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

Abstract

The present invention discloses an image processing method, device, electronic device and computer-readable storage medium. The method comprises: obtaining a first cable tunnel image; obtaining the pixel size of the first cable tunnel image; adjusting the pixel size of the first cable tunnel image to a predetermined pixel size to obtain a second cable tunnel image; performing image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image. The present invention solves the technical problem of large amount of calculation when detecting abnormal conditions in cable tunnels in related technologies.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to an image processing method, device, electronic equipment and computer-readable storage medium. Background Art

[0002] At present, as an important part of the urban power grid, the power tunnel bears the responsibility of the entire power transmission channel. Once a fault occurs and the insulation of the equipment is damaged, it is very easy to cause fire hazards, and it is a high-risk place for power fires. The fire hazard of the cable tunnel is an important factor affecting the risk of power grid operation. Once a fire occurs, it will seriously affect the safe operation of the power grid and the reliability of power supply. Therefore, fire and fire hazard identification and analysis alarm are of great significance to the safe operation of cable tunnels. However, the widely used detection and perception systems such as smoke alarms have problems such as lag and high false alarm rate. In addition, the calculation amount is too large when detecting a large number of real-time images, and fire hazards cannot be discovered in time. Summary of the invention

[0003] The embodiments of the present invention provide an image processing method, device, electronic device and computer-readable storage medium to at least solve the technical problem of large amount of calculation when detecting abnormal conditions in a cable tunnel in the related art.

[0004] According to one aspect of an embodiment of the present invention, there is provided an image processing method, comprising: acquiring a first cable tunnel image; acquiring a pixel size of the first cable tunnel image; adjusting the pixel size of the first cable tunnel image to a predetermined pixel size to obtain a second cable tunnel image; performing image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image.

[0005] Optionally, performing image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image includes: marking the cable area in the second cable tunnel image; performing image detection on the marked second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image.

[0006] Optionally, performing image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image includes: inputting the second cable tunnel image into an image detection model to obtain an abnormality detection result of the second cable tunnel image, wherein the image detection model is trained using multiple groups of sample data, and the training samples include sample cable tunnel images, and abnormality detection results of the sample cable tunnel images.

[0007] Optionally, the sample cable tunnel image includes: a plurality of third cable tunnel images and a plurality of fourth cable tunnel images, wherein the plurality of third cable tunnel images are captured by a camera at a first predetermined position at a predetermined time point, and the fourth cable tunnel images are obtained by adjusting the pixel size and image color parameters of the third cable tunnel images.

[0008] Optionally, it also includes: obtaining a second predetermined position of a camera used to capture the first cable tunnel image; determining a position offset of the second predetermined position relative to the first predetermined position; when the position offset is lower than a predetermined threshold, adjusting an image angle of the second cable tunnel image to obtain a fifth cable tunnel image; and performing image detection on the fifth cable tunnel image to obtain an abnormality detection result of the fifth cable tunnel image.

[0009] Optionally, the abnormality detection result includes: whether there is an abnormality in the cable tunnel, the type of the abnormality in the cable tunnel, and the regional location of the abnormality in the cable tunnel.

[0010] According to one aspect of an embodiment of the present invention, there is provided an image processing device, comprising: a first acquisition module, used to acquire a first cable tunnel image; a second acquisition module, used to acquire the pixel size of the first cable tunnel image; an adjustment module, used to adjust the pixel size of the first cable tunnel image to a predetermined pixel size to obtain a second cable tunnel image; and a detection module, used to perform image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image.

[0011] According to one aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement any one of the above-mentioned image processing methods.

[0012] According to one aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute any one of the above-mentioned image processing methods.

[0013] According to one aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the image processing method described above is implemented.

[0014] In the embodiment of the present invention, by acquiring the first cable tunnel image and the pixel size of the first cable tunnel image, adjusting the pixel size of the first cable tunnel image to a predetermined pixel size, a second cable tunnel image is obtained, and image detection is performed on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image. Because the second cable tunnel image is detected, the second cable tunnel image is an image after the pixel size is adjusted, so the number of pixels to be compared can be reduced during the detection process, which can effectively reduce the amount of calculation during the detection process, thereby solving the technical problem of large amount of calculation when detecting abnormal conditions in cable tunnels in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0016] Figure 1 is a flowchart of an image processing method according to an embodiment of the present invention;

[0017] Figure 2 is a structural block diagram of an image processing apparatus according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] Example 1

[0021] According to an embodiment of the present invention, an embodiment of an image processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] Figure 1 is a flowchart of an image processing method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0023] Step S102, acquiring a first cable tunnel image;

[0024] Step S104, obtaining the pixel size of the first cable tunnel image;

[0025] Step S106, adjusting the pixel size of the first cable tunnel image to a predetermined pixel size to obtain a second cable tunnel image;

[0026] Step S108: performing image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image.

