CN114998527B - High-accuracy three-dimensional human body surface reconstruction system - Google Patents
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Abstract
The invention discloses a three-dimensional human body surface reconstruction system with high accuracy in the technical field of computer vision, which is characterized in that the related data information of human body images is obtained through direct scanning, the denoising and the characteristic point data extraction are carried out, and the characteristic data separation is carried out after classification, so that the human body data characteristics can be directly provided through photos, and the model reconstruction can be automatically matched with a proper model according to the related characteristic model of the human body, so that the reconstruction calculation amount can be effectively reduced, the time can be saved, meanwhile, the texture map construction is carried out by combining the surface characteristics of the human body, the abundant construction of the model surface details is completed, the advantages of a bat algorithm and a cuckoo algorithm are fully utilized through a mixed optimization algorithm combining the bat algorithm and the cuckoo algorithm, and a human body database query plan which is better than the traditional algorithm is obtained, and a required human body model can be quickly queried in a human body database.
Description
Technical Field
The invention relates to the technical field of computer vision, in particular to a three-dimensional human body surface reconstruction system with high accuracy.
Background
In the field of computer vision, reconstructing a three-dimensional surface of a target object is a very classical problem in the field of computer vision, and at present, three-dimensional model construction methods for a human body are various, for example, a method of using a memory efficient multi-layer GPU data structure to reconstruct a three-dimensional model of the human body, or a method of using a KinectFusion method of a movable volume to reconstruct a three-dimensional model of the human body under the condition of outdoor large scene, or three-dimensional surface reconstruction is performed on the target object by using a plurality of two-dimensional images at different angles, or reconstruction is performed in a laser three-dimensional scanning mode, and the like.
In the process of reconstructing the human body model by adopting the reconstruction modes, more equipment is generally needed to be matched, and a laser three-dimensional scanning method needs a special laser three-dimensional scanning instrument, but the instrument has very high price, is difficult to bear by a personal user, is not suitable for common people, has large reconstruction calculation amount, wastes time and is difficult to reconstruct the accurate three-dimensional human body surface rapidly.
Based on the above, the invention designs a three-dimensional human body surface reconstruction system with high accuracy so as to solve the problems.
Disclosure of Invention
The invention aims to provide a three-dimensional human body surface reconstruction system with high accuracy so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the three-dimensional human body surface reconstruction system with high accuracy comprises an information acquisition unit, wherein the information acquisition unit acquires and inputs acquired three-dimensional human body data information into a preprocessing module for preliminary processing, the preprocessing module is used for denoising preprocessing surface characteristic data to obtain depth information without noise smoothness, the preprocessing module transfers the processed data to a characteristic point extraction module, the characteristic point extraction module is used for screening and extracting main characteristic points of the acquired human body data information, the characteristic point extraction module sends the human body characteristic point extraction data to a data separation module, the data separation module is used for dispersing the extracted and categorized human body characteristic data information, the data separation module inputs the separated data to a central processor, the central processing unit is used for integrating data, the data can be transferred to the central processing unit through the model query extraction module, the human body model construction module can acquire model data from the model query extraction module, the human body model construction module is used for combining models constructed according to human body characteristic data information, the model query extraction module is used for searching and searching models constructed by human bodies in a human body database, the model query extraction module and the human body database can be used for acquiring bidirectional information, the human body database is used for storing human body model data information, the human body database is connected with the information updating module for updating human body data in real time, the central processing unit is connected with the head model reconstruction module, the head model reconstruction module is used for modeling the classified human body head data information, and the head model reconstruction module is connected with the human body model construction module to acquire information.
The human body model building module is connected with the connection processing module to form data intercommunication, the connection processing module is used for carrying out connection processing on the human body model, and the connection processing module is sequentially distributed with the surface processing module, the feature adding module, the model mapping processing module and the model display module, so that display building of the human body feature model can be carried out;
The surface treatment module is used for carrying out surface finishing treatment on the reconstructed human body model, and the characteristic adding module is used for adding main characteristic points contained in the human body; the model mapping processing module is used for re-projecting the obtained human body model onto each image and calibrating the position and the direction, and the model display module is used for displaying and viewing the reconstructed model in an image mode.
