WO2020211255A1 - 人体形体及体质数据采集方法、装置及存储介质 - Google Patents
人体形体及体质数据采集方法、装置及存储介质 Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1071—Measuring physical dimensions, e.g. size of the entire body or parts thereof measuring angles, e.g. using goniometers
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1073—Measuring volume, e.g. of limbs
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1074—Foot measuring devices
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1075—Measuring physical dimensions, e.g. size of the entire body or parts thereof for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1077—Measuring of profiles
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1079—Measuring physical dimensions, e.g. size of the entire body or parts thereof using optical or photographic means
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
Definitions
- This application relates to the field of image recognition technology, and in particular to a method and device for collecting human body shape and physical fitness data, and a computer non-volatile readable storage medium.
- the collection of physical examination data of the human body is usually limited to the measurement of health data such as height, weight, and body fat, while the measurement of data on other parts of the human body, such as body measurements and body proportions, requires manual inspection by medical staff. , Resulting in slow detection speed, low efficiency, and even non-detection of such data.
- This application provides a method and device for collecting human body shape and physique data, and a computer non-volatile readable storage medium, the main purpose of which is to collect two-dimensional image information of the human body through a photographing device, and to mark all positions to be measured based on the two-dimensional image information The plane data value of, combined with the corresponding correction scale factor, finally obtain the actual body data and physical data of the human body.
- this application provides a method for collecting human body shape and physical fitness data, which is applied to an electronic device, and the method includes:
- the body parameter data of each part of the human body is determined according to the 3D distribution feature information of the human body, and the body fitness data of the human body is determined according to the body parameter data.
- the present application also provides an electronic device that includes a memory, a processor, and a photographing device.
- the memory includes a human body shape and a physical data collection program.
- the human body shape and physical data collection program are The processor executes the following steps:
- the two-dimensional image information including image information of the front, back, side, top of the head and soles of the human body;
- the body parameter data of each part of the human body is determined according to the 3D distribution feature information of the human body, and the body fitness data of the human body is determined according to the body parameter data.
- the present application also provides a non-volatile computer readable storage medium
- the computer readable storage medium includes a human body shape and a physical data collection program, and the human body shape and a physical data collection program are When the processor is executed, the steps of the above-mentioned human body shape and physical fitness data collection method are realized.
- the human body shape and physique data collection method, device, and computer non-volatile readable storage medium proposed in this application are mainly intended to collect two-dimensional image information of the human body through a photographing device, so as to image various parts of the human body (two-dimensional image) , And annotate the plane data values of all the human body positions to be measured according to the portrait, combined with the corresponding correction scale coefficient, and finally obtain the actual body shape data and physical data of the human body.
- Figure 1 is a schematic diagram of the application environment of an embodiment of the applicant's body shape and physical fitness data collection method
- FIG. 2 is a schematic diagram of a unit of an embodiment of a human body shape and physical fitness data collection program in FIG. 1;
- Fig. 3 is a flowchart 1 of an embodiment of the applicant's body shape and physical data collection method
- Fig. 4 is a second flowchart of an embodiment of the applicant's body shape and physical fitness data collection method.
- This application provides a method for collecting human body shape and physical fitness data, which is applied to an electronic device 1.
- FIG. 1 it is a schematic diagram of an application environment of a preferred embodiment of the applicant's body shape and physical data collection method.
- the electronic device 1 may be a terminal device with arithmetic function, such as a server, a smart phone, a tablet computer, a portable computer, a desktop computer, and the like.
- the electronic device 1 includes a processor 12, a memory 11, a camera 13, a network interface 14, and a communication bus 15.
- the memory 11 includes at least one type of readable storage medium.
- the at least one type of readable storage medium may be a non-volatile storage medium such as flash memory, hard disk, multimedia card, card-type memory 11, and the like.
- the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1.
- the readable storage medium may also be the external memory 11 of the electronic device 1, such as a plug-in hard disk or a smart memory card (Smart Media Card, SMC) equipped on the electronic device 1. , Secure Digital (SD) card, Flash Card, etc.
- SD Secure Digital
- the readable storage medium of the memory 11 is generally used to store the human body shape and the physical data collection program 10, the human body image database, etc. installed in the electronic device 1.
- the memory 11 can also be used to temporarily store data that has been output or will be output.
- the processor 12 may be a central processing unit (CPU), a microprocessor or other data processing chip, which is used to run program codes or process data stored in the memory 11, such as executing human body shapes and Physical data collection program 10 etc.
- CPU central processing unit
- microprocessor or other data processing chip, which is used to run program codes or process data stored in the memory 11, such as executing human body shapes and Physical data collection program 10 etc.
- the photographing device 13 may be a part of the electronic device 1 or may be independent of the electronic device 1.
- the electronic device 1 is a terminal device with a camera such as a smart phone, a tablet computer, or a portable computer, and the photographing device 13 is the camera of the electronic device 1.
- the electronic device 1 may be a server, and the photographing device 13 is independent of the electronic device 1 and is connected to the electronic device 1 via a network.
- the photographing device 13 is installed in a specific place, such as a hospital. A real-time image is captured by the person to be examined, and the captured real-time image is transmitted to the processor 12 via the network.
- the network interface 14 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface), and is generally used to establish a communication connection between the electronic device 1 and other electronic devices.
- the communication bus 15 is used to realize the connection and communication between these components.
- FIG. 1 only shows the electronic device 1 with the components 11-15, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead.
- the electronic device 1 may also include a user interface.
- the user interface may include an input unit such as a keyboard (Keyboard), a voice input device such as a microphone (microphone) and other devices with voice recognition functions, and a voice output device such as audio, earphones, etc.
- the user interface may also include a standard wired interface and a wireless interface.
- the electronic device 1 may also include a display, and the display may also be called a display screen or a display unit.
- the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an organic light-emitting diode (Organic Light-Emitting Diode, OLED) touch device.
- OLED Organic Light-Emitting Diode
- the display is used for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
- the electronic device 1 further includes a touch sensor.
- the area provided by the touch sensor for the user to perform a touch operation is called a touch area.
- the touch sensor described here may be a resistive touch sensor, a capacitive touch sensor, or the like.
- the touch sensor includes not only a contact type touch sensor, but also a proximity type touch sensor and the like.
- the touch sensor may be a single sensor, or may be, for example, a plurality of sensors arranged in an array.
- the area of the display of the electronic device 1 may be the same as or different from the area of the touch sensor.
