Image correction method, electronic equipment and storage medium

Abstract

The invention discloses an image correction method, electronic equipment and a storage medium, and the method comprises the steps: correcting each first object in a to-be-corrected image through a conversion matrix determined according to the coordinate information of at least two target objects having a mapping relation in a training image and a calibration original image, and obtaining a corrected image corresponding to the to-be-corrected image, according to the method, the mapping relation between the target object of the training image and the target object of the calibration original image is established, and the conversion matrix is generated through the mapping relation, so that the to-be-corrected image forms the correction image through the conversion matrix, and only the target object of the training image and the target object of the calibration original image need to be recognized when the conversion matrix is generated; therefore, compared with the prior art, the correction image can be generated through the conversion matrix as long as the first object included in the to-be-corrected image is identified, a stable correction result can be obtained when shooting light is insufficient, and the correction stability is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to image correction technology, and relate to but are not limited to an image correction method, an electronic device, and a storage medium. Background Art

[0002] Due to the tolerance of electronic equipment in production and assembly process, when the camera of the electronic equipment is placed in the same position and faces the same direction to shoot the same target object, the captured image will have different degrees of position deviation. Therefore, the electronic equipment needs to perform image correction on the captured image so that the captured images obtained by different cameras in the same direction can be consistent. Image correction in the prior art generally adopts checkerboard correction, but the checkerboard correction method requires the captured image to be clear. Only clear images can form stable correction results through pixel analysis during checkerboard correction. Clear captured images require sufficient light in the shooting environment. Otherwise, insufficient light will also cause deviations in the correction results of different cameras, which has high requirements for the shooting environment. Summary of the invention

[0003] In view of this, the image correction method, electronic device and storage medium provided in the embodiments of the present application can obtain stable correction results when the shooting light is insufficient, thereby improving the correction stability.

[0004] In a first aspect, an image correction method provided by an embodiment of the present application includes: acquiring an image to be corrected, the image to be corrected including at least one first object, each first object including characters; correcting each first object in the image to be corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected, the transformation matrix being determined based on coordinate information of at least two target objects having a mapping relationship between a training image and a calibration original image, each target object including characters.

[0005] In the above-mentioned image correction method, each first object in the image to be corrected is corrected by determining a transformation matrix based on coordinate information of at least two target objects having a mapping relationship between the training image and the calibration original image, so as to obtain a corrected image corresponding to the image to be corrected. That is, the present application establishes a mapping relationship between the target object of the training image and the target object of the calibration original image, and generates a transformation matrix through the mapping relationship, so that the image to be corrected forms a corrected image through the transformation matrix. Since only the target object of the training image and the target object of the calibration original image need to be identified when generating the transformation matrix, compared with the prior art, the present application only needs to identify the first object included in the image to be corrected, and can generate a corrected image through the transformation matrix, so as to obtain a stable correction result when shooting in insufficient light, thereby improving the correction stability.

[0006] In some embodiments, before acquiring the image to be corrected, the method also includes: photographing the calibration original image to obtain the training image; obtaining at least one first target object of the training image; obtaining at least one second target object of the calibration original image; determining at least one group of target objects, each group of target objects including the first target object and the second target object having a mapping relationship; and generating the transformation matrix according to the coordinate information of the at least one group of target objects.

[0007] In some embodiments, determining at least one group of target objects includes: determining from the training image at least one target first target object that is identical to the characters included in each second target object; determining the target relative position of the characters included in each second target object in the corresponding second target object; determining, from the at least one target first target object corresponding to each second target object, a first target object whose relative position of the characters in the corresponding target first target object is identical to the target relative position; and determining the at least one group of target objects based on the first target object corresponding to each second target object and each second target object.

[0008] In some embodiments, obtaining at least one first target object of the training image includes: performing image recognition on the training image to obtain at least one initial first target object in the training image; stretching the at least one initial first target object to obtain at least one standard first target object so that the at least one standard first target object is a target shape; rotating the at least one standard first target object based on a preset rotation angle to obtain the at least one first target object.

[0009] In some embodiments, there are multiple training images, and different training images are shot at different angles. Obtaining at least one first target object of the training images includes: performing image recognition on each of the multiple training images, and obtaining at least one initial first target object in the multiple training images based on the objects recognized in each training image; stretching the at least one initial first target object to obtain at least one standard first target object so that the at least one standard first target object is a target shape; and using the at least one standard first target object as the at least one first target object.

[0010] In some embodiments, stretching the at least one initial first target object to obtain at least one standard first target object includes: transforming the at least one initial first target object based on a perspective correction matrix corresponding to the target shape to obtain the at least one standard first target object.

[0011] In some embodiments, generating the transformation matrix according to the coordinate information of the at least one group of target objects includes: obtaining the coordinate information of the first target object and the coordinate information of the second target object in each group of target objects; placing the coordinate information of the first target object of the at least one group of target objects into a first array in sequence based on a preset order; placing the coordinate information of the second target object of the at least one group of target objects into a second array in sequence based on the preset order; and calling a matrix generation function according to the first array and the second array to generate the transformation matrix.

