight.

[0015] Identification criteria:

[0016] Waist-Hip Ratio (WHR) is close to 1 (usually 0.8-1.0).

[0017] There is little difference in shoulder and hip width, and the waist is obviously not narrow.

[0018] Your BMI may be in the normal range, but your body fat percentage may be high, especially visceral fat.

[0019] 2) Type A (pear-shaped)

[0020] Characteristics: Fuller hips and thighs, relatively thin waist, narrow upper body. Fat is mainly distributed in the lower body.

[0021] Identification criteria:

[0022] Waist-to-hip ratio (WHR) less than 0.8.

[0023] The hips are significantly larger than the shoulders, and the waist is thinner.

[0024] The body fat percentage is high, especially in the lower body (such as thighs and buttocks).

[0025] It is more common in women and is often assessed using waist-to-hip ratio and waist-to-height ratio (WHtR).

[0026] 3) T-type (inverted triangle)

[0027] Characteristics: Broad shoulders, well-developed chest, narrow waist, small hips. Fat is mainly distributed in the upper body.

[0028] Identification criteria:

[0029] Shoulder-Hip Ratio is greater than 1.

[0030] The circumference of the upper body (especially the shoulders and chest) is significantly larger than that of the lower body (hips and thighs).

[0031] It is more common in men and is usually associated with lower body fat percentage and higher muscle mass, especially in the upper body.

[0032] 4) X-type (hourglass type)

[0033] Features: Broad shoulders and hips, with a noticeably narrow waist, giving it a classic "hourglass" shape.

[0034] Identification criteria:

[0035] Waist-to-hip ratio (WHR) is usually around 0.7.

[0036] The waist circumference is significantly smaller than the shoulder circumference and hip circumference.

[0037] The body fat percentage is moderate or slightly high, and the fat is evenly distributed.

[0038] It is common in women and is usually determined by the ratio of waist to hip circumference.

[0039] These body type classifications can more accurately assess and judge a person's body type and have the following advantages:

[0040] 1) Improved diagnostic accuracy: With clear body type classification, doctors can more accurately determine the body characteristics of patients, which helps to more accurately diagnose health problems related to body type. For example, an H-type body may be more prone to certain metabolic diseases, while an A-type body may be associated with an increased risk of cardiovascular disease.

[0041] 2) Personalized treatment: Different body types may respond differently to medications, treatments, and nutritional needs. By classifying body types, doctors can develop more personalized treatment plans for patients, thereby improving treatment outcomes and patient satisfaction.

[0042] 3) Health risk assessment: Body type classification helps doctors assess patients’ health risks more accurately. For example, an X-shaped body is generally considered to be an ideal body type, while an A-shaped body may be associated with an increased risk of cardiovascular disease. By understanding the patient’s body type, doctors can conduct health risk assessments and formulate preventive measures more specifically.

[0043] 4) Health education and prevention: Body type classification can serve as the basis for health education and prevention strategies. Doctors can provide patients with personalized health advice and prevention suggestions based on the characteristics of different body types to help patients establish healthier lifestyles and habits.

[0044] 5) Promote research: Clear body type classification helps promote the progress of medical research. By comparing the health differences and physiological characteristics between different body types, researchers can gain a deeper understanding of the physiological mechanisms of the human body and the mechanisms of disease occurrence, making greater contributions to the development of medicine.

[0045] The present invention uses a 3D scanning method to collect human body shape data, reconstructs a human body model in 3D, and uses a highly restored 3D model to automatically calculate the human body shape (H-type, A-type, T-type, X-type). This solves the problem that traditional body shape calculation methods cannot fully reflect individual body shapes, and realizes rapid classification of human body shapes.

[0046] 1. Data Collection

[0047] Choose the right scanning device: Choose the right scanning device according to your actual needs. For example, if you need to scan a surface, you can choose a laser scanner or a structured light scanner; if you need to scan an internal structure, you can choose a CT scan or an MRI.

[0048] Data collection: Make sure the subject is relatively still in the center of the scanning device to obtain accurate scanning data. During the collection process, the subject can be scanned at different angles or positions as needed to obtain more comprehensive data.

[0049] 2. Data Preprocessing

[0050] Denoising: Remove noise from scanned data through filtering and other methods to improve the accuracy of subsequent processing.

[0051] Registration: If you are using multiple scanning devices or multiple scans, you will need to register the different data sets to ensure they are in the same coordinate system.