[0027] Through the above steps, by acquiring the first cable tunnel image and the pixel size of the first cable tunnel image, adjusting the pixel size of the first cable tunnel image to a predetermined pixel size, the second cable tunnel image is obtained, and image detection is performed on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image. Because the second cable tunnel image is detected, the second cable tunnel image is an image after the pixel size is adjusted, so the number of pixels to be compared can be reduced during the detection process, which can effectively reduce the amount of calculation during the detection process, thereby solving the technical problem of large amount of calculation when detecting abnormal conditions in cable tunnels in related technologies.

[0028] It should be noted that the above abnormality detection results include: whether there is an abnormality in the cable tunnel, the type of abnormality in the cable tunnel, and the regional location of the abnormality in the cable tunnel, wherein the cable tunnel is the cable tunnel displayed in the first cable tunnel image. The type of abnormality can be the appearance of arc light, electric spark, smoke, etc., which can be specifically set according to the abnormalities that may or have occurred in the actual cable tunnel scene.

[0029] As an optional embodiment, the first cable tunnel image may be obtained by directly photographing a camera at a predetermined position in the scene of the cable tunnel. It may also be obtained by recording a video at a predetermined position in the scene of the cable tunnel and capturing frames in the video. The first cable tunnel image may be a real-time cable tunnel image obtained according to a predetermined time interval based on actual conditions. In the process of selecting the camera device, it is advisable to use the camera model and the DVR (Digital Video Recorder) and NVR (Network Video Recorder) models according to the scene of the cable tunnel to avoid the edge computing terminal obtaining the video protocol and identifying the number of DVR and NVR camera images inaccurately, resulting in inaccurate detection correspondence, or the inability to obtain the video source, or the extracted frame images do not match, or the inability to obtain the frame images.

[0030] It should be noted that after acquiring the first cable tunnel image, the first cable tunnel image can be subjected to image preprocessing for better detection. For example, all pixel values ​​of the first cable tunnel image can be normalized and divided by the maximum value of the pixel range 255 to obtain the pixel range [0, 1) of each pixel in the original image. For example, the RGB image data (36, 25, 254) is normalized to (0.14117647, 0.9803922, 0.99607843). Through the normalization process, the pixel value difference of the pixel point can be more intuitively felt. In addition, other preprocessing can be performed, such as the elimination of images that are greatly different from the cable tunnel scene, such as the elimination of the all-black image captured by the camera when the light bulb is broken, and other processing.

[0031] As an optional embodiment, the pixel size of the first cable tunnel image is obtained, which is determined according to the pixel size of the first cable tunnel pixel, for example, according to different devices, it may be 1500*1050 pixel size, 1800*1200 pixel size, etc. The pixel size of the first cable tunnel image is obtained, so as to adjust the pixel size of the first cable tunnel image to a predetermined pixel size, and obtain the second cable tunnel image. Through the above steps, the technical effect of unifying the pixel size of the image is achieved, for example, it can be unified to 256*256 pixel size. In different scenarios or applications, the first cable tunnel image can be unified into different pixel sizes, so as to detect the image after adjusting the pixel size and obtain an abnormal detection result. By setting different pixel sizes, different effects can be achieved. For example, in ordinary cable tunnel scenes with general safety requirements, that is, in a large number of cable tunnel scenes, the first cable tunnel image can be unified into a pixel size with a smaller pixel size. Under the premise of ensuring the detection accuracy, the number of pixels to be detected and compared is small, thereby reducing the amount of calculation in the detection process. For another example, in a relatively special scenario, in a cable tunnel scenario with strict and extremely high safety requirements, that is, in a few scenarios with extremely strict requirements, the first cable tunnel image can be unified into a pixel size with a larger pixel size, which can be detected more accurately and can detect more subtle changes. In the above two scenarios, whether the first cable tunnel image is unified into a larger pixel size or the first cable tunnel image is unified into a smaller pixel size, it is smaller than the pixel size of the original first cable tunnel image. Therefore, the predetermined pixel size can be set autonomously according to the actual application and scenario, and the amount of calculation in the detection process is reduced while meeting the detection accuracy.