The central processing unit is respectively connected with the first model construction module, the second model construction module and the third model construction module and is used for carrying out model construction on the data information classification of each part of the human body; the head model reconstruction module, the first model construction module, the second model construction module and the third model construction module are respectively connected with the information updating module, and model data are obtained from the human body database through the information updating module.
Preferably, the model query extraction module adopts a mixed optimization algorithm combining a bat algorithm and a cuckoo optimization algorithm, and the specific steps are as follows:
S1, traversing a connection tree to obtain a coding sequence L, initializing various parameters of a hybrid optimization algorithm, calculating the fitness value of the bird nest according to an objective function, determining the current optimal bird nest position and the optimal value, reserving the last generation of the optimal bird nest position, updating the position of the bird nest by using an algorithm of LEVY FLIGHT mechanisms to obtain a new bird nest position, and performing out-of-range processing;
wherein, the algorithm of LEVY FLIGHT mechanism is:
in the method, in the process of the invention, And/>Respectively representing the position vectors of the ith nest in the t generation and the t+1th generation, alpha is a step length adjusting factor, and different values can be taken according to different conditions alpha,/>For point-to-point multiplication, le' vy (λ) is a random search path;
S2, calculating an adaptive value of the new bird nest position by using an objective function, comparing the adaptive value with the previous generation, covering the adaptive value of the previous generation when the adaptive value is superior to the adaptive value of the previous generation, updating the corresponding bird nest position, determining the current optimal bird nest position and the optimal value, and then comparing the discovery probability p α of the foreign egg with random numbers R epsilon [0,1] obeying uniform distribution, wherein if R is more than p α, randomly changing the position of the bird nest, thereby obtaining a group of new bird nest positions;
s3, taking the new bird nest position obtained in the previous step as an initial point of a bat algorithm, updating the position of the bird nest by utilizing the bat algorithm to obtain a group of new bird nest positions, then evaluating the fitness value of the bird nest positions, determining the current optimal bird nest position and the optimal value after comparison, and inquiring a plan of a database corresponding to the optimal bird nest position when the termination condition is reached;
wherein, the bat algorithm comprises the following steps:
the method comprises the steps of initializing the population individual number NP, the maximum pulse volume A 0, the maximum pulse rate R 0, the search pulse frequency range [ f min,fmax ], the attenuation coefficient alpha of volume, the enhancement coefficient gamma of search frequency, the search precision epsilon or the maximum iteration number iter_max, then randomly initializing the bat position x i, searching the current optimal solution x * according to the quality of the fitness value, and changing the search pulse frequency, the search speed and the search position of each generation of individuals in the population evolution process according to the following formula:
fi＝fmin+(fmax-fmin)×β
where, β ε [0,1] is a uniformly distributed random number, f i is the frequency of the search pulse of bat i, Representing the speed of bat i at times t and t-1,/>, respectivelyRespectively representing the positions of bat i at the time t and t-1, x * represents the optimal solutions of all the current bats, generating uniformly distributed random numbers rand, randomly perturbing the current optimal solution to generate a new solution if rand is more than r i, performing out-of-range processing on the new solution, accepting the new solution generated in the previous step if rand is less than A i and f (x i)＜f(x*), and updating r i and A i according to the following formula:
And then outputting the global optimal value and the optimal solution.
Preferably, the information acquisition unit comprises a scanning acquisition module and a data input module, wherein the scanning acquisition module and the data input module can respectively perform three-dimensional scanning equipment information input and data direct input.
Preferably, the scanning acquisition module scans the human body image by adopting an infrared camera to obtain image data with clear outline; the data input module is used for inputting the related data information of the human body.