- the display and the touch sensor are stacked to form a touch display screen. The device detects the touch operation triggered by the user based on the touch screen.
- the electronic device 1 may also include a radio frequency (RF) circuit, a sensor, an audio circuit, etc., which will not be repeated here.
- RF radio frequency
- the memory 11 as a computer storage medium may include an operating system, and a human body shape and physical data collection program 10; the processor 12 executes the human body shape and physical data stored in the memory 11.
- the following steps are implemented in the acquisition program 10:
- the two-dimensional image information including image information of the front, back, side, top of the head and soles of the human body;
- the body parameter data of each part of the human body is determined according to the 3D distribution feature information of the human body, and the body fitness data of the human body is determined according to the body parameter data.
- the photographing device 13 includes a rotatable base for the user to stand on, and a photographing camera arranged around the rotatable base.
- the photographing camera is connected to an external control system.
- the human body stands on the rotatable base, and the photographing camera is controlled by the control system to perform the human body Multi-angle shooting.
- the angle of the shooting camera can be adjusted according to the height of the human body to be detected.
- the step of obtaining the correction scale factor includes: creating a human body image database, and collecting the scale factor between the two-dimensional image data in the database and the corresponding parameters of each part of the actual human body, and then obtaining the correction scale factor of each part of the human body.
- the correction scale factor varies according to different parts of the human body and different angles, such as sideways correction scale factor, front correction scale factor, head correction scale factor, shoulder correction scale factor, chest correction scale factor, etc.
- the human body can be photographed at various angles every 15 degrees or 30 degrees, until the human body rotates 360 degrees, so as to obtain the image information of the human body at various angles to improve 3D The accuracy of the characteristic information.
- the steps of obtaining the correction scale coefficient may include:
- the front human body image area, the back human body image area, the side human body image area, etc. can be identified in a variety of ways.
- Method 1 Train the human front detector, side detector, and back detector by extracting typical human features (such as gradient features, edge features), and then apply the human front detector, side detector, and back detector in the scale and position space
- the device separately judges the front body image, the side body image and the back body image of the human body, and then detects the front body image area, the back body image area, and the side body image area in the front body image.
- a human frontal detector can also be constructed by combining the features of gradient direction histogram and support vector machine.
- Method 2 Detect different parts of the front of the human body, and then use the geometric relationship between the parts to construct a human front detector. For example, divide the front of the human body into multiple parts: head, upper chest, lower chest, waist, buttocks, legs, left arm, right arm, and construct a human body side detector based on these eight parts.
- the constructed human frontal detector recognizes the frontal human body image area based on these eight parts from the frontal full-body image.
- the two-dimensional image data of the human body can be determined according to the front body image area, the side body image area, and the back body image area.
- the 3D distribution feature information of the human body includes the three-dimensional shape information of the human body and various feature points distributed on the three-dimensional shape.
- the feature points include head vertices, hand/foot distribution points, and various joint points of the human body.
- the feature points can be set according to The shape parameter data to be measured is determined, that is, we can measure the shape parameter data through the feature points.
- the body parameter data includes basic parameters and scale parameters: among them, the basic parameters include: body height, weight, arm length, leg length, shoulder width, palm size, sole size, head size, bust, waist, and hip And neck circumference data, etc.; the ratio parameters are obtained according to the basic parameters, and the ratio parameters include: upper and lower body ratio, head-body ratio, head-neck ratio and head-to-shoulder ratio data.
- the step of determining the body mass data of the human body according to the body parameter data includes: obtaining the body mass index according to the body parameter data, and the calculation formula of the body mass index is:
- BMI body mass index
- G body weight
- H body height
- the human body shape and physique data collection method provided in this application also includes traditional measurement steps.
- the body's height, weight, grip strength, blood pressure, heart rate, blood oxygen and other indicators are collected through traditional measurement devices.
- the fingerprint of the person to be tested is first collected to authenticate the user's identity, and then parameter information such as the age and gender of the person to be tested is input into the human body data collection program 10, and the information is displayed to the test person for confirmation, and then The collection, analysis and processing of various indicators or parameters of the human body are realized through the human body data collection program, and the acquired data is displayed and stored in real time, which is convenient for testers to establish health files and later review.
- test parameters may include: height, weight, grip strength, blood pressure, heart rate, blood oxygen, measurements, human body upper and lower body ratio, head to body ratio, head to neck ratio and head to shoulder ratio, body mass index BMI (for body shape analysis), etc.
- body mass index BMI and grip strength BMI a comprehensive body shape analysis and upper limb strength to evaluate the health of the examinee.
- body mass index BMI and grip strength BMI a comprehensive body shape analysis and upper limb strength to evaluate the health of the examinee.
- the measured values of blood pressure and grip strength refer to the corresponding standards for men and women of different age groups, estimate the age of the examinee, and compare their actual age to obtain the degree of aging. If the estimated age is greater than the actual age, it means that the aging rate is faster and the physical fitness declines quickly; on the contrary, it means that the aging rate is slower and the physical fitness is maintained better.
- the electronic device 1 proposed in the above embodiment can collect and analyze human health and physical data in a comprehensive and rapid manner, and learn the current health status of the human body according to existing health standards, and realize the effect of one-time, automated, and comprehensive physical examination, and user experience it is good.
- the human body shape and physical data collection program 10 can also be divided into one or more units, and one or more units are stored in the memory 11 and executed by the processor 12 to complete the application.
- the unit referred to in this application refers to a series of computer program instruction segments that can complete specific functions. Referring to FIG. 2, it is the program unit of the preferred embodiment of the human body shape and physical fitness data collection program 10 in FIG. 1.
- the body shape and physical fitness data collection program 10 can be divided into:
- the photographing unit 110 is used to collect two-dimensional image information of the human body in various positions and multiple angles.
- the 3D distribution feature determining unit 120 obtains corresponding human body 3D distribution feature information through the two-dimensional image information collected by the photographing unit and the corresponding correction scale coefficient.
- the recognition unit 130 determines the parameter data of various parts of the human body to be collected according to the 3D distribution feature information of the human body, and obtains the data of the upper and lower body ratio, the head-body ratio, the head-neck ratio, and the head-to-shoulder ratio.
- the display unit 140 displays the above-mentioned human body upper and lower body ratio, head to body ratio, head to neck ratio, and head to shoulder ratio data in real time.
- the traditional measurement unit is used to collect the body's height, weight, grip strength, blood pressure, heart rate, blood oxygen and other indicators.
- FIG. 3 is the first flow chart of a specific embodiment of the applicant's body shape and physique data collection method.
