CN110555411A - Method and system for measuring size of shoulder and neck, storage medium and electronic equipment - Google Patents
Method and system for measuring size of shoulder and neck, storage medium and electronic equipment Download PDFInfo
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Abstract
the invention provides a method and a system for measuring the size of a shoulder and a neck, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring a front outline image and a side outline image of a person; positioning front neck feature points in the front profile by using a maximum distance method, and positioning side neck feature points in the side profile by using an angle method; calculating the length of the front neck and the length of the side neck according to the front neck characteristic points and the side neck characteristic points respectively; and generating neck circumference data by using the characteristic information of the user, the front neck length and the side neck length as input parameters through a neural network model. The invention provides a new positioning algorithm for human shoulder and neck, and improves the accuracy of human characteristic point positioning.
Description
Technical Field
the invention relates to the field of measurement of human body dimensions, in particular to a method and a system for measuring shoulder and neck dimensions, a storage medium and electronic equipment.
background
with the rapid development of modern information technology, the application of virtual reality technology is more and more common in reality. In the clothing industry, such as human body modeling, online virtual fitting based on a human body model is also emerging in the life of people, the accuracy of human body feature point positioning directly influences the feature parameters of the human body, and the measurement of the human body parameters is the core technology for researching the body type of the human body.
currently, human body feature points are usually located by scanning line detection or template traversal, but since some human body feature points have unique characteristics, such as neck feature points, the neck feature points with the shortest horizontal distance to the neck of the human body are detected by scanning lines, but in the measurement standard, the measurement of the neck circumference has a slope amount of 25 to 30 °, so that the neck circumference error obtained based on the neck feature points detected by the scanning line detection is large. Accordingly, the present invention provides a method for measuring dimensions based on an improved method for locating a characteristic point of a shoulder and neck.
disclosure of Invention
The invention aims to provide a method, a system, a storage medium and electronic equipment for measuring the size of a shoulder and a neck, which realize the purpose of providing a new positioning algorithm for the human body neck circumference and improve the accuracy of positioning the characteristic points of the human body.
The technical scheme provided by the invention is as follows:
the invention provides a method for measuring the size of a shoulder and neck, which comprises the following steps:
acquiring a front outline image and a side outline image of a person;
Positioning front neck feature points in the front profile by using a maximum distance method, and positioning side neck feature points in the side profile by using an angle method;
calculating the length of the front neck and the length of the side neck according to the front neck characteristic points and the side neck characteristic points respectively;
Generating neck circumference data by using the characteristic information of the user, the length of the front neck and the length of the side neck as input parameters through a neural network model;
and positioning shoulder characteristic points in the front profile map, and calculating shoulder width data by combining the front neck characteristic points.
further, the positioning of the front neck feature points in the front contour map by using the maximum distance method specifically includes:
detecting the front profile graph through a profile detection function to obtain left and right extreme points which are respectively left and right hand feature points;
Extracting a front neck contour line, and simultaneously determining the vertex midpoint in the front contour map;
connecting the left-hand feature point, the right-hand feature point and the vertex midpoint to obtain a front neck feature straight line;
and traversing the front neck contour line, and calculating the Euclidean distance from the neck contour point on the front neck contour line to the front neck characteristic straight line, wherein the point with the largest distance is the front neck characteristic point.
Further, positioning the lateral neck feature points in the lateral contour map by using an angle method specifically includes:
Determining a neck horizontal connecting line with the maximum neck horizontal distance value of the side profile graph as a side neck characteristic straight line through scanning line detection;
And determining the characteristic points of the side neck according to the characteristic straight line of the side neck and the preset neck circumference measurement inclination angle.
further, positioning the shoulder feature points in the front contour map, and calculating the shoulder width data by combining the front neck feature points specifically includes:
segmenting the front profile map to determine a shoulder area, and extracting a shoulder contour line from the shoulder area;
Performing curve fitting on the shoulder contour line to obtain a contour curve;
Calculating the curvatures of all contour points on the contour curve, and selecting the contour point with the largest curvature as a shoulder characteristic point; or the like, or, alternatively,
Positioning front shoulder characteristic points based on eight chain codes;
and calculating shoulder width data through the front neck characteristic points and the shoulder characteristic points.