[0012] In some embodiments, the first target object and / or the second target object both include multiple vertices, the preset order includes an object priority order and a vertex priority order, and the coordinate information of the first target object of the at least one group of target objects is sequentially placed into the first array based on the preset order, including: determining a first target object of a first priority object, the first priority object being a group of target objects of the first order in the object priority order; sequentially placing all vertex coordinates in the first target object of the first priority object into the first array based on the vertex priority order; and sequentially placing all vertex coordinates of the first target object in other priority objects into the first array according to the method of placing all vertex coordinates of the first target object in the first priority object;

[0013] The step of placing the coordinate information of the second target object of the at least one group of target objects into the second array in sequence based on the preset order includes: determining the second target object of the first priority object; placing all vertex coordinates of the second target object of the first priority object into the second array in sequence based on the vertex priority order; and placing all vertex coordinates of the second target object in other priority objects into the second array in sequence according to the method of placing all vertex coordinates of the second target object in the first priority object.

[0014] In some embodiments, the at least one group of target objects are all geometric bodies, and the geometric bodies include two-dimensional geometric bodies or three-dimensional geometric bodies.

[0015] In a second aspect, an embodiment of the present application provides an electronic device, which includes: an image acquisition module, used to acquire an image to be corrected, the image to be corrected includes at least one first object, and each first object includes characters; an image correction module, used to correct each first object in the image to be corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected, the transformation matrix is ​​determined based on coordinate information of at least two target objects having a mapping relationship between a training image and a calibration original image, and each target object includes characters.

[0016] In a third aspect, a computer device provided by an embodiment of the present application includes a memory and a processor. The memory stores a computer program that can run on the processor, and when the processor executes the program, the method described in the embodiment of the present application is implemented.

[0017] In a fourth aspect, a computer-readable storage medium provided by an embodiment of the present application stores a computer program thereon, and when the computer program is executed by a processor, the method provided by the embodiment of the present application is implemented.

[0018] It should be understood that the technical solutions of the second to fourth aspects of the embodiments of the present application are consistent with those of the first aspect of the embodiments of the present application, and the beneficial effects obtained by each aspect and the corresponding feasible implementation manners are similar, and will not be elaborated herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings herein are incorporated into the specification and constitute a part of this specification. These drawings illustrate embodiments consistent with the present application and, together with the specification, are used to explain the technical solutions of the present application.

[0020] Figure 1 It is a schematic flowchart of the image correction method provided by the embodiment of the present application;

[0021] Figure 2 It is a comparison diagram of the image correction results provided by the embodiment of the present application;

[0022] Figure 3 It is a schematic flowchart of the image correction method provided by the embodiment of the present application;

[0023] Figure 4 It is an image of the training image and the calibrated original image provided by the embodiment of the present application;

[0024] Figure 5 It is a schematic flowchart of the image correction method provided by the embodiment of the present application;

[0025] Figure 6 It is a schematic flowchart of the image correction method provided by the embodiment of the present application;

[0026] Figure 7 It is a schematic diagram of the images obtained after rotating the rectangle including the character "Bingma" by 0°, 90°, 180°, and 270° respectively, provided by the embodiment of the present application;

[0027] Figure 8 It is a schematic flowchart of the image correction method provided by the embodiment of the present application;

[0028] Fig. 9 It is a schematic flowchart of the image correction method provided by the embodiment of the present application;

[0029] Fig.10 A schematic diagram of a flow chart of an image correction method provided in an embodiment of the present application;

[0030] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0031] Fig.12 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the specific technical solution of the present application will be further described in detail below in conjunction with the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0034] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0035] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present application are used to distinguish similar or different objects, and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0036] The embodiment of the present application provides an image correction method, which is applied to an electronic device, which can be various types of devices with information processing capabilities during implementation. For example, the electronic device can include a personal computer, a laptop computer, a PDA or a server, etc.; the electronic device can also be a mobile terminal, for example, the mobile terminal can include a mobile phone, a car computer, a tablet computer or a projector, etc. The function implemented by the method can be implemented by calling the program code by the processor in the electronic device, and of course the program code can be stored in a computer storage medium. It can be seen that the electronic device at least includes a processor and a storage medium.

[0037] Figure 1 A schematic diagram of a flow chart of an image correction method provided in an embodiment of the present application. Figure 1 As shown, the method may include the following steps:

[0038] Step S101, obtaining an image to be corrected, where the image to be corrected includes at least one first object, and each first object includes a character.

[0039] It should be understood that the above-mentioned image to be corrected is an image that needs to be corrected, which can be a picture taken directly by the user, or can be extracted from a picture taken by the user. For example, when an ID image needs to be uploaded for identity verification, the user points the camera directly at the ID to shoot. At this time, the captured image only contains the ID image and does not contain other images. Alternatively, the user does not point the camera directly at the ID to shoot, and the captured image contains other images in addition to the ID image, for example, the desktop where the ID is placed. At this time, it is necessary to extract the ID image from the picture, that is, the image to be corrected.

[0040] Optionally, the first object may be a geometric body including characters, and the geometric body may be a two-dimensional geometric body or a three-dimensional geometric body, and the two-dimensional geometric body may be a quadrilateral, a pentagon, etc., and the three-dimensional geometric body may be a cube, a cone, etc., and this application does not make any restrictions, and the geometric body whose vertices can be recognized by the electronic device shall prevail. The characters may be symbols, Chinese characters, letters, numbers, etc., and this application does not make any restrictions, and the characters that can be recognized by the electronic device shall prevail.

