Image processing method and device, electronic equipment and storage medium

Abstract

The invention provides an image processing method and device, electronic equipment and a storage medium, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-processed image and the network signal intensity between the local and a cloud server; under the condition that a preset condition is met, performing a first image processing process on the to-be-processed image locally to obtain a first image processing result; wherein the preset condition comprises that the network signal strength is lower than a preset threshold value. According to the invention, when the network signal between the edge device and the cloud server is weak, the to-be-processed image is processed locally and the processing result is obtained, so that the delay caused by uploading the image to the cloud server can be avoided, and the real-time performance of image processing is improved.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to an image processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the rapid development of IoT and AI technologies, image processing is being applied more and more widely in various fields. However, existing image processing methods often encounter numerous problems when facing complex network environments. For example, in pursuit of image processing accuracy, current methods upload images to cloud servers for processing. This approach can achieve high-precision results when network signal strength is strong, but when network signal strength is weak, image upload speed is slow or even fails, resulting in poor real-time performance of image processing. Summary of the Invention

[0003] This invention provides an image processing method, apparatus, electronic device, and storage medium to solve the technical problem of poor real-time performance in image processing in the prior art.

[0004] This invention provides an image processing method, comprising: Acquire the image to be processed and the network signal strength between the local and cloud servers; Under preset conditions, the image to be processed is subjected to a first image processing procedure locally to obtain a first image processing result. The preset conditions include the network signal strength being lower than a preset threshold.

[0005] According to an image processing method provided by the present invention, after performing a first image processing procedure on the image to be processed locally to obtain a first image processing result if the network signal strength is lower than a preset threshold, the method further includes: The network signal strength is acquired every preset time interval; If the network signal strength is higher than the preset threshold, the target image is uploaded to the cloud server, and the target image is part or all of the image to be processed; Obtain the second image processing result obtained by the cloud server after performing a second image processing procedure on the target image; The second image processing result is used to overwrite the first image processing result; The processing accuracy of the second image processing process is greater than that of the first image processing process.

[0006] According to an image processing method provided by the present invention, the first image processing process includes: Identify the target location in the image to be processed; The image of the region where the target location is located is extracted as the target image.

[0007] According to an image processing method provided by the present invention, uploading the target image to the cloud server includes: The target image is compressed and encrypted; The compressed and encrypted target image is uploaded to the cloud server.

[0008] According to an image processing method provided by the present invention, after acquiring the image to be processed and the network signal strength between the local machine and the cloud server, before performing a first image processing procedure on the image to be processed locally to obtain a first image processing result if the network signal strength is lower than a preset threshold, the method further includes: Determine the preset threshold corresponding to the application scenario of the image to be processed; The higher the real-time requirements of the application scenario for image processing, the larger the corresponding preset threshold.

[0009] According to an image processing method provided by the present invention, after acquiring the image to be processed, the method further includes: Identify the target motion speed in the image to be processed; If the target's movement speed is higher than a preset speed, then the first image processing procedure is performed on the image to be processed locally to obtain the first image processing result.

[0010] According to an image processing method provided by the present invention, after acquiring the image to be processed and the network signal strength between the local machine and the cloud server, the method further includes: If the network signal strength is higher than the preset threshold and the target movement speed is lower than the preset speed, then the target image is uploaded to the cloud server, and the target image is part or all of the image to be processed; Obtain the second image processing result obtained by the cloud server after performing a second image processing procedure on the target image.

[0011] According to an image processing method provided by the present invention, after acquiring the image to be processed, the method further includes: Identify the target type in the image to be processed; If the target type is the type that defaults to the second image processing procedure, then the target image is uploaded to the cloud server, and the target image is part or all of the image to be processed; Obtain the second image processing result obtained by the cloud server after performing the second image processing procedure on the target image; The preset conditions also include that the target type is not the type that defaults to the second image processing procedure.

[0012] The present invention also provides an image processing apparatus, comprising: The acquisition module is used to acquire the image to be processed and the network signal strength between the local and cloud servers. The processing module is used to perform a first image processing procedure on the image to be processed locally under preset conditions to obtain a first image processing result; The preset conditions include the network signal strength being lower than a preset threshold.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the image processing method described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the image processing method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the image processing method as described above.