the invention also provides a system for measuring the size of the shoulder and neck, which comprises:
The outline image acquisition module is used for acquiring a front outline image and a side outline image of a person;
The characteristic point positioning module is used for positioning the front neck characteristic points in the front profile acquired by the profile acquisition module by using a maximum distance method and positioning the side neck characteristic points in the side profile acquired by the profile acquisition module by using an angle method;
The neck analysis module is used for calculating the length of the front neck and the length of the side neck according to the front neck characteristic points and the side neck characteristic points positioned by the characteristic point positioning module respectively;
the neck circumference calculation module is used for generating neck circumference data through a neural network model by taking the characteristic information of the user, the front neck length and the side neck length obtained by the neck analysis module as input parameters;
and the shoulder width calculating module is used for positioning the shoulder characteristic points in the front profile acquired by the profile acquisition module and calculating shoulder width data by combining the front neck characteristic points positioned by the characteristic point positioning module.
Further, the feature point positioning module specifically includes:
the feature point analysis unit is used for detecting the front profile map through a profile detection function to obtain left and right extreme points which are respectively left and right hand feature points;
the contour line extraction unit is used for extracting the front neck contour line and determining the vertex midpoint in the front contour map;
The characteristic line analysis unit is used for connecting the left-hand and right-hand characteristic points determined by the characteristic point analysis unit with the vertex midpoint determined by the contour line extraction unit to obtain a front neck characteristic straight line;
the feature point analysis unit traverses the front neck contour line determined by the contour line extraction unit, calculates the Euclidean distance from the neck contour point on the front neck contour line to the front neck feature straight line determined by the feature line analysis unit, and the point with the largest distance is the front neck feature point.
further, the feature point positioning module further includes:
the characteristic line analysis unit determines a neck horizontal connecting line with the maximum neck horizontal distance value of the side profile graph as a side neck characteristic straight line through scanning line detection;
And the characteristic point analysis unit is used for determining the characteristic points of the side neck according to the characteristic straight line of the side neck determined by the characteristic line analysis unit and the preset neck circumference measurement inclination angle.
further, the shoulder width calculating module specifically includes:
the region segmentation unit is used for segmenting the front profile map to determine a shoulder region and extracting a shoulder contour line from the shoulder region;
the curve fitting unit is used for performing curve fitting on the shoulder contour line obtained by the region segmentation unit to obtain a contour curve;
the characteristic point processing unit is used for calculating the curvatures of all the contour points on the contour curve obtained by the curve fitting unit and selecting the contour point with the maximum curvature as a shoulder characteristic point;
and the shoulder width calculating unit calculates shoulder width data through the front neck characteristic points and the shoulder characteristic points obtained by the characteristic point processing unit.
The invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements any of the methods described above.
the invention also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program running on the processor, and the processor implements any one of the methods described above when executing the computer program.
the method, the system, the storage medium and the electronic equipment for measuring the size of the shoulder and neck provided by the invention can bring at least one of the following beneficial effects:
1. In the invention, the size information of the person can be obtained based on the outline of the person. The processing process does not need professional operation, and the processing time is rapid, thereby being suitable for remote dimension measurement.
2. according to the invention, a new positioning algorithm is provided for the human body neck circumference and the shoulders, so that the accuracy of positioning the human body characteristic points is improved. And (4) positioning the neck characteristic points by using a maximum distance method, and conforming to the human body measurement standard. The algorithm firstly positions the shoulder characteristic points by a minimum curvature radius method and then extracts the shoulder neck line of the human body, and experiments show that the algorithm has small error and high practicability.
3. in the invention, the characteristic value of the neck circumference of the neural network training position is adopted, so that the method has wide applicability and high accuracy.
drawings
the above features, technical features, advantages and implementations of a method, system, storage medium and electronic device for measuring a size of a shoulder and neck will be further described in the following detailed description of preferred embodiments in a clearly understandable manner in conjunction with the accompanying drawings.
