Image distortion correction method, head-up display device and vehicle system

Abstract

The present application belongs to the technical field of automobile electronics, and particularly relates to a kind of image distortion correction square, head-up display device and car machine system.Image distortion correction method obtains initial image data and vehicle data of preset area, generates distortion mapping file according to initial image data;Obtain real-time image data, adjust real-time image data according to distortion mapping file, and generate corrected image data;Corrected image data is output externally.Through setting distortion mapping file as adjustment framework, this method not only reduces the complexity of adjustment to avoid using additional chips for real-time correction, but also makes different vehicle models and different HUD hardware adjust according to the framework of distortion mapping file, avoiding the problem of too long development cycle.

Description

Technical Field

[0001] This invention belongs to the field of automotive electronics technology, and particularly relates to an image distortion correction method, a head-up display device, and a vehicle infotainment system. Background Technology

[0002] A head-up display (HUD) is a technology that projects a virtual screen onto a vehicle's windshield to prevent driver distraction caused by looking down at the instrument panel. When projecting a flat image onto the windshield, the image needs to be distorted to fit the curved glass structure, thus avoiding severe geometric distortion.

[0003] In existing HUD image distortion correction processes, software and hardware correction solutions are commonly used. Hardware correction solutions require additional distortion chips and a dedicated system to perform real-time image correction, enabling real-time distortion adjustment of images captured by the camera. However, this approach incurs additional chip material costs. Software correction solutions rely on the upper-layer application for self-correction. This method requires specially designed algorithms for different screens, ultimately necessitating independent adaptation and adjustments for different vehicles, vehicle models, and HUD hardware, thus extending the vehicle development cycle. Summary of the Invention

[0004] To address the technical problem that "existing technologies require additional chip materials for HUD correction, and different vehicles, screens, and models require separate development of adaptation algorithms, resulting in excessively long R&D cycles," this invention proposes an image distortion correction method, a head-up display device, and an in-vehicle infotainment system.

[0005] The present invention solves the above problems through the following technical solution: In a first aspect, the present invention proposes an image distortion correction method, comprising: Obtain initial image data and vehicle model data for a preset region, and generate a distortion mapping file based on the initial image data and vehicle model data; Acquire real-time image data, adjust the real-time image data according to the distortion mapping file, and generate corrected image data; Output the corrected image data.

[0006] The image distortion correction method acquires initial image data and vehicle model data for a preset region, generates a distortion mapping file based on the initial image data, acquires real-time image data, adjusts the real-time image data according to the distortion mapping file, and generates corrected image data, which is then output. By setting the distortion mapping file as the adjustment framework, this approach not only reduces the complexity of the adjustment, thus avoiding the need for additional chips for real-time correction, but also ensures that different vehicle models and different HUD hardware are adjusted using a unified framework based on the distortion mapping file, avoiding the problem of excessively long development cycles.

[0007] In some implementations, initial image data and vehicle model data of a preset area are acquired, and a distortion mapping file is generated based on the initial image data and vehicle model data; including: Obtain initial image data and vehicle model data for a preset region, and generate a distortion mapping file based on the initial image data and vehicle model data; The distortion mapping file is preset in the vehicle and a mapping model is preset. The mapping model is used to select the distortion mapping file and correct the image according to the distortion mapping file.

[0008] In some implementations, real-time image data is acquired, adjusted according to a distortion mapping file, and corrected image data is generated; including: The mapping model acquires real-time image data, and obtains the first coordinate set based on the real-time image data; The mapping model obtains a distortion mapping file based on vehicle model data, and outputs a second coordinate set based on the distortion mapping file and the first coordinate set. The mapping model corrects the real-time image data based on the second coordinate set and generates the first corrected image data, and outputs the first corrected image data as the corrected image data.

[0009] In some implementations, real-time image data is acquired, adjusted according to a distortion mapping file, and corrected image data is generated; including: The mapping model acquires real-time image data and line-of-sight data, and obtains the first coordinate set based on the real-time image data; The mapping model obtains a distortion mapping file based on vehicle model data, and obtains a third coordinate set based on the first coordinate set, the distortion mapping file, and the line-of-sight data; The mapping model corrects the real-time image data based on the third coordinate set and generates the second corrected image data, and outputs the second corrected image data as the corrected image data.

[0010] In some implementations, real-time image data is acquired, the real-time image data is adjusted according to a distortion mapping file, and corrected image data is generated; furthermore: The display channel of the head-up display system is determined and the graphics processor is activated. The mapping model then uses the graphics processor to correct the real-time image data.

