Multi-Exposure Image Modeling Method, Device, Computer Equipment and Storage Medium

Abstract

The present application relates to a multi-exposure image modeling method, apparatus, computer device, and storage medium. The method includes: obtaining multiple sets of first images collected by multiple acquisition devices, where each set of the first images includes first sub-images corresponding to multiple sets of exposure parameters; performing image processing on the multiple first sub-images collected by each acquisition device to obtain first point cloud data, and obtaining multiple pieces of the first point cloud data based on the multiple acquisition devices; and stitching the multiple pieces of the first point cloud data into complete three-dimensional data. By adopting this method, it is possible to adapt to scanning scenarios under different ambient lights by obtaining the first sub-images of multiple sets of exposure parameters, improve the accuracy of the first point cloud data, and further improve the accuracy of the complete three-dimensional data.

Description

Technical Field

[0001] The present application relates to the technical field of three-dimensional modeling, and particularly relates to a multi-exposure image modeling method, apparatus, computer device, and storage medium. Background Art

[0002] The work of generating three-dimensional scan data by measuring a physical object is called three-dimensional scanning (3D scanning). Generally, such three-dimensional scanning can work in the following manner, that is, placing the measurement object on a rotatable turntable, and while rotating the turntable 360 degrees, using a three-dimensional scanner to scan the measurement object, thereby generating three-dimensional scan data; or rotating the three-dimensional scanner around the object to measure the object, thereby generating three-dimensional scan data.

[0003] Binocular three-dimensional scanner devices have a wide range of application prospects due to their characteristics of being portable, easy to operate, and having high cost performance, and they include three-dimensional scanners. When performing scanning, the three-dimensional scanner continuously moves and flips in the spatial position, and the binocular cameras of the three-dimensional scanner gradually measure the surface data of the scanned target, and perform a series of surface reconstructions to synthesize a triangular mesh model of the surface of the scanned object in the computer.

[0004] However, the existing scanning methods can reconstruct better three-dimensional data when the ambient light and the object material are good. However, the user's usage scenario is not fixed, and the intensity of the ambient light and the scanned object will also change. The existing scanning methods use fixed scanning parameters to adapt to unknown scanning scenarios and cannot be adjusted according to different environments, resulting in low accuracy of the three-dimensional data reconstructed in most usage scenarios. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a multi-exposure image modeling method, apparatus, computer device, and storage medium that can improve the scanning accuracy.

[0006] A multi-exposure image modeling method, the method comprising:

[0007] Obtaining multiple groups of first images collected by multiple acquisition devices, wherein each group of the first images includes first sub-images corresponding to multiple groups of exposure parameters;

[0008] Performing image processing on the multiple first sub-images collected by each acquisition device to obtain first point cloud data, and obtaining multiple pieces of the first point cloud data based on the multiple acquisition devices;

[0009] Stitching the multiple pieces of the first point cloud data into complete three-dimensional data.

[0010] In one embodiment, the obtaining multiple groups of first images collected by multiple acquisition devices includes:

[0011] Preset the priority order of multiple acquisition devices;

[0012] Multiple acquisition devices acquire the multiple groups of first images according to the priority order.

[0013] In one embodiment, the obtaining the first point cloud data by performing image processing on multiple first sub-images acquired by each acquisition device includes:

[0014] Perform image processing on the first sub-images corresponding to multiple exposure parameters acquired by each acquisition device to obtain a second image corresponding to each acquisition device, and perform three-dimensional reconstruction on the second image to obtain the first point cloud data.

[0015] In one embodiment, the splicing of the multiple first point cloud data into complete three-dimensional data includes:

[0016] Detect the image parameters of multiple first point cloud data;

[0017] Mutually cover the multiple first point cloud data according to the image parameters to obtain covered point cloud data;

[0018] Splice the covered point cloud data into complete three-dimensional data to establish a three-dimensional model.

[0019] In one embodiment, the performing image processing on the first sub-images corresponding to multiple exposure parameters acquired by each acquisition device to obtain a second image corresponding to each acquisition device includes:

[0020] Detect the pixel parameters of the pixels of multiple first sub-images acquired by each acquisition device;

[0021] According to the pixel parameters, obtain the weights corresponding to the pixel parameters of the corresponding first sub-image pixels;

[0022] Perform weighted processing on the pixel parameters of the pixels at the same position of the first sub-images acquired by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device.

[0023] In one embodiment, the performing weighted processing on the pixel parameters of the pixels at the same position of the first sub-images acquired by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device includes:

[0024] Perform average weighted processing on the pixel parameters of the pixels at the same position of the first sub-images acquired by each acquisition device to obtain a second image corresponding to each acquisition device.

[0025] An image modeling method, the method includes:

[0026] Detect the optimal exposure parameters of each acquisition device, and use the optimal exposure parameters as the preset exposure parameters;

[0027] Obtain multiple groups of third images acquired by multiple acquisition devices according to the preset exposure parameters;

[0028] Perform image processing on multiple groups of the third images to obtain second point cloud data;

[0029] Stitch multiple pieces of the second point cloud data into complete three-dimensional data.

[0030] In one embodiment, the detecting the optimal exposure parameters of each acquisition device and using the optimal exposure parameters as the preset exposure parameters includes:

[0031] Obtain multiple groups of fourth images acquired with multiple groups of exposure parameters;

[0032] Detect the image quality of multiple groups of fourth images;

[0033] Use the exposure parameters corresponding to the fourth image with the optimal image quality as the preset exposure parameters for the acquisition device.