[0041] Optionally, the at least one first object may be evenly distributed in the image to be corrected, or may be scattered in the image with correction. This application does not impose any limitation on this, and it is set by those skilled in the art according to actual conditions.

[0042] Optionally, the above-mentioned image to be corrected can be obtained through an internal camera of the electronic device, or by receiving an image sent by an external camera. This application does not impose any restrictions and is set by technical personnel in this field according to actual conditions.

[0043] Step S102, correcting each first object in the image to be corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected, wherein the transformation matrix is ​​determined based on coordinate information of at least two target objects having a mapping relationship between the training image and the calibration original image, and each target object includes a character.

[0044] It should be understood that the above mapping relationship is the corresponding relationship between at least two target objects in the training image and at least two target objects in the calibrated original image. For example, the training image includes object A, object B, and object C, the calibrated original image includes object D, object E, and object F, and object A in the training image has a mapping relationship with object D in the calibrated original image, object B in the training image has a mapping relationship with object E in the calibrated original image, and object C in the training image has a mapping relationship with object F in the calibrated original image. Then, the transformation matrix is determined according to the coordinate information of object A, object B, and object C and object D, object E, and object F, so that after the training image is processed by the transformation matrix, object A in the training image is transformed into object D, object B in the training image is transformed into object E, and object C in the training image is transformed into object F. Thus, at least one first object in the image to be corrected can obtain a corrected image after being processed by the transformation matrix.

[0045] Exemplarily, as Figure 2 shown, the left figure is the image to be corrected, and the right figure is the corrected image obtained after being processed by the transformation matrix. After being processed by the transformation matrix, the quadrilateral in the image to be corrected in an inverted state with a mapping relationship can be transformed into a rectangle in the standard direction in the corrected image. For example, there is a mapping relationship between the quadrilateral containing the character "Jiama" in the inverted state in the image to be corrected and the rectangle containing the character "Jiama" in the standard direction in the corrected image. Then, after being processed by the transformation matrix, the quadrilateral containing the character "Jiama" in the inverted state in the image to be corrected can be transformed into a rectangle containing the character "Jiama" in the standard direction in the corrected image, and the same applies to other quadrilaterals.

[0046] In the above image correction method, by using the transformation matrix determined according to the coordinate information of at least two target objects with a mapping relationship in the training image and the calibrated original image, each first object in the image to be corrected is corrected to obtain a corrected image corresponding to the image to be corrected. That is, in the present application, by establishing a mapping relationship between the target objects in the training image and the target objects in the calibrated original image, and generating a transformation matrix through the mapping relationship, so that the image to be corrected forms a corrected image through the transformation matrix. Since only the target objects in the training image and the target objects in the calibrated original image need to be recognized when generating the transformation matrix, compared with the prior art, as long as the first object included in the image to be corrected is recognized in the present application, a corrected image can be generated through the transformation matrix, and a stable correction result can be obtained when the shooting light is insufficient, improving the correction stability.

[0047] Figure 3 It is a schematic flowchart of an image correction method provided by an embodiment of the present application. As Figure 3 shown, based on Figure 1 the method shown, the method may further include the following steps:

[0048] Step S103, photographing the calibration original image to obtain a training image.

[0049] It should be understood that the calibration original image is a standard image including all target objects, and the training image shot for the calibration original image also includes all or part of the target objects in the calibration original image, and the number of target objects included depends on the shooting angle.

[0050] Optionally, the above-mentioned method of photographing the calibration original image can be to manually fix the camera at a certain position, and use the position as the shooting position for photographing the calibration original image. When the camera receives a shooting instruction, it shoots the fixed position, and uses the photographed image as a training image; or the camera can judge the object within the shooting range when acquiring the image. If it is consistent with the object of the calibration original image, the shooting operation is performed to obtain the training image. If it is inconsistent with the object of the calibration original image, the shooting operation is not performed.

[0051] Step S104: obtaining at least one first target object of the training image.

[0052] Step S105: obtaining at least one second target object of the calibration original image.

[0053] Optionally, the at least one first target object and the at least one second target object may be obtained by image recognition. When it is recognized that there is content belonging to the target object in the training image and the calibration original image, the content is separately segmented out as the target object.

[0054] Step S106: determining at least one group of target objects, each group of target objects including a first target object and a second target object having a mapping relationship.

[0055] It should be understood that although the training image is obtained by photographing the calibration original image, the training image includes all target objects of the calibration original image. However, due to the influence of the shooting angle, there is a directional inconsistency between the first target object presented in the training image and the second target object presented in the calibration original image. However, the first target object and the second target object are essentially the same content. Therefore, there is at least one group of target objects that have a mapping relationship between the first target object of the training image and the second target object of the calibration original image.

[0056] Optionally, at least one group of target objects can be determined through an image recognition method. First, each target object is segmented according to the interval distance between the target objects. When it is identified that there is only a directional difference between the pixel points of a first target object and a second target object, it is determined that the first target object and the second target object are a group of target objects.

[0057] For example, Figure 4As shown in the figure, taking the left figure as the training image and the right figure as the calibrated original image as an example, there is a mapping relationship between the quadrilateral containing the character "Jiamǎ" in the inverted state in the training image and the rectangle containing the character "Jiamǎ" in the standard direction in the calibrated original image. Then, it can be determined that the quadrilateral containing the character "Jiamǎ" in the inverted state in the training image and the rectangle containing the character "Jiamǎ" in the standard direction in the calibrated original image are a pair of target objects with each other.