- the method can be executed by a device, and the device can be implemented by software and/or hardware.
- the method for collecting human body shape and physical fitness data includes the following steps:
- Step S110 Collect two-dimensional image information of the human body through the photographing device.
- the two-dimensional image information includes image information of the front, back, side, top of the head and soles of the human body.
- the photographing device 13 includes a rotatable base for the user to stand on and a plurality of photographing cameras arranged around the rotatable base.
- the photographing cameras are connected to an external control system.
- the human body stands on the rotatable base and rotates at any angle through the control system.
- Control the shooting camera to take multi-angle shooting of the human body to be detected, and obtain the human body two-dimensional image information.
- the angle of the shooting camera can be adjusted according to the height of the human body to be detected to obtain image information of multiple parts of the human body such as the front, back, side, top of the head, and soles of the human body.
- the step of obtaining the correction scale factor includes: creating a human body image database, and collecting the scale factor between the two-dimensional image data in the database and the corresponding parameters of each part of the actual human body, and then obtaining the correction scale factor of each part of the human body.
- the correction scale factor varies according to different parts of the human body and different angles, such as sideways correction scale factor, front correction scale factor, head correction scale factor, shoulder correction scale factor, chest correction scale factor, etc.
- the human body can be photographed at various angles every 15 degrees or 30 degrees, until the human body rotates 360 degrees, so as to obtain the image information of the human body at various angles to improve 3D The accuracy of feature distribution information.
- the 3D distribution feature information of the human body includes the three-dimensional shape information of the human body and various feature points distributed on the three-dimensional shape.
- the feature points include head vertices, hand/foot distribution points, and various joint points of the human body.
- the feature points can be set according to
- the shape parameter data to be measured is determined, that is, we can measure the shape parameter data through the feature points.
- Step S120 Obtain corresponding human body 3D distribution feature information through the two-dimensional image information and the corresponding correction scale factor; wherein the correction scale factor is acquired based on a deep neural network model.
- the steps of obtaining the correction scale coefficient may include:
- the front human body image area, the back human body image area, the side human body image area, etc. can be identified in a variety of ways.
- Method 1 Train the human body front detector, side detector, and back detector by extracting the typical features of the human body (such as gradient feature, edge feature), and then apply the human body, side detector, and back detector to the scale and position space respectively.
- the front body image, the side body image, and the back body image of the human body are judged, and then the front body image area, the back body image area, and the side body image area in the front body image are detected.
- a human frontal detector can also be constructed by combining the features of the gradient direction histogram and the support vector machine.
- Method 2 Detect different parts of the front of the human body, and then use the geometric relationship between the parts to construct a human front detector. For example, divide the front of the human body into multiple parts: head, upper chest, lower chest, waist, buttocks, legs, left arm, right arm, and construct a human body side detector based on these eight parts.
- the constructed human frontal detector recognizes the frontal human body image area based on these eight parts from the frontal full-body image.
- the two-dimensional image data of the human body can be determined according to the front body image area, the side body image area, and the back body image area.
- Step S130 Determine the body parameter data of each part of the human body according to the 3D distribution feature information of the human body, and determine the physical data of the human body according to the body parameter data.
- the 3D distribution feature information of the human body includes the three-dimensional shape information of the human body and various feature points distributed on the three-dimensional shape.
- the feature points include head vertices, hand/foot distribution points, and various joint points of the human body.
- the feature points can be set according to The shape parameter data to be measured is determined, that is, we can measure the shape parameter data through the feature points.
- the body parameter data includes basic parameters and scale parameters: among them, the basic parameters include: body height, weight, arm length, leg length, shoulder width, palm size, foot size, head size, bust, waist, and hip And neck circumference data, etc.; the ratio parameters are obtained according to the basic parameters, and the ratio parameters include: upper and lower body ratio, head-body ratio, head-neck ratio and head-to-shoulder ratio data.
- the step of determining the body mass data of the human body according to the body parameter data includes: obtaining the body mass index according to the body parameter data, and the calculation formula of the body mass index is:
- BMI is the body mass index
- G is the body weight and the unit can be Kg
- H is the body height and the unit can be m.
- the human body shape and physique data collection method provided in this application also includes traditional measurement steps.
- the body's height, weight, grip strength, blood pressure, heart rate, blood oxygen and other indicators are collected through traditional measurement devices.
- the fingerprint of the person to be tested is first collected to authenticate the user's identity, and then parameter information such as the age and gender of the person to be tested is input into the human body data collection program 10, and the information is displayed to the test person for confirmation, and then The collection, analysis and processing of various indicators or parameters of the human body are realized through the human body data collection program, and the acquired data is displayed and stored in real time, which is convenient for testers to establish health files and later review.
- test parameters may include: height, weight, grip strength, blood pressure, heart rate, blood oxygen, measurements, upper and lower body ratio, head-to-body ratio, head-neck ratio and head-to-shoulder ratio, body mass index BMI (for body shape analysis), etc.
- body mass index BMI and grip strength BMI a comprehensive body shape analysis and upper limb strength to evaluate the health of the examinee.
- body mass index BMI and grip strength BMI a comprehensive body shape analysis and upper limb strength to evaluate the health of the examinee.
- the measured values of blood pressure and grip strength refer to the corresponding standards for men and women of different age groups, estimate the age of the examinee, and compare their actual age to obtain the degree of aging. If the estimated age is greater than the actual age, it means that the aging rate is faster and the physical fitness declines quickly; on the contrary, it means that the aging rate is slower and the physical fitness is maintained better.
- FIG. 4 shows the second process of the embodiment of the applicant's body shape and physique data collection method.
- the Applicant's body shape and physical data collection method further includes the following steps:
- S210 Perform fingerprint collection and information entry for the person to be tested.
- S220 Collect two-dimensional image information of the human body from various angles through the cooperation of the shooting device and the rotating base.
- S240 Determine parameter data of various parts of the human body according to the 3D distribution feature information of the human body.
- S250 Obtain human body measurements, upper and lower body ratio, head to body ratio, head to neck ratio, and head to shoulder ratio data according to the parameter data.
- S260 Save, analyze and process the above data to obtain the health status of the tester.
- the human body shape and physique data collection method proposed in the above embodiment obtains the 3D distribution feature information of the human body through the camera and the correction ratio coefficient, and then determines the shape parameter data and physique data of each part of the human body according to the 3D distribution feature information, the body shape and the physique
- the data is relatively comprehensive, the test operation is simple, and the scope of application is wide.