[0016] The image processing method, apparatus, electronic device, and storage medium provided by this invention can process the image to be processed locally and obtain the processing result when the network signal between the edge device and the cloud server is weak, thereby avoiding the delay caused by uploading the image to the cloud server and improving the real-time performance of image processing. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts of the image processing method provided by the present invention.

[0019] Figure 2 This is the second flowchart of the image processing method provided by the present invention.

[0020] Figure 3 This is a timing diagram of the image processing method provided by the present invention.

[0021] Figure 4 This is a schematic diagram of the image processing device provided by the present invention.

[0022] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] It should be noted that in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a 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 said element. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly, for example, as a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection through an intermediate medium; or a connection within two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention 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, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "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.

[0026] The following is combined with Figures 1-5 This invention describes the image processing method, apparatus, electronic device, and storage medium provided by the present invention.

[0027] The present invention first provides an image processing system, including an edge device, a dynamic decision module, and a cloud server. The dynamic decision module is used to monitor the network signal strength between the edge device and the cloud server in real time, predict the trend of network status changes based on historical data and real-time data, and dynamically determine the image processing mode based on the network signal strength.

[0028] like Figure 1 As shown, the image processing method provided by the present invention includes steps S1 and S2. This image processing method is applied to edge devices.

[0029] Step S1: Obtain the image to be processed and the network signal strength between the local server and the cloud server.

[0030] The images to be processed can be captured in real time by a camera, and edge devices acquire these images. The camera should possess characteristics such as high resolution, high frame rate, and low latency to meet the image acquisition needs of different scenarios. The camera's resolution, frame rate, and other parameters can be configured according to the specific application scenario. For example, in intelligent security scenarios, a 1080P resolution camera with 30 frames per second can be used; in autonomous driving scenarios, cameras with higher resolution and frame rates can be used to meet the requirements for image detail and real-time performance.

[0031] "Local" refers to edge devices. The dynamic decision-making module can use existing network status monitoring tools or algorithms to detect network signal strength, such as by measuring network round-trip time and packet loss rate. Simultaneously, it can predict network status trends based on historical and real-time network data, making decisions in advance. Edge devices acquire the network signal strength detected by the dynamic decision-making module.

[0032] Step S2: Under the condition that the preset conditions are met, perform the first image processing process on the image to be processed locally to obtain the first image processing result; wherein the preset conditions include the network signal strength being lower than a preset threshold.

[0033] The preset conditions can include only network signal strength below a preset threshold. If the network signal strength is below the preset threshold, it means that the network signal is weak. If the image is uploaded to the cloud server for processing, there will be problems such as slow upload speed or upload failure, which will reduce the real-time performance of image processing. Therefore, it is not advisable to upload the image to the cloud server for processing. In this case, the dynamic decision module will notify the edge device to process the image locally.

[0034] The first image processing step involves the edge device performing basic processing on the image to be processed locally. This basic processing includes object classification, counting, and simple behavior analysis. For example, in a smart security scenario, the first image processing step could identify moving objects, resulting in a "live intrusion detected" message. This local image processing allows for timely detection of live intrusions in smart security scenarios. The edge device can also generate corresponding user notifications based on the first image processing results, such as "Intrusion detected" or "Pet activity detected." Notification methods could include sound alarms, flashing lights, and push notifications.

[0035] As can be seen from the above, the image processing method of the present invention selects to process the image to be processed locally and obtain the processing result when the network signal between the edge device and the cloud server is weak, which can avoid the delay caused by uploading the image to the cloud server and improve the real-time performance of image processing.