[0052] Data repair: Repair missing or damaged parts of the data. Data repair can be performed using methods such as interpolation.

[0053] 3. Reconstruct the model

[0054] Select a reconstruction algorithm: Select an appropriate reconstruction algorithm based on the data type and requirements. For example, for point cloud data, you can use a surface reconstruction algorithm such as the Marching Cubes algorithm; for volume data, you can use a voxelization reconstruction algorithm.

[0055] Model reconstruction: Convert the preprocessed data into a 3D model using the selected algorithm. This may involve steps such as mesh generation, surface fitting, and voxelization.

[0056] Point cloud data is a type of data commonly used to generate 3D scanning and perception equipment, which usually does not contain clear connection relationships. In order to build a 3D model, the common process of point cloud data reconstruction is:

[0057] 1. Mesh generation: For point cloud data or volume data, the core of the reconstruction process is to generate polygonal meshes or triangular patches.

[0058] Using the Marching Cubes algorithm, the specific process is as follows:

[0059] 1. Divide the three-dimensional space into a series of regular cubes and mark the vertices of each cube as "inside" or "outside", depending on where the data point is located.

[0060] 2. Within each cube, determine which vertices need to be connected by looking up the edge table.

[0061] 3. Generate triangular patches and connect them to form a mesh. These triangular patches will eventually form the surface of the entire 3D model.

[0062] 2. Surface fitting: For data that requires a smooth surface, the Poisson reconstruction algorithm is used. The basic process is:

[0063] 1. Compute the gradient field and normal vector of the point cloud.

[0064] 2. Through global fitting, these gradient field information are used to generate smooth surfaces.

[0065] 3. Generate the final triangular patch to represent the model surface.

[0066] 4. Model segmentation (optional)

[0067] If surface area and volume calculations are required for specific parts, the model can be segmented. This can be done manually or with an automated segmentation algorithm.

[0068] 1) Manual segmentation

[0069] Manual segmentation is a segmentation method that relies on manual operation and is usually performed by professional technicians using 3D modeling software (such as Blender, MeshLab, etc.).

[0070] step:

[0071] Import the complete 3D model into the modeling software.

[0072] Use the "segmentation tool" in the software to manually select the area that needs to be segmented (such as the head, torso, limbs, etc.).

[0073] Isolate the selected area from the overall model as a separate model part.

[0074] Surface area and volume calculations were performed separately for each sub-region.

[0075] 2) Automatic segmentation algorithm

[0076] Automatic segmentation algorithms use computer programs to automatically segment models according to specific rules or conditions, reducing manual operations and improving efficiency. Common automatic segmentation algorithms include grid-based segmentation, volume-based segmentation, morphology-based segmentation, etc.

[0077] Based on Mesh Segmentation algorithm:

[0078] Mesh preprocessing: After importing the model, preprocess its mesh, such as removing redundant points and mesh smoothing.

[0079] Cutting plane determination: According to the topological structure of the model, the cutting plane is automatically determined by curvature changes, geometric features, etc. For example, the joints of the body (shoulder, elbow, knee) are usually natural segmentation points.

[0080] Region segmentation: Divide the model into multiple sub-regions according to the cutting plane, such as head, torso, limbs, etc.

[0081] Data extraction: Calculate the volume and surface area of ​​each sub-region.

[0082] Voxel-based Segmentation:

[0083] Voxelization: Voxelize the model and convert it into three-dimensional data consisting of a series of regular small cubes (voxels).

[0084] Region growing algorithm: By selecting a seed point, the region growing algorithm is used to expand to neighboring voxels to form a segmented region. For example, region growing can be started from the shoulder or knee joint.

[0085] Segmentation complete: Now that the different body regions have been segmented, the surface area and volume of each region can be measured.

[0086] Based on morphological segmentation algorithm:

[0087] Edge detection: Edge detection is performed through the geometric contours of the model (such as the curvature of the surface) to identify the key points of the model, such as shoulders, waist, knees, etc.

[0088] Morphological operations: Use morphological operations such as dilation and erosion to process the model and clearly segment each area.

[0089] Region division: Divide the model into head, torso, limbs and other parts.

[0090] Surface area and volume calculations: Further surface area and volume calculations are performed on each area using specialized measurement algorithms.

[0091] 5. Measure key body data using the matching model measurement tools.