[0034] A three-dimensional scanning system, the system includes: a three-dimensional scanning device and a data processing device connected to the three-dimensional scanning device;

[0035] The three-dimensional scanning device includes a scanning table and an acquisition device;

[0036] The acquisition device is used to acquire image information of an object to be scanned located on the scanning table, and send the acquired image information to the data processing device;

[0037] The data processing device is used to establish a three-dimensional model based on the image information.

[0038] A multi-exposure image modeling device, the device includes:

[0039] A first scanning module, configured to obtain multiple groups of first images acquired by multiple acquisition devices, wherein each group of the first images includes first sub-images corresponding to multiple groups of exposure parameters;

[0040] A first image processing module, configured to perform image processing on multiple first sub-images acquired by each acquisition device to obtain first point cloud data, and obtain multiple pieces of the first point cloud data based on multiple acquisition devices;

[0041] A first three-dimensional modeling module, configured to stitch multiple pieces of the first point cloud data into complete three-dimensional data.

[0042] An image modeling device, the device includes:

[0043] An exposure parameter optimization module for detecting the optimal exposure parameters of each acquisition device and using the optimal exposure parameters as preset exposure parameters;

[0044] A second scanning module for obtaining multiple sets of third images acquired by multiple acquisition devices according to the preset exposure parameters;

[0045] A second image processing module for performing image processing on multiple sets of the third images to obtain second point cloud data;

[0046] A second 3D modeling module for stitching multiple sets of the second point cloud data into complete 3D data.

[0047] A computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0048] Obtaining multiple sets of first images acquired by multiple acquisition devices, wherein each set of the first images includes first sub-images corresponding to multiple sets of exposure parameters;

[0049] Performing image processing on multiple first sub-images acquired by each acquisition device to obtain first point cloud data, and obtaining multiple sets of the first point cloud data based on multiple acquisition devices;

[0050] Stitching multiple sets of the first point cloud data into complete 3D data.

[0051] A computer-readable storage medium having a computer program stored thereon, and the computer program implementing the following steps when executed by a processor:

[0052] Obtaining multiple sets of first images acquired by multiple acquisition devices, wherein each set of the first images includes first sub-images corresponding to multiple sets of exposure parameters;

[0053] Performing image processing on multiple first sub-images acquired by each acquisition device to obtain first point cloud data, and obtaining multiple sets of the first point cloud data based on multiple acquisition devices;

[0054] Stitching multiple sets of the first point cloud data into complete 3D data.

[0055] A computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0056] Detecting the optimal exposure parameters of each acquisition device and using the optimal exposure parameters as preset exposure parameters;

[0057] Obtaining multiple sets of third images acquired by multiple acquisition devices according to the preset exposure parameters;

[0058] Perform image processing on multiple groups of the third images to obtain second point cloud data;

[0059] Stitch multiple pieces of the second point cloud data into complete three-dimensional data.

[0060] A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0061] Detect the optimal exposure parameters of each acquisition device, and use the optimal exposure parameters as preset exposure parameters;

[0062] Obtain multiple groups of third images acquired by multiple acquisition devices according to the preset exposure parameters;

[0063] Perform image processing on multiple groups of the third images to obtain second point cloud data;

[0064] Stitch multiple pieces of the second point cloud data into complete three-dimensional data.

[0065] The above multi-exposure image modeling method, device, computer device and storage medium, by acquiring the first sub-images of multiple groups of exposure parameters, performing image processing on multiple first sub-images to obtain the corresponding first point cloud data, and stitching multiple pieces of the first point cloud data into complete three-dimensional data, by acquiring the first sub-images of multiple groups of exposure parameters, can adapt to the scanning scenarios under different ambient lights, improve the accuracy of the first point cloud data, and further improve the accuracy of the complete three-dimensional data. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the modules of a three-dimensional scanning system in an embodiment;

[0067] Figure 2 It is a schematic flowchart of a multi-exposure image modeling method in an embodiment;

[0068] Figure 3 It is a schematic flowchart of the image modeling steps in an embodiment;

[0069] Figure 4 It is a schematic flowchart of a multi-exposure image modeling method in another embodiment;

[0070] Figure 5 It is a block diagram of the structure of a multi-exposure image modeling device in an embodiment;

[0071] Figure 6 It is a block diagram of the structure of an image modeling device in an embodiment;

[0072] Figure 7 It is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0074] The multi-exposure image modeling method provided by the present application can be applied to, for example, Figure 1 the three-dimensional scanning system as shown. The three-dimensional scanning system includes: a three-dimensional scanning device and a data processing device connected to the three-dimensional scanning device; the three-dimensional scanning device includes a scanning table and a collection device; the collection device is connected to the data processing device and is used to collect the image information of the object to be scanned located on the scanning table and send the collected image information to the data processing device; the data processing device is used to establish a three-dimensional model according to the image information. Specifically, the three-dimensional scanning device includes a plurality of collection devices and a scanning table, and the plurality of collection devices are arranged around the scanning table to collect the image information of the object to be scanned located on the scanning table. The plurality of collection devices respectively correspond to different parts of the object to be scanned. In one embodiment, the plurality of collection devices are respectively fixed on the top and bottom of the scanning table, and each collection device respectively collects the images of different parts of the object to be scanned on the scanning table. The data processing device is installed on the scanning table and is set as an integrated machine with the three-dimensional scanning device. Of course, the data processing device can also be set independently of the three-dimensional scanning device. Further, the scanning table includes a bracket and tempered glass installed on the bracket. The collection devices are installed on the bracket and are distributed above and below the tempered glass. The collection devices are all oriented towards the scanning area of the tempered glass. The data processing device is installed on the bracket and is located below the tempered glass, and is arranged to avoid the collection devices and does not block the image collection of the collection devices for the scanning area. The scanning area uses a transparent material. If the bracket is in the scanning area or blocks the collection device, it uses a transparent material. If not, it uses a non-transparent material, which is not limited herein.

