Image processing method and device, electronic equipment and readable storage medium

Abstract

The invention discloses an image processing method and device, electronic equipment and a readable storage medium. The method comprises the steps that a target image shot by a first camera is acquired; wherein the target image comprises at least one target point; determining a conversion matrix according to the coordinate of a first calibration area where the target point is located and the coordinate of a second calibration area corresponding to the first calibration area; wherein the first calibration area is shot by the first camera; the second calibration area is shot by a second camera; converting the coordinates of the target point into target coordinates through the conversion matrix; wherein the target coordinate is the coordinate of the target point in the corresponding image shot by the second camera.

Description

Technical Field

[0001] This application belongs to the field of image processing technology, specifically relating to an image processing method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] With the development of artificial intelligence technologies and the improvement of computing power in terminal devices, more and more application scenarios rely on real-time processing results related to computer vision. Ultra-wide-angle cameras can capture images with a wider field of view, but objects in images taken at greater distances appear relatively small, which is not conducive to image recognition and processing by algorithms such as object detection. Image coordinate transformation methods based on binocular cameras require the collection of large amounts of training data and the acquisition of camera depth information to compensate for the transformation results, leading to high complexity in the image coordinate transformation process. Summary of the Invention

[0003] This application provides an image processing method, apparatus, electronic device, and readable storage medium, which can solve the problem that coordinate transformation methods in related technologies require the collection of a large amount of training data for training and also need to obtain information such as the depth of field of the camera to compensate for the transformation results, resulting in a high complexity of the image coordinate transformation process.

[0004] In a first aspect, embodiments of this application provide an image processing method, the method comprising: acquiring a target image captured by a first camera; wherein the target image includes at least one target point; determining a transformation matrix based on the coordinates of a first calibration region where the target point is located and the coordinates of a second calibration region corresponding to the first calibration region; wherein the first calibration region is captured by the first camera; the second calibration region is captured by a second camera; converting the coordinates of the target point into target coordinates through the transformation matrix; wherein the target coordinates are the coordinates of the target point in the corresponding image captured by the second camera.

[0005] Secondly, embodiments of this application provide an image processing apparatus, comprising: an acquisition module for acquiring a target image captured by a first camera; wherein the target image includes at least one target point; a determination module for determining a transformation matrix based on the coordinates of a first calibration region where the target point is located and the coordinates of a second calibration region corresponding to the first calibration region; wherein the first calibration region is captured by the first camera; and the second calibration region is captured by a second camera; and a conversion module for converting the coordinates of the target point into target coordinates using the conversion matrix; wherein the target coordinates are the coordinates of the target point in the corresponding image captured by the second camera.

[0006] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0007] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0008] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the steps of the method described in the first aspect.

[0009] In a sixth aspect, embodiments of this application provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including a program or instructions, which, when executed, implement the steps of the method described in the first aspect.

[0010] In this embodiment, a target image captured by a first camera is acquired. The target image includes at least one target point. A transformation matrix is ​​determined based on the coordinates of the target point in a first calibration region and the coordinates of the second calibration region corresponding to the first calibration region. The coordinates of the target point are then converted into target coordinates using the transformation matrix. This achieves the mapping of the target point in the target image captured by the first camera to the corresponding image captured by the second camera. The conversion of the target point's coordinates utilizes the coordinates of the target point in the first calibration region of the target image and the coordinates in the second calibration region of the corresponding image. This eliminates the need to collect a large amount of training data for training. Furthermore, the coordinates formed by the coordinates of the first and second calibration regions are used to perform coordinate transformation on the determined transformation matrix. It also eliminates the need to obtain information such as the camera's depth of field, thus reducing the complexity of image coordinate transformation. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of an image processing method provided in an embodiment of this application; Figure 2 This is a schematic diagram of a calibration method for calibration points provided in an embodiment of this application; Figure 3a and Figure 3b This is a schematic diagram of a calibration area provided in an embodiment of this application; Figure 4 This is a schematic diagram of a horizontal conversion deviation provided in an embodiment of this application; Figure 5a This is a flowchart illustrating another image processing method provided in an embodiment of this application; Figure 5b This is a schematic diagram of an image processing embodiment provided in this application; Figure 6 This is a schematic diagram of the structure of an image processing device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0014] The image processing method, apparatus, electronic device, and readable storage medium provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0015] Figure 1 This diagram illustrates a flowchart of an image processing method provided in an embodiment of this application, which can be executed by an electronic device. See also... Figure 1 The method may include the following steps.

