Vehicle, readable storage medium, and image processing method and system
Patent Information
- Application Number
- PCT/CN2024/119649
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2024-03-07
- Filing Date
- 2024-09-19
- Publication Date
- 2025-10-02
AI Technical Summary
In the existing technology, how to ensure that the internal texture of the image after deformation is filled when the 2D image is superimposed on the 3D reality, obtain the correct texture graphics, and improve the sense of fit of the AR real scene.
By obtaining the original image to be displayed, performing grid division, stretching the edge points to form the outer contour of the target image, calculating the pixel coordinates of the internal filling points, and performing texture deformation based on the grid interpolation algorithm, the texture of the target image is generated.
The correct filling of the internal texture after image deformation is achieved, which enhances the real-scene fit of the AR image and ensures a good superposition effect between 2D images and 3D reality.
Smart Images
Figure CN2024119649_02102025_PF_FP_ABST
Abstract
Description
Vehicle, readable storage medium, image processing method and system
[0001] CROSS-REFERENCE TO RELATED APPLICATIONS
[0002] This application claims the benefit of Chinese patent application No. 202410259270.8 filed on March 7, 2024, the contents of which are incorporated herein by reference. Technical Field
[0003] The present application relates to image processing, and in particular, to an image processing method, an image processing system, a vehicle, and a readable storage medium. Background Art
[0004] A Head-Up Display (HUD), a driving assistance device used in automobiles, is a comprehensive electronic display device consisting of electronic components, display components, and controllers. It projects information such as vehicle speed, navigation information, and warnings in the form of images and characters in front of the driver via optical components. The driver can view this information directly on the windshield above the hood. Compared to viewing driving information from the instrument cluster, the driver can keep their eyes on the road, requiring less head and eye movement and less time to adjust to road information. Therefore, a HUD increases safety, keeps the driver's eyes on the road, and improves driver comfort.
[0005] Traditional HUDs primarily include C-HUD (Combiner HUD) and W-HUD (Windshield HUD). While both address the potential safety hazard of blind driving caused by drivers needing to look down to check vehicle information, they are essentially a simple migration of central control and instrument panel information. Furthermore, due to limited projection size, they are unable to support the trend of intelligent development in modern vehicles. To fully display information and provide an immersive experience, AR-HUD (Augmented Reality Head-Up Display) has emerged. Based on W-HUD, AR-HUD combines AR technology to overlay the HUD display onto the real road surface, allowing for a realistic and vivid display of driving information within the driver's field of view, resulting in an AR-like real-world fit.
[0006] However, the overlay of 2D images with 3D reality requires deformation of the graphics to better fit the 2D image with the 3D world. OpenGL (Open Graphics Library) provides a texture stretching method that introduces a programmable rendering pipeline. When rendering graphics, images can be deformed in OpenGL's Vertex Shader (vertex shader). Programmers only need to input the correct vertex points and the UV coordinates of the texture corresponding to each point to render the texture graphics with the correct shape in the FrameBuffer. However, how to determine the correct vertex points and UV coordinates is a technical problem that needs to be solved by those skilled in the art.
[0007] Application Contents
[0008] The technical problem to be solved in the first aspect of the present application is to provide an image processing method, which can ensure the internal texture filling after the image is deformed and obtain a correct texture graphic.
[0009] The technical problem to be solved in the second aspect of the present application is to provide an image processing system, which can ensure the internal texture filling after the image is deformed and obtain a correct texture graphic.
[0010] The technical problem to be solved in the third aspect of this application is to provide a vehicle that can make the image projected by the HUD have correct internal texture, thereby improving the sense of fit of the AR real scene.
[0011] The technical problem to be solved in the fourth aspect of this application is to provide a readable storage medium that can be executed by a computer processor so that the processed image has the correct internal texture and improves the sense of fit of AR real scene.