Preferably, the human body database includes first model data, second model data and third model data, which are used for respectively classifying, storing and managing the information of the multiple model data of the human body, and the data stored in the first model data, the second model data and the third model data respectively correspond to the data required to be constructed by the first model construction module, the second model construction module and the third model construction module.
Preferably, the model mapping processing module is configured to re-project the obtained human model onto each image and calibrate the position and direction of the human model, and then project color and texture information in the image onto a model triangulated vertex by using a texture mapping function in OpenGL to complete texture mapping and material restoration, so as to obtain the three-dimensional model after surface treatment.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the invention, the related data information of the human body image is obtained through direct scanning, denoising and extraction of feature point data are carried out, and the feature data after classification are separated, so that the features of the human body data can be directly provided through a photo, and an appropriate model can be automatically matched according to the related feature model of the human body without reconstructing the model again, so that the reconstruction calculation amount can be effectively reduced, the time can be saved, and meanwhile, the texture map construction is carried out by combining the surface features of the human body, so that the abundant construction of the surface details of the model is completed, and the three-dimensional reconstruction of the human body can be rapidly and accurately realized;
2. according to the invention, the related data information of the human body image is acquired through the information acquisition unit, the denoising processing is carried out through the preprocessing module to obtain smooth data information, the human body characteristic data information is disassembled and classified through the characteristic point extraction module, meanwhile, the classified related data are separated, the reconstruction of the head of the human body is carried out through the head reconstruction module, and the corresponding first model data, second model data and third model data are matched according to the first model construction module, the second model construction module and the third model construction module, so that a reconstructed model can be quickly obtained, any one item or a plurality of items can be reconstructed through the head model reconstruction module, the first model construction module, the second model construction module and the third model construction module, and a history model can be selectively matched, so that the model has reasonable selectivity and diversity processing, is simple and easy to implement, and the running speed is improved;
3. After the reconstruction of the human body model is completed through the head model reconstruction module, the first model construction module, the second model construction module and the third model construction module, the reconstructed characteristic data of each part of human body model can be directly sent to the information updating module, and the reconstructed model information can be sent to the human body database for storage through the information updating module, so that new human body model data can be obtained, real-time updating of the human body database can be completed in the process of reconstructing the model, and human body model diversification is increased.
According to the invention, by adopting the mixed optimization algorithm combining the bat algorithm and the cuckoo optimization algorithm, the advantages of the bat algorithm and the cuckoo algorithm can be fully utilized, a human body database query plan better than that of the traditional algorithm is obtained, a required human body model can be quickly queried in the human body database, and a user can conveniently and quickly construct the model.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a schematic flow chart of embodiment 1 of the present invention;
FIG. 3 is a schematic flow chart of embodiment 2 of the present invention;
fig. 4 is a schematic flow chart of embodiment 3 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-4, the present invention provides a technical solution: the three-dimensional human body surface reconstruction system with high accuracy comprises an information acquisition unit, wherein the information acquisition unit acquires and inputs acquired three-dimensional human body data information into a preprocessing module for preliminary processing, the information acquisition unit comprises a scanning acquisition module and a data input module, and the scanning acquisition module and the data input module can respectively perform three-dimensional scanning equipment information input and data direct input; the scanning acquisition module scans the human body image by adopting an infrared camera to obtain image data with clear outline; the data input module is used for inputting the related data information of the human body.
