Image processing method, terminal and storage medium based on image capture

Abstract

The present invention provides an image processing method based on image capture, a terminal and a storage medium. The image processing method based on image capture comprises: detecting a first acquired image frame to determine a first pollutant in the first image frame; the first pollutant is an object other than a component element of a traffic road; determining a background image and a captured image from historical image frames before the first image frame; the captured image is an image containing a moving target associated with the first pollutant; acquiring a second image frame after the first image frame and detecting it to determine a second pollutant in the second image frame; judging whether the second pollutant matches the first pollutant; if they match, associating the second image frame, the captured image and the background image, thereby improving the detection accuracy of pollutants and the accuracy of road pollution alarms.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to an image processing method, a terminal and a storage medium based on image capture. Background Art

[0002] Muck trucks and mixer trucks driving in cities are prone to leakage and spillage of muck or mud, which not only pollutes the road surface, but also increases road safety hazards, and it is necessary to punish illegal vehicles. In the traditional mode, regulatory authorities often review road video surveillance for manual review, which is inefficient and has a large workload. In order to reduce labor costs and improve the efficiency of event processing, more and more regions use real-time control of road monitoring probes and real-time analysis of intelligent servers, and use computer vision technology to realize automatic alarm of road events, replacing the original manual management method, monitoring road pollution events around the clock, and monitoring illegal vehicles. However, when light and shadow, tire water marks or splashes appear on the road surface, other image areas will also be mistakenly detected as pollutants, resulting in poor detection accuracy of pollutants. Summary of the invention

[0003] The main technical problem solved by the present invention is to provide an image processing method, a terminal and a storage medium based on image capture, so as to solve the problem of low detection accuracy of pollutants in the prior art.

[0004] To solve the above technical problems, the first technical solution adopted by the present invention is: to provide an image processing method based on image capture, which includes: detecting the acquired first image frame to determine the first pollutant in the first image frame; the first pollutant is an object other than the component elements of the traffic road; from the historical image frames before the first image frame, determining the background image and the captured image; the captured image is an image containing a moving target associated with the first pollutant; acquiring the second image frame after the first image frame and detecting it to determine the second pollutant in the second image frame; judging whether the second pollutant matches the first pollutant; if they match, associating the second image frame, the captured image and the background image.

[0005] To solve the above technical problems, the second technical solution adopted by the present invention is: to provide a terminal, the terminal includes a memory, a processor and a computer program stored in the memory and running on the processor, and the processor is used to implement the steps of the above image processing method based on image capture.

[0006] To solve the above technical problems, the third technical solution adopted by the present invention is: providing a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above-mentioned image processing method based on image capture are implemented.

[0007] The beneficial effects of the present invention are as follows: different from the prior art, an image processing method, terminal and storage medium based on image capture are provided, and the image processing method based on image capture includes: detecting the acquired first image frame to determine the first pollutant in the first image frame; determining the background image and the captured image from the historical image frames before the first image frame; reacquiring the second image frame after the first image frame and detecting it to determine the second pollutant in the second image frame; then comparing the detected second pollutant with the first pollutant to further verify the first pollutant; if the first pollutant matches the second pollutant, associating the second image frame, the captured image and the background image, thereby improving the accuracy of pollutant detection and the accuracy of road pollution alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a flow chart of an image processing method based on image capture provided by the present invention;

[0009] Figure 2 It is a flowchart of a specific embodiment of an image processing method based on image capture provided by the present invention;

[0010] Figure 3 yes Figure 2 A flowchart of a specific embodiment of step S202 in the image processing method based on image capture is provided;

[0011] Figure 4 yes Figure 2 A flowchart of a specific embodiment of step S206 in the image processing method based on image capture is provided;

[0012] Figure 5 It is a photo captured by the image acquisition device provided by the present invention;

[0013] Figure 6 is a schematic block diagram of an implementation manner of a terminal provided by the present invention;

[0014] Figure 7 It is a schematic block diagram of an embodiment of a computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0015] The scheme of the embodiment of the present application is described in detail below in conjunction with the drawings of the specification.