[0012] In order to solve the above technical problems, the present application provides an image processing method, which includes obtaining an original image to be displayed;
[0013] Dividing the original image into grids to generate a target grid;
[0014] Stretching the edge points of the target grid as control points to form the outer contour of the target image, and obtaining pixel coordinates corresponding to the edge points after stretching;
[0015] Traversing the internal filling points of the target grid, and obtaining the pixel coordinates of the insertion points corresponding to the internal filling points of the target image based on the pixel coordinates corresponding to the edge points after stretching and using a grid interpolation algorithm;
[0016] The texture of the original image is deformed based on the pixel coordinates corresponding to the edge points after stretching and the pixel coordinates of the insertion points to obtain the texture of the target image.
[0017] As a specific implementation of the present application, dividing the original image into grids to generate a target grid includes:
[0018] The original image is divided into M*N equidistant grids to generate a target grid.
[0019] As a specific implementation of the present application, traversing the internal filling points of the target grid and obtaining the pixel coordinates of the insertion points corresponding to the target image and the internal filling points based on the grid interpolation algorithm according to the pixel coordinates corresponding to the edge points after stretching include:
[0020] Traversing the interior filling points of the target grid;
[0021] According to the pixel coordinates of the four vertices of the stretched target grid, the pixel coordinates of the edge point located in the i-th row and the pixel coordinates of the edge point located in the j-th column, the insertion point a corresponding to the internal filling point of the target image and the i-th row and j-th column is obtained based on the grid difference algorithm. ij The pixel coordinates of .
[0022] As a specific embodiment of the present application, the insertion point a ij The horizontal coordinate a ij.x The grid difference calculation formula is:
[0023] Among them, a Mj.x The edge point a in the Mth row and jth column Mj The horizontal axis, a M1.x The edge point a in the Mth row and the first column M1 The horizontal axis, a MN.x The edge point a in the Mth row and Nth column MN The horizontal axis, a 1j.x The edge point a in row 1 and column j 1j The horizontal axis, a 11.x The edge point a in row 1 and column 1 11 The horizontal axis, a 1N.x The edge point a in row 1 and column N 1N The horizontal axis, a iN.x is the edge point a in row i and column N iN The horizontal axis, a i1.x The edge point a in row i and column 1 i1 The horizontal axis of
[0024] The insertion point a ij The vertical coordinate a ij.y The grid difference calculation formula is:
[0025] Among them, a iN.y is the edge point a in row i and column N iN The vertical coordinate, a 1N.y The edge point a in row 1 and column N 1N The vertical coordinate, a MN.y The edge point a in the Mth row and Nth column MN The vertical coordinate, a i1.y The edge point a in row i and column 1 i1 The vertical coordinate, a 11.y The edge point a in row 1 and column 1 11 The vertical coordinate, a M1.y The edge point a in the Mth row and the first column M1 The vertical coordinate of .
[0026] As a specific implementation of the present application, deforming the texture of the original image based on the pixel coordinates corresponding to the edge points after stretching and the pixel coordinates of the insertion points to obtain the texture of the target image includes:
[0027] Performing triangulation on the target mesh to perform triangulation on the texture of the original image;
[0028] Based on the pixel coordinates corresponding to the edge points after stretching and the pixel coordinates of the insertion points, the target image is grid-filled and triangulated;
[0029] According to the triangulation of the target image, the texture of the original image after the triangulation is deformed to obtain the texture of the target image.
[0030] As a specific embodiment of the present application, the image processing method is applied to a HUD, and the method further includes:
[0031] Acquire environmental parameters of a road surface in front of the vehicle, and derive a deformed shape of the target image based on the environmental parameters.
[0032] As a specific implementation of the present application, after deriving the deformed shape of the target image based on the environmental parameters, the method further includes:
[0033] The driver's eye point position parameters are obtained, and the deformed shape of the target image is adjusted based on the eye point position parameters.