[0036] Considering the limited computing resources and image processing precision of edge devices, the accuracy of the resulting first image processing result may be limited, potentially leading to incorrect user prompts and a degraded user experience. Therefore, in some embodiments, after step S2, the image processing method of the present invention may further include: The network signal strength is measured every preset time interval; If the network signal strength is higher than the preset threshold, the target image will be uploaded to the cloud server. The target image is part or all of the image to be processed. Obtain the second image processing result obtained after the cloud server performs a second image processing procedure on the target image; The second image processing result is used to overwrite the first image processing result; The processing accuracy of the second image processing process is greater than that of the first image processing process.

[0037] Under normal circumstances, the network signal between the edge device and the cloud server will not remain weak indefinitely, but will recover over time. The edge device will periodically obtain the network signal strength from the dynamic decision module and request a re-decision. If the obtained network signal strength is higher than a preset threshold, it means that the network signal has recovered to a relatively strong level, and the image can be uploaded to the cloud server.

[0038] After the target image is uploaded to the cloud server, the cloud server performs a high-precision second image processing step to obtain a high-precision second image processing result. Specifically, the cloud server uses deep learning algorithms (such as ResNet and EfficientNet) to perform high-precision analysis on the uploaded target image, identifying detailed information such as object type, behavior, and state. The cloud server has powerful computing capabilities and abundant storage resources, enabling it to run complex deep learning models and achieve high-precision image analysis. For example, ResNet solves the gradient vanishing problem during deep network training by introducing residual connections, improving the model's accuracy; EfficientNet balances the model's depth, width, and resolution through a composite scaling method, achieving efficient computation and high-precision recognition.

[0039] The cloud server feeds back the calculated second image processing result to the edge device. The edge device uses the second image processing result to overwrite the first image processing result and can generate new user prompts based on the second image processing result. For example, in a smart security scenario, if the first image processing result indicates an intruder, the edge device warns the user that someone has entered the premises. If the second image processing result indicates the intruder is the homeowner, then the final image processing result is the homeowner, and the edge device can prompt the user, "Alarm deactivated, family member has returned home." Similarly, in a traffic monitoring scenario, if the first image processing result shows vehicle location and traffic flow, and the second image processing result shows license plate number and traffic violation records, then the final image processing result is the license plate number and traffic violation records. The edge device can display, "Vehicle with license plate number X passed by speeding, has 3 historical traffic violations."

[0040] Since the second image processing result is more accurate than the first image processing result, using the second image processing result to overwrite the first image processing result after the network signal is restored can make up for the accuracy of the image processing in a timely manner after the network signal is restored, making the image processing result more accurate and helping to improve the accuracy of user prompts.

[0041] As mentioned above, the target image can be part or all of the image to be processed. Uploading the entire image to be processed would result in high bandwidth consumption and low transmission efficiency. Therefore, the first image processing step of this invention may include: Identify the target location in the image to be processed; Extract the image of the region where the target location is located as the target image.

[0042] The target location is the location of the target to be identified, such as a person, animal, or license plate.

[0043] Edge devices can utilize lightweight deep learning algorithms (such as MobileNet and YOLO-Lite) to detect targets in images and label their locations. These lightweight algorithms are characterized by low computational complexity and fast inference speed, making them suitable for operation on resource-constrained edge devices. For example, MobileNet employs a depthwise separable convolutional structure, significantly reducing the number of model parameters and computational cost; YOLO-Lite optimizes the YOLO algorithm by adjusting the network structure and hyperparameters, improving the model's inference speed.

[0044] The key part of the image to be processed is the image of the target to be identified. The image of the area where the target is located is extracted as the target image. This is equivalent to uploading only the key area image to the cloud server, which can significantly reduce traffic consumption, especially suitable for scenarios with limited bandwidth.

[0045] In some embodiments, before identifying the target location in the image to be processed and obtaining an image of the region where the target location is located as the target image, the first image processing procedure may further include: The image to be processed is enhanced, denoised, and normalized in sequence.

[0046] Image enhancement can employ methods such as histogram equalization and contrast-limited adaptive histogram equalization to improve image contrast and clarity; noise reduction can use methods such as Gaussian filtering and median filtering to remove noise from the image; normalization can normalize image pixel values ​​to a specific range (such as [0, 1] or [-1, 1]) to facilitate subsequent processing by deep learning algorithms.