[0016] Step 102: Obtain the target image captured by the first camera.

[0017] The target image includes at least one target point.

[0018] Step 104: Determine the transformation matrix based on the coordinates of the first calibration region where the target point is located and the coordinates of the second calibration region corresponding to the first calibration region.

[0019] The first calibration area is captured by the first camera; the second calibration area is captured by the second camera. Both the first and second calibration areas consist of multiple calibration points, and the first calibration area containing the target point can be determined using the ray casting method.

[0020] Step 106: Convert the coordinates of the target point into target coordinates using the transformation matrix.

[0021] Wherein, the target coordinates are the coordinates of the target point in the corresponding image captured by the second camera.

[0022] The coordinate transformation of the target point can be performed using the following formula: .

[0023] in, M represents the coordinates of the target point in the target image, and M is calculated using the coordinates of three pairs of calibration points in the calibration region. Transformation matrix, These are the coordinates of the target point mapped to the corresponding image. It can be seen that mapping a target point only requires one affine transformation, resulting in low transformation complexity.

[0024] In this embodiment, a target image captured by a first camera is acquired. The target image includes at least one target point. A transformation matrix is ​​determined based on the coordinates of the target point in a first calibration region and the coordinates of the second calibration region corresponding to the first calibration region. The coordinates of the target point are then converted into target coordinates using the transformation matrix. This achieves the mapping of the target point in the target image captured by the first camera to the corresponding image captured by the second camera. The conversion of the target point's coordinates utilizes the coordinates of the target point in the first calibration region of the target image and the coordinates in the second calibration region of the corresponding image. This eliminates the need to collect a large amount of training data for training. Furthermore, the coordinates formed by the coordinates of the first and second calibration regions are used to perform coordinate transformation on the determined transformation matrix. It also eliminates the need to obtain information such as the camera's depth of field, thus reducing the complexity of image coordinate transformation.

[0025] It should be noted that the image processing method provided in this application embodiment is based on the dual-camera system which has already undergone distortion correction, i.e., it is performed under ideal distortion-free conditions.

[0026] In one implementation, before determining the transformation matrix in step 104 based on the coordinates of the first calibration region where the target point is located and the coordinates of the second calibration region corresponding to the first calibration region, the method further includes the following steps.

[0027] Step 1031: Obtain a first image of the calibration board captured by the first camera and a second image of the calibration board captured by the second camera.

[0028] The calibration board includes at least one calibration point. The first image includes a first calibration point corresponding to the calibration point, and the second image includes a second calibration point corresponding to the calibration point. The calibration points on the calibration board are the actual calibration points set on the calibration board. The first calibration point is the point corresponding to each calibration point in the first image, and the second calibration point is the point corresponding to each calibration point in the second image. That is, the calibration point, the first calibration point, and the second calibration point correspond to each other.

[0029] Step 1032: Based on the index of the calibration point, the first coordinate of the first calibration point, and the second coordinate of the second calibration point, establish a coordinate mapping relationship between the first coordinate and the second coordinate.

[0030] Among them, for the calibration points on the actual calibration board, there are preset indices for each calibration point. These indices correspond to the first coordinates of the first calibration point and the second coordinates of the second calibration point, so that the first coordinates of the first calibration point and the second coordinates of the second calibration point can be mapped through these indices.

[0031] In the embodiments of this application, see Figure 2 , Figure 2 This diagram illustrates a calibration method for a calibration point according to an embodiment of this application, including a calibration plate 21, a first camera 22, and a second camera 23. The dual-camera setup, including the first camera 22 and the second camera 23, faces the calibration plate 21, ensuring that the cameras are aligned with the center of the calibration plate in both the horizontal and vertical directions, and that the camera position remains stable during shooting. The first camera 22 captures a first image 221 of the calibration plate 21, and the second camera 23 captures a second image 231 of the calibration plate 21. The calibration plate 21 includes at least one calibration point, such as calibration point O1. Correspondingly, the first calibration point O2 in the first image 221 corresponds to calibration point O1, and the second calibration point O3 in the second image 231 corresponds to calibration point O1. By establishing appropriate coordinate systems for the first image 221 and the second image 231, the first coordinates of the first calibration point and the second coordinates of the second calibration point can be obtained respectively. These coordinates are then mapped to the indices of the corresponding calibration points to establish a coordinate mapping relationship between the first and second coordinates. Therefore, given the coordinates of the first calibration point in the first image, the coordinates of the corresponding second calibration point in the second image can be determined based on this coordinate mapping relationship. Alternatively, given the coordinates of the second calibration point in the second image, the coordinates of the corresponding first calibration point in the first image can be determined based on this coordinate mapping relationship. This yields the coordinate pairs of calibration points, which are then used to determine the transformation matrix.