[0092] After the model is segmented, use the matching measurement tools to measure the key data of the body (such as surface area, volume, length, circumference, etc.). Commonly used measurement tools include:

[0093] Surface area and volume measurements:

[0094] Perform surface area and volume measurements on each segmented region, either through built-in tools in your 3D modeling software (such as the “Measure Tool” add-on for Blender) or by writing a custom script.

[0095] The surface area and volume of closed surfaces can be calculated using the Gauss-Bonnet formula or directly by integration.

[0096] Length and girth measurements:

[0097] Use the model's geometric properties to select key points and segments to calculate the length and girth of different parts of the body. For example, you can measure the length from shoulder to wrist, or calculate the waist circumference.

[0098] Calculate precise length and girth data by setting a fixed measurement path or using an algorithm to find the shortest path along a surface.

[0099] 6. Automatic body type classification

[0100] The extracted features are clustered using clustering algorithms. Clustering algorithms include K-means clustering, hierarchical clustering, and density clustering. These algorithms can divide data points into different clusters or categories based on their similarities. In the clustering process, it is necessary to select appropriate clustering parameters (such as the number of clusters, similarity measurement methods, etc.) to obtain the best clustering effect. The clustering results are evaluated to determine the accuracy and reliability of the typing. In addition, the clustering results are displayed through visualization techniques (such as scatter plots, heat maps, etc.) to more intuitively understand the differences between different body types.

[0101] 1.H type (straight type)

[0102] Features: The shoulders, waist, and hips are almost on the same straight line, the difference between waist and hip circumference is small, and the overall body shape is straight.

[0103] Identification criteria:

[0104] Waist-Hip Ratio (WHR) is close to 1 (usually 0.8-1.0).

[0105] There is little difference in shoulder and hip width, and the waist is obviously not narrow.

[0106] Your BMI may be in the normal range, but your body fat percentage may be high, especially visceral fat.

[0107] 2. Type A (pear-shaped)

[0108] Characteristics: Fuller hips and thighs, relatively thin waist, narrow upper body. Fat is mainly distributed in the lower body.

[0109] Identification criteria:

[0110] Waist-to-hip ratio (WHR) less than 0.8.

[0111] The hips are significantly larger than the shoulders, and the waist is thinner.

[0112] The body fat percentage is high, especially in the lower body (such as thighs and buttocks).

[0113] It is more common in women and is often assessed using waist-to-hip ratio and waist-to-height ratio (WHtR).

[0114] 3. T-type (inverted triangle)

[0115] Characteristics: Broad shoulders, well-developed chest, narrow waist, small hips. Fat is mainly distributed in the upper body.

[0116] Identification criteria:

[0117] Shoulder-Hip Ratio is greater than 1.

[0118] The circumference of the upper body (especially the shoulders and chest) is significantly larger than that of the lower body (hips and thighs).

[0119] It is more common in men and is usually associated with lower body fat percentage and higher muscle mass, especially in the upper body.

[0120] 4.X-type (hourglass type)

[0121] Features: Broad shoulders and hips, with a noticeably narrow waist, giving it a classic "hourglass" shape.

[0122] Identification criteria:

[0123] Waist-to-hip ratio (WHR) is usually around 0.7.

[0124] The waist circumference is significantly smaller than the shoulder circumference and hip circumference.

[0125] The body fat percentage is moderate or slightly high, and the fat is evenly distributed.

[0126] It is common in women and is usually determined by the ratio of waist to hip circumference.

[0127] 7. Visualization

[0128] Use 3D rendering technology to visualize the calculation results for a more intuitive understanding and sharing of the results. You can use 3D rendering software or libraries to display the distribution of the model surface area and volume, and generate visual reports or animations.

[0129] A computer-readable medium storing software, the software comprising instructions executable by one or more computers, the instructions causing the one or more computers to perform operations through such execution, the operations comprising the process of the above-mentioned system.

[0130] A computer system comprising:

[0131] one or more processors;

[0132] The memory stores operable instructions, which, when executed by the one or more processors, enable the one or more processors to perform operations, wherein the operations include the process of the above-mentioned system.

[0133] Compared with the prior art, the advantages of the present invention are:

[0134] 1. High precision and high efficiency: 3D scanning technology can acquire a large amount of point cloud data in a very short time to form a highly realistic 3D model. Its data acquisition accuracy is high and it can capture the subtle features of the object. This greatly improves the efficiency of data acquisition and ensures the accuracy of the data, providing a solid foundation for subsequent body type classification.