[0058] Optionally, at least one group of the above target objects can be geometric bodies, and the geometric bodies can be two-dimensional geometric bodies or three-dimensional geometric bodies.

[0059] Step S107, generate a transformation matrix according to the coordinate information of at least one group of target objects.

[0060] Optionally, the method for generating the transformation matrix can be to call a matrix generation function with the coordinates of at least one group of target objects, such as the findHomography function of the opencv platform, etc., to generate a corresponding matrix, and use this matrix as the transformation matrix.

[0061] In the above image correction method, after determining the first target object and the second target object that are a pair of target objects with each other from the training image and the calibrated original image, and generating a transformation matrix according to the coordinate information of at least one group of target objects, a transformation matrix that can convert the training image to the calibrated original image can be accurately obtained, so as to improve the correction stability of converting the image to be corrected to the corresponding corrected image.

[0062] Figure 5 It is a schematic flowchart of an image correction method provided by an embodiment of the present application. As Figure 5 shown, the method may include the following steps:

[0063] Step S201, photograph the calibrated original image to obtain a training image.

[0064] Step S202, obtain at least one first target object of the training image.

[0065] Step S203, obtain at least one second target object of the calibrated original image.

[0066] The execution manners of the above steps S201 to S203 are the same as those of the above steps S103 to S105, and will not be elaborated here.

[0067] Step S204, determine at least one target first target object from the training image that includes the same characters as each second target object.

[0068] Optionally, the characters of the above-mentioned first target object and second target object can be recognized through optical character recognition (OCR).

[0069] Exemplarily, as Figure 4 shown, taking the left figure as the training image and the right figure as the calibrated original image as an example, the characters of the quadrilateral containing the character "Jiamǎ" in the inverted state in the training image are the same as those of the rectangle containing the character "Jiamǎ" in the standard direction in the calibrated original image. In this embodiment, the standard direction is the horizontal direction, that is, the quadrilateral containing the character "Jiamǎ" in the inverted state in the training image can be used as the target first target object corresponding to the rectangle containing the character "Jiamǎ" in the standard direction in the calibrated original image.

[0070] Step S205, determine the target relative positions of the characters included in each second target object in the corresponding second target object.

[0071] Step S206, from at least one target first target object corresponding to each second target object, determine the first target object in which the relative position of the character in the corresponding target first target object is the same as the target relative position.

[0072] Step S207, according to the first target object corresponding to each second target object and each second target object, determine at least one group of target objects.

[0073] There may be multiple target first target objects in the calibrated original image that have the same characters as those included in the second target object. Therefore, in order to determine the target first target object that best matches the second target object from multiple target first target objects, it is necessary to judge the relative positions of the characters to improve the matching accuracy between objects.

[0074] Exemplarily, in the rectangle of the character "Jiamǎ" in the calibrated original image, the character "Jiamǎ" is located at a position偏左 (left - biased) in the rectangle. Then, it is necessary to determine from the multiple quadrilaterals including the character "Jiamǎ" in the training image the quadrilateral in which the character "Jiamǎ" is also located at a left - biased position in the quadrilateral.

[0075] Step S208, generate a transformation matrix according to the coordinate information of at least one group of target objects.

[0076] The execution manner of the above - mentioned step S208 is the same as that of the above - mentioned step S107, and will not be elaborated here.

[0077] Step S209, obtain the image to be corrected. The image to be corrected includes at least one first object, and each first object includes characters.

[0078] Step S210, correcting each first object in the image to be corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected, wherein the transformation matrix is ​​determined based on coordinate information of at least two target objects having a mapping relationship between the training image and the calibration original image, and each target object includes a character.

[0079] The execution method of the above steps S209 to S210 is consistent with the execution method of the above steps S101 to S102, and will not be repeated here.

[0080] In the above-mentioned image correction method, by determining the first target object whose characters and relative positions are consistent with the second target object from the training image, the first target object and the second target object which are a group of target objects are obtained, thereby improving the matching accuracy between the first target object and the second target object.

[0081] Figure 6 A flowchart of an image correction method provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the method may include the following steps:

[0082] Step S301, photographing the calibration original image to obtain a training image.

[0083] Step S302: performing image recognition on the training image to obtain at least one initial first target object in the training image.

[0084] Step S303: stretch at least one initial first target object to obtain at least one standard first target object, so that the at least one standard first target object is a target shape, and the target shape is consistent with the shape of the second target object.

[0085] In the training image, due to the shooting angle, the first target object presented in the training image is mostly in a skewed state. For example, the target object that is a rectangle in the calibration original image becomes a quadrilateral in the training image. Therefore, after obtaining the quadrilateral in the training image, the quadrilateral needs to be stretched to obtain the same shape as the calibration original. Figure 1 The characters in the quadrilateral are also in a horizontal direction to facilitate character recognition by electronic devices.

[0086] Optionally, the stretching method may be: transforming at least one initial first target object based on a perspective correction matrix corresponding to the target shape to obtain at least one standard first target object.

[0087] Step S304: Rotate at least one standard first target object based on a preset rotation angle to obtain at least one first target object.

