Video conference implementation method and device for beauty education and computer storage medium

Abstract

The invention discloses a video conference implementation method and device for beauty education and a computer storage medium, which realize seamless integration of video conferences in beauty education by performing intelligent layout on conference windows and teaching contents and synchronizing audio and video data of the video conferences and operation data of the teaching contents in real time, overcome geographical restrictions and improve the efficiency of the beauty education. The coverage range of high-quality educational resources is expanded, meanwhile, each file version in the beauty education teaching process is updated and stored, changes in each step in the creation process can be tracked and compared, reflection and improvement of the creation process are facilitated, understanding of artistic creation is deepened, and therefore improvement on layout implementation and file version management is facilitated, and the artistic creation efficiency is improved. And the teaching efficiency and quality of beauty education are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aesthetic education, and particularly to a method, device, and computer storage medium for realizing a video conference for aesthetic education. Background Art

[0002] Currently, intelligent interactive large screens have been used for teaching in the field of aesthetic education, but most systems are limited to simple content display and basic interaction functions. In the prior art, some systems support video conferencing functions, but they are often independent modules and lack deep integration with teaching content. At the same time, although some systems support multi-user collaboration, their functions in image processing and 3D operations are relatively limited. In addition, existing systems also have deficiencies in file version management and it is difficult to effectively track and manage the creative iterations in the aesthetic education teaching process. Summary of the Invention

[0003] The technical problem to be solved by the present invention is: to provide a method, device, and computer storage medium for realizing a video conference for aesthetic education, which can improve the teaching efficiency and quality of aesthetic education.

[0004] To solve the above technical problem, a technical solution adopted by the present invention is:

[0005] A method for realizing a video conference for aesthetic education includes the steps of:

[0006] Receiving a video conference initiation request;

[0007] Creating a corresponding meeting room according to the video conference initiation request, and intelligently arranging the meeting window and the teaching content interface;

[0008] Real-time synchronizing the audio and video data of the video conference and the operation data of the teaching content;

[0009] Updating and saving each file version generated during the aesthetic education teaching process.

[0010] To solve the above technical problem, another technical solution adopted by the present invention is:

[0011] A device for realizing a video conference for aesthetic education includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above method for realizing a video conference for aesthetic education are implemented.

[0012] To solve the above technical problem, another technical solution adopted by the present invention is:

[0013] A computer-readable storage medium stores computer program instructions thereon. When the computer program instructions are executed by a processor, the steps of the above method for implementing a video conference for aesthetic education are realized.

[0014] The beneficial effects of the present invention are as follows: In the implementation of a video conference for aesthetic education, by intelligently arranging the conference window and teaching content, and synchronizing the audio and video data of the video conference and the operation data of the teaching content in real time, seamless integration of the video conference in aesthetic education is achieved, overcoming geographical restrictions, expanding the coverage of high-quality educational resources. At the same time, by updating and saving each file version in the teaching process of aesthetic education, it is possible to track and compare every change in the creation process, which is beneficial to reflecting on and improving the creation process and deepening the understanding of artistic creation. Therefore, the improvements in layout implementation and file version management greatly improve the teaching efficiency and quality of aesthetic education. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flowchart of the steps of a method for implementing a video conference for aesthetic education according to an embodiment of the present invention;

[0016] Figure 2 It is a schematic structural diagram of a device for implementing a video conference for aesthetic education according to an embodiment of the present invention;

[0017] Figure 3 It is a business flowchart of the method for implementing a video conference for aesthetic education according to an embodiment of the present invention;

[0018] Figure 4 It is a schematic structural diagram of a system for implementing a video conference for aesthetic education according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To describe in detail the technical content, achieved objectives and effects of the present invention, the following is described in conjunction with the embodiments and with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , a method for implementing a video conference for aesthetic education, includes the steps of:

[0021] Receiving a video conference initiation request;

[0022] Creating a corresponding meeting room according to the video conference initiation request, and intelligently arranging the conference window and the teaching content interface;

[0023] Synchronizing the audio and video data of the video conference and the operation data of the teaching content in real time;

[0024] Updating and saving each file version generated in the teaching process of aesthetic education.