[0032] As an optional embodiment, image detection is performed on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image. During the detection process, the cable area in the second cable tunnel image can be marked, so that the image detection of the marked second cable tunnel image is performed to obtain the abnormality detection result of the second cable tunnel image. That is, the cable area in the second cable tunnel image is marked, and the marked area can be focused on or specially detected. Because it is in the scene of the cable tunnel, the location where the abnormality is likely to occur is easy to determine, for example, abnormalities are likely to occur in the pipeline, abnormalities are likely to occur at the intersection of cables, and so on. At this time, the pipeline part and the cable intersection gathering part will be marked for key detection, or the pipeline part and the cable intersection gathering part will be specially detected respectively, so as to achieve more accurate detection.

[0033] As an optional embodiment, when performing image detection on the second cable tunnel image and obtaining an abnormality detection result of the second cable tunnel image, detection can also be performed by means of a model. That is, the second cable tunnel image can be input into an image detection model to obtain an abnormality detection result of the second cable tunnel image, wherein the image detection model is trained using multiple sets of sample data, and the training samples include sample cable tunnel images and abnormality detection results of the sample cable tunnel images. By performing detection by means of a model, the model can better learn the relationship between the cable tunnel image and the abnormality, and can detect the abnormality more accurately.

[0034] It should be noted that this optional embodiment has made improvements to the training sample part of the model, wherein the sample cable tunnel image includes: a plurality of third cable tunnel images and a plurality of fourth cable tunnel images, wherein the plurality of third cable tunnel images are obtained by shooting at a predetermined time point by a camera at a first predetermined position, and the fourth cable tunnel image is obtained by adjusting the pixel size and image color parameters of the third cable tunnel image, and the image color parameters may be parameters such as image brightness, image contrast, image randomness, and image hue. For example, the third cable tunnel image may be a camera at a first predetermined position that collects image data from zero to twenty-four o'clock in a day at intervals of 30 seconds, totaling 24*60*2=2880 images. The fourth cable tunnel image may be an image obtained by color transformation by adjusting the third cable tunnel image by random brightness, random contrast, random saturation, and random hue. Images under different lighting conditions are simulated, and the number of samples is expanded. The model can be fully, extensively, and targetedly trained.

[0035] It should also be noted that the sample cable tunnel image and the first cable tunnel image are of the same scene, that is, it can be understood that the sample cable tunnel image and the first cable tunnel image are in the same cable tunnel scene and are photographed in the same cable tunnel. Therefore, using the sample cable tunnel image as a training sample can train the model more specifically.

[0036] As an optional embodiment, in order to make the image detection more accurate, the second predetermined position of the camera that shoots the first cable tunnel image can be obtained, the position offset of the second predetermined position relative to the first predetermined position can be determined, and when the position offset is lower than the predetermined threshold, the image angle of the second cable tunnel image is adjusted to obtain the fifth cable tunnel image, and the image detection is performed on the fifth cable tunnel image to obtain the abnormal detection result of the fifth cable tunnel image. Because the sample cable tunnel image and the first cable tunnel image are in the same cable tunnel scene, the sample cable tunnel image and the first cable tunnel image can be obtained by the camera at the same position or by the camera at different positions. In the case where the sample cable tunnel image and the first cable tunnel image are obtained by the camera at the same position, the image detection can be performed directly on the image. In the case where the sample cable tunnel image and the first cable tunnel image are obtained by the camera at different positions, the above steps can be performed. Among them, the predetermined threshold is set to avoid the first predetermined position and the second predetermined position being too different, and the same angle as the sample cable tunnel image cannot be obtained by adjusting the image angle alone, or the image angle adjusted too large will cause large image distortion, thereby affecting the detection result. By adjusting the image angle of the second cable tunnel image to be the same as that of the sample cable tunnel image, it is possible to ensure that the detection of the fifth cable tunnel image is more accurate.

[0037] It should be noted that after obtaining the anomaly detection results, if there is an anomaly in the cable tunnel, the category of the anomaly in the cable tunnel and the regional location of the anomaly in the cable tunnel can be sent to the alarm personnel, so that the alarm personnel can further determine and handle the anomaly in a timely manner, thereby resolving the anomaly.

[0038] The technical solution recorded in this application can reduce the time for the cable company command center to manually view and retrieve video fault images, improve the efficiency of discovering and solving abnormalities, promptly discover fire hazards, and promptly send the abnormal category and abnormal area location of the cable tunnel to the alarm personnel in the event of an abnormality, so as to make a quick decision and provide the cable center with accurate and effective decision-making information. It ensures the normal operation of equipment in the cable tunnel, reduces direct economic losses caused by equipment operation, cable aging and other hidden faults, and greatly improves the reliability of power supply of the power grid.