[0032] Optionally, the coordinates of the calibration points of each set of first and second images can be stored in a coordinate mapping grid as shown in Table 1, stored row by row.

[0033] Table 1

[0034] The row and column indices are used to store the row and column numbers of the calibration points in the calibration board. The x-coordinate and y-coordinate of the first calibration point are used to store the coordinate position of the first calibration point in the first image. The x-coordinate and y-coordinate of the second calibration point are used to store the coordinate position of the Dür calibration point in the second image.

[0035] In one implementation, step 104 above determines the transformation matrix based on the coordinates of the first calibration region where the target point is located and the coordinates of the second calibration region corresponding to the first calibration region, including the following steps.

[0036] Step 1041: Determine the index corresponding to the coordinates of the first calibration area based on the coordinates of the target point in the first calibration area and the coordinate mapping relationship.

[0037] The first calibration region is composed of a preset number of first calibration points in the first image, for example, such as... Figure 3a As shown, the first calibration region where the target point P is located in the first image 31 is composed of four first calibration points (Q1, Q2, Q3, Q4). That is to say, given the coordinates of the first calibration region, which is also the coordinates of each first calibration point constituting the first calibration region, the index corresponding to the coordinates of each first calibration point can be found according to the coordinate mapping relationship shown in Table 1 above.

[0038] Step 1042: Determine the coordinates of the second calibration region based on the index corresponding to the coordinates of the first calibration region and the coordinate mapping relationship.

[0039] The second calibration region is composed of a preset number of second calibration points in the second image, for example, such as... Figure 3a As shown, the second calibration region where the target point P is located in the second image 32 consists of four second calibration points (Q1', Q2', Q3', and Q4'). Q1' corresponds to Q1, Q2' to Q2, Q3' to Q3, and Q4' to Q4. In other words, given the known indices of the coordinates of each first calibration point constituting the first calibration region, the coordinates of each second calibration point constituting the second calibration region can be found according to the coordinate mapping relationship shown in Table 1 above.

[0040] Step 1043: Based on the coordinates of the first calibration region where the target point is located and the coordinates of the second calibration region, determine the transformation matrix to transform the target point from the target image to the corresponding image.

[0041] Based on the characteristics of the transformation matrix, three pairs of coordinates are selected from the coordinate pairs corresponding to the first and second calibration regions mentioned above for calculating the transformation matrix, such as... Figure 3b As shown.

[0042] In this embodiment, the index corresponding to the coordinates of the first calibration region where the target point is located is first determined based on the coordinates of the first calibration region and the coordinate mapping relationship. Then, the coordinates of the second calibration region are determined based on the determined index corresponding to the coordinates of the first calibration region and the coordinate mapping relationship. Thus, the coordinate pair corresponding to the first calibration region and the second calibration region where the target point is located is determined. Based on the coordinates of the first calibration region and the second calibration region where the target point is located, a transformation matrix is ​​determined to convert the target point from the target image to the corresponding image. Through this transformation matrix, the target point can be converted from the target image captured by the first camera to the corresponding image captured by the second camera.

[0043] In one implementation, the calibration board is a symmetrical circular grid, with the center of the symmetrical circle being the calibration point; the index of the calibration point includes a row index and a column index. Before step 1031, which involves acquiring the first image of the calibration board captured by the first camera and the second image of the calibration board captured by the second camera, the method further includes the following steps.

[0044] Step 1030a1: Determine the width of the calibration plate based on the field of view of the first camera in the horizontal direction and the calibration distance.

[0045] The calibration distance is the distance from the first camera or the second camera to the calibration board.

[0046] The width (W) of the calibration plate can be determined using the following formula, based on the horizontal field of view (HFOV) of the first camera and the calibration distance (dist): .

[0047] Step 1030a2: Establish the row index based on the width of the calibration plate.