[0025] As can be seen from the above description, the beneficial effects of the present invention are as follows: In the implementation of video conferencing for aesthetic education, by intelligently arranging the conference window and teaching content, and synchronizing the audio and video data of the video conference and the operation data of the teaching content in real time, seamless integration of video conferencing in aesthetic education is achieved, overcoming geographical limitations, expanding the coverage of high-quality educational resources. At the same time, by updating and saving each file version in the teaching process of aesthetic education, every step change in the creation process can be tracked and compared, which is conducive to reflecting on and improving the creation process and deepening the understanding of artistic creation. Therefore, the improvements in layout implementation and file version management greatly improve the teaching efficiency and quality of aesthetic education.

[0026] Further, before the real-time synchronization of the audio and video data of the video conference and the operation data of the teaching content, the following steps are also included:

[0027] Judge whether a multi-user collaboration request is received. If not, execute the step of real-time synchronizing the audio and video data of the video conference and the operation data of the teaching content;

[0028] If so, start the multi-user collaboration function and synchronize the operation data of each user.

[0029] As can be seen from the above description, through the multi-user collaboration function, real-time interaction and co-creation between teachers and students and among students are promoted, and the teamwork ability and innovative thinking of students are cultivated.

[0030] Further, after synchronizing the operation data of each user, the following steps are also included:

[0031] Judge whether an image processing request is received. If so, perform image editing operations according to the image processing request;

[0032] If not, judge whether an image 3D operation request is received. If so, perform 3D model operations according to the image 3D operation request;

[0033] If not, execute the step of updating and saving each file version generated in the teaching process of aesthetic education.

[0034] As can be seen from the above description, by judging whether an image processing request and an image 3D operation request are received and performing corresponding operations respectively, corresponding operations can be executed according to specific requests, better meeting the functional requirements under multi-user collaboration and improving the flexibility in image processing and 3D model operations.

[0035] Further, the updating and saving of each file version generated in the teaching process of aesthetic education includes:

[0036] Update and save each node of each user's operation in the teaching process of aesthetic education;

[0037] Generate a corresponding version identifier for each node;

[0038] Construct a version tree based on all the generated version identifiers and record the relationships between the versions.

[0039] As can be seen from the above description, by automatically saving each node of the user's operation, generating corresponding identifiers for each node, and constructing a version tree to record the relationships between the versions, the flexibility of the user to view each version can be improved, and the creative iteration in the aesthetic education teaching process can be effectively tracked and managed.

[0040] Furthermore, the image editing operation includes:

[0041] Implement multi-scale feature extraction using an improved multi-scale feature extraction algorithm;

[0042] The extracted multi-scale features are expressed as:

[0043] L(x,y,σ) = G(x,y,kσ) - G(x,y,σ)

[0044]

[0045] where x represents the horizontal coordinate position in the image plane, y represents the vertical coordinate position in the image plane, σ represents the standard deviation of the Gaussian function, k represents the scale factor, and G() represents the Gaussian function;

[0046] Perform localization optimization on the extracted multi-scale features:

[0047]

[0048] where D represents the scale space difference and X represents the multi-scale feature vector;

[0049] Perform image editing operations based on the multi-scale features after localization optimization.

[0050] As can be seen from the above description, by using an improved multi-scale feature extraction algorithm to extract image features in the image editing operation and performing localization optimization on the extracted multi-scale features, it can be better applied to image processing in scenarios of seamless integration and real-time interaction between video conferencing and teaching content, and improve the flexibility of image processing.

[0051] Furthermore, the image editing operation includes:

[0052] Perform artistic style feature extraction based on an improved Gabor filter:

[0053]

[0054] Wherein, x' and y' represent the positions in the new coordinate system after the rotation of the original coordinates (x, y), f represents the center frequency, θ represents the direction angle, and σ x and σ y represent the standard deviations of the Gaussian envelope;

[0055] Perform image editing operations according to the extracted artistic style features.

[0056] As can be seen from the above description, the extraction of artistic style features is realized through an improved Gabor filter, which can be better applied to image processing in the scenarios of seamless integration and real-time interaction of video conferencing and teaching content, and improve the flexibility of image processing.