[0011] In some implementations, real-time image data is acquired, adjusted according to a distortion mapping file, and corrected image data is generated; including: The mapping model acquires real-time image data, and obtains the first coordinate set based on the real-time image data; The mapping model obtains the distortion mapping file, real-time image data, and dynamic adjustment data, and obtains the fourth coordinate set based on the first coordinate set, the distortion mapping file, and the dynamic adjustment data; The mapping model corrects the real-time image data based on the fourth coordinate set and generates the third corrected image data, and outputs the third corrected image data as the corrected image data. The dynamically adjusted data includes one or more of rotation, offset, and scaling.

[0012] In some implementations, the mapping model acquires a distortion mapping file, real-time image data, and dynamic adjustment data, and acquires a fourth coordinate set based on a first coordinate set, the distortion mapping file, and the dynamic adjustment data; it also includes: The mapping model acquires distortion mapping files, real-time image data, and dynamic adjustment data; the mapping model then loads the coloring module. The coloring module recombines the red, green, blue, and transparency channel components of the real-time image data based on dynamically adjusted parameters and distortion mapping files to form a fourth coordinate set.

[0013] In some implementations, initial image data and vehicle model data of a preset area and / or real-time image data are acquired through the following methods: The system obtains initial image data and vehicle model data from the first system through a first preset protocol and / or obtains real-time image data from the first system through the first preset protocol.

[0014] In some implementations, outputting the corrected image data includes the following sub-steps: By invoking the direct rendering manager and hardware compositor, the corrected image data is output via a second preset protocol.

[0015] Secondly, the present invention provides a head-up display device, including a first system, a second system, and a projection module; The first system includes a first data sending module, a first data receiving module, and a projection docking module. The first data sending module acquires and sends initial image data, vehicle model data, and real-time image data to the second system. The first data receiving module receives corrected image data sent by the second system. The projection docking module connects to and sends corrected image data to the projection module. The second system includes a second data receiving module, a second data sending module, and a data processing module. The second data receiving module is used to receive initial image data, vehicle model data, and / or real-time image data from the first system. The data processing module is used to generate and save a distortion mapping file and / or to adjust the real-time image data according to the distortion mapping file and generate corrected image data. The second data sending module is used to receive the corrected image data and send the corrected image data to the first system. The projection module is used to output the corrected image data.

[0016] Thirdly, the present invention proposes a vehicle infotainment system, characterized in that it includes: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of an image distortion correction method as proposed in any of the first aspects.

[0017] The beneficial effects of the image distortion correction method, head-up display device, and vehicle infotainment system of the present invention are as follows: The image distortion correction method acquires initial image data and vehicle model data for a preset region, generates a distortion mapping file based on the initial image data, acquires real-time image data, adjusts the real-time image data according to the distortion mapping file, and generates corrected image data, which is then output. By setting the distortion mapping file as the adjustment framework, this method not only reduces the complexity of the adjustment, thus avoiding the need for additional chips for real-time correction, but also ensures that different vehicle models and different HUD hardware use a unified framework based on the distortion mapping file for correction, avoiding the problem of excessively long development cycles.

[0018] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0019] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 The flowchart of the image distortion correction method of the present invention is as follows: Figure 2 The flowchart below shows steps 110 to 120 of the image distortion correction method of the present invention: Figure 3 This is a flowchart of steps 211 to 231 of the image distortion correction method of the present invention; Figure 4 This is a flowchart of steps 212 to 232 of the image distortion correction method of the present invention; Figure 5 This is a flowchart of steps 213 to 233 of the image distortion correction method of the present invention; Figure 6 This is the initial image data for step 100 of the image distortion correction method of the present invention; Figure 7 This refers to the corrected image data in step 300 of the image distortion correction method of the present invention; Figure 8 This is a frame diagram of the head-up display device proposed in Embodiment 2 of the present invention; Figure 9 This is a framework diagram of the vehicle infotainment system proposed in Embodiment 3 of the present invention; Figure 10 This is a diagram of the vehicle infotainment system structure proposed in Embodiment 3 of the present invention.