The preprocessing module is used for carrying out denoising preprocessing on surface characteristic data to obtain depth information without noise smoothness, the preprocessing module transfers processed data to the characteristic point extraction module, the characteristic point extraction module is used for screening and extracting main characteristic points of acquired human body data information, the characteristic point extraction module is used for sending human body characteristic point extraction data to the data separation module, the data separation module is used for dispersing the extracted and classified human body characteristic data information, the data separation module inputs the separated data to the central processing unit, the central processing unit carries out data integration, the model inquiry extraction module can call the extracted data to the central processing unit to obtain model data from the model inquiry extraction module, the human body model construction module can be used for combining models constructed according to the human body characteristic data information, the model inquiry extraction module and the human body database can carry out bidirectional information acquisition, the human body database is used for storing human body model data information, the human body database comprises first model data, second model data and third model data, the model information is stored in the first model data, the third model data is stored in the model inquiry extraction module and the third model data is stored in the model inquiry extraction module, the model inquiry extraction module is used for carrying out search and the model construction of model constructed according to the human body model constructed by the model information.
The human body database is connected with the information updating module to update human body data in real time, the central processing unit is connected with the head model reconstruction module, the head model reconstruction module is used for modeling the head data information of the human body after classification, and the head model reconstruction module is connected with the human body model construction module to acquire information.
The human body model building module is connected with the connection processing module to form data intercommunication, the connection processing module is used for carrying out connection processing on the human body model, and the connection processing module is sequentially distributed with the surface processing module, the feature adding module, the model mapping processing module and the model display module, so that display building of the human body feature model can be carried out;
The surface treatment module is used for carrying out surface finishing treatment on the reconstructed human body model, and the characteristic adding module is used for adding main characteristic points contained in the human body; the model mapping processing module is used for re-projecting the obtained human body model onto each image and calibrating the position and the direction, and the model display module is used for displaying and viewing the reconstructed model in an image mode.
The model mapping processing module is used for re-projecting the obtained human body model onto each image and calibrating the positions and directions, and then projecting color and texture information in the image onto a model triangulated vertex by utilizing a texture mapping function in OpenGL to complete texture mapping and material restoration, so as to obtain the three-dimensional model after surface treatment.
The central processing unit is respectively connected with the first model construction module, the second model construction module and the third model construction module and is used for carrying out model construction on the data information classification of each part of the human body; the head model reconstruction module, the first model construction module, the second model construction module and the third model construction module are respectively connected with the information updating module, and model data are obtained from the human body database through the information updating module.
The model query extraction module adopts a mixed optimization algorithm combining a bat algorithm and a cuckoo optimization algorithm, and comprises the following specific steps:
S1, traversing a connection tree to obtain a coding sequence L, initializing various parameters of a hybrid optimization algorithm, calculating the fitness value of the bird nest according to an objective function, determining the current optimal bird nest position and the optimal value, reserving the last generation of the optimal bird nest position, updating the position of the bird nest by using an algorithm of LEVY FLIGHT mechanisms to obtain a new bird nest position, and performing out-of-range processing;
wherein, the algorithm of LEVY FLIGHT mechanism is:
in the method, in the process of the invention, And/>Respectively representing the position vectors of the ith nest in the t generation and the t+1th generation, alpha is a step length adjusting factor, and different values can be taken according to different conditions alpha,/>For point-to-point multiplication, le' vy (λ) is a random search path;
S2, calculating an adaptive value of the new bird nest position by using an objective function, comparing the adaptive value with the previous generation, covering the adaptive value of the previous generation when the adaptive value is superior to the adaptive value of the previous generation, updating the corresponding bird nest position, determining the current optimal bird nest position and the optimal value, and then comparing the discovery probability p α of the foreign egg with random numbers R epsilon [0,1] obeying uniform distribution, wherein if R is more than p α, randomly changing the position of the bird nest, thereby obtaining a group of new bird nest positions;
s3, taking the new bird nest position obtained in the previous step as an initial point of a bat algorithm, updating the position of the bird nest by utilizing the bat algorithm to obtain a group of new bird nest positions, then evaluating the fitness value of the bird nest positions, determining the current optimal bird nest position and the optimal value after comparison, and inquiring a plan of a database corresponding to the optimal bird nest position when the termination condition is reached;
wherein, the bat algorithm comprises the following steps:
the method comprises the steps of initializing the population individual number NP, the maximum pulse volume A 0, the maximum pulse rate R 0, the search pulse frequency range [ f min,fmax ], the attenuation coefficient alpha of volume, the enhancement coefficient gamma of search frequency, the search precision epsilon or the maximum iteration number iter_max, then randomly initializing the bat position x i, searching the current optimal solution x * according to the quality of the fitness value, and changing the search pulse frequency, the search speed and the search position of each generation of individuals in the population evolution process according to the following formula:
fi＝fmin+(fmax-fmin)×β
where, β ε [0,1] is a uniformly distributed random number, f i is the frequency of the search pulse of bat i, Representing the speed of bat i at times t and t-1,/>, respectivelyRespectively representing the positions of bat i at the time t and t-1, x * represents the optimal solutions of all the current bats, generating uniformly distributed random numbers rand, randomly perturbing the current optimal solution to generate a new solution if rand is more than r i, performing out-of-range processing on the new solution, accepting the new solution generated in the previous step if rand is less than A i and f (x i)＜f(x*), and updating r i and A i according to the following formula:
And then outputting the global optimal value and the optimal solution.