[0057] Furthermore, the intelligent layout of the conference window and the teaching content interface includes:

[0058] Map the 3D points obtained in the video conference to the 2D plane through projective transformation, and match to obtain the corresponding 2D projection points;

[0059] Fuse the 2D projection points with the teaching content interface to realize the intelligent layout of the conference window and the teaching content interface.

[0060] Furthermore, the matching to obtain the corresponding 2D projection points includes:

[0061] Map the 3D points to the 2D plane through the projective transformation matrix to obtain the corresponding projection points;

[0062] Calculate the reprojection error between the projection points and the actual observation points;

[0063] Set an adaptive error threshold, and judge whether the reprojection error is less than the adaptive error threshold. If so, determine the projection points as the corresponding 2D projection points;

[0064] If not, adopt an improved LM algorithm for adaptive iteration, dynamically adjust the adaptive error threshold according to the number of iterations, and return to the step of judging whether the reprojection error is less than the adaptive error threshold.

[0065] As can be seen from the above description, the matching of 3D-2D feature points is realized by using an optimized feature point mapping algorithm, which can improve the accuracy of the 3D-2D feature point correspondence relationship, optimize the camera pose estimation result, enhance the real-time performance through an adaptive strategy, ensure the stability of the system in practical applications, introduce an adaptive error threshold, which can be dynamically adjusted according to the number of iterations, and adopt an improved LM algorithm to accelerate convergence, thereby realizing dynamic precision control and ensuring the balance of precision and efficiency.

[0066] Please refer to Figure 2, A video conferencing implementation device for aesthetic education, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned video conferencing implementation method for aesthetic education are realized.

[0067] A computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the steps of the above-mentioned video conferencing implementation method for aesthetic education are realized.

[0068] The above-mentioned video conferencing implementation method, device, and computer storage medium for aesthetic education can be applicable to the scenario of video teaching in the process of aesthetic education, which will be described below through specific implementation manners:

[0069] In an alternative implementation manner, as Figure 1 shown, a video conferencing implementation method for aesthetic education includes the steps of:

[0070] Receiving a video conferencing initiation request;

[0071] Creating a corresponding meeting room according to the video conferencing initiation request, and intelligently arranging the meeting window and the teaching content interface. Specifically, when implementing, the video conferencing API can be called to create a virtual meeting room;

[0072] Real-time synchronizing the audio and video data of the video conference and the operation data of the teaching content;

[0073] Updating and saving each file version generated during the aesthetic education teaching process;

[0074] Specifically, as Figure 3 shown, before the step of real-time synchronizing the audio and video data of the video conference and the operation data of the teaching content, the steps further include:

[0075] Judging whether a multi-user collaboration request is received. If not, then executing the step of real-time synchronizing the audio and video data of the video conference and the operation data of the teaching content;

[0076] If so, then starting the multi-user collaboration function and synchronizing the operation data of each user;

[0077] When performing multi-user collaboration operations, the operation permissions of multiple users can be identified and authorized, a unique operation identifier can be assigned to each user, and the operation data of each user, including information such as mouse movement, clicks, and drawing, can be synchronized in real time. A conflict detection algorithm can be used to solve the conflicts that may occur when multiple users operate simultaneously.

[0078] After synchronizing the operation data of each user, the steps further include:

[0079] Determine whether an image processing request is received. If so, perform an image editing operation according to the image processing request. Specifically, when implemented, image processing can be achieved by calling an image processing module;

[0080] If not, determine whether an image 3D operation request is received. If so, perform a 3D model operation according to the image 3D operation request. Specifically, when implemented, 3D operations can be executed by calling a 3D operation module;

[0081] If not, then execute the step of updating and saving each file version generated during the aesthetic education and teaching process.

[0082] In another alternative embodiment, the updating and saving of each file version generated during the aesthetic education and teaching process includes:

[0083] Update and save each node of each user operation during the aesthetic education and teaching process;

[0084] Generate a corresponding version identifier for each node;

[0085] Construct a version tree based on all the generated version identifiers to record the relationships between various versions.