[0135] 2. Non-contact measurement: 3D scanning technology uses non-contact measurement, which does not require physical contact with the target object, so it can be measured without damaging or contaminating the object. This avoids errors caused by human factors and improves the accuracy and reliability of the measurement.

[0136] 3. Strong applicability: 3D scanning technology can work in various environmental conditions, including indoors, outdoors, and in dark environments, which makes it widely used in many fields. In human body typing, this strong adaptability enables the technology to cope with various complex environments and ensure the smooth progress of data collection.

[0137] 4. Intuitiveness and diversity of results: The collected point cloud data not only contains spatial information, but also color information and reflectivity values, which can truly reproduce the object scene. In human body typing, this rich information can help us understand the morphological structure of the human body more comprehensively and improve the accuracy of typing. At the same time, one measurement can output multiple results without repeated measurements, which improves work efficiency.

[0138] 5. High degree of automation: By combining advanced computer vision and machine learning algorithms, it is possible to automatically process and analyze 3D scan data, thereby automatically performing body type classification. This highly automated processing method greatly reduces the need for manual intervention and improves the efficiency and accuracy of classification.

[0139] 6. Personalized services: Human body type classification based on 3D scanning data can provide personalized services according to the specific morphological structure of individuals. For example, in the clothing industry, suitable clothing can be customized according to people of different body types; in the fitness industry, personalized fitness plans and suggestions can be provided for people of different body types. BRIEF DESCRIPTION OF THE DRAWINGS

[0140] Figure 1 It is the data collection interface;

[0141] Figure 2 Rebuild the interface for the data;

[0142] Figure 3 Split the interface for the model;

[0143] Figure 4 It is the measurement parting interface. DETAILED DESCRIPTION

[0144] A human body model measurement and typing method for three-dimensional reconstruction of the human body.

[0145] The BMI index method and the body mass index method are both simple calculations based on height and weight. Although they can preliminarily judge a person's body shape, they cannot fully and accurately reflect a person's body shape and health status. Therefore, in medical evaluation, it is necessary to combine other indicators (such as waist circumference, body fat percentage, muscle mass, etc.) for comprehensive analysis. By further dividing the human body shape into H type, A type, T type, and X type,

[0146] 1) H type (straight type)

[0147] Features: The shoulders, waist, and hips are almost on the same straight line, the difference between waist and hip circumference is small, and the overall body shape is straight.

[0148] Identification criteria:

[0149] Waist-Hip Ratio (WHR) is close to 1 (usually 0.8-1.0).

[0150] There is little difference in shoulder and hip width, and the waist is obviously not narrow.

[0151] Your BMI may be in the normal range, but your body fat percentage may be high, especially visceral fat.

[0152] 2) Type A (pear-shaped)

[0153] Characteristics: Fuller hips and thighs, relatively thin waist, narrow upper body. Fat is mainly distributed in the lower body.

[0154] Identification criteria:

[0155] Waist-to-hip ratio (WHR) less than 0.8.

[0156] The hips are significantly larger than the shoulders, and the waist is thinner.

[0157] The body fat percentage is high, especially in the lower body (such as thighs and buttocks).

[0158] It is more common in women and is often assessed using waist-to-hip ratio and waist-to-height ratio (WHtR).

[0159] 3) T-type (inverted triangle)

[0160] Characteristics: Broad shoulders, well-developed chest, narrow waist, small hips. Fat is mainly distributed in the upper body.

[0161] Identification criteria:

[0162] Shoulder-Hip Ratio is greater than 1.

[0163] The circumference of the upper body (especially the shoulders and chest) is significantly larger than that of the lower body (hips and thighs).

[0164] It is more common in men and is usually associated with lower body fat percentage and higher muscle mass, especially in the upper body.

[0165] 4) X-type (hourglass type)

[0166] Features: Broad shoulders and hips, with a noticeably narrow waist, giving it a classic "hourglass" shape.

[0167] Identification criteria:

[0168] Waist-to-hip ratio (WHR) is usually around 0.7.

[0169] The waist circumference is significantly smaller than the shoulder circumference and hip circumference.

[0170] The body fat percentage is moderate or slightly high, and the fat is evenly distributed.