[0020] Figure label: 1. Correct image data; 2. Preset area; 3. Windshield. Detailed Implementation

[0021] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0022] Example 1: like Figures 1-3 , Figure 6 , Figure 7 As shown, the image distortion correction method proposed in this invention includes: Step 100: Obtain the initial image data and vehicle model data of the preset area, and generate a distortion mapping file based on the initial image data and vehicle model data; Specifically, step 100 is typically performed before the vehicle leaves the factory or before it is officially driven. During step 100, debugging can be performed on the cloud, PC, etc. Initial image data and vehicle model data for a preset area are acquired. The preset area is the area set for HUD projection, and the initial image data is a specific top-mounted map of the preset area. The initial image data is the uncorrected projection data, which can be collected via a camera and modeled on the cloud or PC to determine the specific top-mounted map, i.e., the initial image data. The specific top-mounted map is usually coordinate set data; in some embodiments, it can be an array structure of multiple point coordinates. Simultaneously, to perform customized design based on the vehicle model, vehicle model data is also required. This data is used to set different distortion mapping files for different vehicles. Vehicle model data mainly includes the surface data of the windshield, the vehicle's HUD device data, driver's seat height, etc., to ensure better image correction effect of the distortion mapping file.

[0023] Based on the initial image data, corrected data modeling can be completed via PC or cloud. This involves correcting the offset image portion into a normal image based on the forward visual perspective. In some practical implementations, the initial image data is a structure of multiple coordinate points arranged irregularly, while the target data typically has a regular arrangement, forming a rectangular structure. Furthermore, to adapt to the curvature of different vehicle windshields, the windshield curvature needs to be factored in during correction to account for the deviation. In some preferred embodiments, AI modeling and correction can be used to output normal data. The distortion mapping file stores the correction parameters of the initial image data, allowing for rapid correction when projecting onto the same area by reading the parameters from the image coordinates. This method not only enables rapid data correction but also avoids the increased cost, reduced functionality, and unstable HUD display issues associated with using separate terminals and chips for correction. In some embodiments, the distortion mapping file can be understood as a pre-built algorithm.

[0024] Step 200: Acquire real-time image data, adjust the real-time image data according to the distortion mapping file, and generate corrected image data; Specifically, in practical use, the real-time image data to be projected, such as real-time instrument data, can be corrected according to the parameters in the distortion mapping file to form the required corrected image data. For example, if the coordinate point (0,0) in the initial image data needs to be moved by the X vector, then the (0,0) coordinate point in the real-time image data will also be adjusted and corrected by the X vector. Further correction can also include rotation / scaling / offset methods. This method avoids the problems of increased cost, reduced functionality, and unstable HUD display caused by using separate terminals and chips for correction. At the same time, this method can also be adjusted quickly, avoiding the need for a large amount of computing power.

[0025] Step 300: Output the corrected image data.

[0026] Specifically, after acquiring the corrected image data, the vehicle identifies the target HUD display channel and outputs the corrected image data to the outside world.

[0027] In some more specific embodiments, step 100 involves capturing a specific calibration map of the HUD projection using a camera, and then using PC-based host computer software to analyze and generate a distortion mapping table file (i.e., a correction algorithm) for the vehicle model. This file uses RGBA channel-encoded coordinate mapping relationships. Step 200 involves creating a service and listening for and receiving vehicle correction instructions via a communication protocol, such as SOME / IP, and performing real-time image data correction to generate corrected image data. Step 300 involves identifying the HUD channels and outputting the corrected data.

[0028] The image distortion correction method in steps 100 to 300 uses a distortion mapping file as an adjustment framework. This approach not only reduces the complexity of the adjustment, thus avoiding the need for additional chips for real-time correction, but also allows different car models and different HUD hardware to be adjusted using a unified framework based on the distortion mapping file, avoiding the problem of excessively long development cycles.

[0029] In some embodiments, initial image data of a preset region and vehicle model data are acquired, and a distortion mapping file is generated based on the initial image data; including: Step 110: Obtain initial image data and vehicle model data for a preset area, and generate a distortion mapping file based on the initial image data and the vehicle model data; Specifically, such as Figures 3-4 The system collects raw image data from a preset area, including vehicle model information, and may further include windshield curve data. It then uses PCs and cloud platforms to model and calculate the required target data. For example, it collects the calibration chart image projected by the HUD and extracts the actual observed coordinates q_observed_i and their corresponding theoretical ideal coordinates q_ideal_i for N feature points in the image. Specifically, based on vehicle model data, the corresponding set of distortion functions transferred to the target data is calculated. This set of distortion functions can be a collection of functions used to transform each original image data point into the target data. For example, loss function construction: a multi-objective loss function is constructed, weighted by point calibration error terms and area calibration error terms.