The three-dimensional human body surface reconstruction system adopts the following steps: the method comprises the steps of directly acquiring relevant data information of a human body image through an information acquisition unit, transmitting the relevant data information to a preprocessing module, carrying out denoising processing through the preprocessing module, extracting the data information of the human body characteristic points through a characteristic point extracting module, separating the classified characteristic data through a data separating module, directly transmitting the separated characteristic data to a model query extracting module through the transmission of a central processing unit, directly matching a corresponding human body model from a human body database at the moment by the model query extracting module, directly constructing the model by a human body model constructing module, carrying out model reconstruction by a connecting processing module, carrying out surface processing on the model by a surface processing module, adding relevant human body characteristics by a characteristic adding module, and finally carrying out model information map enriching processing by a model map processing module, and carrying out model display by a model display module;
The technical effects achieved by adopting the steps are as follows: the method has the advantages that the related data information of the human body image is obtained through direct scanning, denoising and characteristic point data extraction are carried out, and characteristic data separation after classification is carried out, so that the human body data characteristics can be directly provided through photos, an appropriate model can be automatically matched according to the related characteristic model of the human body, reconstruction of the model is not needed, the reconstruction calculation amount can be effectively reduced, time can be saved, meanwhile, texture mapping construction is carried out by combining the surface characteristics of the human body, the abundant construction of the surface details of the model is completed, reconstruction of multiple parts of the model is not needed, and therefore the three-dimensional human body reconstruction can be rapidly and accurately realized;
The steps of selectively matching the corresponding stage models are: the method comprises the steps of directly acquiring related data information of a human body image through an information acquisition unit, transmitting the related data information to a preprocessing module, carrying out denoising processing through the preprocessing module, extracting the data information of the human body characteristic points through a characteristic point extraction module, separating the classified characteristic data through a data separation module, transmitting the classified characteristic data through a central processing unit, reconstructing a head model through a head model reconstruction module, constructing a model through a first model construction module, a second model construction module and a third model construction module, simultaneously, matching the model in a corresponding human body database through a model query extraction module, directly transmitting the model to a human body model construction module for construction processing, carrying out model reconstruction through a connection processing module, carrying out surface processing on the model through a surface processing module, adding human body related characteristics through a characteristic adding module, and finally carrying out model information map rich processing through a model map processing module, and carrying out model display through a model display module.
The technical effects achieved by adopting the steps are as follows: the method comprises the steps of acquiring related data information of a human body image through an information acquisition unit, denoising through a preprocessing module to obtain smooth data information, carrying out disassembly and classification on the human body characteristic data information through a characteristic point extraction module, separating the classified related data, reconstructing the head of the human body through a head reconstruction module, matching corresponding first model data, second model data and third model data according to a first model construction module, a second model construction module and a third model construction module, quickly obtaining a reconstructed model, reconstructing any one item or a plurality of items through the head model reconstruction module, the first model construction module, the second model construction module and the third model construction module, and selectively matching a history model, so that the model has reasonable selectivity and diversity, is simple and easy to implement, and improves the running speed.