FIG. 1 is a flow chart of one embodiment of a method of measuring a size of a shoulder and neck of the present invention;
FIG. 2 is a schematic illustration of the shoulder width length of a human body;
FIG. 3 is a flow chart of another embodiment of a method of measuring a size of a shoulder and neck of the present invention;
FIG. 4 is a schematic diagram of a front outline of a person segmented according to the golden ratio;
FIG. 5 is a schematic diagram of locating frontal neck feature points based on the maximum distance method;
FIG. 6 is a schematic illustration of the angular based positioning of lateral neck feature points;
FIG. 7 is a flow chart of another embodiment of a method of measuring a size of a shoulder and neck of the present invention;
FIG. 8 is a schematic diagram of locating shoulder feature points based on curve fitting;
FIG. 9 is a schematic diagram of locating shoulder feature points based on eight chain codes;
fig. 10 is a schematic structural view of an embodiment of a measurement system for the size of the shoulder and neck of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain specific embodiments of the present invention with reference to the drawings of the specification. It is obvious that the drawings in the following description are only some examples of the invention, from which other drawings and embodiments can be derived by a person skilled in the art without inventive effort.
for the sake of simplicity, only the parts relevant to the present invention are schematically shown in the drawings, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "one" means not only "only one" but also a case of "more than one".
one embodiment of the present invention, as shown in fig. 1, is a method of measuring a size of a shoulder and neck, comprising:
S100, acquiring a front outline image and a side outline image of a person;
specifically, a front outline and a side outline of the person are obtained. Firstly, a front person image and a side person image of a preset posture are obtained, then the front person image and the side person image are respectively subjected to the same preprocessing, and contours are extracted to obtain a corresponding front contour map and a corresponding side contour map.
the purpose of the preset posture of the character image is to ensure that the information of the user can be comprehensively and correctly acquired at each angle. For example, when the front person image is acquired, the user's heel is closed and both hands are opened at about 45 °, and when the side person image is acquired, the user's hands are close to the root of the thigh. In addition, in order to obtain a front person image and a side person image which meet the requirements, the preset posture includes not only the state requirements of the user to be photographed, but also the requirements for the photographing environment, such as the photographing background, the distance between the camera and the human body, the distance between the camera and the ground, and the like.
secondly, the obtained front person image and the side person image can be selected from the existing pictures, and can also be shot in real time through a camera device. In addition, no matter the existing picture or real-time shooting is carried out, in order to avoid size ratio distortion during shooting, the plane where the lens is located is ensured to be parallel to the vertical plane where the human body is located, and the lens does not need to look up or look down the user during shooting.
S200, positioning front neck characteristic points in the front outline by using a maximum distance method, and positioning side neck characteristic points in the side outline by using an angle method;
specifically, a neck region in the front contour map is segmented based on the human body image, a front neck contour line is determined, and then front neck characteristic points are located by using a maximum distance method. Similarly, the neck region in the side contour map is segmented based on the human body image, the front side neck contour line is determined, and then the front side neck characteristic point is positioned by using the maximum distance method angle method.
s300, calculating the length of the front neck and the length of the side neck according to the front neck characteristic points and the side neck characteristic points respectively;
Specifically, each neck contour line has a neck feature point, so that the front neck feature point and the side neck feature point have two points respectively, the front neck length is calculated according to the front neck feature point, and the side neck length is calculated according to the side neck feature point.
s400, generating neck circumference data through a neural network model by taking the characteristic information of the user, the length of the front neck and the length of the side neck as input parameters;
Specifically, the characteristic information of the user is obtained, wherein the characteristic information is information influencing the three-dimensional size of the user, such as height information, sex, shoe size and the like of the user, which influence the body proportion of the user. And then taking the characteristic information, the length of the front neck and the length of the side neck as input parameters, obtaining the plurality of input parameters by an input layer of the neural network model, and sending the plurality of input parameters to each neuron in the first hidden layer, wherein the number of the neurons in the input layer is the same as that of the input parameters.
each neuron in the first hidden layer trains a plurality of input parameters to obtain first hidden layer output parameters respectively, and the first hidden layer output parameters are sent to each neuron in the next hidden layer respectively. And then, training the neuron in each of the rest hidden layers which are not the first hidden layer by using the hidden layer output parameter of the previous hidden layer as an input parameter to obtain a corresponding hidden layer output parameter, and respectively sending the hidden layer output parameter to each neuron in the next hidden layer until all the hidden layers are trained, and respectively sending the hidden layer output parameter to the output layer by the neuron in the last hidden layer. And the output layer processes the received hidden layer output parameters to obtain neck circumference data.