- an embodiment of the present application also proposes a computer-readable storage medium that includes a human body shape and a physical data collection program, and the following operations are implemented when the human body shape and physical data collection program is executed by a processor :
- the two-dimensional image information including image information of the front, back, side, top of the head and soles of the human body;
- the body parameter data of each part of the human body is determined according to the 3D distribution feature information of the human body, and the body fitness data of the human body is determined according to the body parameter data.
- the step of obtaining the correction scale coefficient based on a deep neural network model includes:
- the correction scale coefficient is obtained according to the two-dimensional image data and the corresponding size parameters of each part of the actual human body.
- the body parameter data includes basic parameters and scale parameters: wherein,
- the basic parameters include: human body height, weight, arm length, leg length, shoulder width, palm size, foot size, head size, bust, waist, hip and neck data;
- the ratio parameters include: upper and lower body ratio, head-body ratio, head-neck ratio, and head-to-shoulder ratio data.
- the step of determining the physical data of the human body according to the physical parameter data includes:
- the body mass index is obtained according to the body shape parameter data, and the calculation formula of the body mass index is:
- BMI is the body mass index
- G is the body weight in Kg
- H is the body height in m.
- the correction ratio includes a sideways correction ratio, a front correction ratio, a head correction ratio, a shoulder correction ratio, and a chest correction ratio.
- the specific implementation of the computer-readable storage medium of the present application is substantially the same as the specific implementation of the above-mentioned human body shape and physical data collection method, and electronic device, and will not be repeated here.
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Abstract
一种人体形体及体质数据采集的方法、装置及存储介质。该方法包括:通过拍摄装置(13)采集人体二维图像信息,二维图像信息包括人体正面、背面、侧面、头顶及脚底图像信息(S110);通过二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息,其中,校正比例系数基于深度神经网络模型获取(S120);根据人体3D分布特征信息确定人体各部位的形体参数数据,并根据形体参数数据确定人体的体质数据(S130)。该方法能对人体健康数据及形体数据进行采集分析,并根据健康标准,获知人体当前的健康状况。
Description
本申请要求于2019年4月17日提交中国专利局,申请号为201910308190.6、发明名称为“人体形体及体质数据采集方法、装置及存储介质”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
本申请涉及图像识别技术领域,尤其涉及一种人体形体及体质数据采集方法、装置及计算机非易失性可读存储介质。
随着生活水平的提高和养生理念逐渐受到重视,近年来体检和健身逐渐走进了千家万户。然而目前市场上的体检***功能相对单一,或者功能较多但操作复杂,只有专业技术人员才能操作。且通常的体检***给出的结果都是一个个测量参数,普通使用者一般很难通过一个个数字得出分析结果和当前的健康状况。
例如,在传统体检过程中,对人体的体检数据采集,通常限制在身高、体重以及体脂等健康数据的测量,而对于人体的三围、人体比例等其他部位数据的测量,需要医护人员手动检测,导致检测速度慢、效率低,甚至不检测此类数据等问题。
发明内容
本申请提供一种人体形体及体质数据采集方法、装置及计算机非易失性可读存储介质,其主要目的在于通过拍摄装置采集人体二维图像信息,并根据二维图像信息标注所有待测位置的平面数据值,结合对应的校正比例系数,最终获取人体的实际形体数据及体质数据。
第一方面,本申请提供一种人体形体及体质数据采集方法,应用于电子装置,所述方法包括:
通过拍摄装置采集人体二维图像信息,所述二维图像信息包括人体正面、 背面、侧面、头顶及脚底图像信息;
通过所述二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息;其中,所述校正比例系数基于深度神经网络模型获取;
根据所述人体3D分布特征信息确定人体各部位的形体参数数据,并根据所述形体参数数据确定人体的体质数据。