[0171] It is common in women and is usually determined by the ratio of waist to hip circumference.

[0172] These body type classifications can more accurately assess and judge a person's body type and have the following advantages:

[0173] 1) Improved diagnostic accuracy: With clear body type classification, doctors can more accurately determine the body characteristics of patients, which helps to more accurately diagnose health problems related to body type. For example, an H-type body may be more prone to certain metabolic diseases, while an A-type body may be associated with an increased risk of cardiovascular disease.

[0174] 2) Personalized treatment: Different body types may respond differently to medications, treatments, and nutritional needs. By classifying body types, doctors can develop more personalized treatment plans for patients, thereby improving treatment outcomes and patient satisfaction.

[0175] 3) Health risk assessment: Body type classification helps doctors assess patients’ health risks more accurately. For example, an X-shaped body is generally considered to be an ideal body type, while an A-shaped body may be associated with an increased risk of cardiovascular disease. By understanding the patient’s body type, doctors can conduct health risk assessments and formulate preventive measures more specifically.

[0176] 4) Health education and prevention: Body type classification can serve as the basis for health education and prevention strategies. Doctors can provide patients with personalized health advice and prevention suggestions based on the characteristics of different body types to help patients establish healthier lifestyles and habits.

[0177] 5) Promote research: Clear body type classification helps promote the progress of medical research. By comparing the health differences and physiological characteristics between different body types, researchers can gain a deeper understanding of the physiological mechanisms of the human body and the mechanisms of disease occurrence, making greater contributions to the development of medicine.

[0178] The present invention uses a 3D scanning method to collect human body shape data, reconstructs a human body model in 3D, and uses a highly restored 3D model to automatically calculate the human body shape (H-type, A-type, T-type, X-type). This solves the problem that traditional body shape calculation methods cannot fully reflect individual body shapes, and realizes rapid classification of human body shapes.

[0179] 1. Data Collection

[0180] Choose the right scanning device: Choose the right scanning device according to your actual needs. For example, if you need to scan a surface, you can choose a laser scanner or a structured light scanner; if you need to scan an internal structure, you can choose a CT scan or an MRI.

[0181] Data collection: Make sure the subject is relatively still in the center of the scanning device to obtain accurate scanning data. During the collection process, the subject can be scanned at different angles or positions as needed to obtain more comprehensive data.

[0182] 2. Data Preprocessing

[0183] Denoising: Remove noise from scanned data through filtering and other methods to improve the accuracy of subsequent processing.

[0184] Registration: If you are using multiple scanning devices or multiple scans, you will need to register the different data sets to ensure they are in the same coordinate system.

[0185] Data repair: Repair missing or damaged parts of the data. Data repair can be performed using methods such as interpolation.

[0186] 3. Reconstruct the model

[0187] Select a reconstruction algorithm: Select an appropriate reconstruction algorithm based on the data type and requirements. For example, for point cloud data, you can use a surface reconstruction algorithm such as the Marching Cubes algorithm; for volume data, you can use a voxelization reconstruction algorithm.

[0188] Model reconstruction: Convert the preprocessed data into a 3D model using the selected algorithm. This may involve steps such as mesh generation, surface fitting, and voxelization.

[0189] Point cloud data is a type of data commonly used to generate 3D scanning and perception equipment, which usually does not contain clear connection relationships. In order to build a 3D model, the common process of point cloud data reconstruction is:

[0190] 1. Mesh generation: For point cloud data or volume data, the core of the reconstruction process is to generate polygonal meshes or triangular patches.

[0191] Using the Marching Cubes algorithm, the specific process is as follows:

[0192] 1. Divide the three-dimensional space into a series of regular cubes and mark the vertices of each cube as "inside" or "outside", depending on where the data point is located.

[0193] 2. Within each cube, determine which vertices need to be connected by looking up the edge table.

[0194] 3. Generate triangular patches and connect them to form a mesh. These triangular patches will eventually form the surface of the entire 3D model.

[0195] 2. Surface fitting: For data that requires a smooth surface, the Poisson reconstruction algorithm is used. The basic process is:

[0196] 1. Compute the gradient field and normal vector of the point cloud.

[0197] 2. Through global fitting, these gradient field information are used to generate smooth surfaces.

[0198] 3. Generate the final triangular patch to represent the model surface.