[0088] After obtaining the standard first target object, to increase the probability of identifying the first target object that forms a pair of target objects with the second target object, it is necessary to enhance the presentation effect of the first target object at different angles. Therefore, the standard first target object is rotated at different angles.

[0089] Optionally, when there are multiple preset rotation angles, the rotation angle of the current first target object can be determined by the change in the vertex coordinates after rotation.

[0090] Exemplarily, Figure 7 For the rectangle including the character "Bingma", the images obtained after rotating it at angles of 0°, 90°, 180°, and 270° respectively are shown. Correspondingly, the vertex coordinates of the rectangle also change. For example, the vertex numbers of the upper left corner point, upper right corner point, lower right corner point, and lower left corner point of the rectangle are No. 1, No. 2, No. 3, and No. 4 respectively. Initially at the 0° angle, the coordinate of No. 1 is A, but after rotating 90°, the coordinate of No. 1 becomes B, and the other vertex coordinates also change accordingly. Then the electronic device can determine the current rotation angle through the coordinate change of the rectangle vertices.

[0091] Step S305, obtain at least one second target object of the calibrated original image.

[0092] Step S306, determine at least one group of target objects, where each group of target objects includes a first target object and a second target object with a mapping relationship.

[0093] Step S307, generate a transformation matrix according to the coordinate information of at least one group of target objects.

[0094] The execution manners of the above steps S305 to S307 are the same as those of the above steps S105 to S107, and will not be elaborated here.

[0095] Step S308, obtain the image to be corrected, where the image to be corrected includes at least one first object, and each first object includes characters.

[0096] Step S309, correct each first object in the image to be corrected according to the preset transformation matrix to obtain a corrected image corresponding to the image to be corrected. The transformation matrix is determined according to the coordinate information of at least two target objects with a mapping relationship in the training image and the calibrated original image, and each target object includes characters.

[0097] The execution manners of the above steps S308 to S309 are the same as those of the above steps S101 to S102, and will not be elaborated here.

[0098] In the above image correction method, after obtaining the first target object, the initial first target object is stretched and rotated to obtain a result corresponding to the calibrated original Figure 1 The first target object and the second target object are identified as the same target object, thereby increasing the probability of recognizing that the first target object and the second target object are a group of target objects.

[0099] Figure 8 A flowchart of an image correction method provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, the method may include the following steps:

[0100] Step S401, photographing the calibration original image to obtain a training image.

[0101] Step S402, performing image recognition on each of the multiple training images, and obtaining at least one initial first target object in the multiple training images according to the object recognized in each training image, wherein different training images are shot at different angles.

[0102] Step S403: stretching the at least one initial first target object to obtain at least one standard first target object, so that the at least one standard first target object is a target shape, and the target shape is consistent with the shape of the second target object.

[0103] Step S404: taking at least one standard first target object as at least one first target object.

[0104] In this embodiment, since there are multiple training images and each training image is shot at a different angle, it is possible to ensure that the first target object of the character at different angles is obtained, which is consistent with the presentation effect of the first target object obtained after the rotation in step S404. Therefore, in this embodiment, it is only necessary to stretch the initial first target object into a target shape, thereby determining the first target object that is consistent with the second target object in terms of the relative position between characters.

[0105] Step S405: obtaining at least one second target object of the calibration original image.

[0106] Step S406: determining at least one group of target objects, each group of target objects including a first target object and a second target object having a mapping relationship.

[0107] Step S407: generating a transformation matrix according to coordinate information of at least one set of target objects.

[0108] Step S408: acquiring an image to be corrected, wherein the image to be corrected includes at least one first object, and each first object includes a character.

[0109] Step S409, correcting each first object in the image to be corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected, wherein the transformation matrix is ​​determined based on coordinate information of at least two target objects having a mapping relationship between the training image and the calibration original image, and each target object includes a character.

[0110] The execution method of the above steps S405 to S409 is consistent with the execution method of the above steps S305 to S309, and will not be repeated here.

[0111] In the above-mentioned image correction method, by shooting multiple training images, and each training image is shot at a different angle, so as to obtain the first target objects at different angles, thereby determining the first target object that is consistent with the second target object in both characters and character relative positions among the first target objects in the multiple training images, thereby determining a group of target objects.

[0112] Fig. 9 A flowchart of an image correction method provided in an embodiment of the present application is shown in FIG. Fig. 9 As shown, the method may include the following steps:

[0113] Step S501, photographing the calibration original image to obtain a training image.

[0114] Step S502: obtaining at least one first target object of a training image.

[0115] Step S503: obtaining at least one second target object of the calibration original image.

[0116] Step S504: determining at least one group of target objects, each group of target objects including a first target object and a second target object having a mapping relationship.

[0117] The execution method of the above steps S501 to S504 is consistent with the execution method of the above steps S103 to S106, and will not be repeated here.

[0118] Step S505: obtaining coordinate information of the first target object and coordinate information of the second target object in each group of target objects.

[0119] Optionally, the coordinate information of the first target object and the second target object may be the coordinate information of the contour points of the first target object and the second target object. In some embodiments, the first target object and / or the second target object both include multiple vertices, the coordinate information of the first target object is the coordinates of the multiple vertices of the first target object, and the coordinate information of the second target object is the coordinates of the multiple vertices of the second target object. For example, taking the first target object or the second target object as an example, the rectangle includes an upper left corner point, an upper right corner point, a lower right corner point, and a lower left corner point. Figure 7As shown, each vertex can be identified by a vertex number, and the vertex numbers of the upper left corner, upper right corner, lower right corner, and lower left corner are 1, 2, 3, and 4 respectively.