In the first embodiment, the information acquisition unit directly acquires the related data information of the human body image, the related data information is transmitted to the preprocessing module, the preprocessing module performs denoising processing, the feature point extraction module extracts the data information of the human body feature points, the data separation module separates the classified feature data, the head is subjected to reconstruction processing according to the head features, and the separated model is subjected to construction connection processing, wherein before the step, whether the human body model data with the corresponding features exist in the human body database is judged, if so, the separated model is subjected to construction connection processing, then the model is subjected to surface processing, secondary modification and addition of the features are performed, and then the model surface mapping processing is performed.
In the second embodiment, the difference from the first embodiment is that the reconstruction processing is performed on the head according to the head characteristics, and the reconstruction processing is performed on the other three data according to the characteristics, and then the connection processing is performed on the separation model.
In the third embodiment, the part different from the second embodiment is that it is no longer necessary to determine whether the human body model data with corresponding features exists in the human body database, and after the reconstruction processing is performed on the head according to the head features and the reconstruction processing is performed on the other three parts of data according to the features, the reconstructed human body information is uploaded to the database.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. The three-dimensional human body surface reconstruction system with high accuracy comprises an information acquisition unit, wherein the information acquisition unit acquires and inputs acquired three-dimensional human body data information into a preprocessing module for preliminary processing, the preprocessing module is used for denoising preprocessing surface characteristic data to obtain depth information without noise smoothness, the preprocessing module transfers the processed data to a characteristic point extraction module, the characteristic point extraction module is used for screening and extracting main characteristic points of the acquired human body data information, the characteristic point extraction module sends the human body characteristic point extraction data to a data separation module, the data separation module is used for dispersing the extracted and categorized human body characteristic data information, the data separation module inputs the separated data to a central processor, the central processing unit is used for integrating data, the data can be transferred to the central processing unit through the model query extraction module, the human body model construction module can acquire model data from the model query extraction module, the human body model construction module is used for combining models constructed according to human body characteristic data information, the model query extraction module is used for searching and searching models constructed by human bodies in a human body database, the model query extraction module and the human body database can be used for acquiring bidirectional information, the human body database is used for storing human body model data information, the human body database is connected with the information updating module for updating human body data in real time, the central processing unit is connected with the head model reconstruction module, the head model reconstruction module is used for modeling the classified human body head data information, the head model reconstruction module is connected with the human body model construction module to acquire information;
The human body model building module is connected with the connection processing module to form data intercommunication, the connection processing module is used for carrying out connection processing on the human body model, and the connection processing module is sequentially distributed with the surface processing module, the feature adding module, the model mapping processing module and the model display module, so that display building of the human body feature model can be carried out;
the surface treatment module is used for carrying out surface finishing treatment on the reconstructed human body model, and the characteristic adding module is used for adding main characteristic points contained in the human body; the model mapping processing module is used for re-projecting the obtained human body model onto each image and calibrating the position and the direction, and the model display module is used for displaying and viewing the reconstructed model in an image mode;
the central processing unit is respectively connected with the first model construction module, the second model construction module and the third model construction module and is used for carrying out model construction on the data information classification of each part of the human body; the head model reconstruction module, the first model construction module, the second model construction module and the third model construction module are respectively connected with the information updating module, and model data are obtained from the human body database through the information updating module.