S500, positioning shoulder characteristic points in the front contour map, and calculating shoulder width data by combining the front neck characteristic points.
Specifically, the shoulder characteristic points in the front profile are positioned through curve fitting or eight-chain codes, the shoulder and neck contour lines are determined according to all the shoulder characteristic points and the neck characteristic points, as shown in fig. 2, the shoulder width data is determined through the shoulder and neck contour lines and the neck width, wherein the neck width is the distance between the two front neck characteristic points.
in this embodiment, a BP neural network is created for data regression by combining the existing machine learning method. And taking the characteristic information, the front neck length and the side neck length as input parameters, using a plurality of hidden layers, and finally outputting data of the collar.
Another embodiment of the present invention is an optimized embodiment of the foregoing embodiment, as shown in fig. 3, the main improvement of this embodiment compared with the foregoing embodiment is that, S200, locating the front neck feature point in the front profile by using the maximum distance method, and locating the side neck feature point in the side profile by using the angle method specifically includes:
s210, detecting the front profile through a profile detection function to obtain left and right extreme points which are respectively left and right hand feature points;
Specifically, the front profile is detected through a profile detection function such as cvFindContours to obtain left and right extreme points, wherein the left and right extreme points are respectively left and right hand feature points.
s220, extracting a front neck contour line, and simultaneously determining the vertex midpoint in the front contour map;
S230, connecting the left-hand feature point, the right-hand feature point and the vertex midpoint to obtain a front neck feature straight line;
s240, traversing the front neck contour line, and calculating the Euclidean distance from a neck contour point on the front neck contour line to the front neck feature straight line, wherein the point with the largest distance is the front neck feature point;
specifically, first, image segmentation is performed on the front profile to determine the neck region, and as shown in fig. 4, the image segmentation based on the golden ratio of the human body divides the human body into 7.5 parts, wherein the neck region is 0.5 to 1.5.
As shown in fig. 5, the contour line of the neck region is obtained as the front neck contour line, the vertex midpoint in the front contour map is determined, the left-hand feature point and the right-hand feature point of the two front hand feature points are respectively connected with the vertex midpoint to obtain two front neck feature straight lines, and the straight lines are marked as α1,α2. Two frontal neck contours are denoted κ1，κ2traversing contour points on the front neck contour, and marking the contour points as beta respectively1，β2wherein beta is1∈κ1，β2∈κ2calculating beta1，β2alpha to the respective side1,α2of Euclidean distance d1,d2. The characteristic point of the front collar part is gamma1＝max(d1),Γ2＝max(d2)。
s250, determining a neck horizontal connecting line with the maximum neck horizontal distance value of the side profile graph as a side neck characteristic straight line through scanning line detection;
s260, determining a side neck characteristic point according to the side neck characteristic straight line and the preset neck circumference measurement inclination angle.
Specifically, the side profile map is subjected to image segmentation to determine a neck region, and then a corresponding side neck contour line is obtained. As shown in fig. 6, the scanning line detects the side neck contour lines, the neck horizontal distance of the neck horizontal connecting line corresponding to each contour line in the side neck contour lines is determined, the neck horizontal connecting line with the largest distance value is the side neck characteristic straight line, and the side neck characteristic points are determined according to the side neck characteristic straight line and the preset neck circumference measurement inclination angle. The intersection point of the side neck characteristic straight line and the neck contour line on the front side of the person is a side neck characteristic point, the side neck characteristic straight line is rotated counterclockwise by a preset neck circumference with the side neck characteristic as the center to measure an inclination angle, for example, 25 degrees to 30 degrees, and then another side neck characteristic point on the neck contour line on the front side of the person is obtained. The preset collar circumference measurement inclination angle can be respectively set according to different clothing requirements.