[0199] 4. Model segmentation (optional)

[0200] If surface area and volume calculations are required for specific parts, the model can be segmented. This can be done manually or with an automated segmentation algorithm.

[0201] 1) Manual segmentation

[0202] Manual segmentation is a segmentation method that relies on manual operation and is usually performed by professional technicians using 3D modeling software (such as Blender, MeshLab, etc.).

[0203] step:

[0204] Import the complete 3D model into the modeling software.

[0205] Use the "segmentation tool" in the software to manually select the area that needs to be segmented (such as the head, torso, limbs, etc.).

[0206] Isolate the selected area from the overall model as a separate model part.

[0207] Surface area and volume calculations were performed separately for each sub-region.

[0208] 2) Automatic segmentation algorithm

[0209] Automatic segmentation algorithms use computer programs to automatically segment models according to specific rules or conditions, reducing manual operations and improving efficiency. Common automatic segmentation algorithms include grid-based segmentation, volume-based segmentation, morphology-based segmentation, etc.

[0210] Based on Mesh Segmentation algorithm:

[0211] Mesh preprocessing: After importing the model, preprocess its mesh, such as removing redundant points and mesh smoothing.

[0212] Cutting plane determination: According to the topological structure of the model, the cutting plane is automatically determined by curvature changes, geometric features, etc. For example, the joints of the body (shoulder, elbow, knee) are usually natural segmentation points.

[0213] Region segmentation: Divide the model into multiple sub-regions according to the cutting plane, such as head, torso, limbs, etc.

[0214] Data extraction: Calculate the volume and surface area of ​​each sub-region.

[0215] Voxel-based Segmentation:

[0216] Voxelization: Voxelize the model and convert it into three-dimensional data consisting of a series of regular small cubes (voxels).

[0217] Region growing algorithm: By selecting a seed point, the region growing algorithm is used to expand to neighboring voxels to form a segmented region. For example, region growing can be started from the shoulder or knee joint.

[0218] Segmentation complete: Now that the different body regions have been segmented, the surface area and volume of each region can be measured.

[0219] Based on morphological segmentation algorithm:

[0220] Edge detection: Edge detection is performed through the geometric contours of the model (such as the curvature of the surface) to identify the key points of the model, such as shoulders, waist, knees, etc.

[0221] Morphological operations: Use morphological operations such as dilation and erosion to process the model and clearly segment each area.

[0222] Region division: Divide the model into head, torso, limbs and other parts.

[0223] Surface area and volume calculations: Further surface area and volume calculations are performed on each area using specialized measurement algorithms.

[0224] 5. Measure key body data using the matching model measurement tools.

[0225] After the model is segmented, use the matching measurement tools to measure the key data of the body (such as surface area, volume, length, circumference, etc.). Commonly used measurement tools include:

[0226] Surface area and volume measurements:

[0227] Perform surface area and volume measurements on each segmented region, either through built-in tools in your 3D modeling software (such as the “Measure Tool” add-on for Blender) or by writing a custom script.

[0228] The surface area and volume of closed surfaces can be calculated using the Gauss-Bonnet formula or directly by integration.

[0229] Length and girth measurements:

[0230] Use the model's geometric properties to select key points and segments to calculate the length and girth of different parts of the body. For example, you can measure the length from shoulder to wrist, or calculate the waist circumference.

[0231] Calculate precise length and girth data by setting a fixed measurement path or using an algorithm to find the shortest path along a surface.

[0232] 6. Automatic body type classification

[0233] The extracted features are clustered using clustering algorithms. Clustering algorithms include K-means clustering, hierarchical clustering, and density clustering. These algorithms can divide data points into different clusters or categories based on their similarities. In the clustering process, it is necessary to select appropriate clustering parameters (such as the number of clusters, similarity measurement methods, etc.) to obtain the best clustering effect. The clustering results are evaluated to determine the accuracy and reliability of the typing. In addition, the clustering results are displayed through visualization techniques (such as scatter plots, heat maps, etc.) to more intuitively understand the differences between different body types.

[0234] 1.H type (straight type)

[0235] Features: The shoulders, waist, and hips are almost on the same straight line, the difference between waist and hip circumference is small, and the overall body shape is straight.

[0236] Identification criteria:

[0237] Waist-Hip Ratio (WHR) is close to 1 (usually 0.8-1.0).

[0238] There is little difference in shoulder and hip width, and the waist is obviously not narrow.