2. The high-accuracy three-dimensional human body surface reconstruction system according to claim 1, wherein the model query extraction module adopts a hybrid optimization algorithm combining a bat algorithm and a cuckoo optimization algorithm, and comprises the following specific steps:
S1, traversing a connection tree to obtain a coding sequence L, initializing various parameters of a hybrid optimization algorithm, calculating the fitness value of the bird nest according to an objective function, determining the current optimal bird nest position and the optimal value, reserving the last generation of the optimal bird nest position, updating the position of the bird nest by using an algorithm of LEVY FLIGHT mechanisms to obtain a new bird nest position, and performing out-of-range processing;
wherein, the algorithm of LEVY FLIGHT mechanism is:
in the method, in the process of the invention, And/>Respectively representing the position vectors of the ith nest in the t generation and the t+1th generation, alpha is a step length adjusting factor, and different values can be taken according to different conditions alpha,/>For point-to-point multiplication, le' vy (λ) is a random search path;
S2, calculating an adaptive value of the new bird nest position by using an objective function, comparing the adaptive value with the previous generation, covering the adaptive value of the previous generation when the adaptive value is superior to the adaptive value of the previous generation, updating the corresponding bird nest position, determining the current optimal bird nest position and the optimal value, and then comparing the discovery probability p α of the foreign egg with random numbers R epsilon [0,1] obeying uniform distribution, wherein if R is more than p α, randomly changing the position of the bird nest, thereby obtaining a group of new bird nest positions;
s3, taking the new bird nest position obtained in the previous step as an initial point of a bat algorithm, updating the position of the bird nest by utilizing the bat algorithm to obtain a group of new bird nest positions, then evaluating the fitness value of the bird nest positions, determining the current optimal bird nest position and the optimal value after comparison, and inquiring a plan of a database corresponding to the optimal bird nest position when the termination condition is reached;
wherein, the bat algorithm comprises the following steps:
the method comprises the steps of initializing the population individual number NP, the maximum pulse volume A 0, the maximum pulse rate R 0, the search pulse frequency range [ f min,fmax ], the attenuation coefficient alpha of volume, the enhancement coefficient gamma of search frequency, the search precision epsilon or the maximum iteration number iter_max, then randomly initializing the bat position x i, searching the current optimal solution x * according to the quality of the fitness value, and changing the search pulse frequency, the search speed and the search position of each generation of individuals in the population evolution process according to the following formula:
fi＝fmin+(fmax-fmin)×β
where, β ε [0,1] is a uniformly distributed random number, f i is the frequency of the search pulse of bat i, Representing the speed of bat i at times t and t-1,/>, respectivelyRespectively representing the positions of bat i at the time t and t-1, x * represents the optimal solutions of all the current bats, generating uniformly distributed random numbers rand, randomly perturbing the current optimal solution to generate a new solution if rand is more than r i, performing out-of-range processing on the new solution, accepting the new solution generated in the previous step if rand is less than A i and f (x i)＜f(x*), and updating r i and A i according to the following formula:
ri t+1＝R0[1-exp(-λt)]
And then outputting the global optimal value and the optimal solution.
3. The high-accuracy three-dimensional human body surface reconstruction system according to claim 1, wherein the information acquisition unit comprises a scanning acquisition module and a data input module, and the scanning acquisition module and the data input module can respectively perform three-dimensional scanning equipment information input and data direct input.
4. A three-dimensional human body surface reconstruction system with high accuracy according to claim 3, wherein the scanning acquisition module scans human body images by using an infrared camera to obtain image data with clear outline; the data input module is used for inputting the related data information of the human body.
5. The three-dimensional human body surface reconstruction system with high accuracy according to claim 1, wherein the human body database comprises first model data, second model data and third model data, which are used for respectively classifying, storing and managing a plurality of model data information of the human body, and the data stored in the first model data, the second model data and the third model data respectively correspond to the data required to be constructed by the first model construction module, the second model construction module and the third model construction module.
6. The system of claim 1, wherein the model mapping processing module is configured to re-project the obtained human model onto each image and calibrate the position and direction of the human model, and then project color and texture information in the image onto the triangulated vertices of the model by using the texture mapping function in OpenGL to complete texture mapping and material restoration, so as to obtain the three-dimensional model after surface treatment.
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