[0030] in:

[0031] This is the point calibration error term, used to measure the deviation between the output of the mapping function Φ and the actual observed coordinates;

[0032] λ is the surface calibration error term, used to integrate the second derivative of the mapping function Φ to constrain its curvature; λ is the regularization coefficient (λ>0), used to balance the calibration accuracy of the balancing point with the surface smoothness. Optimization solution: The mapping function Φ is represented by a parameterized model, and the loss function ε is iteratively minimized using a numerical optimization algorithm until the convergence criterion is met, thus obtaining the optimal mapping function Φ*. Distortion mapping file generation: The mapping relationship of the optimal mapping function Φ* is discretized into a lookup table (LUT) with a predetermined resolution. Each data unit of the lookup table stores the coordinate offset in a specific encoding format, and the final output is a distortion mapping table file.

[0033] Understandably, a distortion mapping file can be understood as a set of function mappings that transfer all pixels in an uncorrected image to pixels in a properly corrected image.

[0034] Step 120: Preset the distortion mapping file in the vehicle and preset the mapping model. The mapping model is used to select the distortion mapping file and correct the image according to the distortion mapping file.

[0035] Specifically, distortion mapping files are stored in the vehicle information system, allowing them to be stored locally in the vehicle and retrieved directly without a network connection. A pre-defined mapping model, which can be a system service within the vehicle, is used to receive distortion correction commands and correct the image according to steps 100-300. The mapping model receives vehicle control commands, including selecting the appropriate distortion mapping file based on the vehicle model. This approach allows for the storage of multiple distortion mapping files to accommodate the correction needs of different vehicles and vehicle types. In some more specific implementations, the mapping model can be the SurfaceFlinger module of the Android system.

[0036] In some embodiments, real-time image data is acquired, the real-time image data is adjusted according to a distortion mapping file, and corrected image data is generated; including: Step 211: The mapping model acquires real-time image data, and obtains the first coordinate set based on the real-time image data; Specifically, the mapping model acquires real-time image data, obtains a first coordinate set based on the real-time image data, and the first coordinate set can be obtained according to the pixels of the real-time image data. For example, the lower left corner can be defined as the origin of the coordinate system (0, 0) and a coordinate system can be established. The first coordinate set of all pixels is saved according to the coordinate system.

[0037] Step 221: The mapping model obtains the distortion mapping file based on the vehicle model data, and outputs the second coordinate set based on the distortion mapping file and the first coordinate set; Specifically, the corresponding distortion mapping file is obtained based on the vehicle model data. The distortion mapping file is the coordinates in the first coordinate set. The corresponding transformation function in the distortion mapping file is found for each coordinate in the first coordinate set. Each coordinate in the first coordinate set is input into the corresponding transformation function in the distortion mapping file to obtain the final second coordinate set.

[0038] Step 231: The mapping model corrects the real-time image data according to the second coordinate set and generates the first corrected image data, and outputs the first corrected image data as the corrected image data.

[0039] Specifically, the mapping model stretches, scales, and rotates real-time image data according to the second coordinate set to form the first corrected image data. In other words, each pixel in the real-time image data is rearranged according to the second coordinate set to form the first corrected image data, and this first corrected image data is output as the corrected image data.

[0040] In some embodiments, initial image data and vehicle model data of a preset area and / or real-time image data are obtained through the following methods: The system obtains initial image data and vehicle model data from the first system through a first preset protocol and / or obtains real-time image data from the first system through the first preset protocol.

[0041] Specifically, the first preset protocol is the SOME / IP protocol.

[0042] In some embodiments, outputting the corrected image data includes the following sub-steps: By invoking the Direct Rendering Manager and the Hardware Composer, the corrected image data is output via a second preset protocol. Specifically, the corrected image data is sequentially input into the Hardware Composer (HWC) and the Direct Rendering Manager (DRM), and then output through the Direct Rendering Manager. This allows the corrected image data to be processed and output within a system such as Android. The second preset protocol can be the HAB protocol.