In the embodiment, the neck characteristic points in the side profile map are positioned by combining the inclination of the collar on the side in the actual use process of the garment, so that the acquired neck characteristic points are ensured to be more suitable for practical application.
another embodiment of the present invention is an optimized embodiment of the above embodiment, as shown in fig. 7, the main improvement of this embodiment compared with the above embodiment is that S500 locates the shoulder feature points in the front contour map, and the calculating the shoulder width data in combination with the front neck feature points specifically includes:
S510, segmenting the front profile map to determine a shoulder area, and extracting a shoulder contour line from the shoulder area;
s520, performing curve fitting on the shoulder contour line to obtain a contour curve;
S530, calculating the curvatures of all contour points on the contour curve, and selecting the contour point with the maximum curvature as a shoulder characteristic point; or positioning the front shoulder characteristic points based on eight-chain codes;
S540 calculates shoulder width data from the frontal neck feature points and shoulder feature points.
specifically, the frontal contour map is subjected to image segmentation to determine the shoulder area, and as shown in fig. 4, the image segmentation based on the golden ratio of the human body divides the human body into 7.5 parts, wherein the shoulder area is the region of-1.5 of the neck contour point.
as shown in FIG. 8, a shoulder contour line is extracted, and a curve is fitted by the least square method at a point X1，X2，X3...XnFunction value y of1,y2,y3...ynobtaining a polynomial p (x) a0+a1x+...+anxkfor determining the value of a under load conditions, the equation for the right-hand side aiK, the partial derivatives are calculated to obtain k +1 equations:
And (3) the equation is collated to obtain:
formula for curvature:the greater the curvature of a certain point of the curve, the greater the degree of curvature of the curve. The contour point with the maximum curvature is the shoulder contour point, shoulder width data are calculated through the front neck characteristic point and the shoulder characteristic point, the shoulder width data are equal to the sum of a shoulder neck contour line and a neck width, the shoulder neck contour line is two contour lines respectively taking the front neck characteristic point and the shoulder characteristic point on the same side as two ends, and the neck width is the distance between the two front neck characteristic points.
In a further embodiment, the shoulder feature points are located using the eight-chain code set forth in Freeman, as shown in fig. 9. The eight-chain code value proposed by Freeman assigns a value to each pixel of the contour in the direction. The eight-chain code theory adopts 0 to 7 eight marks to represent pixel points in eight neighborhoods of a certain pixel point counterclockwise. Therefore, each continuous human body contour line can be represented by eight-chain code values of pixel points on the contour. Taking the analysis of the contour line of the shoulder of a human body as an example, starting from a pixel f0 to f8, wherein the 9 pixels all have the same code value of "0". At the f9 pixel point, the code value changes to "7", so pixels f0 through f8 can be considered as vector a 0. Similarly, the pixels f8 to f9 can be regarded as vectors a1, and the vectors are connected end to form the contour line of the shoulder of the human body. By studying the direction change between adjacent vectors in the shoulder contour line, for example, the change trend of the feature vector of the right shoulder is (0, 7, 0, 7, 0, 0, 7, 0, 7, 7, 7, 0, 0) as shown in fig. 9, 12 feature points are determined by the change trend, and then the 6 th feature point is selected as the feature point of the right shoulder. If the determined characteristic points are even numbers, the characteristic points corresponding to the middle number of the even numbers are taken as shoulder characteristic points, and for example, the 6 th characteristic point in the 12 characteristic points is taken as the corresponding shoulder characteristic point. If the odd number of feature points is determined, the middle feature point is taken as the shoulder feature point, for example, the 5 th feature point in the 11 feature points is the corresponding shoulder feature point. Then, the shoulder characteristic points can be located by traversing and querying the outline of the whole shoulder.