[0047] Improving, denoising, and normalizing the image to be processed sequentially can improve image quality and thus improve image processing efficiency.

[0048] In some embodiments, uploading the target image to a cloud server in this invention may further include: Compress and encrypt the target image; The compressed and encrypted target image is uploaded to the cloud server.

[0049] Compression can employ common image compression algorithms such as JPEG and H.264; encryption can use encryption algorithms such as AES and RSA. After the compressed and encrypted target image is uploaded to the cloud server, the cloud server will first decompress and decrypt the target image, and then perform a second image processing procedure.

[0050] Compressing and encrypting the target image during transmission can reduce bandwidth consumption and improve the security of image data.

[0051] The preset threshold of this invention can be a fixed value. However, in different application scenarios, a fixed preset threshold may lead to the following problems: In application scenarios with low real-time requirements for image processing, the preset threshold may be too high, causing images to be processed locally even when the network signal is strong, resulting in limited accuracy of the image processing results; In application scenarios with high real-time requirements for image processing, the preset threshold may be too low, causing images to be uploaded to the cloud server for processing when the network signal is weak, reducing the timeliness of image processing. In order to balance the accuracy of image processing results and the timeliness of image processing, in some embodiments, after step S1 and before step S2, the image processing method of this invention may further include: Determine the preset threshold corresponding to the application scenario of the image to be processed; The higher the real-time requirements of the application scenario for image processing, the larger the corresponding preset threshold.

[0052] The preset threshold can be adjusted according to the specific application scenario. For example, in intelligent security scenarios where the real-time requirements for image processing are relatively low, the preset threshold can be set to 2 bars of signal strength. As long as the network signal strength is higher than 2 bars, the image will be uploaded to the cloud server for processing, ensuring the accuracy of the image processing results. In autonomous driving scenarios where the real-time requirements for image processing are high, the preset threshold can be set to 3 bars of signal strength. As long as the network signal strength is lower than 3 bars, the image will be processed locally, ensuring the timeliness of image processing.

[0053] In some embodiments, after acquiring the image to be processed, the image processing method of the present invention may further include: Identify the speed of target motion in the image to be processed; If the target's movement speed is higher than the preset speed, the first image processing procedure is performed on the image to be processed locally to obtain the first image processing result.

[0054] Considering the high real-time requirements for image processing in scenarios involving high-speed target movement, uploading images of such scenarios to a cloud server for processing would reduce the real-time performance of identifying high-speed moving targets. Targets moving at speeds exceeding a preset speed are considered to be in a high-speed motion state, suitable for local processing, and thus improve the real-time performance of identifying high-speed moving targets.

[0055] In some embodiments, after step S1, the image processing method of the present invention may further include: If the network signal strength is higher than the preset threshold and the target movement speed is lower than the preset speed, the target image will be uploaded to the cloud server. The target image is part or all of the image to be processed. Obtain the second image processing result obtained after the cloud server performs a second image processing procedure on the target image.

[0056] If the network signal strength is higher than a preset threshold, it indicates a strong network signal, suitable for directly uploading the image to the cloud server for processing. If the target's movement speed is lower than a preset speed, it indicates a slow target movement speed, and local processing is not necessary. For example, in a traffic monitoring scenario, after the cloud server identifies the license plate number, it sends the license plate number back to the edge device. The edge device can then further query vehicle information (such as owner information, traffic violation records, etc.) and display it to the user.

[0057] This allows images to be uploaded directly to a cloud server for processing when the network signal is strong and the target is not moving at high speed, thus improving the accuracy of the image processing results.

[0058] In some embodiments, after acquiring the image to be processed, the image processing method of the present invention may further include: Identify the type of target in the image to be processed; If the target type is the type that will undergo the second image processing procedure by default, then the target image will be uploaded to the cloud server; Obtain the second image processing result obtained after the cloud server performs a second image processing procedure on the target image; The preset conditions also include that the target type is not the type that will be processed in the second image processing procedure by default.