[0075] Among them, the data processing device can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, portable wearable devices, independent servers or a server cluster composed of multiple servers. In this embodiment, the three-dimensional scanning device is a foot scanner, and by scanning the feet of the user, a three-dimensional model of the two feet is constructed.

[0076] In one embodiment, as Figure 2 shown, a multi-exposure image modeling method is provided. Taking the method applied to the three-dimensional scanning system in Figure 1 as an example, the method includes the following steps:

[0077] Step 202, obtaining multiple groups of first images collected by a plurality of collection devices, where each group of the first images includes first sub-images corresponding to multiple groups of exposure parameters.

[0078] Among them, the first sub-image is an image collected by the same acquisition device for the same area of the object to be scanned based on the same exposure parameters and / or different exposure parameters. The exposure parameters include exposure value and exposure time. The exposure value (Exposure Value, EV) represents all camera aperture and shutter combinations that can give the same exposure; the exposure time refers to the time interval from when the shutter opens to when it closes. During this period, the object can leave an image on the negative film. Specifically, multiple acquisition devices are used to acquire multiple groups of first images of different parts of the object to be scanned. Each acquisition device acquires a group of first images, and the first image includes multiple first sub-images collected by the same acquisition device for the same acquisition area according to different exposure parameters and / or the same exposure parameters. The acquisition areas corresponding to different groups of first images are different. Of course, the acquisition areas corresponding to different groups of first images generally have overlapping areas. In one embodiment, the multiple groups of exposure parameters include a first exposure parameter corresponding to low ambient light, a second exposure parameter corresponding to medium ambient light, and a third exposure parameter corresponding to high ambient light. The first exposure parameter under low ambient light uses a shorter exposure time, the second exposure parameter under medium ambient light uses a medium exposure time, and the third exposure parameter under high ambient light uses a longer exposure time to ensure that the pattern projected by the acquisition device can obtain a clear pattern under low, medium, and high ambient lights. Further, low ambient light refers to a lightless environment, high ambient light refers to an environment with direct light source, and medium ambient light refers to an environment with or without direct light source, an environment between low ambient light and high ambient light. The acquisition device acquires the first sub-images of the first exposure parameter, the first sub-images of the second exposure parameter, and the first sub-images of the third exposure parameter respectively according to the multiple groups of exposure parameters. In another embodiment, the multiple groups of exposure parameters are the same exposure parameters, and the acquisition device acquires multiple groups of first sub-images of the same exposure parameters respectively according to the multiple groups of exposure parameters. In still another embodiment, the multiple groups of exposure parameters include a first exposure parameter corresponding to low ambient light, a second exposure parameter corresponding to medium ambient light, a third exposure parameter corresponding to high ambient light, and a fourth exposure parameter the same as the third exposure parameter. The acquisition device acquires the first sub-images of the first exposure parameter, the first sub-images of the second exposure parameter, the first sub-images of the third exposure parameter, and the first sub-images of the fourth exposure parameter respectively according to the multiple groups of exposure parameters.

[0079] The acquiring multiple groups of first images collected by multiple acquisition devices includes: presetting the priority order of multiple acquisition devices; and multiple acquisition devices acquiring multiple groups of the first images according to the priority order.

[0080] Specifically, multiple acquisition devices are set with a priority order. When the three-dimensional scanning device performs scanning, the multiple acquisition devices are controlled to scan the image information of the object to be scanned according to the priority order, and multiple groups of the first images are acquired. In one embodiment, the number of acquisition devices is 11, numbered A1 to A11 respectively, and their priority order is A1, A2, A3... A10, A11. The acquisition order is that after the A1 acquisition device finishes acquisition, the A2 acquisition device performs acquisition; after the A2 acquisition device finishes acquisition, the A3 acquisition device performs acquisition, and acquisition is carried out in the priority order until all 11 acquisition devices have completed acquisition.

[0081] Step 204, perform image processing on the multiple first sub-images acquired by each acquisition device to obtain first point cloud data, and obtain multiple pieces of the first point cloud data based on the multiple acquisition devices.

[0082] Specifically, the data processing device detects the pixel points of the multiple first sub-images, combines the pixel points at the same position of the multiple first sub-images acquired by the same acquisition device, and only retains an image with one pixel point at the same position in the multiple first sub-images. The image with multiple pixel points is processed to obtain a second image, and the second image is three-dimensionally reconstructed to obtain first point cloud data. Similarly, multiple acquisition devices obtain corresponding first point cloud data in this way. Among them, the same position refers to the same coordinates in the first sub-image, that is, the same position on the camera imaging plane.

[0083] The performing image processing on the multiple first sub-images acquired by each acquisition device to obtain first point cloud data includes: performing image processing on the multiple first sub-images corresponding to multiple exposure parameters acquired by each acquisition device to obtain a second image corresponding to each acquisition device, and three-dimensionally reconstructing the second image to obtain first point cloud data.