[0048] Step 1030b1: Determine the height of the calibration plate based on the field of view of the second camera in the vertical direction and the calibration distance.

[0049] The height (H) of the calibration plate can be determined using the following formula, based on the vertical field of view (VFOV) of the second camera and the calibration distance (dist): .

[0050] Step 1030b2: Establish the column index based on the height of the calibration plate.

[0051] In this embodiment, the width of the calibration board is determined based on the horizontal field of view of the first camera and the calibration distance, thereby establishing a row index for the calibration board. The height of the calibration board is determined based on the vertical field of view of the second camera and the calibration distance, thereby establishing a column index for the calibration board. Based on the established row and column indices of the calibration board, a coordinate mapping relationship is established with the first coordinates of the first calibration point in the first calibration region and the second coordinates of the second calibration point in the second calibration region.

[0052] In one implementation, the image processing method described above further includes the following steps.

[0053] Step 1081: Obtain the first distance between the optical axis center of the first camera and the optical axis center of the second camera, the second distance between the first camera or the second camera and the target point, and the third distance between the first camera or the second camera and the calibration plate.

[0054] Step 1082: Determine the conversion deviation value of the target point based on the first distance, the second distance, and the third distance.

[0055] In the embodiments of this application, see Figure 4 , Figure 4 This illustration shows a horizontal conversion deviation according to an embodiment of this application. Under ideal conditions, without considering image distortion, the coordinate mapping deviation mainly originates from the distance between the optical axis centers of the two cameras. The horizontal conversion deviation value of the target point (and similarly for the vertical direction) can be determined using the following formula: ): .

[0056] in, The distance between the centers of the optical axes of the two cameras. This refers to the actual shooting distance, which is the distance between the camera and the target point. This refers to the distance between the camera and the calibration board.

[0057] Figure 5aThis diagram illustrates a flowchart of another image processing method provided in one implementation of this application. In this embodiment, the first camera is a telephoto camera; the second camera is an ultra-wide-angle camera. See also... Figure 5a The method may include the following steps.

[0058] Step 501: Determine the width of the calibration board based on the field of view of the telephoto camera in the horizontal direction and the calibration distance, and determine the height of the calibration board based on the field of view of the ultra-wide-angle camera in the vertical direction and the calibration distance, and establish the index of the calibration points in the calibration board.

[0059] The calibration plate uses a symmetrical circular grid.

[0060] Step 502: Obtain the first image of the calibration board taken by the telephoto camera and the second image of the calibration board taken by the ultra-wide-angle camera.

[0061] Step 503: Identify the calibration points in the calibration board based on the first image and the second image, establish a coordinate mapping relationship between the identified calibration points and the telephoto image and the ultra-wide-angle image, and store the coordinate mapping relationship.

[0062] The telephoto view is captured by the telephoto camera, and the ultra-wide-angle view is captured by the ultra-wide-angle camera. The format for storing the coordinate mapping relationship is shown in Table 1 above.

[0063] Step 504: Locate the first calibration area where the target point is located in the telephoto image using the ray method.

[0064] The target point is a specific point within the telephoto image captured by the telephoto camera.

[0065] Step 505: Based on the index of the calibration point corresponding to the first calibration area, find the second calibration area where the target point is located in the ultra-wide-angle image through coordinate mapping relationship.

[0066] Step 506: Calculate the transformation matrix of the affine transformation based on the coordinate pairs formed by the coordinates of the calibration points corresponding to the first calibration region and the coordinates of the calibration points corresponding to the second calibration region.

[0067] Specifically, three pairs of coordinates are selected to calculate the transformation matrix.

[0068] Step 507: Transform the coordinates of the target point using a transformation matrix to obtain the target coordinates of the target point in the ultra-wide-angle image.

[0069] Step 508: Determine the conversion deviation value of the target point based on the distance between the optical axis centers of the telephoto camera and the ultra-wide-angle camera, the actual distance from the camera to the target point, and the calibration distance from the camera to the calibration board.