[0120] Step S506: placing the coordinate information of the first target object of at least one group of target objects into the first array in sequence based on a preset order.

[0121] Optionally, the preset order includes an object priority order and a vertex priority order, and the above step S506 may include the following steps:

[0122] Step S5061, determining a first target object of a first priority object, where the first priority object is a group of target objects that are ranked first in the object priority sequence;

[0123] Step S5062, placing all vertex coordinates in the first target object of the first priority object into the first array in sequence based on the vertex priority order;

[0124] Step S5063: According to the method of placing all vertex coordinates of the first target object in the first priority object, all vertex coordinates of the first target object in other priority objects are placed in the first array in sequence.

[0125] Step S507: placing the coordinate information of the second target object of at least one group of target objects into the second array in sequence based on a preset order.

[0126] Optionally, the preset order includes an object priority order and a vertex priority order, and the above step S507 may include the following steps:

[0127] Step S5071, determining a second target object of a first priority object, where the first priority object is a group of target objects that are ranked first in the object priority sequence;

[0128] Step S5072, placing all vertex coordinates in the second target object of the first priority object into the second array in sequence based on the vertex priority order;

[0129] Step S5073: According to the method of placing all vertex coordinates of the second target object in the first priority object, all vertex coordinates of the second target object in other priority objects are placed in the second array in sequence.

[0130] Step S508, calling a matrix generation function according to the first array and the second array to generate a conversion matrix.

[0131] The above matrix generation function adopts common technical means in the prior art. The specific function is set by technical personnel in this field according to actual needs, and this application does not impose any limitation.

[0132] Step S509: acquiring an image to be corrected, where the image to be corrected includes at least one first object, and each first object includes a character.

[0133] Step S510, correcting each first object in the image to be corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected, wherein the transformation matrix is ​​determined based on coordinate information of at least two target objects having a mapping relationship between the training image and the calibration original image, and each target object includes a character.

[0134] The execution method of the above steps S509 to S510 is consistent with the execution method of the above steps S101 to S102, and will not be repeated here.

[0135] In the above-mentioned image correction method, by placing at least one first target object in the first array in sequence, placing at least one second target object in the second array in sequence, and then calling the existing matrix generation function through the first array and the second array, the matrix generation process can be modularized to reduce the consumption of computing resources.

[0136] The following describes an exemplary application of the embodiments of the present application in a practical application scenario.

[0137] Fig.10 A schematic diagram of a flow chart of an image correction method provided in an embodiment of the present application. Fig.10 As shown, the method comprises the following steps:

[0138] Step S601, set Figure 4 The calibration original image is shown in the figure on the right. The calibration original image includes a 5*5 rectangle with Chinese characters written in it, and the Chinese characters are obviously biased to the left. The coordinates of the Chinese characters are recorded from left to right and from top to bottom in the calibration original image, such as Jia Wei, Jia Sun, Jia Ma... Shan Liu, Shan Zhou, and the coordinates of each rectangle are recorded in the order of upper left, upper right, lower right, and lower left.

[0139] Step S602: photograph the calibration original image to obtain a training image. Figure 4 As shown in the left figure.

[0140] Step S603, identifying all quadrilaterals in the training image through an image processing algorithm, and recording the vertex coordinate information of the quadrilaterals.

[0141] Step S604, each identified quadrilateral is converted into a standard rectangle through the standard transformation matrix H, and the rectangle generated after the conversion is rotated by four angles and saved respectively. Each quadrilateral in the training image will generate four rectangles, as shown in the figure.

[0142] Step S605, start OCR recognition for all rectangles, obtain the characters in each rectangle, and identify whether the characters in each rectangle are consistent with the Chinese characters in the calibration original image. If not, delete the rectangle and make a judgment on the next rectangle. If they are consistent, record the coordinate information of the recognized character. After all rectangles are recognized, find the rectangle whose position coordinates relative to the rectangle are consistent with the position coordinates of the Chinese characters in the calibration original image, and record the vertex information of the corresponding rectangle. Record the vertex information of the rectangle in the order of upper left, upper right, lower right, and left. According to the recognized Chinese characters, obtain the coordinate information of each vertex in the calibration original image in the recognition order.

[0143] Step S606, saving the coordinates of all the rectangular vertices and the corresponding points of the calibration original image found in step S605 in two arrays arr1 and arr2 respectively.

[0144] Step S607, call the findHomography function of the opencv platform or other platform functions according to the array arr1 and the array arr2 to generate the matrix M, and the matrix M is the transformation matrix we finally obtain.

[0145] Step S608, recording the generated matrix M inside the electronic device, and after each photo is taken, the image to be corrected can be normalized by using the matrix M to obtain a calibration image corresponding to the image to be corrected.