[0084] Specifically, multiple first sub-images are acquired, and the pixel parameters of each pixel point in the multiple first sub-images are detected. According to the pixel parameters of the pixel points, the corresponding weights are looked up in a preset mapping table. The pixel points at the same position are weighted and processed to be combined into one pixel point, and the image at the position of the pixel point is obtained. The pixel points at the same position in each of the multiple first sub-images are processed in turn, and the processed pixel points are combined to obtain a second image. Among them, the images acquired by the acquisition device are all grayscale images. The second image is three-dimensionally reconstructed to obtain the first point cloud data corresponding to the second image. Similarly, the data processing device processes the first images acquired by multiple acquisition devices to obtain multiple second images, and three-dimensionally reconstructs the multiple second images to obtain multiple first point cloud data. Among them, the pixel parameters include exposure and / or grayscale value. The grayscale value is the value of a certain pixel coordinate point in the image, with a total of 256 levels from 0 to 255; the exposure is the exposure degree of a certain pixel coordinate point in the image. In one embodiment, the pixel parameter is the grayscale value, and the weight is the reciprocal of the number of first sub-images. That is, if the number of first sub-images is 10, the reciprocal is 1 / 10, and the weight is 1 / 10. The pixel parameters of the pixels at the same position in the first sub-images acquired by each acquisition device are averaged and weighted, and the second image is generated according to the weighted pixel parameters, that is, the weighted pixel parameters are the pixel parameters of the pixels at this position in the second image. The multiple first sub-images of each acquisition device are processed to obtain the second image corresponding to each acquisition device. In another embodiment, the weight is the value corresponding to the pixel parameter, and different numerical mapping tables are set according to different pixel parameters. The weight of the pixel point in the corresponding image in the numerical mapping table is obtained according to the pixel parameter. The pixel parameters of the pixels at the same position in the first sub-images acquired by each acquisition device are weighted according to the weight, and the second image is generated according to the weighted pixel parameters. The multiple first sub-images of each acquisition device are processed to obtain the second image corresponding to each acquisition device. Among them, the numerical mapping table includes exposure parameters, pixel parameters, and weights, and the exposure parameters and pixel parameters correspond to a weight. For example, when the exposure parameter is T1 and the pixel parameter is G1, the weight is Z1; when the exposure parameter is T1 and the pixel parameter is G2, the weight is Z2. If the exposure parameter is the exposure time and the pixel parameter is the grayscale value, the exposure time and the grayscale value correspond to a weight. When the exposure time is T1 and the grayscale value is G1, the weight is Z1; when the exposure time is T1 and the grayscale value is G2, the weight is Z2. If the exposure parameter is the exposure value and the pixel parameter is the grayscale value, the exposure value and the grayscale value correspond to a weight. When the exposure value is T1 and the grayscale value is G1, the weight is Z1; when the exposure value is T1 and the grayscale value is G2, the weight is Z2.

[0085] Step 206, stitching the multiple first point cloud data into complete three-dimensional data.

[0086] Specifically, the data processing device splices a plurality of the first point cloud data into complete three-dimensional data, and establishes a three-dimensional model according to the complete three-dimensional data.

[0087] The splicing of a plurality of the point cloud data into complete three-dimensional data includes: obtaining the image parameters of a plurality of the first point cloud data, mutually covering the plurality of the first point cloud data according to the image parameters to obtain covered point cloud data; splicing the covered point cloud data into complete three-dimensional data and establishing a three-dimensional model. Among them, the mutual covering means fusing the point clouds at the same position in a plurality of the first point cloud data in the world unified coordinate system into one point cloud, and retaining the points at different positions. The image parameters are the calibration data corresponding to the scanning table, indicating the relative position relationship of a plurality of probe heads. The image parameters are preset in the acquisition device and / or the data processing device.

[0088] Specifically, the data processing device processes the first image collected by a certain acquisition device, reads the camera parameters preset in the acquisition device, and performs three-dimensional reconstruction on the first image according to the camera parameters to obtain the first point cloud data. When the data processing device processes a plurality of the first images and obtains a plurality of the first point cloud data, the plurality of the first point cloud data are input into the world unified coordinate system, and the points at the same position in the plurality of the first point cloud data in the world unified coordinate system are fused into one point, and the points at different positions are retained to obtain covered point cloud data. The covered point cloud data is translated and rotated according to the image parameters, spliced into complete three-dimensional data, and a three-dimensional model is established according to the complete three-dimensional data.

[0089] Among them, the camera parameters are preset in the acquisition device and / or the data processing device. The camera parameters are the internal parameters and external parameters of the acquisition device; the internal parameters are the parameters related to the characteristics of the camera itself and are the inherent attributes of the camera, such as the focal length, principal point, etc. of the camera; the external parameters are the rotation and translation matrix between two cameras in the probe, indicating the relative position relationship between the two cameras. The world unified coordinate system is the absolute coordinate system of the system, and the coordinates of all points on the screen are determined by the origin of this coordinate system. In one embodiment, the object to be scanned is the feet, and the image parameters include the first calibration data corresponding to the left foot and the second calibration data corresponding to the right foot. Multiple point cloud data are input into the world unified coordinate system according to the first calibration data or the second calibration data and are mutually covered to obtain the covered point cloud data. The points in the covered point cloud data are distinguished into the point cloud data of the left foot and the point cloud data of the right foot according to the coordinate sizes of the points in the covered point cloud data. The point cloud data of the left foot is spliced according to the first calibration data, the point cloud data of the right foot is spliced according to the second calibration data, the point cloud data of the left foot and the point cloud data of the right foot are subjected to operations such as removing miscellaneous points, noise, spikes, etc. to obtain the complete three-dimensional data of the feet. A three-dimensional model of the feet is established according to the complete three-dimensional data, and data surface smoothing processing is performed to obtain the final three-dimensional model.

[0090] In the above multi-exposure image modeling method, by collecting the first sub-images of multiple groups of exposure parameters, multiple first sub-images are subjected to image processing to obtain the corresponding first point cloud data, and multiple pieces of the first point cloud data are spliced into complete three-dimensional data. By obtaining the first sub-images of multiple groups of exposure parameters, it is possible to adapt to the scanning scenarios under different ambient lights, improve the accuracy of the first point cloud data, and further improve the accuracy of the complete three-dimensional data.