第二方面,本申请还提供一种电子装置,该电子装置包括:存储器、处理器及拍摄装置,所述存储器中包括人体形体及体质数据采集程序,所述人体形体及体质数据采集程序被所述处理器执行时实现如下步骤:
通过拍摄装置采集人体二维图像信息,所述二维图像信息包括人体正面、背面、侧面、头顶及脚底图像信息;
通过所述二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息;其中,所述校正比例系数基于深度神经网络模型获取;
根据所述人体3D分布特征信息确定人体各部位的形体参数数据,并根据所述形体参数数据确定人体的体质数据。
此外,为实现上述目的,本申请还提供一种计算机非易失性可读存储介质,所述计算机可读存储介质中包括人体形体及体质数据采集程序,所述人体形体及体质数据采集程序被处理器执行时,实现如上所述的人体形体及体质数据采集方法的步骤。
本申请提出的人体形体及体质数据采集方法、装置及计算机非易失性可读存储介质,其主要目的在于通过拍摄装置采集人体二维图像信息,以对人体各部分进行画像(二维图像),并根据画像标注所有人体待测位置的平面数据值,结合对应的校正比例系数,最终获取人体的实际形体数据及体质数据。
图1为本申请人体形体及体质数据采集方法实施例的应用环境示意图;
图2为图1中人体形体及体质数据采集程序实施例的单元示意图;
图3为本申请人体形体及体质数据采集方法实施例的流程图一;
图4为本申请人体形体及体质数据采集方法实施例的流程图二。
本申请目的的实现、功能特点及优点将结合实施例,参照附图做进一步说明。
应当理解,此处所描述的具体实施例仅仅用以解释本申请,并不用于限定本申请。
本申请提供一种人体形体及体质数据采集方法,应用于一种电子装置1。参照图1所示,为本申请人体形体及体质数据采集方法较佳实施例的应用环境示意图。
在本实施例中,电子装置1可以是服务器、智能手机、平板电脑、便携计算机、桌上型计算机等具有运算功能的终端设备。
该电子装置1包括:处理器12、存储器11、拍摄装置13、网络接口14及通信总线15。
存储器11包括至少一种类型的可读存储介质。所述至少一种类型的可读存储介质可为如闪存、硬盘、多媒体卡、卡型存储器11等的非易失性存储介质。在一些实施例中,所述可读存储介质可以是所述电子装置1的内部存储单元,例如该电子装置1的硬盘。在另一些实施例中,所述可读存储介质也可以是所述电子装置1的外部存储器11,例如所述电子装置1上配备的插接式硬盘,智能存储卡(Smart Media Card,SMC),安全数字(Secure Digital,SD)卡,闪存卡(Flash Card)等。
在本实施例中,所述存储器11的可读存储介质通常用于存储安装于所述电子装置1的人体形体及体质数据采集程序10、人体图像数据库等。所述存储器11还可以用于暂时地存储已经输出或者将要输出的数据。
处理器12在一些实施例中可以是一中央处理器(Central Processing Unit,CPU),微处理器或其他数据处理芯片,用于运行存储器11中存储的程序代码或处理数据,例如执行人体形体及体质数据采集程序10等。
拍摄装置13既可以是所述电子装置1的一部分,也可以独立于电子装置1。在一些实施例中,所述电子装置1为智能手机、平板电脑、便携计算机等具有摄像头的终端设备,则所述拍摄装置13即为所述电子装置1的摄像头。在其他实施例中,所述电子装置1可以为服务器,所述拍摄装置13独立于该电子装置1、与该电子装置1通过网络连接,例如,该拍摄装置13安装于特定场所,如医院,对待体检人员拍摄得到实时图像,通过网络将拍摄得到的实时图像传输至处理器12。
网络接口14可选地可以包括标准的有线接口、无线接口(如WI-FI接口),通常用于在该电子装置1与其他电子设备之间建立通信连接。
通信总线15用于实现这些组件之间的连接通信。
图1仅示出了具有组件11-15的电子装置1,但是应理解的是,并不要求实施所有示出的组件,可以替代的实施更多或者更少的组件。
可选地,该电子装置1还可以包括用户接口,用户接口可以包括输入单元比如键盘(Keyboard)、语音输入装置比如麦克风(microphone)等具有语音识别功能的设备、语音输出装置比如音响、耳机等,可选地用户接口还可以包括标准的有线接口、无线接口。
可选地,该电子装置1还可以包括显示器,显示器也可以称为显示屏或显示单元。在一些实施例中可以是LED显示器、液晶显示器、触控式液晶显示器以及有机发光二极管(Organic Light-Emitting Diode,OLED)触摸器等。显示器用于显示在电子装置1中处理的信息以及用于显示可视化的用户界面。
可选地,该电子装置1还包括触摸传感器。所述触摸传感器所提供的供用户进行触摸操作的区域称为触控区域。此外,这里所述的触摸传感器可以为电阻式触摸传感器、电容式触摸传感器等。而且,所述触摸传感器不仅包括接触式的触摸传感器,也可包括接近式的触摸传感器等。此外,所述触摸传感器可以为单个传感器,也可以为例如阵列布置的多个传感器。
此外,该电子装置1的显示器的面积可以与所述触摸传感器的面积相同,也可以不同。可选地,将显示器与所述触摸传感器层叠设置,以形成触摸显示屏。该装置基于触摸显示屏侦测用户触发的触控操作。
可选地,该电子装置1还可以包括射频(Radio Frequency,RF)电路,传感器、音频电路等等,在此不再赘述。
在图1所示的装置实施例中,作为一种计算机存储介质的存储器11中可以包括操作***、以及人体形体及体质数据采集程序10;处理器12执行存储器11中存储的人体形体及体质数据采集程序10时实现如下步骤:
通过拍摄装置采集人体二维图像信息,所述二维图像信息包括人体正面、背面、侧面、头顶及脚底图像信息;
通过所述二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息;其中,所述校正比例系数基于深度神经网络模型获取;
根据所述人体3D分布特征信息确定人体各部位的形体参数数据,并根据所述形体参数数据确定人体的体质数据。
其中,拍摄装置13包括供用户站立的可旋转底座、设置在可旋转底座四周的拍摄相机,拍摄相机与外界控制***连接,人体站在可旋转底座上,通过控制***操控拍摄相机对待检测人体进行多角度的拍摄。其中,拍摄相机的角度可根据待检测的人体高度进行调整。
获取校正比例系数的步骤包括:创建人体图像数据库,并采集数据库中的二维图像数据与对应的实际人体各部位参数之间的比例系数,进而获得人体各部位的校正比例系数。该校正比例系数根据人体部位的不同、角度的不同也存在差异,例如包括侧身校正比例系数,正面校正比例系数、头部校正比例系数、肩部校正比例系数、胸部校正比例系数等。
此外,也可在可旋转底座的辅助作用下,每旋转15度或者30度等对人体进行全方位多角度拍照,直至人体旋转360度为止,从而获得人体在各个角度的图像信息,以提高3D特征信息的准确度。
当基于深度神经网络模型获取校正比例系数时,校正比例系数的获取步骤可以包括:
1、创建卷积神经网络模型,并通过预训练模型对卷积神经网络模型的参数进行初始化。
2、将人体图像数据库内的图像输入初始化处理后的卷积神经网络模型进行训练,提取图像中的特征信息,并获取与图像对应的二维图像数据。
3、根据二维图像数据及对应的实际人体各部位的尺寸参数获取所述校正比例系数。二维图像数据与对应的实际人体各部位参数之间的比例系数即为校正比例系数。
在根据二维图像分析获取二维图像数据的过程中,可以通过多种方式识别正面人体图像区域、背面人体图像区域、侧面人体图像区域等。