[0016] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0017] The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship. In addition, "many" in this article means two or more than two.

[0018] In order to enable those skilled in the art to better understand the technical solution of the present invention, an image processing method based on image capture provided by the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] See also Figure 1 , Figure 1 1 is a flow chart of an image processing method based on image capture provided by the present invention. In this embodiment, an image processing method based on image capture is provided, which is suitable for monitoring whether a muck truck or a mixer truck causes pollution to the road surface when passing through the road surface. The image processing method based on image capture includes the following steps.

[0020] S11: Detecting the acquired first image frame to determine a first pollutant in the first image frame.

[0021] Specifically, the image acquisition device captures the image of the road surface in real time, and then obtains the first image frame, performs target detection on the first image frame based on the target detection model of the deep learning framework, and determines the information of the first pollutant contained in the first image frame. The information of the first pollutant includes the position information of the first pollutant in the first image frame and the category of the first pollutant, and marks the detection frame of the first pollutant in the first image frame. For example, the first pollutant can be a static pollutant such as slag, mud, sand and gravel.

[0022] S12: Determine a background image and a captured image from historical image frames before the first image frame.

[0023] Specifically, feature extraction and comparison are performed on the historical image frames within the preset time period and the first image frame respectively; the historical image frames in which the first pollutant is not detected within the preset time period are screened and retained; the historical image frames with the shortest time interval with the first image frame are selected from the retained historical image frames, and the selected historical image frames are determined as the background image. Moving target detection is performed on the historical image frames before the first image frame, and the historical image frames containing moving targets are screened; the historical image frames with the shortest time interval with the first image frame and containing moving targets whose positions are closest to the positions of the first pollutants are determined, and the historical image frames are used as captured images. The captured image is an image containing a moving target associated with the first pollutant.

[0024] S13: Acquire a second image frame after the first image frame and perform detection to determine a second pollutant in the second image frame.

[0025] Specifically, when the first pollutant is detected in the first image frame, the second image frame is continuously captured by the image capture device at a preset interval after the first image frame, and the target detection model of the first image frame is used to detect the target in the second image frame. When the second pollutant is detected in the second image frame, the position information of the second pollutant in the second image frame and the category of the second pollutant are obtained, and the detection frame of the second pollutant in the second image frame is marked. The position of the image capture device is fixed, the first image frame and the second image frame are captured at the same position on the same road surface, and the first image frame and the second image frame are captured at different times.

[0026] S14: Determine whether the second pollutant matches the first pollutant.

[0027] Specifically, when the second pollutant is acquired in the second image frame, it is determined whether the second pollutant in the second image frame is the same as the first pollutant in the first image frame. In a specific embodiment, it is determined whether the position coordinates of the second pollutant in the second image frame are the same as the position coordinates of the first pollutant in the first image frame, and it is determined whether the category of the second pollutant is the same as the category of the first pollutant, and then it is determined whether the second pollutant matches the first pollutant, thereby improving the inspection accuracy of the first pollutant.

[0028] When it is determined that the second pollutant matches the first pollutant, the process directly jumps to step S14; when it is determined that the second pollutant does not match the first pollutant, the process directly jumps to step S15.

[0029] S15: Determine to associate the second image frame, the captured image, and the background image.

[0030] Specifically, when it is determined that the second pollutant matches the first pollutant, it indicates that the category of the second pollutant in the second image frame is the same as the category of the first pollutant in the first image frame, and the coordinate position of the second pollutant in the second image frame is also the same as the coordinate position of the first pollutant in the first image frame. That is, it can be determined that the first pollutant is a real road pollutant, the first pollutant can be determined as the detection result, and the second image frame, the captured image, and the background image are associated to generate an alarm basis.

[0031] S16: Determine that the first pollutant detected in the first image frame is a false detection.