[0091] In one embodiment, as Figure 3 shown, an image modeling method is provided. Taking the method applied to the Figure 1 three-dimensional scanning system as an example for illustration, it includes the following steps:

[0092] Step 302, detect the optimal exposure parameters of each acquisition device, and use the optimal exposure parameters as the preset exposure parameters.

[0093] The detecting the optimal exposure parameters of each acquisition device and using the optimal exposure parameters as the preset exposure parameters includes: obtaining multiple groups of fourth images collected by multiple groups of exposure parameters; detecting the image quality of multiple groups of fourth images; using the exposure parameters corresponding to the fourth image with the optimal image quality as the preset exposure parameters of the acquisition device.

[0094] Specifically, a single acquisition device acquires fourth images corresponding to each set of exposure parameters, detects the image quality of multiple sets of fourth images, selects the fourth image with the optimal image quality, obtains the exposure parameters corresponding to the fourth image, and sets the exposure parameters as the preset exposure parameters of multiple acquisition devices. Further, multiple acquisition devices respectively acquire fourth images corresponding to each set of exposure parameters, each of the multiple acquisition devices detects the image quality of multiple sets of fourth images, selects the fourth image with the optimal image quality, obtains the exposure parameters corresponding to the fourth image, and sets the exposure parameters as its own preset exposure parameters. In one embodiment, the data processing device analyzes the smoothness of multiple sets of fourth images and the number of fracture line displays, generates a value corresponding to the image quality, obtains the exposure parameters corresponding to the fourth image with the highest value corresponding to the image quality, and sets the exposure parameters as the preset exposure parameters of multiple acquisition devices.

[0095] Step 304, obtain multiple sets of third images acquired by multiple acquisition devices according to preset exposure parameters.

[0096] Specifically, each acquisition device acquires a set of third images according to preset exposure parameters. Among them, the images in the third images can be one or multiple.

[0097] Step 306, perform image processing on multiple sets of the third images to obtain second point cloud data.

[0098] Specifically, the data processing device performs image processing on multiple sets of third images acquired by each acquisition device to obtain second point cloud data.

[0099] Performing image processing on multiple of the third images to obtain second point cloud data includes: performing image processing on the third images corresponding to the preset exposure parameters acquired by each acquisition device, and performing three-dimensional reconstruction on the third images to obtain second point cloud data.

[0100] In one embodiment, the preset exposure parameters are optimal exposure parameters. Each acquisition device acquires one third image according to the preset exposure parameters and generates point cloud data based on the third image. In another embodiment, the preset exposure parameters are multiple sets of exposure parameters. Each acquisition device acquires multiple sets of third images according to multiple sets of exposure parameters, performs weighted processing on the pixel parameters of the same-position pixels of multiple third images acquired by each acquisition device according to weights, and generates corresponding second images based on the pixel parameters after weighted processing.

[0101] Step 308, splice multiple of the second point cloud data into complete three-dimensional data.

[0102] Specifically, the data processing device splices multiple of the second point cloud data into complete three-dimensional data and establishes a three-dimensional model based on the complete three-dimensional data.

[0103] Splicing the multiple pieces of point cloud data into complete 3D data includes: detecting the image parameters of the multiple pieces of second point cloud data, mutually covering the multiple pieces of second point cloud data according to the image parameters to obtain covered point cloud data; splicing the covered point cloud data into complete 3D data and establishing a 3D model.

[0104] In one embodiment, the object to be scanned is both feet, the image parameters include first calibration data corresponding to the left foot and second calibration data corresponding to the right foot. The point cloud data is input into the world unified coordinate system according to the first calibration data and / or the second calibration data and mutually covered to obtain covered point cloud data. The points in the covered point cloud data are classified into point cloud data of the left foot or the right foot according to the coordinate magnitudes of the points in the covered point cloud data. The point cloud data of the left foot is spliced according to the first calibration data, the point cloud data of the right foot is spliced according to the second calibration data. The point cloud data of the left foot and the right foot are subjected to operations such as removing miscellaneous points, noise, spikes, etc. to obtain complete 3D data of both feet. A 3D model of both feet is established according to the complete 3D data, and data surface smoothing processing is performed to obtain the final 3D model.

[0105] In another embodiment, as Figure 4 shown, a multi-exposure image modeling method is provided. Taking the method applied to the Figure 1 3D scanning system as an example for illustration, it includes the following steps:

[0106] Step 402, the acquisition device in the 3D scanning device sequentially acquires images of the object to be scanned with three exposure parameters and sends the images to the data processing device.

[0107] Specifically, the data processing device controls multiple acquisition devices according to software to sequentially acquire images of the object to be scanned with three exposure parameters and receives the acquired images transmitted back by the acquisition devices. When any one of the acquisition devices finishes acquiring an image, the image is immediately transmitted to the data processing device, and the data processing device processes the image. For example, when the first acquisition device finishes acquiring an image and transmits it to the data processing device, and the second acquisition device finishes acquiring an image and transmits it to the data processing device, even if the image of the first acquisition device has not been processed completely, the data processing device also processes the image of the second acquisition device.

[0108] Step 404, the data processing device receives all the images acquired by the acquisition devices, performs 3D reconstruction on the images to obtain point cloud data corresponding to the images of each acquisition device, fuses and splices the multiple pieces of point cloud data, and establishes a 3D model according to the fused and spliced point cloud data.