[0070] In this embodiment, a calibration board is photographed using a telephoto camera and an ultra-wide-angle camera. Calibration points are identified in the captured images, and a coordinate mapping relationship from the telephoto view to the ultra-wide-angle view is established based on the identification results. The coordinate mapping process for the image of the target object utilizes an existing mapping relationship to look up coordinate pairs, determines the transformation matrix, and then performs an affine transformation on the coordinates in the telephoto view using the transformation matrix to obtain the corresponding coordinates in the ultra-wide-angle view. This achieves the conversion from the telephoto view to the ultra-wide-angle view. Figure 5b As shown, there is no need to collect a large amount of training data for training, nor is it necessary to obtain information such as the camera's depth of field, which reduces the complexity of image coordinate transformation.

[0071] It should be noted that the image processing method provided in this application embodiment can be executed by an image processing device or a control module within that image processing device for executing the image processing method. This application embodiment uses an image processing device executing the image processing method as an example to illustrate the image processing device provided in this application embodiment.

[0072] Figure 6 A schematic diagram of the structure of an image processing apparatus provided in an embodiment of this application is shown. Figure 6 As shown, the device 600 includes: an acquisition module 61, a determination module 62, and a conversion module 63.

[0073] The acquisition module 61 is used to acquire a target image captured by a first camera; wherein the target image includes at least one target point; the determination module 62 is used to determine a transformation matrix based on the coordinates of a first calibration area where the target point is located and the coordinates of a second calibration area corresponding to the first calibration area; wherein the first calibration area is captured by the first camera; and the second calibration area is captured by a second camera; the conversion module 63 is used to convert the coordinates of the target point into target coordinates through the conversion matrix; wherein the target coordinates are the coordinates of the target point in the corresponding image captured by the second camera.

[0074] In one implementation, the image processing device 600 described above may further include a calibration module for acquiring a first image of the calibration board captured by the first camera and a second image of the calibration board captured by the second camera; wherein the calibration board includes at least one calibration point, the first image includes a first calibration point corresponding to the calibration point, and the second image includes a second calibration point corresponding to the calibration point; and a coordinate mapping relationship between the first coordinate and the second coordinate is established based on the index of the calibration point, the first coordinate of the first calibration point, and the second coordinate of the second calibration point.

[0075] In one implementation, the determining module 62 described above can be used to determine the index corresponding to the coordinates of the first calibration region based on the coordinates of the first calibration region where the target point is located and the coordinate mapping relationship; wherein, the first calibration region is composed of a preset number of first calibration points in the first image; determine the coordinates of the second calibration region based on the index corresponding to the coordinates of the first calibration region and the coordinate mapping relationship; wherein, the second calibration region is composed of a preset number of second calibration points in the second image; and determine a transformation matrix for converting the target point from the target image to the corresponding image based on the coordinates of the first calibration region where the target point is located and the coordinates of the second calibration region.

[0076] In one implementation, the calibration plate is a symmetrical circular grid, and the center of the symmetrical circle is the calibration point; the index of the calibration point includes a row index and a column index; the calibration module described above can also be used to determine the width of the calibration plate based on the field of view of the first camera in the horizontal direction and the calibration distance; establish the row index based on the width of the calibration plate; determine the height of the calibration plate based on the field of view of the second camera in the vertical direction and the calibration distance; and establish the column index based on the height of the calibration plate.

[0077] In one implementation, the image processing device 600 described above can also be used to obtain a first distance between the optical axis center of the first camera and the optical axis center of the second camera, a second distance between the first camera or the second camera and the target point, and a third distance between the first camera or the second camera and the calibration plate; and determine the conversion deviation value of the target point based on the first distance, the second distance, and the third distance.

[0078] In one implementation, the first camera is a telephoto camera; the second camera is an ultra-wide-angle camera.

[0079] The image processing device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device; for example, a mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. Non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), television sets (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0080] The image processing device in this application embodiment can be a device with an operating system. The operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system.

[0081] The image processing apparatus provided in this application embodiment can achieve... Figures 1 to 5b To avoid repetition, the various processes implemented in the method embodiments will not be described again here.

[0082] Based on the same technical concept, this application also provides an electronic device for performing the above-described image processing method. Figure 7 This is a schematic diagram of the structure of an electronic device to implement the various embodiments of this application. The electronic device can vary significantly due to differences in configuration or performance, and may include a processor 701, a communications interface 702, a memory 703, and a communication bus 704. The processor 701, communications interface 702, and memory 703 communicate with each other via the communication bus 704. The processor 701 can call a computer program stored in the memory 703 and executable on the processor 701 to perform the various steps of the above-described image processing method embodiments, achieving the same technical effects. To avoid repetition, further details are omitted here.