[0034] A second aspect of the present application provides an image processing system, comprising:
[0035] An acquisition module, used for acquiring the original image to be displayed;
[0036] The image processing module includes a grid division unit, an edge stretching unit, a coordinate calculation unit and a texture filling unit, wherein:
[0037] The grid division unit is used to divide the original image into grids to generate a target grid;
[0038] The edge stretching unit is configured to stretch the edge points of the target grid as control points to form an outer contour of the target image, and obtain pixel coordinates corresponding to the edge points after stretching;
[0039] The coordinate calculation unit is used to traverse the internal filling points of the target grid and obtain the pixel coordinates of the insertion points corresponding to the internal filling points of the target image based on the pixel coordinates corresponding to the edge points after stretching based on a grid interpolation algorithm;
[0040] The texture filling unit is configured to deform the texture of the original image based on the pixel coordinates corresponding to the edge points after stretching and the pixel coordinates of the insertion points to obtain the texture of the target image.
[0041] A third aspect of the present application provides a vehicle, comprising the image processing system described in the above technical solution, and executing the image processing method as described in any one of the above technical solutions, wherein the image processing system and the image processing method are applied to HUD.
[0042] A fourth aspect of the present application provides a readable storage medium, which stores executable instructions. The executable instructions are used to be read by a processor of a computer to execute the image processing method described in any one of the above technical solutions.
[0043] Through the above solution, the beneficial effects of this application are as follows:
[0044] The image processing method of the present application only needs to control the position of the edge points of the target image, and can accurately obtain the pixel coordinates of the insertion points inside the target image based on the grid interpolation algorithm, so that the pixel coordinates, which are the coordinates of the Vertex point, can be used to automatically fill the internal texture of the target image when deforming the image in the Vertex Shader of OpenGL, rendering a texture graphic of the correct shape, thereby enhancing the real-scene fit of the AR image.
[0045] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the following detailed description, they are used to explain the present application but do not constitute a limitation of the present application. In the accompanying drawings:
[0047] FIG1 is a flow chart of the image processing method of the present application;
[0048] FIG2 is a schematic diagram of dividing the original image into M*N grids;
[0049] FIG3 is a schematic diagram of the edge points of the target grid;
[0050] FIG4 is a schematic diagram of the outer contour of a target image formed by stretching the edge points of a target grid;
[0051] FIG5 is a schematic diagram of a grid filling of a target image;
[0052] FIG6 is a schematic diagram showing the division of the original image into 6*6 grids;
[0053] FIG7 is a schematic diagram of triangulating the image shown in FIG6 ;
[0054] FIG8 is a schematic diagram of a mesh filling formed after the target mesh shown in FIG6 is stretched;
[0055] Figure 9 is the test texture;
[0056] FIG10 is the deformed texture obtained by calculating the grid interpolation algorithm in FIG9;
[0057] FIG11 is a structural block diagram of the image processing system of the present application. DETAILED DESCRIPTION
[0058] The specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described here are only used to illustrate and explain the present application, and the scope of protection of the present application is not limited to the specific implementation methods described below.
[0059] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "formed," "provided with," "set," "connected," etc. should be understood in a broad sense. For example, the connection may be a direct connection or an indirect connection through an intermediate medium; it may be a fixed connection, a detachable connection, or an integral connection; it may be a direct connection or an indirect connection through an intermediate connector; it may be internal communication between two elements or an interaction relationship between two elements. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.
[0060] The present application provides an image processing method, as shown in FIG1 , which includes:
[0061] Step S110: Acquire the original image to be displayed.
[0062] Taking the application in HUD as an example, when the HUD display screen is displayed, driving information is selected according to the driver's needs, or the image to be displayed is automatically identified based on the road conditions, and the corresponding original image is called for display. In order to be able to superimpose the HUD display screen on the real road surface, the original image needs to be stretched and deformed so that it can reasonably and vividly superimpose the driving information in the driver's field of view, and have an AR real-scene fit.
[0063] Step S120: Divide the original image into grids to generate a target grid.