方法一:通过提取人体典型特征(如梯度特征,边缘特征)训练出人体正面检测器、侧面检测器、背面检测器,再在尺度和位置空间上应用人体正面检测器、侧面检测器、背面检测器分别对人体的正面全身图像、侧面全身图像和背面全身图像进行判断,进而检测出正面全身图像中的正面人体图像区域、背面人体图像区域、侧面人体图像区域等。此外,还可以通过梯度方向直方 图特征结合支持向量机构建人体正面检测器。
方法二:检测人体正面的不同部位,再利用各部位之间的几何关系构造出人体正面检测器。例如:将人体正面分成多个部位:头部、上胸部、下胸部、腰部、臀部、腿部、左臂、右臂,就构造基于这八个部位的人体侧面检测器。构建的人体正面检测器从正面全身图像中基于这八个部位识别出正面人体图像区域。
进而根据正面人体图像区域、侧面全身图像区域和背面全身图像区域可确定人体的二维图像数据。
另外,人体3D分布特征信息包括人体的立体形状信息和分布在所述立体形状上的各特征点,特征点包括头顶点、手/脚掌分布点以及人体各关节点等,特征点的设置可以根据待量取的形体参数数据来确定,即通过特征点我们可以量取形体参数数据。
作为具体示例,形体参数数据包括基础参数及比例参数:其中,基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据等;比例参数根据基础参数获取,比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据等。
最后,根据形体参数数据确定人体的体质数据的步骤包括:根据体型参数数据获取体质指数,体质指数的计算公式为:
BMI=G÷H
2
其中,BMI为体质指数;G为人体体重;H为人体身高。
需要说明的是,本申请提供的人体形体及体质数据采集方法,还包括传统测量步骤,通过传统测量装置采集人体的身高、体重、握力、血压、心率和血氧等指标。
为方便对采集后的数据进行存储及分析,还可以在数据采集前,先对待检测人进行个人信息记录。具体地,首先对待检测人的指纹进行采集,以便对用户身份进行认证,然后在人体形体数据采集程序10内输入检测人的年龄、性别等参数信息,并将该信息向测试人显示确认,然后通过人体形体数据采集程序实现对人体的各指标或参数的采集、分析及处理,并将获取的各项数据进行实时显示、存储,便于测试人员建立健康档案,及后期查阅等。
上述测试参数可以包括:身高、体重、握力、血压、心率、血氧、三围、 人体上下身比例、头身比例、头颈比例及头肩比例、体质指数BMI(用于体型分析)等。
进而通过计算体质指数BMI和握力体重指数,综合体型分析和上肢力量,以评估体检者的健康状况。同时,根据血压和握力测量值,参考不同年龄段男女对应的标准,估算体检者年龄,并对比其实际年龄,从而得到其衰老程度。若估算年龄大于实际年龄,则表示衰老速度较快,体质下降偏快;反之,则表示衰老速度较慢,体质保持较好。
上述实施例提出的电子装置1,能够全面快速的对人体健康及形体数据进行采集分析,并根据现有健康标准,获知人体当前的健康状况,实现一次性、自动化、全面体检的效果,用户体验好。
在其他实施例中,人体形体及体质数据采集程序10还可以被分割为一个或者多个单元,一个或者多个单元被存储于存储器11中,并由处理器12执行,以完成本申请。本申请所称的单元是指能够完成特定功能的一系列计算机程序指令段。参照图2所示,为图1中人体形体及体质数据采集程序10较佳实施例的程序单元。
所述人体形体及体质数据采集程序10可以被分割为:
拍摄单元110,用于采集人体各方位多角度的二维图像信息。
3D分布特征确定单元120,通过所述拍摄单元采集的二维图像信息及对应的校正比例系数获取对应的人体3D分布特征信息。
识别单元130,根据人体3D分布特征信息确定待采集的人体各部位参数数据,并获取人体上下身比例、头身比例、头颈比例及头肩比例数据。
显示单元140,对上述人体上下身比例、头身比例、头颈比例及头肩比例数据进行实时展示。
此外,还包括校正比例系数获取单元和传统测量单元;传统测量单元用于采集人体的身高、体重、握力、血压、心率和血氧等指标。
此外,本申请还提供一种人体形体及体质数据采集方法。参照图3所示,图3为本申请人体形体及体质数据采集方法具体实施例的流程一。该方法可以由一个装置执行,该装置可以由软件和/或硬件实现。
在本实施例中,人体形体及体质数据采集方法包括以下所示步骤:
步骤S110:通过拍摄装置采集人体二维图像信息,二维图像信息包括人 体正面、背面、侧面、头顶及脚底图像信息。
其中,拍摄装置13包括供用户站立的可旋转底座以及设置在可旋转底座四周的多个拍摄相机,拍摄相机均与外界控制***连接,人体站在可旋转底座上进行任意角度旋转,通过控制***操控拍摄相机对待检测人体进行多角度的拍摄,并获取人体二维图像信息。其中,拍摄相机的角度可根据待检测的人体高度进行调整,以获得人体正面、背面、侧面、头顶及脚底等多个部位的图像信息。
获取校正比例系数的步骤包括:创建人体图像数据库,并采集数据库中的二维图像数据与对应的实际人体各部位参数之间的比例系数,进而获得人体各部位的校正比例系数。该校正比例系数根据人体部位的不同、角度的不同也存在差异,例如包括侧身校正比例系数,正面校正比例系数、头部校正比例系数、肩部校正比例系数、胸部校正比例系数等。
此外,也可在可旋转底座的辅助作用下,每旋转15度或者30度等对人体进行全方位多角度拍照,直至人体旋转360度为止,从而获得人体在各个角度的图像信息,以提高3D特征分布信息的准确度。
其中,人体3D分布特征信息包括人体的立体形状信息和分布在所述立体形状上的各特征点,特征点包括头顶点、手/脚掌分布点以及人体各关节点等,特征点的设置可以根据待量取的形体参数数据来确定,即通过特征点我们可以量取形体参数数据。
步骤S120:通过所述二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息;其中,所述校正比例系数基于深度神经网络模型获取。
当基于深度神经网络模型获取校正比例系数时,校正比例系数的获取步骤可以包括:
1、创建卷积神经网络模型,并通过预训练模型对卷积神经网络模型的参数进行初始化。
2、将人体图像数据库内的图像输入初始化处理后的卷积神经网络模型进行训练,提取图像中的特征信息,并获取与图像对应的二维图像数据。
3、根据二维图像数据及对应的实际人体各部位的尺寸参数获取所述校正比例系数。二维图像数据与对应的实际人体各部位参数之间的比例系数即为校正比例系数。
在根据二维图像分析获取二维图像数据的过程中,可以通过多种方式识别正面人体图像区域、背面人体图像区域、侧面人体图像区域等。
方法一:通过提取人体典型特征(如梯度特征,边缘特征)训练出人体正面检测器、侧面检测器、背面检测器,再在尺度和位置空间上应用人体、侧面检测器、背面检测器分别对人体的正面全身图像、侧面全身图像和背面全身图像进行判断,进而检测出正面全身图像中的正面人体图像区域、背面人体图像区域、侧面人体图像区域等。此外,还可以通过梯度方向直方图特征结合支持向量机构建人体正面检测器。
方法二:检测人体正面的不同部位,再利用各部位之间的几何关系构造出人体正面检测器。例如:将人体正面分成多个部位:头部、上胸部、下胸部、腰部、臀部、腿部、左臂、右臂,就构造基于这八个部位的人体侧面检测器。构建的人体正面检测器从正面全身图像中基于这八个部位识别出正面人体图像区域。
进而根据正面人体图像区域、侧面全身图像区域和背面全身图像区域可确定人体的二维图像数据。
步骤S130:根据所述人体3D分布特征信息确定人体各部位的形体参数数据,并根据所述形体参数数据确定人体的体质数据。