[0032] Specifically, when it is determined that the second pollutant does not match the first pollutant, it indicates that the category of the second pollutant in the second image frame is different from the category of the first pollutant in the first image frame, or the coordinate position of the second pollutant in the second image frame is different from the coordinate position of the first pollutant in the first image frame. That is, it can be determined that the first pollutant is not a real pollutant. In another optional embodiment, if the second pollutant is not detected in the second image frame, it can also be confirmed that the first pollutant detected in the first image frame is a false detection. Among them, the first pollutant may be a pollutant such as light and shadow, tire watermarks or water splashes, and such pollutants will undergo drastic changes in appearance or disappear over time. If the first pollutant is water splashes, after a period of time, the water will evaporate and will not appear on the road surface. Therefore, when the second image frame is detected, the second pollutant matching the first pollutant cannot be detected in the second image frame. Pollutants such as light and shadow, tire watermarks or water splashes are not real pollutants on the road surface and will not affect the road surface. Therefore, when the second pollutant is not detected in the second image frame, or the second pollutant detected in the second image frame does not match a pollutant detected in the first image frame, it is determined that the first pollutant detected in the first image frame is a false detection.

[0033] The image processing method based on image capture provided in this embodiment mainly detects the acquired first image frame to determine the first pollutant in the first image frame; determines the background image and the captured image from the historical image frames before the first image frame; re-acquires the second image frame after the first image frame and detects it to determine the second pollutant in the second image frame; then compares the detected second pollutant with the first pollutant to further verify the first pollutant; if the first pollutant matches the second pollutant, the second image frame, the captured image and the background image are associated, thereby improving the accuracy of pollutant detection and the accuracy of road pollution alarms.

[0034] See also Figure 2 , Figure 2 1 is a flow chart of a specific embodiment of an image processing method based on image capture provided by the present invention. In this embodiment, an image processing method based on image capture is provided, and the image processing method based on image capture includes the following steps.

[0035] S201: Detecting a first acquired image frame to determine a first pollutant in the first image frame.

[0036] Specifically, the image acquisition device captures the image of the road surface in real time, and then obtains the first image frame, performs target detection on the first image frame based on the target detection model of the deep learning framework, and determines the information of the first pollutant contained in the first image frame. The information of the first pollutant includes the position information of the first pollutant in the first image frame and the category of the first pollutant, and marks the detection frame of the first pollutant in the first image frame. For example, the first pollutant can be a static pollutant such as slag, mud, sand and gravel.

[0037] See also Figure 3 and Figure 5 , Figure 3 yes Figure 2 A flowchart of a specific embodiment of step S202 in the image processing method based on image capture is provided; Figure 5 It is a photo captured by the image acquisition device provided by the present invention.

[0038] S202: Determine a background image in historical image frames before the first image frame according to the first image frame.

[0039] Specifically, see Figure 3 Determining the background image of the first image frame from the continuous historical image frames before the first image frame specifically includes the following steps.

[0040] S2021: Extract features from the historical image frames and the first image frame within a preset time period and perform comparisons.

[0041] Specifically, multiple historical image frames within a preset time period are selected from the historical image frames before the first image frame. The target detection model based on the deep learning framework performs feature extraction on the first image frame, and also performs feature extraction on the selected multiple historical image frames within the preset time period respectively, and compares the feature map extracted from the first image frame with the feature map extracted from the historical image frames one by one, and determines which feature maps extracted from the historical image frames do not contain the first pollutant relative to the feature map extracted from the first image frame, and which feature maps extracted from the historical image frames contain the first pollutant relative to the feature map extracted from the first image frame.

[0042] S2022: Filter and retain historical image frames in which the first pollutant is not detected within a preset period of time.

[0043] Specifically, all historical image frames that do not contain the first pollutant are screened out, that is, the screened historical image frames do not detect the first pollutant compared to the first image frames, the screened historical image frames that do not detect the first pollutant are retained, and the historical image frames that detect the first pollutant are removed.

[0044] S2023: Select a historical image frame having the shortest time interval with the first image frame from among the retained historical image frames, and determine the selected historical image frame as the background image.

[0045] Specifically, the shooting time of the retained historical image frame is compared with the shooting time of the first image frame, and a historical image frame with the closest shooting time to the first image frame is selected, and the historical image frame is determined as the background image. Figure 5 (a) This is to reduce errors and avoid that the first image frame and the background image have no other differences except the first pollutant, thereby improving the subsequent detection accuracy.