[0043] Example 2: like Figure 4 As shown, in some other embodiments, step 200, which involves acquiring real-time image data, adjusting the real-time image data according to the distortion mapping file, and generating corrected image data, includes: Step 212: The mapping model acquires real-time image data and line-of-sight data, and obtains the first coordinate set based on the real-time image data; Specifically, real-time image data and gaze data are used. Real-time image data is acquired from cameras, while gaze data can be obtained through vehicle-mounted cameras or eye-tracking devices to capture eye movements. A mapping model is then used to obtain the real-time image data, and a first coordinate set is derived from this data. This first coordinate set can be obtained pixel-wise from the real-time image data; for example, the lower left corner can be defined as the origin (0, 0), and a coordinate system is established. The first coordinate set of all pixels is then stored according to this coordinate system. This method is used to adapt to the gaze of users at different heights, allowing for more personalized correction for different users.

[0044] Step 222: The mapping model obtains the distortion mapping file based on the vehicle model data, and obtains the third coordinate set based on the first coordinate set, the distortion mapping file, and the line-of-sight data; Specifically, based on the vehicle model data, the corresponding distortion mapping file is obtained. The distortion mapping file is the coordinates within the first coordinate set. For each coordinate in the first coordinate set, the corresponding conversion function in the distortion mapping file is found. Each coordinate in the first coordinate set is input into the corresponding conversion function in the distortion mapping file, and a second coordinate set is output. Furthermore, the second coordinate set is used to output a third coordinate set through line-of-sight data. That is, based on the adjustment of the distortion mapping file, the line-of-sight data is adjusted. For example, when looking straight ahead, the offset of the line-of-sight data can be 0. If the line of sight is too high, a downward coordinate offset can be added to the coordinates adjusted in the distortion mapping file to adapt to different user line-of-sight heights, ultimately forming a third coordinate set with line-of-sight adjustment.

[0045] Step 232: The mapping model corrects the real-time image data according to the third coordinate set and generates the second corrected image data, and outputs the second corrected image data as the corrected image data.

[0046] Specifically, the mapping model stretches, scales, and rotates real-time image data according to the third coordinate set to form the corrected second image data. In other words, each pixel in the real-time image data is rearranged according to the third coordinate set to form the second corrected image data, which is then output as the corrected image data.

[0047] In other embodiments, the process includes acquiring real-time image data, adjusting the real-time image data according to a distortion mapping file, and generating corrected image data; it also includes determining the display channel of the head-up display system and activating the graphics processor, with the mapping model correcting the real-time image data through the graphics processor.

[0048] Specifically, before calibration, the display channel of the HUD is obtained and the graphics processing unit (GPU) is enabled to avoid consuming CPU computing power during adjustment, so that the computing power is allocated more reasonably during calibration.

[0049] Example 3: like Figure 5 As shown, in some other embodiments, step 210, which involves acquiring real-time image data, adjusting the real-time image data according to the distortion mapping file, and generating corrected image data, includes: Step 213: The mapping model acquires real-time image data, and obtains the first coordinate set based on the real-time image data; Specifically, the mapping model acquires real-time image data, obtains a first coordinate set based on the real-time image data, and the first coordinate set can be obtained according to the pixels of the real-time image data. For example, the lower left corner can be defined as the origin of the coordinate system (0, 0) and a coordinate system can be established. The first coordinate set of all pixels is saved according to the coordinate system.

[0050] Step 223: The mapping model obtains the distortion mapping file, real-time image data, and dynamic adjustment data, and obtains the fourth coordinate set based on the first coordinate set, the distortion mapping file, and the dynamic adjustment data; Specifically, based on the vehicle model data, a corresponding distortion mapping file is obtained. This distortion mapping file represents the coordinates within the first coordinate set. For each coordinate in the first coordinate set, a transformation function is found within the distortion mapping file. Each coordinate in the first coordinate set is then input into the corresponding transformation function in the distortion mapping file, outputting a second coordinate set. Furthermore, this second coordinate set is dynamically adjusted to output a fourth coordinate set. The dynamic adjustment data includes one or more of rotation, offset, and scaling. This allows for real-time user adjustments, making the adjustment method more flexible.

[0051] Step 233: The mapping model corrects the real-time image data according to the fourth coordinate set and generates the third corrected image data, and outputs the third corrected image data as the corrected image data; Specifically, the mapping model stretches, scales, and rotates real-time image data according to the fourth coordinate set to form the corrected third image data. In other words, each pixel in the real-time image data is rearranged according to the fourth coordinate set to form the third corrected image data. This third corrected image data is then output as the corrected image data.