[0086] In another alternative embodiment, important nodes of user operations can be stored and a version tree can be constructed. In this embodiment, by storing each important node during the user operation process and generating a unique version identifier to record the relationships between various versions, a version comparison and rollback function can be provided, allowing users to view the differences between different versions and select to restore to a specific version, which can effectively track and manage the creative iterations during the aesthetic education and teaching process.

[0087] In another alternative embodiment, the image editing operation includes:

[0088] Implement multi-scale feature extraction using an improved multi-scale feature extraction algorithm;

[0089] The extracted multi-scale features are expressed as:

[0090] L(x,y,σ)=G(x,y,kσ)-G(x,y,σ)

[0091]

[0092] where x represents the horizontal coordinate position in the image plane, y represents the vertical coordinate position in the image plane, σ represents the standard deviation of the Gaussian function, k represents the scale factor, and G() represents the Gaussian function;

[0093] Perform positioning optimization on the extracted multi-scale features:

[0094]

[0095] In the formula, D represents the scale space difference, and X represents the multi-scale feature vector;

[0096] Perform image editing operations based on the multi-scale features optimized by localization.

[0097] In another alternative embodiment, the image editing operations include:

[0098] Extract artistic style features based on an improved Gabor filter:

[0099]

[0100] In the formula, x' and y' represent the positions in the new coordinate system after the rotation of the original coordinates (x, y), f represents the central frequency, θ represents the direction angle, σ x and σ y represent the standard deviation of the Gaussian envelope;

[0101] Perform image editing operations according to the extracted artistic style features.

[0102] In another alternative embodiment, the 3D model operations include:

[0103] Implement 3D rotation using an optimized quaternion rotation algorithm. Specifically, when implementing:

[0104] The quaternion is expressed as:

[0105]

[0106] In the formula, θ is the rotation angle, represents the unit vector of the rotation axis;

[0107] The rotation matrix conversion process is as follows:

[0108] For any point P(x, y, z) in space, represent it in the form of a column vector;

[0109] The rotated point P' is obtained through matrix multiplication:

[0110] x' = (1 - 2y 2 - 2z 2 )x + (2xy - 2wz)y + (2xz + 2wy)z

[0111] y' = (2xy + 2wz)x + (1 - 2x 2 - 2z 2 )y + (2yz - 2wx)z

[0112] z' = (2xz - 2wy)x + (2yz + 2wx)y + (1 - 2x 2 - 2y 2 )z

[0113] Where (x', y', z') are the new coordinates of point P after rotation, and w, x, y, z are the components of the unit quaternion; the rotation matrix R ensures the orthogonality of the rotation transformation, that is, it keeps the distance and angle unchanged:

[0114]

[0115] Where q = [w, x, y, z] is the unit quaternion; w represents the real part (scalar part) of the quaternion, equal to cos(θ / 2), where θ is the rotation angle; x represents the first component of the imaginary part of the quaternion, equal to the x component of the unit vector of the rotation axis multiplied by sin(θ / 2); y represents the second component of the imaginary part of the quaternion, equal to the y component of the unit vector of the rotation axis multiplied by sin(θ / 2); z represents the third component of the imaginary part of the quaternion, equal to the z component of the unit vector of the rotation axis multiplied by sin(θ / 2); these four components together describe the rotation transformation in three-dimensional space, where w determines the rotation angle, and x, y, z determine the direction of the rotation axis.

[0116] In another alternative embodiment, an improved perspective interpolation algorithm is also adopted when performing 3D model operations:

[0117] The optimized spherical linear interpolation is expressed as:

[0118]

[0119] The result of SLERP interpolation is to obtain a new unit quaternion q = [w, x, y, z]: This quaternion represents a certain rotation state between q1 and q2, and the specific meaning is as follows:

[0120] When t = 0, the result is equal to q1;

[0121] When t = 1, the result is equal to q2;

[0122] When t = 0.5, the result is the "middle" rotation between q1 and q2;

[0123] Other t values will obtain rotations at corresponding proportional positions, where t ∈ [0, 1] is the interpolation parameter, and q1, q2 are the starting and ending quaternions.