[0046] S203: Extract features of the first pollutant in the first image frame to obtain a pollutant feature map.

[0047] Specifically, a target detection model based on a deep learning framework performs feature extraction on the first pollutant in the detection frame marked in the first image frame to obtain a pollutant feature map, so as to facilitate the subsequent identification of the type of the first pollutant and improve the identification accuracy.

[0048] S204: Determine whether the pollutant feature map in the first image frame matches a preset feature map in the database.

[0049] Specifically, in order to identify whether the first pollutant in the first image frame appears for the first time, the pollutant feature map extracted from the first image frame is compared one by one with the preset feature maps in the pre-established database to determine whether the pollutant feature map is the same as the preset feature map in the database. In another optional embodiment, due to the influence of the shooting angle factor, the image of the same pollutant may also have errors. In order to reduce the error, the similarity between the pollutant feature map in the first image frame and each preset feature map in the database can be calculated, and then the obtained similarity can be compared with the preset threshold value to determine whether the pollutant feature map in the first image frame matches the preset feature map in the database. Among them, the preset threshold value can be set to 99%.

[0050] When it is determined that the pollutant feature map in the first image frame matches a preset feature map in the database, the category of the preset feature map corresponding to the pollutant feature map is used as the category of the first pollutant, and then the process directly jumps to step S206; when it is determined that the pollutant feature map in the first image frame does not match any preset feature map in the database, it is necessary to identify the pollutant feature map and detect the category of the first pollutant, and then the process directly jumps to step S205.

[0051] S205: Extracting a first image frame.

[0052] Specifically, if the pollutant feature map in the first image frame does not match any preset feature map in the database, the first image frame, the category and the position of the first pollutant are used to generate a new first image frame, see Figure 5 (b). That is, the first pollutant in the first image frame appears for the first time, and the pollutant feature map is detected based on the target detection model, and then the category of the first pollutant is determined by detecting and identifying the pollutant feature map. In another embodiment, the pollutant feature map corresponding to the first pollutant in the first image frame and its corresponding category are updated to the database to facilitate subsequent comparison with the pollutant feature maps extracted from other video frames.

[0053] S206: According to the first image frame, determine a captured image in historical image frames before the first image frame.

[0054] See also Figure 4 , Figure 4 yes Figure 2 A flowchart of a specific embodiment of step S206 in an image processing method based on image capture is provided. Specifically, the step of determining a captured image in a historical image frame before the first image frame according to the first image frame specifically includes the following steps.

[0055] S2061: Perform moving target detection on historical image frames before the first image frame, and select historical image frames containing moving targets.

[0056] Specifically, based on the moving target detection model, moving target detection is performed on the historical image frames within a preset time period before the first image frame, and the moving targets and detection frames of the moving targets contained in the historical image frames are detected. The moving targets include transport vehicles such as muck trucks and mixer trucks. The detection results include the category, location, license plate and other relevant information that helps to identify the moving targets. All historical image frames containing moving targets are screened and retained.

[0057] S2062: Determine a historical image frame having the shortest time interval with the first image frame and containing a moving target whose position is closest to the position of the first pollutant, and use the historical image frame as a captured image.

[0058] Specifically, the shooting time of the retained historical image frame is compared with the shooting time of the first image frame one by one, and the position of the moving target in the historical image frame is compared with the position of the first pollutant in the first image frame one by one, and a historical image frame with the closest shooting time to the first image frame and the moving target contained in it is closest to the position of the first pollutant is selected, and the moving target in the historical image frame is determined as the moving target that threw the first pollutant, and the historical image frame and the shooting time and location are generated as a captured image, see Figure 5(c) This is to reduce the error and thus improve the accuracy of determining the moving target that threw the first pollutant.

[0059] S207: Acquire a second image frame after the first image frame and perform detection to determine a second pollutant in the second image frame.

[0060] Specifically, when the first pollutant is detected in the first image frame, the second image frame is continuously captured by the image acquisition device at a preset interval after the first image frame, and the target detection model of the first image frame is used to detect the target in the second image frame. When the second pollutant is detected in the second image frame, the position information of the second pollutant in the second image frame and the category of the second pollutant are obtained, and the detection frame of the second pollutant in the second image frame is marked. The position of the image acquisition device is fixed, and the first image frame and the second image frame captured are the same position on the same road surface.