[0064] Referring to FIG2 , as a specific implementation, the original image is divided into M*N equidistant grids, that is, the grid lines are arranged in M rows and N columns, and the spacing between adjacent horizontal grid lines is equal, and the spacing between adjacent vertical grid lines is equal, thereby generating M*N grid points.
[0065] Step S130: stretching the edge points of the target grid as control points to form the outer contour of the target image, and obtaining the pixel coordinates corresponding to the edge points after stretching.
[0066] Specifically, referring to FIG2 and FIG3, the edge points include the four vertices A of the target mesh. 11 (the point in row 1 and column 1), A 1N (the point at row 1 and column N), A M1 (the point at row M and column 1) and A MN (the point at row M and column N) and the control points on the four edges, and obtain their corresponding UV coordinates. According to the shape of the desired target image, stretch the control points to the corresponding positions shown in Figure 4, such as point A. 11 Stretch to the edge point a of the target image 11 , point A 1N Stretch to the edge point a of the target image 1N , and obtain the pixel coordinates of the edge points of the target image corresponding to the above control points after stretching.
[0067] It should be noted that the shape of the target image can be pre-set, i.e., the pixel coordinates of each edge point of the target image are preset and correspond to the UV coordinates of the edge points of the target grid. Furthermore, during the AR-HUD display process, due to changes in environmental parameters of the road ahead of the vehicle, such as changes in road slope or road dividing lines, the shape of the target image needs to change to better fit the actual scene. Therefore, the shape of the target image can be adjusted based on these environmental parameters to adjust the deformed shape of the target image, thereby adjusting the outer contour of the target image and adjusting its internal texture deformation. Specifically, based on the detected environmental parameters of the road ahead of the vehicle, the pixel coordinates of the edge points of the target image corresponding to the stretched control points are adjusted to re-determine the outer contour of the target image. The environmental parameters of the road ahead of the vehicle can be acquired by sensors on the vehicle body, such as one or more of a camera, lidar, and millimeter-wave radar. The vehicle captures multi-angle video of the environment ahead of the vehicle and uploads the images to a processor for analysis and processing. This processor can be an onboard processor or a cloud-based processor transmitted via network communication.
[0068] It should also be noted that the image pictures observed from different perspectives are different. During the vehicle driving process, the driver's body parameters and driving posture are different, and the eye position parameters will also be different. In order to further enable the driver to view the display image of the AR-HUD to better fit the actual scene and increase the sense of fit with the real scene, the driver's eye position parameters can also be obtained. Based on the eye position parameters, the deformation shape of the target image can be adjusted. The pixel coordinates of the edge points of the target image corresponding to the control point after stretching can be adjusted in combination with the detected road environment parameters in front of the vehicle, so as to redefine the outer contour of the target image. Among them, the driver's eye position parameters can be obtained by capturing the driver's eye images through the driver monitoring camera in the car and uploading them to the processor for analysis and processing to obtain the eye position parameters. Alternatively, the driver's eye position parameters can be calculated by adjusting the seat posture parameters (seat front and rear adjustment parameters, left and right adjustment parameters, up and down adjustment parameters, backrest adjustment parameters, etc.) adjusted by the driver.
[0069] Step S140: After obtaining the pixel coordinates corresponding to the stretched edge points, traverse the internal filling points of the target grid, and according to the pixel coordinates corresponding to the stretched edge points, based on the grid interpolation algorithm, obtain the pixel coordinates of the insertion points corresponding to the target image and the internal filling points.
[0070] Specifically, referring to FIG2 and FIG5, for the internal filling point A in the i-th row and j-th column of the target grid ij , the corresponding insertion point in the target image is a ij, according to the four vertices of the target mesh after stretching (a 11 、a 1N 、a M1 and a MN ) pixel coordinates, edge points located in row i (a i1 、a iN ) and the pixel coordinates of the edge point (a 1j 、a Mj ) pixel coordinates, based on the grid difference algorithm, the insertion point a corresponding to the internal filling point of the target image and the i-th row and j-th column is obtained. ij The pixel coordinates of .