另外,人体3D分布特征信息包括人体的立体形状信息和分布在所述立体形状上的各特征点,特征点包括头顶点、手/脚掌分布点以及人体各关节点等,特征点的设置可以根据待量取的形体参数数据来确定,即通过特征点我们可以量取形体参数数据。
为具体示例,形体参数数据包括基础参数及比例参数:其中,基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据等;比例参数根据基础参数获取,比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据等。
最后,根据形体参数数据确定人体的体质数据的步骤包括:根据体型参数数据获取体质指数,体质指数的计算公式为:
BMI=G÷H
2
其中,BMI为体质指数;G为人体体重,单位可以为Kg;H为人体身高,单位可以为m。
需要说明的是,本申请提供的人体形体及体质数据采集方法,还包括传统测量步骤,通过传统测量装置采集人体的身高、体重、握力、血压、心率和血氧等指标。
为方便对采集后的数据进行存储及分析,还可以在数据采集前,先对待检测人进行个人信息记录。具体地,首先对待检测人的指纹进行采集,以便对用户身份进行认证,然后在人体形体数据采集程序10内输入检测人的年龄、性别等参数信息,并将该信息向测试人显示确认,然后通过人体形体数据采集程序实现对人体的各指标或参数的采集、分析及处理,并将获取的各项数据进行实时显示、存储,便于测试人员建立健康档案,及后期查阅等。
上述测试参数可以包括:身高、体重、握力、血压、心率、血氧、三围、人体上下身比例、头身比例、头颈比例及头肩比例、体质指数BMI(用于体型分析)等。
进而通过计算体质指数BMI和握力体重指数,综合体型分析和上肢力量,以评估体检者的健康状况。同时,根据血压和握力测量值,参考不同年龄段男女对应的标准,估算体检者年龄,并对比其实际年龄,从而得到其衰老程度。若估算年龄大于实际年龄,则表示衰老速度较快,体质下降偏快;反之,则表示衰老速度较慢,体质保持较好。
作为具体实施例,图4示出了本申请人体形体及体质数据采集方法实施例的流程二。
如图4所示,本申请人体形体及体质数据采集方法还包括以下步骤:
S210:对待测试人员进行指纹采集及信息录入。
S220:通过拍摄装置及旋转底座的配合,采集人体各角度二维图像信息。
S230:通过上述人体二维图像信息及对应的校正比例系数获取人体3D分布特征信息。
S240:根据人体3D分布特征信息确定人体各部位参数数据。
S250:根据所述参数数据获取人体三围、上下身比例、头身比例、头颈比例及头肩比例数据。
S260:对上述数据进行保存、分析处理,获取测试人员的健康状况。
上述实施例提出的人体形体及体质数据采集方法,通过拍摄装置及校正比例系数,获取人体3D分布特征信息,进而根据3D分布特征信息确定人体 各部位的形体参数数据和体质数据,人体形体及体质数据比较全面,且测试操作简单,适用范围广。
此外,本申请实施例还提出一种计算机可读存储介质,所述计算机可读存储介质中包括人体形体及体质数据采集程序,所述人体形体及体质数据采集程序被处理器执行时实现如下操作:
通过拍摄装置采集人体二维图像信息,所述二维图像信息包括人体正面、背面、侧面、头顶及脚底图像信息;
通过所述二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息;其中,所述校正比例系数基于深度神经网络模型获取;
根据所述人体3D分布特征信息确定人体各部位的形体参数数据,并根据所述形体参数数据确定人体的体质数据。
优选地,所述基于深度神经网络模型获取所述校正比例系数的步骤包括:
创建卷积神经网络模型,并通过预训练模型对卷积神经网络模型的参数进行初始化;
将人体图像数据库内的图像输入初始化处理后的卷积神经网络模型进行训练,提取所述图像中的特征信息,并获取与所述图像对应的二维图像数据;
根据所述二维图像数据及对应的实际人体各部位的尺寸参数获取所述校正比例系数。
优选地,所述形体参数数据包括基础参数及比例参数:其中,
所述基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据;
所述比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据。
优选地,根据所述形体参数数据确定人体的体质数据的步骤包括:
根据所述体型参数数据获取体质指数,所述体质指数的计算公式为:
BMI=G÷H
2
其中,BMI为体质指数;G为人体体重,单位为Kg;H为人体身高,单位为m。
优选地,所述校正比例系数包括侧身校正比例系数、正面校正比例系数、头部校正比例系数、肩部校正比例系数及胸部校正比例系数。
本申请之计算机可读存储介质的具体实施方式与上述人体形体及体质数 据采集方法、电子装置的具体实施方式大致相同,在此不再赘述。
需要说明的是,在本文中,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、装置、物品或者方法不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、装置、物品或者方法所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括该要素的过程、装置、物品或者方法中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述,不代表实施例的优劣。通过以上的实施方式的描述,本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现,当然也可以通过硬件,但很多情况下前者是更佳的实施方式。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中,包括若干指令用以使得一台终端设备(可以是手机,计算机,服务器,或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例,并非因此限制本申请的专利范围,凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换,或直接或间接运用在其他相关的技术领域,均同理包括在本申请的专利保护范围内。
Claims (20)
- 一种人体形体及体质数据采集方法,应用于电子装置,其特征在于,所述方法包括:通过拍摄装置采集人体二维图像信息,所述二维图像信息包括人体正面、背面、侧面、头顶及脚底图像信息;通过所述二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息;其中,所述校正比例系数基于深度神经网络模型获取;根据所述人体3D分布特征信息确定人体各部位的形体参数数据,并根据所述形体参数数据确定人体的体质数据。
- 根据权利要求1所述的人体形体及体质数据采集方法,其特征在于,所述基于深度神经网络模型获取所述校正比例系数的步骤包括:创建卷积神经网络模型,并通过预训练模型对卷积神经网络模型的参数进行初始化;将人体图像数据库内的图像输入初始化处理后的卷积神经网络模型进行训练,提取所述图像中的特征信息,并获取与所述图像对应的二维图像数据;根据所述二维图像数据及对应的实际人体各部位的尺寸参数获取所述校正比例系数。