[0052] In some embodiments, real-time image data is acquired, adjusted according to a distortion mapping file, and corrected image data is generated; Acquire real-time image data and vehicle status data; Adjust real-time image data based on distortion mapping files and vehicle status data, and generate vehicle status data.

[0053] Specifically, real-time image data is acquired, along with vehicle status data, including data on the vehicle's left and right turns and up and down vibrations during movement. This allows for the adjustment of the real-time image data based on the distortion mapping file and the vehicle status data. Furthermore, based on the existing image distortion correction method as described in Example 1, a vehicle status data offset is added. For example, after the original data adjustment, if the status data shows upward vibration, the real-time image data can be shifted upward by an additional amount, making the adjustment more relevant to the real-world scenario.

[0054] In some embodiments, the mapping model acquires a distortion mapping file, real-time image data, and dynamic adjustment data, and acquires a fourth coordinate set based on a first coordinate set, the distortion mapping file, and the dynamic adjustment data; it also includes: The mapping model acquires distortion mapping files, real-time image data, and dynamic adjustment data, and loads a shading module. Specifically, the mapping model is equipped with a Skia-based rendering engine, which acquires distortion mapping files, real-time image data, and dynamic adjustment data. At the same time, the mapping model writes SkiL shaders, which are shading modules. The coloring module recombines the red, green, blue, and alpha channel components of the real-time image data based on dynamically adjusted parameters and a distortion mapping file to form a fourth coordinate set. Specifically, the coloring module recombines the RGBA channel components to form the target's fourth coordinate set.

[0055] Example 4: like Figure 8 As shown, the present invention also proposes a head-up display device, including: a first system 410, a second system 420 and a projection module 430; The first system 410 is equipped with a first data sending module 411, a first data receiving module 412, and a projection docking module 413; the first data sending module 411 acquires initial image data, vehicle model data, and real-time image data and sends them to the second system 420; the first data receiving module 412 receives corrected image data from the second system 420; the projection docking module 413 connects to and sends corrected image data to the projection module 430. The second system 420 is provided with a second data receiving module 421 and a second data sending module 423; the second data receiving module 421 is used to receive the initial image data, vehicle model data and real-time image data of the first system 410; the second data sending module 423 is used to send corrected image data to the first system 410; the data processing module 422 is used to generate and save the distortion mapping file and / or the data processing module 422 is used to adjust the real-time image data according to the distortion mapping file and generate corrected image data; Projection module 430 is used to output corrected image data to the outside world.

[0056] In some more specific embodiments, the first system is a QNX system, the second system is an Android system, the first data sending module 411 can be a QNX system APP, the first data receiving module 412 can be a data receiving interface for the QNX system, wherein the first data sending module 411 and the first data receiving module 412, the second data receiving module 421 can be an Android system interface for HUD data reception, and the second data sending module 423 can be an Android system DRM. The data processing module 422 includes at least: a SurfaceFlinger module for generating and saving distortion mapping files and converting real-time image data into corrected image data, a RenderEengine module in the SurfaceFlinger module for rendering, correction, and image overlay, an input hardware compositor (HWC), and a direct rendering manager (DRM).

[0057] Example 5: like Figures 9-10 The diagram shown is a structural schematic of an embodiment of the in-vehicle infotainment system. The specific embodiments of the present invention do not limit the specific implementation of the in-vehicle infotainment system.

[0058] As shown in the figure, the vehicle infotainment system may include: processor 502, communication interface 504, memory 506, and communication bus 508.

[0059] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508. Communication interface 504 is used to communicate with other network elements, such as clients or other servers. Processor 502 executes program 510, specifically performing the relevant steps described above in the embodiment of the image distortion correction method.

[0060] Specifically, program 510 may include program code, which includes computer-executable instructions.

[0061] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The vehicle infotainment system includes one or more processors, which may be of the same type, such as one or more CPUs; or they may be of different types, such as one or more CPUs and one or more ASICs.

[0062] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0063] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments of this invention are not directed to any particular programming language.

[0064] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. Similarly, for the sake of brevity and to aid in understanding one or more aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0065] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

[0066] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. An image distortion correction method, characterized in that, include: Acquire initial image data and vehicle model data for a preset region, and generate a distortion mapping file based on the initial image data and the vehicle model data; Acquire real-time image data, adjust the real-time image data according to the distortion mapping file, and generate corrected image data; The corrected image data is then output to the outside world.