[0146] It should be understood that although Figure 1-Figure 10 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-Figure 10 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0147] Based on the foregoing embodiments, an embodiment of the present application provides an electronic device, which includes the modules included and the units included in the modules, which can be implemented by a processor; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0148] Fig.11A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig.11 As shown, the electronic equipment includes:

[0149] The image acquisition module 1101 is used to acquire an image to be corrected, where the image to be corrected includes at least one first object, and each first object includes a character;

[0150] The image correction module 1102 is used to correct each first object in the image to be corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected. The transformation matrix is ​​determined based on the coordinate information of at least two target objects having a mapping relationship between the training image and the calibration original image, and each target object includes characters.

[0151] In some embodiments, the electronic device may further include:

[0152] A training image acquisition module is used to photograph the calibration original image to obtain a training image;

[0153] A first object obtaining module, used to obtain at least one first target object of a training image;

[0154] A second object obtaining module, used to obtain at least one second target object of the calibration original image;

[0155] A target object determination module, used to determine at least one group of target objects, each group of target objects including a first target object and a second target object having a mapping relationship;

[0156] The transformation matrix generation module is used to generate a transformation matrix according to coordinate information of at least one group of target objects.

[0157] In some embodiments, the target object determination module may include:

[0158] A first determination submodule, configured to determine from the training image at least one target first target object that is identical to the characters included in each second target object;

[0159] A second determination submodule, used to determine a target relative position of a character included in each second target object in the corresponding second target object;

[0160] A third determination submodule is used to determine, from at least one target first target object corresponding to each second target object, a first target object whose relative position of the character in the corresponding target first target object is the same as the target relative position;

[0161] The fourth determination submodule is used to determine at least one group of target objects according to the first target object corresponding to each second target object and each second target object.

[0162] In some embodiments, the first object obtaining module may include:

[0163] An initial recognition submodule, used to perform image recognition on a training image to obtain at least one initial first target object in the training image;

[0164] A stretching processing submodule, configured to stretch at least one initial first target object to obtain at least one standard first target object, so that the at least one standard first target object is a target shape;

[0165] The rotation processing submodule is used to rotate at least one standard first target object based on a preset rotation angle to obtain at least one first target object.

[0166] In some embodiments, there are multiple training images, and different training images are shot at different angles. The first object acquisition module may include:

[0167] an initial recognition submodule, configured to perform image recognition on each of the plurality of training images, and obtain at least one initial first target object in the plurality of training images according to the object recognized in each training image;

[0168] A stretching processing submodule, configured to stretch at least one initial first target object to obtain at least one standard first target object, so that the at least one standard first target object is a target shape;

[0169] The target determination submodule is used to use at least one standard first target object as at least one first target object.

[0170] In some embodiments, the stretching processing submodule may include:

[0171] The standard matrix conversion unit is used to convert at least one initial first target object based on a perspective correction matrix corresponding to the target shape to obtain at least one standard first target object.

[0172] In some embodiments, the above-mentioned conversion matrix generation module may include:

[0173] A coordinate information obtaining submodule, used to obtain coordinate information of a first target object and coordinate information of a second target object in each group of target objects;

[0174] A first array processing submodule, used for sequentially placing coordinate information of a first target object of at least one group of target objects into a first array based on a preset order;

[0175] A second array processing submodule, used for sequentially placing coordinate information of second target objects of at least one group of target objects into a second array based on a preset order;

[0176] The matrix generation submodule is used to call a matrix generation function according to the first array and the second array to generate a conversion matrix.

[0177] In some embodiments, the first target object and / or the second target object each include a plurality of vertices, the preset order includes an object priority order and a vertex priority order, and the first array processing submodule may include:

[0178] A first priority determination unit, used to determine a first target object of a first priority object, where the first priority object is a group of target objects that are ranked first in the object priority sequence;

[0179] A vertex coordinate placing unit, used for placing all vertex coordinates of the first target object of the first priority object into the first array in sequence based on the vertex priority order;

[0180] The remaining priority determination units are used to sequentially place all vertex coordinates of the first target object in other priority objects into the first array according to the method of placing all vertex coordinates of the first target object in the first priority object.

[0181] The second array processing submodule may include:

[0182] The first priority determination unit is further used to determine a second target object of the first priority object;

[0183] The vertex coordinate placing unit is further used to place all vertex coordinates of the second target object of the first priority object into the second array in sequence based on the vertex priority order;

[0184] The remaining priority determination units are further used to sequentially place all vertex coordinates of the second target objects in other priority objects into the second array according to the method of placing all vertex coordinates of the second target object in the first priority object.

[0185] The description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of the present application, please refer to the description of the method embodiment of the present application for understanding.

[0186] It should be noted that in the embodiments of this application Fig.11The division of modules in the electronic device shown is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. It may also be implemented in the form of a combination of software and hardware.

[0187] It should be noted that in the embodiment of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium, including several instructions to enable an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0188] The embodiment of the present application provides a computer device, which may be a server, and its internal structure diagram may be as follows: Fig.12 As shown. The computer device includes a processor, a memory and a network interface 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. 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, an image correction method is implemented.

[0189] An embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the method provided in the above embodiment are implemented.

[0190] An embodiment of the present application provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the steps of the method provided in the above method embodiment.

[0191] Those skilled in the art will understand that Fig.12The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0192] In one embodiment, the electronic device provided by the present application can be implemented in the form of a computer program, which can be used in Fig.12 The computer device shown in the figure is run. The memory of the computer device can store various program modules constituting the sampling device. The computer program composed of various program modules enables the processor to execute the steps of the image correction method of each embodiment of the present application described in this specification.