- 根据权利要求2所述的人体形体及体质数据采集方法,其特征在于,所述获取与所述图像对应的二维图像数据的过程包括:体取人体的典型特征并训练出正面检测器、侧面检测器、背面检测器;通过所述正面检测器、侧面检测器、背面检测器分别对人体的正面全身图像、侧面全身图像和背面全身图像进行判断,并分别检测出正面人体图像区域、侧面全身图像区域和背面全身图像区域;基于所述正面人体图像区域、侧面全身图像区域和背面全身图像区域确定所述二维图像数据。
- 根据权利要求3所述的人体形体及体质数据采集方法,其特征在于,所述典型特征包括:人体梯度特征、边缘特征、梯度方向直方图特征。
- 根据权利要求1所述的人体形体及体质数据采集方法,其特征在于,所述校正比例系数包括侧身校正比例系数、正面校正比例系数、头部校正比例系数、肩部校正比例系数及胸部校正比例系数;所述形体参数数据包括基础参数及比例参数:其中,所述基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据;所述比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据。
- 根据权利要求2所述的人体形体及体质数据采集方法,其特征在于,所述校正比例系数包括侧身校正比例系数、正面校正比例系数、头部校正比例系数、肩部校正比例系数及胸部校正比例系数;所述形体参数数据包括基础参数及比例参数:其中,所述基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据;所述比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据。
- 根据权利要求3所述的人体形体及体质数据采集方法,其特征在于,所述校正比例系数包括侧身校正比例系数、正面校正比例系数、头部校正比例系数、肩部校正比例系数及胸部校正比例系数;所述形体参数数据包括基础参数及比例参数:其中,所述基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据;所述比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据。
- 根据权利要求4所述的人体形体及体质数据采集方法,其特征在于,所述校正比例系数包括侧身校正比例系数、正面校正比例系数、头部校正比例系数、肩部校正比例系数及胸部校正比例系数;所述形体参数数据包括基础参数及比例参数:其中,所述基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据;所述比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据。
- 根据权利要求1所述的人体形体及体质数据采集方法,其特征在于,所述人体3D分布特征信息包括人体的立体形状信息和分布在所述立体形状上的特征点;所述特征点包括头顶点、手/脚掌分布点、人体各关节点。
- 一种电子装置,其特征在于,该电子装置包括:存储器、处理器及 拍摄装置,所述存储器中包括人体形体及体质数据采集程序,所述人体形体及体质数据采集程序被所述处理器执行时实现如下步骤:通过拍摄装置采集人体二维图像信息,所述二维图像信息包括人体正面、背面、侧面、头顶及脚底图像信息;通过所述二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息;其中,所述校正比例系数基于深度神经网络模型获取;根据所述人体3D分布特征信息确定人体各部位的形体参数数据,并根据所述形体参数数据确定人体的体质数据。
- 根据权利要求10所述的电子装置,其特征在于,所述基于深度神经网络模型获取所述校正比例系数的步骤包括:创建卷积神经网络模型,并通过预训练模型对卷积神经网络模型的参数进行初始化;将人体图像数据库内的图像输入初始化处理后的卷积神经网络模型进行训练,提取所述图像中的特征信息,并获取与所述图像对应的二维图像数据;根据所述二维图像数据及对应的实际人体各部位的尺寸参数获取所述校正比例系数。
- 根据权利要求11所述的电子装置,其特征在于,所述获取与所述图像对应的二维图像数据的过程包括:体取人体的典型特征并训练出正面检测器、侧面检测器、背面检测器;通过所述正面检测器、侧面检测器、背面检测器分别对人体的正面全身图像、侧面全身图像和背面全身图像进行判断,并分别检测出正面人体图像区域、侧面全身图像区域和背面全身图像区域;基于所述正面人体图像区域、侧面全身图像区域和背面全身图像区域确定所述二维图像数据。
- 根据权利要求12所述的电子装置,其特征在于,所述典型特征包括:人体梯度特征、边缘特征、梯度方向直方图特征。
- 根据权利要求10所述的电子装置,其特征在于,所述校正比例系数包括侧身校正比例系数、正面校正比例系数、头部校正比例系数、肩部校正比例系数及胸部校正比例系数;所述形体参数数据包括基础参数及比例参数:其中,所述基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据;所述比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据。
- 一种计算机非易失性可读存储介质,其特征在于,所述计算机可读存储介质中包括人体形体及体质数据采集程序,所述人体形体及体质数据采集程序被处理器执行时,实现如下步骤:通过拍摄装置采集人体二维图像信息,所述二维图像信息包括人体正面、背面、侧面、头顶及脚底图像信息;通过所述二维图像信息及对应的校正比例系数获取相应的人体3D分布特征信息;其中,所述校正比例系数基于深度神经网络模型获取;根据所述人体3D分布特征信息确定人体各部位的形体参数数据,并根据所述形体参数数据确定人体的体质数据。
- 根据权利要求15所述的计算机非易失性可读存储介质,其特征在于,所述基于深度神经网络模型获取所述校正比例系数的步骤包括:创建卷积神经网络模型,并通过预训练模型对卷积神经网络模型的参数进行初始化;将人体图像数据库内的图像输入初始化处理后的卷积神经网络模型进行训练,提取所述图像中的特征信息,并获取与所述图像对应的二维图像数据;根据所述二维图像数据及对应的实际人体各部位的尺寸参数获取所述校正比例系数。
- 根据权利要求16所述的计算机非易失性可读存储介质,其特征在于,所述获取与所述图像对应的二维图像数据的过程包括:体取人体的典型特征并训练出正面检测器、侧面检测器、背面检测器;通过所述正面检测器、侧面检测器、背面检测器分别对人体的正面全身图像、侧面全身图像和背面全身图像进行判断,并分别检测出正面人体图像区域、侧面全身图像区域和背面全身图像区域;基于所述正面人体图像区域、侧面全身图像区域和背面全身图像区域确定所述二维图像数据。
- 根据权利要求17所述的计算机非易失性可读存储介质,其特征在于,所述典型特征包括:人体梯度特征、边缘特征、梯度方向直方图特征。
- 根据权利要求15所述的计算机非易失性可读存储介质,其特征在于,所述校正比例系数包括侧身校正比例系数、正面校正比例系数、头部校正比例系数、肩部校正比例系数及胸部校正比例系数;所述形体参数数据包括基础参数及比例参数:其中,所述基础参数包括:人体身高、体重、臂长、腿长、肩宽、手掌尺寸、脚掌尺寸、头部尺寸、胸围、腰围、臀围及颈围数据;所述比例参数包括:上下身比例、头身比例、头颈比例及头肩比例数据。
- 根据权利要求15所述的计算机非易失性可读存储介质,其特征在于,所述人体3D分布特征信息包括人体的立体形状信息和分布在所述立体形状上的特征点;所述特征点包括头顶点、手/脚掌分布点、人体各关节点。
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