[0124] In another alternative embodiment, the intelligent layout of the conference window and the teaching content interface includes:

[0125] Mapping the 3D points obtained in the video conference to the 2D plane through projection transformation to match the corresponding 2D projection points;

[0126] Fuse the 2D projection points with the teaching content interface to achieve the intelligent layout of the meeting window and the teaching content interface.

[0127] Specifically, the matching to obtain the corresponding 2D projection points includes:

[0128] Map the 3D points to the 2D plane through the projection transformation matrix to obtain the corresponding projection points;

[0129] Calculate the reprojection error between the projection points and the actual observation points;

[0130] Set an adaptive error threshold, and judge whether the reprojection error is less than the adaptive error threshold. If so, determine the projection points as the corresponding 2D projection points;

[0131] If not, use the improved LM algorithm for adaptive iteration, dynamically adjust the adaptive error threshold according to the number of iterations, and return to the step of judging whether the reprojection error is less than the adaptive error threshold;

[0132] When specifically implemented:

[0133] The projection transformation matrix is: Wherein, K is the camera internal parameter matrix, R is the rotation matrix, and t is the translation vector;

[0134] Minimize the reprojection error:

[0135] E = ∑||x′ i - P·X i || 2

[0136] Wherein, x i ′ is the projection point, X i is the 3D point coordinate, i represents the index value of the projection point, and E represents the reprojection error;

[0137] Wherein, the acquisition of the 3D point coordinate (X i ) can be achieved through the following methods:

[0138] 1) The point cloud data obtained by the 3D scanning device;

[0139] 2) The known 3D model vertex coordinates;

[0140] 3) The 3D feature points obtained by multi-view reconstruction;

[0141] 4) The pre-calibrated 3D reference points;

[0142] The acquisition of the projection point (x i ) can be achieved through the following methods:

[0143] 1) Images captured by a camera;

[0144] 2) Extract feature points from the images using feature detection algorithms (such as SIFT, SURF, etc.);

[0145] 3) Corresponding points in the manually or automatically labeled images;

[0146] 4) Corner points or feature points detected by the image processing algorithm;

[0147] Establishment of the correspondence:

[0148] 1) It is necessary to ensure that X i and x i ' are paired, that is, they describe the same feature point;

[0149] 2) Establish the correspondence between 3D points and 2D points through the feature matching algorithm.

[0150] During the feature point matching process, an adaptive error threshold is constructed:

[0151] The adaptive error threshold τ(n) is mainly used in the feature point mapping optimization process, and the specific application scenarios are as follows:

[0152] Reprojection error judgment: Used to judge whether the reprojection error E = ∑||x i '- P·X i ||2 meets the threshold requirement; when the error is less than the current threshold τ(n), it is considered that the optimization meets the requirement;

[0153] Iteration termination condition: As the dynamic termination condition for the optimization iteration process; as the iteration number n increases, the threshold τ(n) gradually decreases; enabling the optimization process to converge quickly in the initial stage and be more accurate in the later stage;

[0154] Precision control: Balance the optimization precision and computational efficiency by adjusting the initial threshold τ0 and the attenuation factor λ, avoiding waste of computational resources caused by excessive iteration; the design of this threshold ensures that the feature point mapping optimization process can not only ensure precision but also maintain good real-time performance.

[0155] Its calculation formula is as follows:

[0156] τ(n) = τ0·exp(-λn)

[0157] Where: τ0 is the initial threshold, n is the iteration number, and λ is the attenuation factor;

[0158] In another alternative implementation, an improved Levenberg - Marquardt algorithm can be used to accelerate the iterative convergence. Specifically:

[0159] (J T ·J + μI)δ = -J T ·ε

[0160] Where J is the Jacobian matrix, μ is the damping factor, and ε is the error vector;

[0161] Based on the improved Levenberg - Marquardt algorithm, the specific implementation method for accelerating iterative convergence: Dynamic adjustment of the damping factor μ: When the error decreases, reduce the value of μ (approaching the Newton method); when the error increases, increase the value of μ (approaching the gradient descent method); achieve fast convergence through dynamic adjustment;

[0162] The iterative steps are as follows:

[0163] Step 1: Calculate the current error ε and the Jacobian matrix J. In this embodiment, the current error is the reprojection error E mentioned above;