[0071] Specifically, insertion point a ij The horizontal coordinate a ij.x The grid difference calculation formula is:
[0072] Among them, a Mj.x is the edge point a in the Mth row and jth column Mj The horizontal axis, a M1.x is the edge point a in the Mth row and the first column M1 The horizontal axis, a MN.x is the edge point a in the Mth row and Nth column MN The horizontal axis, a 1j.x is the edge point a in row 1 and column j 1j The horizontal axis, a 11.x is the edge point a in row 1 and column 1 11 The horizontal axis, a 1N.x is the edge point a in row 1 and column N 1N The horizontal axis, a iN.x is the edge point a in row i and column N iN The horizontal axis, a i1.x is the edge point a in the i-th row and the first column i1 The horizontal axis of
[0073] Insertion point a ij The vertical coordinate a ij.y The grid difference calculation formula is:
[0074] Among them, a iN.y is the edge point a in row i and column N iN The vertical coordinate, a 1N.y is the edge point a in row 1 and column N 1N The vertical coordinate, a MN.y is the edge point a in the Mth row and Nth column MN The vertical coordinate, a i1.y is the edge point a in the i-th row and the first column i1 The vertical coordinate, a 11.yis the edge point a in row 1 and column 1 11 The vertical coordinate, a M1.y is the edge point a in the Mth row and the first column M1 The vertical coordinate of .
[0075] Taking Figure 6 as an example, it is divided into 6 rows and 6 columns to obtain a total of 20 edge points numbered 1 to 20, and these 20 edge points are used as control points and stretched to the edge points of the outer contour of the target image, thereby obtaining the filled grid shown in Figure 8. The triangular points in the grid are inserted points, point 16 is set as point A, point 2 is set as point B, point 13 is set as point C, point 14 is set as point D, point 15 is set as point E, point 20 is set as point F, point 1 is set as point G, and point 6 is set as point H. The following settings are made, and the coordinates of point A are (A x ,A y ), the coordinates of point B are (B x ,B y ), the coordinates of point C are (C x ,C y ), the coordinates of point D are (D x ,D y ), the coordinates of point E are (E x ,E y ), the coordinates of point F are (F x ,F y ), the coordinates of point G are (G x ,G y ), the coordinates of point H are (H x ,H y ), calculate the point P as follows to get the coordinates of point P (P x ,P y ):
[0076] Step S150: deforming the texture of the original image based on the pixel coordinates corresponding to the stretched edge points and the pixel coordinates of the insertion points to obtain the texture of the target image.
[0077] After the original image is meshed, it can be triangulated as shown in Figure 7, thereby triangulating the texture of the original image. Based on the pixel coordinates corresponding to the edge points after stretching and the pixel coordinates of the insertion points, the target image is meshed and triangulated as shown in the figure, so that the triangular facets of the target image correspond one-to-one with the triangular facets of the original image. According to the triangulation of the target image, the texture of the triangulated original image is deformed to obtain the texture of the target image to form a complete target image. The target image has the correct internal texture deformation effect, so that when the target image is projected onto the front windshield of the vehicle, it has a good sense of real-scene fit. In addition, when realizing the texture deformation inside the image, it is only necessary to control the position of the texture edge points, and the internal changes of the texture can be automatically filled according to the grid interpolation algorithm.
[0078] To better illustrate the technical solution of the present application, the image deformation effect formed after the present application is applied on a computer is described below. The image is deformed in the OpenGL Vertex Shader. FIG9 is a test texture, and a grid is divided into 6 rows and 6 columns as shown in FIG6. A total of 20 edge points numbered 1 to 20 are used as control points. The UV coordinates of the 20 edge points and the pixel coordinates (Vertex point coordinates) of the corresponding 20 edge points after stretching are obtained. The test data is as follows:
[0079] According to the above-mentioned grid interpolation algorithm, the coordinates of each Vertex point are correctly calculated, so that the deformation effect shown in Figure 10 can be rendered in the FrameBuffer. It can be clearly seen that the internal texture of the image after deformation is correctly filled, and the correct texture graphics can be obtained, ensuring that the 2D image projected by the HUD can be superimposed on the 3D reality and have a good sense of fit with the AR real scene. In addition, the image processing method of the present application only needs to control the position of the texture edge point, that is, to determine the UV coordinates and pixel coordinates (Vertex point coordinates) corresponding to the edge point after the image is stretched, and it can automatically complete the filling of the internal changes of the texture. The calculation is simple and efficient, and the internal texture deformation of the image is correct.