[0061] S208: Determine whether the second pollutant matches the first pollutant.

[0062] Specifically, after the second pollutant is acquired in the second image frame, it is determined whether the second pollutant in the second image frame is the same as the first pollutant in the first image frame. In a specific embodiment, it is determined whether the position of the second pollutant in the second image frame is the same as the position of the first pollutant in the first image frame; if the position of the second pollutant in the second image frame is the same as the position of the first pollutant in the first image frame, it is determined whether the similarity between the category of the second pollutant and the category of the first pollutant exceeds a preset value; if the similarity exceeds the preset value, it is determined that the first pollutant matches the second pollutant, thereby improving the detection accuracy of the first pollutant.

[0063] When it is determined that the second pollutant matches the first pollutant, the process directly jumps to step S209 ; when it is determined that the second pollutant does not match the first pollutant, the process directly jumps to step S212 .

[0064] S209: Determine the first pollutant as the detection result.

[0065] Specifically, when it is determined that the second pollutant matches the first pollutant, it indicates that the category of the second pollutant in the second image frame is the same as the category of the first pollutant in the first image frame, and the coordinate position of the second pollutant in the second image frame is also the same as the coordinate position of the first pollutant in the first image frame. That is, it can be determined that the first pollutant is a real road pollutant, and the first pollutant can be determined as the detection result.

[0066] S210: extracting a second image frame.

[0067] Specifically, a new second image frame is generated according to the detection frame of the second pollutant detected in the second image frame, the second image frame, and the shooting time and location of the second image frame, see Figure 5 (d).

[0068] S211: Associating the second image frame, the captured image, and the background image.

[0069] Specifically, the background image, the captured image, the first image frame, and the second image frame are associated and combined to generate an alarm basis.

[0070] S212: Determine that the first pollutant detected in the first image frame is a false detection.

[0071] Specifically, when it is determined that the second pollutant does not match the first pollutant, it indicates that the category of the second pollutant in the second image frame is different from the category of the first pollutant in the first image frame, or the coordinate position of the second pollutant in the second image frame is different from the coordinate position of the first pollutant in the first image frame. That is, it can be determined that the first pollutant is not a real pollutant. In another optional embodiment, if the second pollutant is not detected in the second image frame, it can also be confirmed that the first pollutant detected in the first image frame is a false detection. Among them, the first pollutant may be a pollutant such as light and shadow, tire watermarks or water splashes, and such pollutants will undergo drastic changes in appearance or disappear over time. If the first pollutant is water splashes, after a period of time, the water will evaporate and will not appear on the road surface. Therefore, when the second image frame is detected, the second pollutant matching the first pollutant cannot be detected in the second image frame. Pollutants such as light and shadow, tire watermarks or water splashes are not real pollutants on the road surface and will not affect the road surface. Therefore, when the second pollutant is not detected in the second image frame, or the second pollutant detected in the second image frame does not match a pollutant detected in the first image frame, it is determined that the first pollutant detected in the first image frame is a false detection.

[0072] The pollutant detection method provided in this embodiment includes detecting the acquired first image frame to determine the first pollutant in the first image frame; determining the background image and the captured image from the historical image frames before the first image frame; reacquiring the second image frame after the first image frame and detecting it to determine the second pollutant in the second image frame; then comparing the detected second pollutant with the first pollutant to further verify the first pollutant; if the first pollutant matches the second pollutant, associating the second image frame, the captured image and the background image, thereby improving the accuracy of pollutant detection and the accuracy of road pollution alarms.

[0073] See also Figure 6 , Figure 6It is a schematic block diagram of an embodiment of a terminal provided by the present invention. The terminal 70 in this embodiment includes: a processor 71, a memory 72, and a computer program stored in the memory 72 and executable on the processor 71. When the computer program is executed by the processor 71, the image processing method based on image capture described above is implemented. To avoid repetition, it is not described one by one here.