[0239] Your BMI may be in the normal range, but your body fat percentage may be high, especially visceral fat.

[0240] 2. Type A (pear-shaped)

[0241] Characteristics: Fuller hips and thighs, relatively thin waist, narrow upper body. Fat is mainly distributed in the lower body.

[0242] Identification criteria:

[0243] Waist-to-hip ratio (WHR) less than 0.8.

[0244] The hips are significantly larger than the shoulders, and the waist is thinner.

[0245] The body fat percentage is high, especially in the lower body (such as thighs and buttocks).

[0246] It is more common in women and is often assessed using waist-to-hip ratio and waist-to-height ratio (WHtR).

[0247] 3. T-type (inverted triangle)

[0248] Characteristics: Broad shoulders, well-developed chest, narrow waist, small hips. Fat is mainly distributed in the upper body.

[0249] Identification criteria:

[0250] Shoulder-Hip Ratio is greater than 1.

[0251] The circumference of the upper body (especially the shoulders and chest) is significantly larger than that of the lower body (hips and thighs).

[0252] It is more common in men and is usually associated with lower body fat percentage and higher muscle mass, especially in the upper body.

[0253] 4.X-type (hourglass type)

[0254] Features: Broad shoulders and hips, with a noticeably narrow waist, giving it a classic "hourglass" shape.

[0255] Identification criteria:

[0256] Waist-to-hip ratio (WHR) is usually around 0.7.

[0257] The waist circumference is significantly smaller than the shoulder circumference and hip circumference.

[0258] The body fat percentage is moderate or slightly high, and the fat is evenly distributed.

[0259] It is common in women and is usually determined by the ratio of waist to hip circumference.

[0260] 7. Visualization

[0261] Use 3D rendering technology to visualize the calculation results for a more intuitive understanding and sharing of the results. You can use 3D rendering software or libraries to display the distribution of the model surface area and volume, and generate visual reports or animations.

[0262] A computer-readable medium storing software, the software comprising instructions executable by one or more computers, the instructions causing the one or more computers to perform operations through such execution, the operations comprising the process of the above-mentioned system.

[0263] A computer system comprising:

[0264] one or more processors;

[0265] The memory stores operable instructions, which, when executed by the one or more processors, enable the one or more processors to perform operations, wherein the operations include the process of the above-mentioned system.

[0266] In this embodiment,

[0267] 1. High precision and high efficiency: 3D scanning technology can acquire a large amount of point cloud data in a very short time to form a highly realistic 3D model. Its data acquisition accuracy is high and it can capture the subtle features of the object. This greatly improves the efficiency of data acquisition and ensures the accuracy of the data, providing a solid foundation for subsequent body type classification.

[0268] 2. Non-contact measurement: 3D scanning technology uses non-contact measurement, which does not require physical contact with the target object, so it can be measured without damaging or contaminating the object. This avoids errors caused by human factors and improves the accuracy and reliability of the measurement.

[0269] 3. Strong applicability: 3D scanning technology can work in various environmental conditions, including indoors, outdoors, and in dark environments, which makes it widely used in many fields. In human body typing, this strong adaptability enables the technology to cope with various complex environments and ensure the smooth progress of data collection.

[0270] 4. Intuitiveness and diversity of results: The collected point cloud data not only contains spatial information, but also color information and reflectivity values, which can truly reproduce the object scene. In human body typing, this rich information can help us understand the morphological structure of the human body more comprehensively and improve the accuracy of typing. At the same time, one measurement can output multiple results without repeated measurements, which improves work efficiency.

[0271] 5. High degree of automation: By combining advanced computer vision and machine learning algorithms, it is possible to automatically process and analyze 3D scan data, thereby automatically performing body type classification. This highly automated processing method greatly reduces the need for manual intervention and improves the efficiency and accuracy of classification.

[0272] 6. Personalized services: Human body type classification based on 3D scanning data can provide personalized services according to the specific morphological structure of individuals. For example, in the clothing industry, suitable clothing can be customized according to people of different body types; in the fitness industry, personalized fitness plans and suggestions can be provided for people of different body types.

[0273] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the definition of the claims.