[0059] The default types for the second image processing step can include faces, license plates, and other images requiring high-precision recognition. The target type is the default type for the second image processing step, indicating that the target in the image requires high-precision recognition. Local processing would reduce the accuracy of the image processing results. In this case, uploading the target image to a cloud server for processing can improve the accuracy of the processing results for images requiring high-precision recognition.

[0060] In some embodiments, the image to be processed in this invention can be an image captured by a smart security camera, a traffic monitoring camera, or an autonomous driving camera.

[0061] In other words, the image processing method of the present invention is applicable to various scenarios such as intelligent security, traffic monitoring, and autonomous driving, and has broad market application prospects.

[0062] The complete image processing flow of this invention is as follows: Figure 2As shown, the process includes: an edge device acquiring an image to be processed from a camera; performing preprocessing on the image, such as enhancement, denoising, and normalization; identifying objects in the image and marking their bounding boxes; a dynamic decision module detecting the network signal strength between the edge device and the cloud server; if the network signal is weak, performing basic processing on the image locally and notifying the user, and uploading the key areas of the object to the cloud server promptly after the network recovers; if the network signal is strong, directly uploading the key areas of the object to the cloud server; the cloud server analyzing the key areas of the object and feeding the analysis results back to the edge device; and the edge device integrating the cloud analysis results and displaying them to the user. Figure 2 The corresponding timing diagram for cloud-edge collaboration is as follows: Figure 3 As shown, it will not be elaborated further here.

[0063] like Figure 4 As shown, the present invention also provides an image processing apparatus, comprising: The acquisition module is used to acquire the image to be processed and the network signal strength between the local and cloud servers. The processing module is used to perform a first image processing procedure on the image to be processed locally under preset conditions to obtain a first image processing result. Among the preset conditions is that the network signal strength is lower than a preset threshold.

[0064] In some implementations, the acquisition module can also be used to: acquire network signal strength once every preset time interval; The image processing device may further include: an upload module, used to upload the target image to a cloud server if the network signal strength is higher than a preset threshold, wherein the target image is part or all of the image to be processed; The acquisition module can also be used to: acquire the second image processing result obtained by the cloud server after performing a second image processing process on the target image; The image processing apparatus may further include: an update module for overwriting the first image processing result with the second image processing result; The processing accuracy of the second image processing process is greater than that of the first image processing process.

[0065] In some implementations, the first image processing procedure may include: Identify the target location in the image to be processed; Extract the image of the region where the target location is located as the target image.

[0066] In some implementations, the upload module can also be used for: Compress and encrypt the target image; The compressed and encrypted target image is uploaded to the cloud server.

[0067] In some embodiments, the image processing apparatus may further include: The determination module is used to determine the preset threshold corresponding to the application scenario of the image to be processed. The higher the real-time requirements of the application scenario for image processing, the larger the corresponding preset threshold.

[0068] In some implementations, the processing module can also be used for: Identify the speed of target motion in the image to be processed; If the target's movement speed is higher than the preset speed, the first image processing procedure is performed on the image to be processed locally to obtain the first image processing result.

[0069] In some implementations, the upload module can also be used to: upload the target image to the cloud server if the network signal strength is higher than a preset threshold and the target movement speed is lower than a preset speed, wherein the target image is part or all of the image to be processed; The acquisition module can also be used to acquire the second image processing result obtained by the cloud server after performing a second image processing process on the target image.

[0070] In some implementations, the processing module can also be used for: Identify the type of target in the image to be processed; If the target type is the type that will undergo the second image processing procedure by default, then the target image will be uploaded to the cloud server; Obtain the second image processing result obtained after the cloud server performs a second image processing procedure on the target image; The preset conditions also include that the target type is not the type that will be processed in the second image processing procedure by default.

[0071] It should be noted that the image processing apparatus provided by the present invention can execute the image processing method of any of the above embodiments during specific operation, and this embodiment will not elaborate on this.

[0072] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 5As shown, the electronic device may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can invoke logical instructions in the memory to execute an image processing method, which includes: acquiring the image to be processed and the network signal strength between the local machine and the cloud server; and, under preset conditions, performing a first image processing procedure on the image to be processed locally to obtain a first image processing result; wherein the preset conditions include the network signal strength being lower than a preset threshold.