[0164] Step 2: Solve the equation (J^T·J + μI)δ = -J^T·ε;

[0165] Step 3: Update the parameters: x_new = x + δ, where x represents the optimization variable and δ represents the update step size;

[0166] Step 4: Calculate the new error ε_new;

[0167] Step 5: Adjust μ according to the error change:

[0168] - If ε_new < ε:

[0169] Accept the update, μ = μ / 10;

[0170] - Otherwise:

[0171] Reject the update, μ = μ * 10;

[0172] Step 6: Determine whether the convergence condition is satisfied. Otherwise, return to Step 1.

[0173] Acceleration strategy: Intelligently initialize the value of μ to avoid being too large or too small, use sparse matrix to optimize the calculation efficiency, use caching mechanism to reduce repeated calculations, and skip the recalculation of the Jacobian matrix at appropriate times;

[0174] Convergence judgment: Combine the error threshold τ(n) to monitor the magnitude of the update amount δ and check the improvement amplitude for multiple consecutive times.

[0175] In this embodiment, the improved LM algorithm improves the convergence speed while ensuring convergence through the dynamic adjustment strategy, and is particularly suitable for non - linear least - squares problems such as feature point mapping optimization.

[0176] In another alternative embodiment, as Figure 2 shown, a video conferencing implementation device for aesthetic education includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the video conferencing implementation method for aesthetic education described in any one of the above embodiments.

[0177] In another alternative embodiment, as Figure 4 shown, a collaborative system for aesthetic education based on a large-screen intelligent assistant including various modules can be set up to implement a video conference for aesthetic education. Each module is respectively used to implement the corresponding method steps in each of the above embodiments.

[0178] In a specific application scenario:

[0179] In an art design class, the teacher uses this system for remote collaborative teaching. First, the teacher initiates a video conference through the system and invites remote students to join. The system automatically performs intelligent layout on the video conference window and the teaching content interface, enabling teachers and students to clearly see the shared teaching content while viewing each other.

[0180] The teacher begins to explain the composition techniques of a work of art. She uses the image processing module of the system to zoom in and crop the work, highlighting the key areas. At the same time, the system synchronizes these operations to all students in real time, enabling them to clearly see the teacher's demonstration process.

[0181] Next, the teacher invites the students to participate in collaboration and jointly modify this work. Multiple students edit the work simultaneously, some adjusting the colors and some modifying the lines. The multi-user collaboration module of the system ensures that all operations can be smoothly integrated without conflicts.

[0182] When discussing three-dimensional composition, the teacher loads a 3D model. She uses the 3D rotation module to display the model from different angles to help students understand the spatial structure. The students can also independently manipulate the model, and the system synchronizes the perspective changes of each student to others, promoting in-depth discussion and understanding.

[0183] Throughout the teaching process, the file version management module of the system automatically saves multiple versions of the creation.

[0184] At the end of the course, the teacher reviews the entire creation process and shows the students how the work has been gradually improved by comparing different versions. The students can also return to any historical version to understand the intention and effect of each modification.

[0185] This interactive and collaborative teaching method greatly improves the students' participation and creativity, making aesthetic education more vivid and efficient.

[0186] In another alternative embodiment, a computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the steps of a method for implementing a video conference for aesthetic education described in any one of the above embodiments are realized.

[0187] In summary, a method, apparatus, and computer storage medium for implementing a video conference for aesthetic education provided by the present invention. The seamless integration of the video conference improves the interactivity and immediacy of remote aesthetic education, overcomes geographical restrictions, and expands the coverage of high-quality educational resources; the multi-user collaboration function promotes real-time interaction and co-creation between teachers and students and among students, and cultivates the teamwork ability and innovative thinking of students; the advanced image processing and 3D rotation functions provide more intuitive and flexible demonstration and operation tools for aesthetic education, helping students better understand and master art creation techniques; the file version management system enables teachers and students to track and compare every change in the creation process, which is beneficial to reflecting on and improving the creation process and deepening the understanding of art creation; the overall design of the system improves the efficiency and quality of aesthetic education, provides strong support for personalized teaching and the cultivation of innovative talents, and greatly improves the teaching efficiency and quality of aesthetic education.