Claims

1. A human body model measurement and typing method based on human body three-dimensional reconstruction, characterized in that: The following steps are involved: S1. Data collection Data collection: Make sure the subject is relatively still and placed in the center of the scanning device. During the data collection process, the subject can be scanned at different angles or positions as needed; S2. Data Preprocessing Denoising: Remove noise from scanned data through filtering and other methods to improve the accuracy of subsequent processing; Registration: If you use multiple scanning devices or multiple scans, you need to register the different data sets to ensure that they are in the same coordinate system; Data repair: Repair missing or damaged parts of the data. Data repair can be performed using methods such as interpolation; S3. Reconstruction model Select reconstruction algorithm: Select an appropriate reconstruction algorithm based on the data type and requirements; Model reconstruction: Convert the preprocessed data into a 3D model using the selected algorithm. S4. Model segmentation If you need to calculate the surface area and volume of a specific part, you can segment the model, which can be done manually or with an automatic segmentation algorithm: S5 key body data measurement, use the matching model measurement tool to measure key body data; S6. Automatic body type classification The extracted features are clustered using clustering algorithms.

2. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, characterized in that: Building a 3D model requires point cloud data reconstruction. The specific method is as follows: S3.

1. Grid generation: S3.

11. Divide the three-dimensional space into a series of regular cubes and label the vertices of each cube as "inside" or "outside", depending on where the data point is located; S3.

12. In each cube, determine which vertices need to be connected by looking up the edge table; S3.

13. Generate triangular patches and connect them to form a mesh. These triangular patches will eventually form the surface of the entire three-dimensional model. S3.2, Surface fitting: For data that requires a smooth surface, use the Poisson reconstruction algorithm: S3.

21. Compute the gradient field and normal vector of the point cloud; S3.

22. Through global fitting, these gradient field information are used to generate a smooth surface; S3.

23. Generate the final triangular patch to represent the model surface.

3. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, characterized in that: The manual segmentation method in S4. is as follows: Import the complete 3D model into the modeling software; use the "segmentation tool" in the software to manually select the area to be segmented; the selected area is separated from the overall model as a separate model part. Surface area and volume calculations were performed separately for each sub-region.

4. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, characterized in that: The automatic segmentation algorithm method in S4. is as follows: Based on Mesh Segmentation algorithm: Mesh preprocessing: After importing the model, preprocess its mesh. Cutting plane determination: According to the topological structure of the model, the cutting plane is automatically determined by curvature changes, geometric features, etc. Region segmentation: The model is divided into multiple sub-regions according to the cutting plane. Data extraction: Calculate the volume and surface area of ​​each sub-region. Voxel-based Segmentation: Voxelization: voxelize the model and convert it into three-dimensional data consisting of a series of regular small cubes (voxels); Region growing algorithm: By selecting seed points, the region growing algorithm is used to expand to neighboring voxels to form a segmented region; Segmentation completed: The different body regions are segmented, and the surface area and volume of each region can be measured; Based on morphological segmentation algorithm: Edge detection: edge detection is performed through the geometric contours of the model (such as surface curvature) to identify the key points of the model; Morphological operations: Use morphological operations such as dilation and erosion to process the model and clearly segment each area; Region division: divide the model into head, torso, limbs and other parts; Surface area and volume calculations: Further surface area and volume calculations are performed on each area using specialized measurement algorithms.

5. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, characterized in that: After the model is segmented, use the matching measurement tools to measure the key data of the body (such as surface area, volume, length, circumference, etc.): Surface Area and Volume Measurements: Perform surface area and volume measurements on each segmented region using built-in tools in your 3D modeling software or by writing a custom script. The surface area and volume of a closed surface can be calculated using the Gauss-Bonnet formula or directly by integration; Length and girth measurements: Use the model's geometric properties to select key points and line segments to calculate the length and girth of different parts of the body; calculate accurate length and girth data by setting a fixed measurement path or using an algorithm to find the shortest path along a surface.

6. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, characterized in that: The clustering algorithm can divide data points into different clusters or categories according to the similarities between them. During the clustering process, it is necessary to select appropriate clustering parameters to obtain the best clustering effect and evaluate the clustering results.

7. A computer-readable medium storing software, the software comprising instructions executable by one or more computers, the instructions causing the one or more computers to perform operations by such execution, the operations comprising the process of the above-mentioned human body model measurement and typing method.

8. A computer system comprising: one or more processors; A memory stores operable instructions, wherein when the instructions are executed by the one or more processors, the one or more processors are caused to perform operations, wherein the operations include the process of the above-mentioned human body model measurement and typing method.

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