[0080] In further embodiments of the present application, the image processing method can also be applied to scenarios such as AR glasses, but the present application is not limited to this.
[0081] Correspondingly, the second aspect of the present application provides an image processing system, as shown in FIG11 , which includes:
[0082] An acquisition module, used for acquiring the original image to be displayed;
[0083] The image processing module includes a grid division unit, an edge stretching unit, a coordinate calculation unit and a texture filling unit, wherein:
[0084] A grid division unit, used for dividing the original image into grids to generate a target grid;
[0085] An edge stretching unit is used to stretch the edge points of the target grid as control points to form the outer contour of the target image and obtain the pixel coordinates corresponding to the edge points after stretching;
[0086] A coordinate calculation unit is used to traverse the internal filling points of the target grid and obtain the pixel coordinates of the insertion points corresponding to the target image and the internal filling points based on the pixel coordinates corresponding to the edge points after stretching and a grid interpolation algorithm;
[0087] The texture filling unit is used to deform the texture of the original image based on the pixel coordinates corresponding to the stretched edge points and the pixel coordinates of the insertion points to obtain the texture of the target image.
[0088] Therefore, once the deformed outer contour of the image to be projected is determined, that is, the position of the texture edge points is confirmed, the image processing module can automatically fill in the internal changes of the texture, obtain the correct texture image, and ensure that the projected 2D image can be superimposed on the 3D reality, with a good sense of fit between the AR and the real scene.
[0089] The third aspect of the present application provides a vehicle, including the image processing system provided by the second aspect of the present application, and executing the image processing method provided by the first aspect of the present application. The image processing system and the image processing method are applied to the vehicle HUD.
[0090] In addition, the fourth aspect of the present application provides a readable storage medium, wherein the readable storage medium stores executable instructions, and the executable instructions are used to be obtained by the processor of the computer.
[0091] The preferred embodiments of the present application are described in detail above in conjunction with the accompanying drawings. However, the present application is not limited to the specific details in the above embodiments. Within the technical concept of the present application, various simple modifications can be made to the technical solution of the present application, and these simple modifications all fall within the scope of protection of the present application.
[0092] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this application will not further describe various possible combinations.
[0093] In addition, the various implementation methods of the present application may be arbitrarily combined, and as long as they do not violate the concept of the present application, they should also be regarded as the contents disclosed in the present application.
Claims
1. An image processing method, characterized in that: The method includes: Obtaining the original image to be displayed; Dividing the original image into grids to generate a target grid; Stretching the edge points of the target grid as control points to form the outer contour of the target image, and obtaining pixel coordinates corresponding to the edge points after stretching; Traversing the internal filling points of the target grid, and obtaining the pixel coordinates of the insertion points corresponding to the internal filling points of the target image based on the pixel coordinates corresponding to the edge points after stretching and using a grid interpolation algorithm; The texture of the original image is deformed based on the pixel coordinates corresponding to the edge points after stretching and the pixel coordinates of the insertion points to obtain the texture of the target image.
2. The image processing method according to claim 1, wherein: The gridding of the original image to generate the target grid includes: The original image is divided into M*N equidistant grids to generate a target grid.