2. The image distortion correction method according to claim 1, characterized in that, The step of acquiring initial image data and vehicle model data for a preset region, and generating a distortion mapping file based on the initial image data and the vehicle model data, includes: Acquire initial image data and vehicle model data for a preset region, and generate a distortion mapping file based on the initial image data and the vehicle model data; The distortion mapping file is preset in the vehicle and a mapping model is preset. The mapping model is used to select the distortion mapping file and correct the image according to the distortion mapping file.

3. The image distortion correction method according to claim 2, characterized in that, The process of acquiring real-time image data, adjusting the real-time image data according to the distortion mapping file, and generating corrected image data includes: The mapping model acquires the real-time image data and obtains a first coordinate set based on the real-time image data; The mapping model obtains the distortion mapping file based on the vehicle model data, and outputs a second coordinate set based on the distortion mapping file and the first coordinate set; The mapping model corrects the real-time image data according to the second coordinate set and generates first corrected image data, and outputs the first corrected image data as the corrected image data.

4. The image distortion correction method according to claim 2, characterized in that, The process of acquiring real-time image data, adjusting the real-time image data according to the distortion mapping file, and generating corrected image data includes: The mapping model acquires the real-time image data and line-of-sight data, and obtains a first coordinate set based on the real-time image data; The mapping model obtains the distortion mapping file based on the vehicle model data, and obtains a third coordinate set based on the first coordinate set, the distortion mapping file, and the line-of-sight data; The mapping model corrects the real-time image data according to the third coordinate set and generates second corrected image data, and outputs the second corrected image data as the corrected image data.

5. The image distortion correction method according to claim 2, characterized in that, The process of acquiring real-time image data, adjusting the real-time image data according to the distortion mapping file, and generating corrected image data further includes: The display channel of the head-up display system is determined and the graphics processor is activated. The mapping model corrects the real-time image data through the graphics processor.

6. The image distortion correction method according to claim 2, characterized in that, The process of acquiring real-time image data, adjusting the real-time image data according to the distortion mapping file, and generating corrected image data includes: The mapping model acquires the real-time image data and obtains a first coordinate set based on the real-time image data; The mapping model acquires the distortion mapping file, the real-time image data, and the dynamic adjustment data, and acquires a fourth coordinate set based on the first coordinate set, the distortion mapping file, and the dynamic adjustment data; The mapping model corrects the real-time image data according to the fourth coordinate set and generates third corrected image data, and outputs the third corrected image data as the corrected image data; The dynamically adjusted data includes any one or more of rotation, offset, and scaling.

7. The image distortion correction method according to claim 6, characterized in that, The mapping model acquires the distortion mapping file, the real-time image data, and the dynamic adjustment data, and acquires a fourth coordinate set based on the first coordinate set, the distortion mapping file, and the dynamic adjustment data; it also includes: The mapping model acquires the distortion mapping file, the real-time image data, and the dynamic adjustment data; the mapping model loads the coloring module. The coloring module recombines the red, green, blue, and transparency channel components of the real-time image data according to the dynamic adjustment parameters and the distortion mapping file to form the fourth coordinate set.

8. The image distortion correction method according to claim 1, characterized in that, The acquisition of initial image data and vehicle model data of the preset area and / or the acquisition of real-time image data are obtained through the following methods: The initial image data and vehicle model data are obtained from the first system through the first preset protocol, and / or the real-time image data is obtained from the first system through the first preset protocol.

9. The image distortion correction method according to claim 8, characterized in that, The step of outputting the corrected image data includes the following sub-steps: By invoking the direct rendering manager and hardware compositor, the corrected image data is output outward via a second preset protocol.

10. A head-up display device, characterized in that, Includes the first system, the second system, and the projection module; The first system is equipped with a first data sending module, a first data receiving module, and a projection docking module; the first data sending module acquires and sends initial image data, vehicle model data, and / or real-time image data to the second system; the first data receiving module receives corrected image data sent by the second system; and the projection docking module connects to and sends the corrected image data to the projection module. The second system includes a second data receiving module, a second data sending module, and a data processing module. The second data receiving module receives the initial image data, the vehicle model data, and / or the real-time image data from the first system. The data processing module generates and saves a distortion mapping file and / or adjusts the real-time image data according to the distortion mapping file to generate corrected image data. The second data sending module receives the corrected image data and sends it to the first system. The projection module is used to output the corrected image data to the outside world.

11. A vehicle infotainment system, characterized in that, Includes: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of the image distortion correction method as described in any one of claims 1-8.

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