[0039] It should also be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0040] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0041] Example 2

[0042] According to an embodiment of the present invention, a device for implementing the above image processing method is also provided. Figure 2 is a structural block diagram of an image processing device according to an embodiment of the present invention. Figure 2 As shown, the device includes: a first acquisition module 202, a second acquisition module 204, an adjustment module 206 and a detection module 208. The device is described in detail below.

[0043] The first acquisition module 202 is used to acquire the first cable tunnel image; the second acquisition module 204 is connected to the first acquisition module 202 and is used to acquire the pixel size of the first cable tunnel image; the adjustment module 206 is connected to the second acquisition module 204 and is used to adjust the pixel size of the first cable tunnel image to a predetermined pixel size to obtain the second cable tunnel image; the detection module 208 is connected to the adjustment module 206 and is used to perform image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image.

[0044] It should be noted here that the above-mentioned first acquisition module 202, second acquisition module 204, adjustment module 206 and detection module 208 correspond to steps S102 to S108 in implementing the image processing method, and the instances and application scenarios implemented by the multiple modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0045] Example 3

[0046] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor, wherein the processor is configured to execute the instructions to implement any one of the above-mentioned image processing methods.

[0047] Example 4

[0048] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute any of the above-mentioned image processing methods.

[0049] Example 5

[0050] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, any of the above-mentioned image processing methods is implemented.

[0051] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0052] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0053] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0054] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0055] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit 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 software functional units.

[0056] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution 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 a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0057] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An image processing method, characterized in that: include: acquiring a first cable tunnel image; Obtaining a pixel size of the first cable tunnel image; adjusting the pixel size of the first cable tunnel image to a predetermined pixel size to obtain a second cable tunnel image; performing image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image; The performing of image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image includes: marking a cable area in the second cable tunnel image, wherein the cable area includes a point where cable intersections converge; Performing image detection on the annotated second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image, wherein performing image detection on the annotated second cable tunnel image includes performing special detection on the cable intersection point; The performing of image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image includes: The second cable tunnel image is input into an image detection model to obtain an anomaly detection result of the second cable tunnel image, wherein the image detection model is trained using multiple groups of training samples, the training samples include sample cable tunnel images, and anomaly detection results of the sample cable tunnel images, and the cable tunnel corresponding to the sample cable tunnel image is the cable tunnel corresponding to the first cable tunnel image.

2. The method according to claim 1, characterized in that: The sample cable tunnel images include: a plurality of third cable tunnel images and a plurality of fourth cable tunnel images, wherein the plurality of third cable tunnel images are obtained by photographing a camera at a first predetermined position at a predetermined time point, and the fourth cable tunnel images are obtained by adjusting the pixel size and image color parameters of the third cable tunnel images.

3. The method according to claim 2, characterized in that Also includes: acquiring a second predetermined position of a camera for capturing the first cable tunnel image; determining a position offset of the second predetermined position relative to the first predetermined position; When the position offset is lower than a predetermined threshold, adjusting the image angle of the second cable tunnel image to obtain a fifth cable tunnel image; Performing image detection on the fifth cable tunnel image to obtain an abnormality detection result of the fifth cable tunnel image.

4. The method according to any one of claims 1 to 3, characterized in that The abnormality detection result includes: whether there is an abnormality in the cable tunnel, the type of the abnormality in the cable tunnel, and the regional location of the abnormality in the cable tunnel.

5. An image processing device, characterized in that: include: A first acquisition module, used for acquiring a first cable tunnel image; A second acquisition module, used to acquire the pixel size of the first cable tunnel image; An adjustment module, configured to adjust the pixel size of the first cable tunnel image to a predetermined pixel size to obtain a second cable tunnel image; a detection module, configured to perform image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image; The performing of image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image includes: marking a cable area in the second cable tunnel image, wherein the cable area includes a point where cable intersections converge; Performing image detection on the annotated second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image, wherein performing image detection on the annotated second cable tunnel image includes performing special detection on the cable intersection point; The performing of image detection on the second cable tunnel image to obtain an abnormality detection result of the second cable tunnel image includes: The second cable tunnel image is input into an image detection model to obtain an anomaly detection result of the second cable tunnel image, wherein the image detection model is trained using multiple groups of training samples, the training samples include sample cable tunnel images, and anomaly detection results of the sample cable tunnel images, and the cable tunnel corresponding to the sample cable tunnel image is the cable tunnel corresponding to the first cable tunnel image.

6. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the image processing method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the image processing method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the image processing method according to any one of claims 1 to 4 is implemented.

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