[0193] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned image correction method when executing the computer program.

[0194] 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 above-mentioned image correction method is implemented.

[0195] It should be noted here that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0196] It should be understood that "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in one embodiment" or "in some embodiments" appearing throughout the specification may not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments. The above description of each embodiment tends to emphasize the differences between the various embodiments, and the same or similar aspects can be referenced to each other. For the sake of brevity, this article will not repeat them.

[0197] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist at the same time, and object B exists alone.

[0198] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0199] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical or other forms.

[0200] The modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules; they may be located in one place or distributed on multiple network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0201] In addition, all functional modules in the embodiments of the present application may be integrated into one processing unit, or each module may be a separate unit, or two or more modules may be integrated into one unit; the above-mentioned integrated modules may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0202] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, etc., various media that can store program codes.

[0203] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0204] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0205] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0206] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0207] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An image correction method, characterized in that: Applied to electronic equipment, the method comprises: Acquire an image to be corrected, wherein the image to be corrected includes at least one first object, and each first object includes a character; Each first object in the image to be corrected is corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected, wherein the transformation matrix is ​​determined based on coordinate information of at least two target objects having a mapping relationship between a training image and a calibration original image, and each target object includes a character.

2. The method according to claim 1, characterized in that Before acquiring the image to be corrected, the method further includes: photographing the calibration original image to obtain the training image; obtaining at least one first target object of the training image; Obtaining at least one second target object of the calibration original image; Determine at least one group of target objects, each group of target objects including the first target object and the second target object having a mapping relationship; The transformation matrix is ​​generated according to the coordinate information of the at least one set of target objects.

3. The method according to claim 2, characterized in that The determining of at least one group of target objects comprises: determining from the training image at least one target first target object that is identical to the character included in each second target object; Determining a target relative position of a character included in each second target object in the corresponding second target object; From the at least one target first target object corresponding to each second target object, determine a first target object whose relative position of the character in the corresponding target first target object is the same as the target relative position; The at least one group of target objects is determined according to the first target object corresponding to each second target object and each second target object.

4. The method according to claim 2, characterized in that The obtaining of at least one first target object of the training image comprises: Performing image recognition on the training image to obtain at least one initial first target object in the training image; Stretching the at least one initial first target object to obtain at least one standard first target object, so that the at least one standard first target object is a target shape, and the target shape is consistent with the shape of the second target object; The at least one standard first target object is rotated based on a preset rotation angle to obtain the at least one first target object.

5. The method according to claim 2, characterized in that There are multiple training images, and different training images are shot at different angles. The obtaining of at least one first target object of the training images includes: Performing image recognition on each of the plurality of training images, and obtaining at least one initial first target object in the plurality of training images according to the object recognized in each training image; Stretching the at least one initial first target object to obtain at least one standard first target object, so that the at least one standard first target object is a target shape, and the target shape is consistent with the shape of the second target object; The at least one standard first target object is used as the at least one first target object.

6. The method according to claim 4 or 5, characterized in that The step of stretching the at least one initial first target object to obtain at least one standard first target object includes: The at least one initial first target object is transformed based on a perspective correction matrix corresponding to the target shape to obtain the at least one standard first target object.

7. The method according to claim 2, characterized in that The generating the transformation matrix according to the coordinate information of the at least one group of target objects comprises: Obtaining coordinate information of the first target object and coordinate information of the second target object in each group of target objects; Placing the coordinate information of the first target object of the at least one group of target objects into a first array in sequence based on a preset order; placing the coordinate information of the second target object of the at least one group of target objects into a second array in sequence based on the preset order; A matrix generation function is called according to the first array and the second array to generate the conversion matrix.

8. The method according to claim 7, characterized in that The first target object and / or the second target object each include a plurality of vertices, the preset order includes an object priority order and a vertex priority order, and placing the coordinate information of the first target object of the at least one group of target objects into the first array in sequence based on the preset order includes: Determine a first target object of a first priority object, where the first priority object is a group of target objects that are first in the object priority order; placing all vertex coordinates of the first target object of the first priority object into the first array in sequence based on the vertex priority order; According to the method of placing all vertex coordinates of the first target object in the first priority object, place all vertex coordinates of the first target object in other priority objects into the first array in sequence; The step of sequentially placing the coordinate information of the second target object of the at least one group of target objects into a second array based on the preset order includes: Determine a second target object of the first priority object; placing all vertex coordinates of the second target object of the first priority object into the second array in sequence based on the vertex priority order; According to the method of placing all vertex coordinates of the second target object in the first priority object, all vertex coordinates of the second target object in other priority objects are placed in the second array in sequence.

9. The method according to claim 2, characterized in that The at least one group of target objects are all geometric bodies, and the geometric bodies include two-dimensional geometric bodies or three-dimensional geometric bodies.

10. An electronic device, characterized in that: The electronic device comprises: An image acquisition module, used for acquiring an image to be corrected, wherein the image to be corrected includes at least one first object, and each first object includes a character; An image correction module is used to correct each first object in the image to be corrected according to a preset transformation matrix to obtain a corrected image corresponding to the image to be corrected, wherein the transformation matrix is ​​determined based on the coordinate information of at least two target objects having a mapping relationship between a training image and a calibration original image, and each target object includes a character.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

Images