[0188] The above are only embodiments of the present invention, and thus do not limit the patent scope of the present invention. All equivalent transformations made using the content of the specification and drawings of the present invention, or directly or indirectly applied in related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. A method for realizing a video conference for aesthetic education, characterized in that, Including the steps: Receiving a video conference initiation request; Creating a corresponding meeting room according to the video conference initiation request, and intelligently arranging the meeting window and the teaching content interface; Real-time synchronizing the audio and video data of the video conference and the operation data of the teaching content; Updating and saving each file version generated during the aesthetic education teaching process.

2. The video conferencing implementation method for aesthetic education according to claim 1, wherein Before the step of real-time synchronizing the audio and video data of the video conference and the operation data of the teaching content, it further includes the steps: Judging whether a multi-user collaboration request is received. If not, then executing the step of real-time synchronizing the audio and video data of the video conference and the operation data of the teaching content; If so, starting the multi-user collaboration function and synchronizing the operation data of each user.

3. The method for realizing a video conference for aesthetic education according to claim 2, wherein After synchronizing the operation data of each user, it further includes the steps: Judging whether an image processing request is received. If so, performing image editing operations according to the image processing request; If not, judging whether an image 3D operation request is received. If so, performing 3D model operations according to the image 3D operation request; If not, then executing the step of updating and saving each file version generated during the aesthetic education teaching process.

4. A method for realizing a video conference for aesthetic education according to any one of claims 1 to 3, characterized in that, The updating and saving each file version generated during the aesthetic education teaching process includes: Updating and saving each node of each user's operation during the aesthetic education teaching process; Generating a corresponding version identifier for each node; Constructing a version tree according to all the generated version identifiers and recording the relationships between each version.

5. The method for realizing a video conference for aesthetic education according to claim 3, wherein, The image editing operations include: Implementing multi-scale feature extraction by using an improved multi-scale feature extraction algorithm; The extracted multi-scale features are expressed as: L(x,y,σ)=G(x,y,kσ)-G(x,y,σ) In the formula, x represents the horizontal coordinate position in the image plane, y represents the vertical coordinate position in the image plane, σ represents the standard deviation of the Gaussian function, k represents the scale factor, and G() represents the Gaussian function; Performing positioning optimization on the extracted multi-scale features: In the formula, D represents the scale space difference, and X represents the multi-scale feature vector; Performing image editing operations according to the multi-scale features after positioning optimization.

6. The method for implementing a video conference for aesthetic education according to claim 3, wherein, The image editing operations include: Performing artistic style feature extraction based on an improved Gabor filter: where x' and y' represent the positions in the new coordinate system after the rotation of the original coordinates (x, y), f represents the center frequency, θ represents the direction angle, and σ x and σ y represent the standard deviations of the Gaussian envelope; Performing image editing operations according to the extracted artistic style features.

7. A method for realizing a video conference for aesthetic education according to any one of claims 1 to 3, characterized in that, The intelligent arrangement of the meeting window and the teaching content interface includes: Mapping the 3D points obtained in the video conference to the 2D plane through projective transformation and matching to obtain the corresponding 2D projection points; Fusing the 2D projection points with the teaching content interface to realize the intelligent arrangement of the meeting window and the teaching content interface.

8. A method for realizing a video conference for aesthetic education according to claim 7, characterized in that The matching to obtain the corresponding 2D projection points includes: Mapping the 3D points to the 2D plane through the projective transformation matrix to obtain the corresponding projection points; Calculating the reprojection error between the projection points and the actual observation points; Setting an adaptive error threshold, judging whether the reprojection error is less than the adaptive error threshold. If so, determining the projection points as the corresponding 2D projection points; If not, an improved LM algorithm is used for adaptive iteration, the adaptive error threshold is dynamically adjusted according to the number of iterations, and the step of judging whether the reprojection error is less than the adaptive error threshold is returned.

9. A video conferencing implementation device for aesthetic education, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of a video conference implementation method for aesthetic education as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the steps of a video conference implementation method for aesthetic education as described in any one of claims 1 to 8 are implemented.

Images