3. The image processing method according to claim 2, wherein: Traversing the internal filling points of the target grid and obtaining the pixel coordinates of the insertion points corresponding to the internal filling points of the target image based on the pixel coordinates corresponding to the edge points after stretching using a grid interpolation algorithm includes: Traversing the interior filling points of the target grid; According to the pixel coordinates of the four vertices of the stretched target grid, the pixel coordinates of the edge point located in the i-th row and the pixel coordinates of the edge point located in the j-th column, the insertion point a corresponding to the internal filling point of the target image and the i-th row and j-th column is obtained based on the grid difference algorithm. ij The pixel coordinates of .
4. The image processing method according to claim 3, wherein: The insertion point a ij The horizontal coordinate a ij.x The grid difference calculation formula is: Among them, a Mj.x The edge point a in the Mth row and jth column Mj The horizontal axis, a M1.x The edge point a in the Mth row and the first column M1 The horizontal axis, a MN.x The edge point a in the Mth row and Nth column MN The horizontal axis, a 1j.x The edge point a in row 1 and column j 1j The horizontal axis, a 11.x The edge point a in row 1 and column 1 11 The horizontal axis, a 1N.x The edge point a in row 1 and column N 1N The horizontal axis, a iN.x is the edge point a in row i and column N iN The horizontal axis, a i1.x The edge point a in row i and column 1 i1 The horizontal axis of The insertion point a ij The vertical coordinate a ij.y The grid difference calculation formula is: Among them, a iN.y is the edge point a in row i and column N iN The vertical coordinate, a 1N.y The edge point a in row 1 and column N 1N The vertical coordinate, a MN.y The edge point a in the Mth row and Nth column MN The vertical coordinate, a i1.y The edge point a in row i and column 1 i1 The vertical coordinate, a 11.y The edge point a in row 1 and column 1 11 The vertical coordinate, a M1.y The edge point a in the Mth row and the first column M1 The vertical coordinate of .
5. The image processing method according to any one of claims 1 to 4, characterized in that: The deforming the texture of the original image based on the pixel coordinates corresponding to the edge points after stretching and the pixel coordinates of the insertion points to obtain the texture of the target image includes: Performing triangulation on the target mesh to perform triangulation on the texture of the original image; Based on the pixel coordinates corresponding to the edge point after stretching and the pixel coordinates of the insertion point Coordinates, grid filling and triangle division of the target image; According to the triangulation of the target image, the texture of the original image after the triangulation is deformed to obtain the texture of the target image.
6. The image processing method according to any one of claims 1 to 4, characterized in that: The image processing method is applied to a HUD, and the method further includes: Acquire environmental parameters of a road surface in front of the vehicle, and derive a deformed shape of the target image based on the environmental parameters.
7. The image processing method according to claim 6, characterized in that: After obtaining the deformed shape of the target image based on the environmental parameters, the method further includes: The driver's eye point position parameters are obtained, and the deformed shape of the target image is adjusted based on the eye point position parameters.
8. An image processing system, characterized in that: include: An acquisition module, used for acquiring the original image to be displayed; The image processing module includes a grid division unit, an edge stretching unit, a coordinate calculation unit and a texture filling unit, wherein: The grid division unit is used to divide the original image into grids to generate a target grid; The edge stretching unit is configured to stretch the edge points of the target grid as control points to form an outer contour of the target image, and obtain pixel coordinates corresponding to the edge points after stretching; The coordinate calculation unit is used to traverse the internal filling points of the target grid and obtain the pixel coordinates of the insertion points corresponding to the internal filling points of the target image based on the pixel coordinates corresponding to the edge points after stretching based on a grid interpolation algorithm; The texture filling unit is configured to deform the texture of the original image based on the pixel coordinates corresponding to the edge points after stretching and the pixel coordinates of the insertion points to obtain the texture of the target image.
9. A vehicle, characterized in that: The image processing system comprises the image processing system according to claim 8 and executes the image processing method according to any one of claims 1 to 7, wherein the image processing system and the image processing method are applied to a vehicle HUD.
10. A readable storage medium, characterized in that: The readable storage medium stores executable instructions, which are used to be read by a processor of a computer to execute the image processing method according to any one of claims 1 to 7.