[0109] Specifically, after the data processing device finishes processing the images collected by all the acquisition devices, it performs 3D reconstruction on all the images to obtain corresponding point cloud data. It detects the image quality of the point cloud data at the same pixel position, covers the point cloud data with the best image quality over the point cloud data with poor image quality, splices the covered point cloud data to obtain complete 3D data, and establishes a 3D model based on the complete 3D data. It should be understood that although Figures 2 to 4 the steps in the flowchart of Figures 2 to 4 are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover,

[0110] In one embodiment, as Figure 5 shown, a multi-exposure image modeling device is provided, including: a first scanning module 510, a first image processing module 520, and a first 3D modeling module 530, where:

[0111] The first scanning module 510 is configured to obtain multiple groups of first images collected by multiple acquisition devices, where each group of the first images includes first sub-images corresponding to multiple exposure parameters.

[0112] The first image processing module 520 is configured to perform image processing on the multiple first sub-images collected by each acquisition device to obtain first point cloud data, and obtain multiple pieces of the first point cloud data based on multiple acquisition devices.

[0113] The first 3D modeling module 530 is configured to splice multiple pieces of the first point cloud data into complete 3D data.

[0114] The first scanning module 510 is further configured to preset the priority order of multiple acquisition devices; the multiple acquisition devices collect the multiple groups of first images according to the priority order.

[0115] The first image processing module 520 is further configured to perform image processing on the first sub-images corresponding to multiple exposure parameters collected by each acquisition device to obtain a second image corresponding to each acquisition device, and perform 3D reconstruction on the second image to obtain first point cloud data.

[0116] The first image processing module 520 is further configured to detect pixel parameters of multiple first sub-image pixels collected by each acquisition device; obtain weights corresponding to the pixel parameters of the corresponding first sub-image pixels according to the pixel parameters; perform weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device.

[0117] The first image processing module 520 is further configured to perform average weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device to obtain a second image corresponding to each acquisition device.

[0118] The first 3D modeling module 530 is further configured to detect image parameters of multiple first point cloud data; mutually cover the multiple first point cloud data according to the image parameters to obtain covered point cloud data; splice the covered point cloud data into complete 3D data to establish a 3D model.

[0119] In one embodiment, as Figure 6 shown, an image modeling device is provided, including: an exposure parameter optimization module 610, a second scanning module 620, a second image processing module 630, and a second 3D modeling module 640, where:

[0120] The exposure parameter optimization module 610 is configured to detect the optimal exposure parameter of each acquisition device and use the optimal exposure parameter as a preset exposure parameter;

[0121] The second scanning module 620 is configured to obtain multiple groups of third images collected by multiple acquisition devices according to the preset exposure parameter.

[0122] The second image processing module 630 is configured to perform image processing on the multiple groups of third images to obtain second point cloud data.

[0123] The second 3D modeling module 640 is configured to splice the multiple second point cloud data into complete 3D data.

[0124] The second scanning module 620 is further configured to obtain multiple groups of fourth images collected with multiple groups of exposure parameters; detect the image quality of the multiple groups of fourth images; use the exposure parameter corresponding to the fourth image with the optimal image quality as the preset exposure parameter for the acquisition device.

[0125] For the specific limitations of the multi-exposure image modeling device, reference may be made to the limitations of the multi-exposure image modeling method in the foregoing text, which will not be elaborated here. Each module in the above multi-exposure image modeling device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0126] In one embodiment, a computer device is provided. The computer device may be a data processing device, and its internal structure diagram may be as Figure 7 shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a multi-exposure image modeling method. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0127] Those skilled in the art can understand that Figure 7 the structure shown in

[0128] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0129] Obtain multiple groups of first images collected by multiple acquisition devices. Among them, each group of the first images includes first sub-images corresponding to multiple groups of exposure parameters;

[0130] Perform image processing on multiple first sub-images collected by each acquisition device to obtain first point cloud data, and obtain multiple pieces of the first point cloud data based on multiple acquisition devices;

[0131] Stitch multiple pieces of the first point cloud data into complete three-dimensional data.

[0132] In one embodiment, when the processor executes the computer program, the following steps are further implemented: The obtaining multiple groups of first images collected by multiple acquisition devices includes:

[0133] Preset the priority order of multiple acquisition devices;

[0134] Multiple acquisition devices collect the multiple groups of first images according to the priority order.

[0135] In one embodiment, when the processor executes the computer program, the following steps are further implemented: The step of obtaining the first point cloud data by performing image processing on the multiple first sub-images collected by each acquisition device includes:

[0136] Perform image processing on the first sub-images corresponding to multiple exposure parameters collected by each acquisition device to obtain a second image corresponding to each acquisition device, and perform three-dimensional reconstruction on the second image to obtain the first point cloud data.

[0137] In one embodiment, when the processor executes the computer program, the following steps are further implemented: The step of stitching the multiple first point cloud data into complete three-dimensional data includes:

[0138] Detect the image parameters of the multiple first point cloud data;

[0139] Mutually cover the multiple first point cloud data according to the image parameters to obtain the covered point cloud data;

[0140] Stitch the covered point cloud data into complete three-dimensional data to establish a three-dimensional model.

[0141] In one embodiment, when the processor executes the computer program, the following steps are further implemented: The step of performing image processing on the first sub-images corresponding to multiple exposure parameters collected by each acquisition device to obtain a second image corresponding to each acquisition device includes:

[0142] Detect the pixel parameters of the pixels of the multiple first sub-images collected by each acquisition device;

[0143] According to the pixel parameters, obtain the weights corresponding to the pixel parameters of the corresponding first sub-image pixels;

[0144] Perform weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device.