[0083] The above electronic device structure does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or arrange them differently. For example, an input unit may include a Graphics Processing Unit (GPU) and a microphone, and a display unit may use a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar display panels. User input units include at least one of a touch panel and other input devices. A touch panel is also called a touchscreen. Other input devices may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be elaborated further here.

[0084] Memory can be used to store software programs and various data. Memory can primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area can store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, memory can include volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0085] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor, wherein the application processor mainly handles operations related to the operating system, user interface, and applications, while the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor.

[0086] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described image processing method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0087] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0088] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described image processing method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0089] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0090] This application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes a program or instructions. When the program or instructions are executed, they implement the various processes of the above-described image processing method embodiments and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0091] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0093] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. An image processing method, characterized in that, include: Acquire a target image captured by a first camera; wherein the target image includes at least one target point; A transformation matrix is ​​determined based on the coordinates of the first calibration area where the target point is located and the coordinates of the second calibration area corresponding to the first calibration area; wherein, the first calibration area is captured by the first camera; and the second calibration area is captured by the second camera. The coordinates of the target point are converted into target coordinates using the transformation matrix; wherein the target coordinates are the coordinates of the target point in the corresponding image captured by the second camera.

2. The method according to claim 1, characterized in that, Before determining the transformation matrix based on the coordinates of the first calibration region where the target point is located and the coordinates of the second calibration region corresponding to the first calibration region, the method further includes: Acquire a first image of the calibration board captured by the first camera and a second image of the calibration board captured by the second camera; wherein the calibration board includes at least one calibration point, the first image includes a first calibration point corresponding to the calibration point, and the second image includes a second calibration point corresponding to the calibration point; Based on the index of the calibration point, the first coordinate of the first calibration point, and the second coordinate of the second calibration point, a coordinate mapping relationship between the first coordinate and the second coordinate is established.

3. The method according to claim 2, characterized in that, The step of determining the transformation matrix based on the coordinates of the first calibration region where the target point is located and the coordinates of the second calibration region corresponding to the first calibration region includes: Based on the coordinates of the first calibration region where the target point is located and the coordinate mapping relationship, the index corresponding to the coordinates of the first calibration region is determined; wherein, the first calibration region is composed of a preset number of the first calibration points in the first image; The coordinates of the second calibration region are determined based on the index corresponding to the coordinates of the first calibration region and the coordinate mapping relationship; wherein, the second calibration region is composed of a preset number of second calibration points in the second image; Based on the coordinates of the first calibration region where the target point is located and the coordinates of the second calibration region, a transformation matrix is ​​determined to transform the target point from the target image to the corresponding image.

4. The method according to claim 2, characterized in that, The calibration board is a symmetrical circular grid, and the center of the symmetrical circle is the calibration point; the index of the calibration point includes a row index and a column index; before acquiring the first image of the calibration board captured by the first camera and the second image of the calibration board captured by the second camera, the method further includes: The width of the calibration plate is determined based on the horizontal field of view of the first camera and the calibration distance; The row index is established based on the width of the calibration board; The height of the calibration plate is determined based on the field of view of the second camera in the vertical direction and the calibration distance; The column index is established based on the height of the calibration plate.

5. The method according to claim 2, characterized in that, The method further includes: Obtain a first distance between the optical axis center of the first camera and the optical axis center of the second camera, a second distance between the first camera or the second camera and the target point, and a third distance between the first camera or the second camera and the calibration plate; The conversion deviation value of the target point is determined based on the first distance, the second distance, and the third distance.

6. The method according to claim 1, characterized in that, The first camera is a telephoto camera; the second camera is an ultra-wide-angle camera.

7. An image processing apparatus, characterized in that, include: An acquisition module is used to acquire a target image captured by a first camera; wherein the target image includes at least one target point; The determination module is used to determine a transformation matrix based on the coordinates of the first calibration area where the target point is located and the coordinates of the second calibration area corresponding to the first calibration area; wherein, the first calibration area is captured by the first camera; and the second calibration area is captured by the second camera. A conversion module is used to convert the coordinates of the target point into target coordinates through the conversion matrix; wherein the target coordinates are the coordinates of the target point in the corresponding image captured by the second camera.

8. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the image processing method as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the image processing method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including programs or instructions that, when executed, implement the steps of the image processing method as described in any one of claims 1 to 6.

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