one embodiment of the present invention, as shown in fig. 10, is a shoulder and neck size measurement system 100 comprising:
the outline image acquisition module 110 is used for acquiring a front outline image and a side outline image of a person;
a feature point positioning module 120, which positions the front neck feature points in the front profile acquired by the profile acquisition module 110 by using a maximum distance method, and positions the side neck feature points in the side profile acquired by the profile acquisition module 110 by using an angle method;
the feature point positioning module 120 specifically includes:
a feature point analysis unit 121, which detects the front profile through a profile detection function to obtain left and right extreme points, where the left and right extreme points are left and right hand feature points, respectively;
The contour line extracting unit 122 extracts a front neck contour line and determines the vertex midpoint in the front contour map;
a feature line analyzing unit 123, which connects the feature points of the left and right hands determined by the feature point analyzing unit 121 and the vertex midpoint determined by the contour line extracting unit 122 to obtain a front neck feature straight line;
the feature point analyzing unit 121 traverses the front neck contour line determined by the contour line extracting unit 122, and calculates an euclidean distance from a neck contour point on the front neck contour line to the front neck feature straight line determined by the feature line analyzing unit 123, where a point with the largest distance is a front neck feature point;
The characteristic line analysis unit 123 determines, through scanning line detection, that a neck horizontal connection line of the side profile diagram with the maximum neck horizontal distance value is a side neck characteristic straight line;
the characteristic point analysis unit 121 determines a characteristic point of the side neck according to the characteristic straight line of the side neck determined by the characteristic line analysis unit 123 and a preset neck circumference measurement inclination angle;
a neck analysis module 130, which calculates the front neck length and the side neck length according to the front neck feature points and the side neck feature points, respectively, positioned by the feature point positioning module 120;
The neck circumference calculation module 140 is configured to generate neck circumference data through a neural network model by using the characteristic information of the user, the front neck length and the side neck length obtained by the neck analysis module 130 as input parameters;
a shoulder width calculation module 150 for locating shoulder feature points in the frontal contour map obtained by the contour map obtaining module 110 and calculating shoulder width data in combination with the frontal neck feature points located by the feature point location module 120;
The shoulder width calculation module 150 specifically includes:
an area dividing unit 151 that divides the front profile map to determine a shoulder area and extracts a shoulder contour from the shoulder area;
a curve fitting unit 152 that performs curve fitting on the shoulder contour line obtained by the region dividing unit 151 to obtain a contour curve;
A feature point processing unit 153 configured to calculate curvatures of all contour points on the contour curve obtained by the curve fitting unit 152, and select a contour point with the largest curvature as a shoulder feature point;
A shoulder width calculating unit 154 for calculating shoulder width data from the front neck feature point and the shoulder feature point obtained by the feature point processing unit 153.
The specific operation modes of the modules in this embodiment have been described in detail in the corresponding method embodiments, and thus are not described in detail again.
an embodiment of the invention provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out all or part of the method steps of the first embodiment.
The present invention can implement all or part of the flow in the method of the first embodiment, and can also be implemented by using a computer program to instruct related hardware, where the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments can be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
An embodiment of the present invention further provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program running on the processor, and the processor executes the computer program to implement all or part of the method steps in the first embodiment.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. a method of measuring a size of a shoulder and neck, comprising:
Acquiring a front outline image and a side outline image of a person;
Positioning front neck feature points in the front profile by using a maximum distance method, and positioning side neck feature points in the side profile by using an angle method;
calculating the length of the front neck and the length of the side neck according to the front neck characteristic points and the side neck characteristic points respectively;
generating neck circumference data by using the characteristic information of the user, the length of the front neck and the length of the side neck as input parameters through a neural network model;
and positioning shoulder characteristic points in the front profile map, and calculating shoulder width data by combining the front neck characteristic points.
2. The method of claim 1, wherein locating the frontal neck feature points in the frontal contour map using a maximum distance method specifically comprises:
detecting the front profile graph through a profile detection function to obtain left and right extreme points which are respectively left and right hand feature points;
Extracting a front neck contour line, and simultaneously determining the vertex midpoint in the front contour map;
Connecting the left-hand feature point, the right-hand feature point and the vertex midpoint to obtain a front neck feature straight line;
and traversing the front neck contour line, and calculating the Euclidean distance from the neck contour point on the front neck contour line to the front neck characteristic straight line, wherein the point with the largest distance is the front neck characteristic point.