[0074] See also Figure 7 , Figure 7 It is a schematic block diagram of an embodiment of a computer-readable storage medium provided by the present invention.

[0075] A computer-readable storage medium 90 is also provided in an embodiment of the present application. The computer-readable storage medium 90 stores a computer program 901. The computer program 901 includes program instructions. The processor executes the program instructions to implement the pollutant detection method provided in the embodiment of the present application.

[0076] The computer-readable storage medium 90 may be an internal storage unit of the computer device of the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium 90 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device.

[0077] The above are only implementation modes of the present invention, and are not intended to limit the patent protection scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An image processing method based on image capture, characterized in that: The method comprises: Detecting the acquired first image frame to determine a first pollutant in the first image frame; the first pollutant is an object other than a component element of the traffic road; Determine a background image and a captured image from historical image frames before the first image frame; the captured image is an image containing a moving target associated with the first pollutant; the background image is a historical image frame having a minimum time interval with the first image frame and in which the first pollutant is not detected; After the first image frame, continue to acquire a second image frame at a preset interval and perform target detection to determine a second pollutant in the second image frame; determining whether the second pollutant matches the first pollutant; If they match, the second image frame, the captured image, and the background image are associated.

2. The image processing method based on image capture according to claim 1, characterized in that: The step of determining the background image and the captured image from the historical image frames before the first image frame specifically includes: Extracting features of the first pollutant in the first image frame to obtain a pollutant feature map; Determining whether the pollutant feature map in the first image frame matches a preset feature map in a database; If the pollutant feature map in the first image frame does not match any of the preset feature maps in the database, the first image frame is extracted.

3. The image processing method based on image capture according to claim 2, characterized in that: The step of determining the background image and the captured image from the historical image frames before the first image frame specifically includes: Extracting features from the historical image frames and the first image frames within a preset time period and performing comparisons respectively; Screening and retaining the historical image frames in which the first pollutant is not detected within the preset time period; The historical image frame having the shortest time interval with the first image frame among the retained historical image frames is selected, and the selected historical image frame is determined as the background image.

4. The image processing method based on image capture according to claim 3 is characterized in that: The step of determining the background image and the captured image from the historical image frames before the first image frame specifically includes: Performing moving target detection on the historical image frames before the first image frame, and screening the historical image frames containing the moving target; Determine the historical image frame having the shortest time interval with the first image frame and containing the moving target whose position is closest to the position of the first pollutant, and use the historical image frame as the captured image.

5. The image processing method based on image capture according to claim 4, characterized in that: After the step of associating the second image frame, the captured image and the background image, the method further includes: An alarm is issued based on the associated background image, the captured image, the first image frame and the second image frame.

6. The image processing method based on image capture according to claim 1, characterized in that: The image processing method of the image capture also includes: If the second pollutant does not match the first pollutant, it is determined that the first pollutant detected in the first image frame is a false detection.

7. The image processing method based on image capture according to claim 1, characterized in that: The step of detecting the acquired first image frame to determine the first pollutant in the first image frame specifically includes: Detecting the first acquired image frame based on a deep learning target detection model to determine the type and location of the first pollutant in the first image frame; The step of acquiring and detecting a second image frame after the first image frame to determine a second pollutant in the second image frame specifically includes: The acquired second image frame is detected based on a deep learning target detection model to determine the type and position of the second pollutant in the second image frame.

8. The image processing method based on image capture according to claim 7, characterized in that: The step of determining whether the second pollutant matches the first pollutant specifically includes: determining whether a position of the second pollutant in the second image frame is the same as a position of the first pollutant in the first image frame; If the position of the second pollutant in the second image frame is the same as the position of the first pollutant in the first image frame, determining whether the similarity between the category of the second pollutant and the category of the first pollutant exceeds a preset value; If the similarity exceeds the preset value, it is determined that the first pollutant matches the second pollutant.

9. A terminal, characterized in that: The terminal includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor is used to execute program data to implement the steps in the image processing method based on image capture as claimed in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the image processing method based on image capture according to any one of claims 1 to 8 are implemented.

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