[0145] In one embodiment, when the processor executes the computer program, the following steps are further implemented: The step of performing weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device includes:

[0146] Perform average weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device to obtain a second image corresponding to each acquisition device.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0148] Obtain multiple groups of first images collected by multiple acquisition devices, where each group of the first images includes first sub-images corresponding to multiple groups of exposure parameters;

[0149] Perform image processing on multiple first sub-images collected by each acquisition device to obtain first point cloud data, and obtain multiple pieces of the first point cloud data based on multiple acquisition devices;

[0150] Stitch multiple pieces of the first point cloud data into complete three-dimensional data.

[0151] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: The obtaining of multiple groups of first images collected by multiple acquisition devices includes:

[0152] Preset the priority order of multiple acquisition devices;

[0153] Multiple acquisition devices collect the multiple groups of first images according to the priority order.

[0154] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: The performing of image processing on multiple first sub-images collected by each acquisition device to obtain first point cloud data includes:

[0155] Perform image processing on the first sub-images corresponding to multiple groups of exposure parameters collected by each acquisition device to obtain a second image corresponding to each acquisition device, and perform three-dimensional reconstruction on the second image to obtain first point cloud data.

[0156] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: The stitching of multiple pieces of the first point cloud data into complete three-dimensional data includes:

[0157] Detect the image parameters of multiple pieces of first point cloud data;

[0158] Mutually cover multiple pieces of first point cloud data according to the image parameters to obtain covered point cloud data;

[0159] Stitch the covered point cloud data into complete three-dimensional data to establish a three-dimensional model.

[0160] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: The performing of image processing on the first sub-images corresponding to multiple groups of exposure parameters collected by each acquisition device to obtain a second image corresponding to each acquisition device includes:

[0161] Detect the pixel parameters of pixels of multiple first sub-images collected by each acquisition device;

[0162] Obtain the weights corresponding to the pixel parameters of the corresponding first sub-image pixels according to the pixel parameters;

[0163] Perform weighted processing on the pixel parameters of the pixels at the same position in the first sub-images collected by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device.

[0164] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: The step of performing weighted processing on the pixel parameters of the pixels at the same position in the first sub-images collected by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device includes:

[0165] Perform average weighted processing on the pixel parameters of the pixels at the same position in the first sub-images collected by each acquisition device to obtain a second image corresponding to each acquisition device.

[0166] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0167] Detect the optimal exposure parameters of each acquisition device and use the optimal exposure parameters as preset exposure parameters;

[0168] Obtain multiple groups of third images collected by multiple acquisition devices according to the preset exposure parameters;

[0169] Perform image processing on multiple groups of the third images to obtain second point cloud data;

[0170] Stitch multiple pieces of the second point cloud data into complete three-dimensional data.

[0171] In one embodiment, when the processor executes the computer program, the following steps are further implemented: The step of detecting the optimal exposure parameters of each acquisition device and using the optimal exposure parameters as preset exposure parameters includes:

[0172] Obtain multiple groups of fourth images collected with multiple groups of exposure parameters;

[0173] Detect the image quality of multiple groups of fourth images;

[0174] Use the exposure parameters corresponding to the fourth image with the optimal image quality as the preset exposure parameters for the acquisition device.

[0175] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0176] Detect the optimal exposure parameters of each acquisition device and use the optimal exposure parameters as preset exposure parameters;

[0177] Obtain multiple groups of third images collected by multiple acquisition devices according to the preset exposure parameters;

[0178] Performing image processing on multiple groups of the third images to obtain second point cloud data;

[0179] Stitching multiple groups of the second point cloud data into complete three-dimensional data.

[0180] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: The detecting the optimal exposure parameter of each acquisition device and taking the optimal exposure parameter as the preset exposure parameter includes:

[0181] Obtaining multiple groups of fourth images acquired with multiple groups of exposure parameters;

[0182] Detecting the image quality of multiple groups of fourth images;

[0183] Taking the exposure parameter corresponding to the fourth image with the optimal image quality as the preset exposure parameter adopted by the acquisition device.

[0184] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0185] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0186] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for multi-exposure image modeling, characterized in that, the method includes: Obtaining multiple groups of first images collected by multiple acquisition devices, wherein each group of the first images includes first sub-images corresponding to multiple groups of exposure parameters, at least some of the multiple groups of exposure parameters are the same, the exposure parameters include exposure value and exposure time, and the multiple groups of exposure parameters correspond to image acquisition environments under at least two different light intensities; Detecting the pixel parameters of the pixels of multiple first sub-images collected by each acquisition device; according to the pixel parameters, obtaining the weights corresponding to the pixel parameters of the corresponding first sub-image pixels; performing weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device, performing three-dimensional reconstruction on the second image to obtain first point cloud data, and obtaining multiple pieces of the first point cloud data based on multiple acquisition devices; Stitching multiple pieces of the first point cloud data into complete three-dimensional data.

2. The method according to claim 1, characterized in that, the obtaining multiple groups of first images collected by multiple acquisition devices includes: Pre-setting the priority order of multiple acquisition devices; Multiple acquisition devices collect the multiple groups of first images according to the priority order.

3. The method according to claim 1, characterized in that, the stitching multiple pieces of the first point cloud data into complete three-dimensional data includes: Detecting the image parameters of multiple pieces of first point cloud data; Mutually covering multiple pieces of first point cloud data according to the image parameters to obtain covered point cloud data; Stitching the covered point cloud data into complete three-dimensional data to establish a three-dimensional model.

4. The method according to claim 1, characterized in that, the performing weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device includes: Performing average weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device to obtain a second image corresponding to each acquisition device.