3. The method of claim 1, wherein locating the lateral neck feature points in the side profile using an angular method specifically comprises:
determining a neck horizontal connecting line with the maximum neck horizontal distance value of the side profile graph as a side neck characteristic straight line through scanning line detection;
and determining the characteristic points of the side neck according to the characteristic straight line of the side neck and the preset neck circumference measurement inclination angle.
4. the method of claim 1, wherein locating shoulder feature points in the frontal contour map and calculating shoulder width data in combination with the frontal neck feature points comprises:
segmenting the front profile map to determine a shoulder area, and extracting a shoulder contour line from the shoulder area;
Performing curve fitting on the shoulder contour line to obtain a contour curve;
calculating the curvatures of all contour points on the contour curve, and selecting the contour point with the largest curvature as a shoulder characteristic point; or the like, or, alternatively,
positioning front shoulder characteristic points based on eight chain codes;
And calculating shoulder width data through the front neck characteristic points and the shoulder characteristic points.
5. A system for measuring a size of a shoulder and neck, comprising:
The outline image acquisition module is used for acquiring a front outline image and a side outline image of a person;
the characteristic point positioning module is used for positioning the front neck characteristic points in the front profile acquired by the profile acquisition module by using a maximum distance method and positioning the side neck characteristic points in the side profile acquired by the profile acquisition module by using an angle method;
the neck analysis module is used for calculating the length of the front neck and the length of the side neck according to the front neck characteristic points and the side neck characteristic points positioned by the characteristic point positioning module respectively;
the neck circumference calculation module is used for generating neck circumference data through a neural network model by taking the characteristic information of the user, the front neck length and the side neck length obtained by the neck analysis module as input parameters;
And the shoulder width calculating module is used for positioning the shoulder characteristic points in the front profile acquired by the profile acquisition module and calculating shoulder width data by combining the front neck characteristic points positioned by the characteristic point positioning module.
6. the system of claim 5, wherein the feature point locating module specifically comprises:
The feature point analysis unit is used for detecting the front profile map through a profile detection function to obtain left and right extreme points which are respectively left and right hand feature points;
The contour line extraction unit is used for extracting the front neck contour line and determining the vertex midpoint in the front contour map;
The characteristic line analysis unit is used for connecting the left-hand and right-hand characteristic points determined by the characteristic point analysis unit with the vertex midpoint determined by the contour line extraction unit to obtain a front neck characteristic straight line;
the feature point analysis unit traverses the front neck contour line determined by the contour line extraction unit, calculates the Euclidean distance from the neck contour point on the front neck contour line to the front neck feature straight line determined by the feature line analysis unit, and the point with the largest distance is the front neck feature point.
7. The system of claim 5, wherein the feature point locating module further comprises:
the characteristic line analysis unit determines a neck horizontal connecting line with the maximum neck horizontal distance value of the side profile graph as a side neck characteristic straight line through scanning line detection;
And the characteristic point analysis unit is used for determining the characteristic points of the side neck according to the characteristic straight line of the side neck determined by the characteristic line analysis unit and the preset neck circumference measurement inclination angle.
8. the system for measuring a shoulder and neck dimension of claim 5, wherein the shoulder width calculation module specifically comprises:
the region segmentation unit is used for segmenting the front profile map to determine a shoulder region and extracting a shoulder contour line from the shoulder region;
The curve fitting unit is used for performing curve fitting on the shoulder contour line obtained by the region segmentation unit to obtain a contour curve;
The characteristic point processing unit is used for calculating the curvatures of all the contour points on the contour curve obtained by the curve fitting unit and selecting the contour point with the maximum curvature as a shoulder characteristic point;
And the shoulder width calculating unit calculates shoulder width data through the front neck characteristic points and the shoulder characteristic points obtained by the characteristic point processing unit.
9. A storage medium having a computer program stored thereon, characterized in that: the computer program, when executed by a processor, implements the method of any of claims 1 to 4.
10. an electronic device comprising a memory and a processor, the memory having stored thereon a computer program that runs on the processor, characterized in that: the processor, when executing the computer program, implements the method of any of claims 1 to 4.
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