[0073] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] On the other hand, the present invention also provides 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 program instructions, and when the program instructions are executed by a computer, the computer is able to execute the image processing method provided in the above embodiments, the method including: acquiring an image to be processed and the network signal strength between a local machine and a cloud server; and, under the condition of satisfying preset conditions, performing a first image processing process on the image to be processed locally to obtain a first image processing result; wherein the preset conditions include the network signal strength being lower than a preset threshold.

[0075] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program is implemented to perform the image processing method provided in the above embodiments. The method includes: acquiring an image to be processed and the network signal strength between a local machine and a cloud server; and, under the condition that preset conditions are met, performing a first image processing procedure on the image to be processed locally to obtain a first image processing result; wherein the preset conditions include the network signal strength being lower than a preset threshold.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, 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 can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An image processing method, characterized in that, include: Acquire the image to be processed and the network signal strength between the local and cloud servers; Under preset conditions, the image to be processed is subjected to a first image processing procedure locally to obtain a first image processing result. The preset conditions include the network signal strength being lower than a preset threshold.

2. The image processing method according to claim 1, characterized in that, If the network signal strength is lower than a preset threshold, then after performing a first image processing procedure on the image to be processed locally to obtain a first image processing result, the method further includes: The network signal strength is acquired every preset time interval; If the network signal strength is higher than the preset threshold, the target image is uploaded to the cloud server, and the target image is part or all of the image to be processed; Obtain the second image processing result obtained by the cloud server after performing a second image processing procedure on the target image; The second image processing result is used to overwrite the first image processing result; The processing accuracy of the second image processing process is greater than that of the first image processing process.

3. The image processing method according to claim 2, characterized in that, The first image processing procedure includes: Identify the target location in the image to be processed; The image of the region where the target location is located is extracted as the target image.

4. The image processing method according to claim 2, characterized in that, Uploading the target image to the cloud server includes: The target image is compressed and encrypted; The compressed and encrypted target image is uploaded to the cloud server.

5. The image processing method according to claim 1, characterized in that, After acquiring the image to be processed and the network signal strength between the local machine and the cloud server, before performing the first image processing procedure on the image to be processed locally and obtaining the first image processing result if the network signal strength is lower than a preset threshold, the method further includes: Determine the preset threshold corresponding to the application scenario of the image to be processed; The higher the real-time requirements of the application scenario for image processing, the larger the corresponding preset threshold.

6. The image processing method according to claim 1, characterized in that, After acquiring the image to be processed, the process further includes: Identify the target motion speed in the image to be processed; If the target's movement speed is higher than a preset speed, then the first image processing procedure is performed on the image to be processed locally to obtain the first image processing result.

7. The image processing method according to claim 6, characterized in that, After acquiring the image to be processed and the network signal strength between the local machine and the cloud server, the process further includes: If the network signal strength is higher than the preset threshold and the target movement speed is lower than the preset speed, then the target image is uploaded to the cloud server, and the target image is part or all of the image to be processed; Obtain the second image processing result obtained by the cloud server after performing a second image processing procedure on the target image.

8. The image processing method according to claim 1, characterized in that, After acquiring the image to be processed, the process further includes: Identify the target type in the image to be processed; If the target type is the type that defaults to the second image processing procedure, then the target image is uploaded to the cloud server, and the target image is part or all of the image to be processed; Obtain the second image processing result obtained by the cloud server after performing the second image processing procedure on the target image; The preset conditions also include that the target type is not the type that defaults to the second image processing procedure.

9. An image processing apparatus, characterized in that, include: The acquisition module is used to acquire the image to be processed and the network signal strength between the local and cloud servers. The processing module is used to perform a first image processing procedure on the image to be processed locally under preset conditions to obtain a first image processing result; The preset conditions include the network signal strength being lower than a preset threshold.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the image processing method as described in any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the image processing method as described in any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the image processing method as described in any one of claims 1 to 8.

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