5. An image modeling method, characterized in that, the method includes: Obtaining multiple groups of fourth images collected with multiple groups of exposure parameters; detecting the image quality of multiple groups of fourth images; using the exposure parameters corresponding to the fourth image with the optimal image quality as the preset exposure parameters adopted by the acquisition device; Obtaining multiple groups of third images collected by multiple acquisition devices according to the preset exposure parameters; Performing weighted processing on the pixel parameters of the pixels at the same position of multiple third images collected by each acquisition device according to the weights corresponding to the preset exposure parameters to obtain a corresponding fourth image, obtaining a fourth image corresponding to each acquisition device, performing three-dimensional reconstruction on the fourth image to obtain second point cloud data; Stitching multiple pieces of the second point cloud data into complete three-dimensional data.

6. A three-dimensional scanning system, characterized in that, the system includes: a three-dimensional scanning device and a data processing device connected to the three-dimensional scanning device; the three-dimensional scanning device includes a scanning table and an acquisition device; The acquisition device is used to acquire multiple sets of first images of the object to be scanned located on the scanning table, and send the multiple first images to the data processing device. Wherein, each set of the first images includes first sub-images corresponding to multiple sets of exposure parameters, at least some of the multiple sets of exposure parameters are the same, the exposure parameters include exposure value and exposure time, and the multiple sets of exposure parameters correspond to image acquisition environments under at least two different light intensities. Or, the acquisition device is used to acquire multiple sets of third images of the object to be scanned located on the scanning table according to preset exposure parameters, and send the multiple third images to the data processing device. Wherein, the preset exposure parameters are the exposure parameters corresponding to the fourth image with the optimal image quality obtained by the data processing device detecting the image quality of multiple sets of fourth images acquired by multiple sets of exposure parameters; The data processing device is used to detect the pixel parameters of the pixels of multiple first sub-images acquired by each acquisition device; according to the pixel parameters, obtain the weights corresponding to the pixel parameters of the corresponding first sub-image pixels; perform weighted processing on the pixel parameters of the pixels at the same position of the first sub-images acquired by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device, perform three-dimensional reconstruction on the second image to obtain first point cloud data, and obtain multiple pieces of the first point cloud data based on multiple acquisition devices; detect the image parameters of the multiple pieces of first point cloud data; mutually cover the multiple pieces of first point cloud data according to the image parameters to obtain covered point cloud data, where mutual covering means fusing the point clouds at the same position in the multiple pieces of first point cloud data in the world unified coordinate system into one point cloud, and retaining the points at different positions; splice the covered point cloud data into complete three-dimensional data to establish a three-dimensional model. Or, the data processing device is used to perform weighted processing on the pixel parameters of the pixels at the same position of multiple third images acquired by each acquisition device according to the weights corresponding to the preset exposure parameters to obtain corresponding fourth images, obtain the fourth images corresponding to each acquisition device, perform three-dimensional reconstruction on the fourth images to obtain second point cloud data, and splice the multiple pieces of second point cloud data into complete three-dimensional data.

7. A multi-exposure image modeling device, characterized in that, the device includes: A first scanning module, configured to obtain multiple sets of first images acquired by multiple acquisition devices. Wherein, each set of the first images includes first sub-images corresponding to multiple sets of exposure parameters, at least some of the multiple sets of exposure parameters are the same, the exposure parameters include exposure value and exposure time, and the multiple sets of exposure parameters correspond to image acquisition environments under at least two different light intensities; A first image processing module, configured to detect pixel parameters of a plurality of first sub-image pixels collected by each acquisition device; obtain weights corresponding to the pixel parameters of the corresponding first sub-image pixels according to the pixel parameters; perform weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device according to the weights to obtain a second image corresponding to each acquisition device, perform three-dimensional reconstruction on the second image to obtain first point cloud data, and obtain a plurality of the first point cloud data based on a plurality of acquisition devices; A first three-dimensional modeling module, configured to detect image parameters of a plurality of first point cloud data; perform mutual coverage on the plurality of first point cloud data according to the image parameters to obtain first covered point cloud data, where mutual coverage means fusing the point clouds at the same position in the plurality of first point cloud data in the world unified coordinate system into one point cloud and retaining the points at different positions; splice the first covered point cloud data into complete three-dimensional data to establish a three-dimensional model.

8. The apparatus according to claim 7, wherein, the first scanning module is further configured to preset a priority order of a plurality of acquisition devices; and the plurality of acquisition devices acquire the plurality of groups of first images according to the priority order.

9. The apparatus according to claim 7, wherein, the first image processing module is further configured to perform average weighted processing on the pixel parameters of the pixels at the same position of the first sub-images collected by each acquisition device to obtain a second image corresponding to each acquisition device.

10. An image modeling apparatus, wherein, the apparatus includes: A second scanning module is configured to acquire a plurality of groups of fourth images collected with a plurality of exposure parameters; detect the image quality of the plurality of groups of fourth images; use the exposure parameter corresponding to the fourth image with the optimal image quality as the preset exposure parameter for the acquisition device; acquire a plurality of groups of third images acquired by a plurality of acquisition devices according to the preset exposure parameter; A second image processing module, configured to perform weighted processing on the pixel parameters of the pixels at the same position of multiple third images collected by each acquisition device according to the weights corresponding to the preset exposure parameter to obtain a corresponding fourth image, obtain a fourth image corresponding to each acquisition device, perform three-dimensional reconstruction on the fourth image to obtain second point cloud data; A second three-dimensional modeling module, configured to splice the plurality of second point cloud data into complete three-dimensional data.

11. A computer device, including a memory and a processor, the memory stores a computer program, wherein, when the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 or claim 5 are implemented.

12. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 or claim 5 are implemented.

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