Image processing method, device, terminal and system for fecal health detection

Abstract

The application discloses an image processing method, device, terminal and system for excrement health detection. The method comprises the following steps: acquiring an original image of a fecal sample; converting the original image to obtain a visually friendly image based on the visual features of the original image; processing the original image to generate an access information encoding image; and synthesizing the visually friendly image and the access information encoding image to obtain a target sample image. By converting the visually friendly image, the original image can be replaced in the user interaction interface, which fundamentally solves the psychological barrier of home excrement detection and improves the user experience. Meanwhile, through parallel double-path processing, the access information encoding image of the original image is generated based on chain encryption, so that the original image data can be requested from the encrypted storage server, and then subsequent health analysis can be carried out based on the encrypted transmission of the original image, thereby ensuring the analysis result accuracy and user information security.

Description

Technical Field

[0001] This invention relates to the field of health detection technology, and in particular to an image processing method, apparatus, terminal and system for fecal health detection. Background Technology

[0002] With the widespread adoption of mobile health (mHealth) and home-based monitoring technologies, using smart devices (such as smartphones) to capture images of feces for preliminary health screening has become an important health management method. Existing solutions typically rely on users directly photographing feces and uploading the images to the cloud for artificial intelligence (AI) analysis. This requires users to store, transmit, and even repeatedly view clear, raw fecal images, which can easily trigger strong psychological discomfort and aversion, representing the biggest obstacle to long-term, regular use. To address this issue, traditional solutions often employ simple image masking or blurring. While this alleviates visual discomfort, it is destructive and directly renders subsequent analyses ineffective. On the other hand, to ensure the accuracy of the analysis results, AI models require high-resolution raw images. However, these raw images contain sensitive personal information, posing a risk of leakage during local storage and cloud transmission. Summary of the Invention

[0003] This invention provides an image processing method, apparatus, terminal, and system for fecal health detection, which improves user experience, enhances the accuracy of analysis results, and safeguards user information security.

[0004] In a first aspect, embodiments of the present invention provide an image processing method for fecal health detection, the method comprising: Obtain the raw image of the fecal sample; Based on the visual features of the original image, the original image is converted into a visually friendly image; The original image is processed to generate an access information encoded image; The visually friendly image and the access information encoded image are combined to obtain the target sample image.

[0005] Optionally, the step of converting the original image into a visually friendly image based on the visual features of the original image includes: A neural style transfer model is used to convert the original image into a cartoon-style image, a watercolor-style image, or an abstract color block image based on the visual features.

[0006] Optionally, processing the original image to generate an access information encoded image includes: Generate the data fingerprint of the original image; The original image is encrypted using a randomly generated symmetric encryption key to obtain the image ciphertext; The encrypted image is uploaded to an encrypted storage server for storage, and an access token is received from the encrypted storage server. The access information encoded image is generated based on the data fingerprint, the access token, and the public key encryption result of the symmetric encryption key.

[0007] Optionally, the method further includes: In response to the user's detection request, the target sample image is parsed to obtain the data fingerprint, the access token, and the public key encryption result; The access token and the public key encryption result are sent to the cloud security gateway. After the cloud security gateway verifies the legitimacy, it requests the image ciphertext from the encrypted storage server. The private key is used to decrypt the public key encryption result to obtain the symmetric encryption key. The symmetric encryption key is then used to decrypt the image ciphertext to obtain the original image. After the original image is verified based on the data fingerprint, it is sent to the intelligent analysis server for analysis. Receive the fecal sample analysis results returned by the cloud security gateway.

[0008] Optionally, the step of synthesizing the visually friendly image and the access information encoded image to obtain the target sample image includes: The access information encoded image is embedded as a watermark in the corner of the visually friendly image to obtain the target sample image.

[0009] Optionally, before converting the original image into a visually friendly image based on the visual features of the original image, the method further includes: Identify and crop the target fecal body region in the original image; and / or, White balance and illumination compensation are applied to the original image.

[0010] Secondly, embodiments of the present invention also provide an image processing apparatus for fecal health detection, the apparatus comprising: The image acquisition module is used to acquire raw images of fecal samples; An image conversion module is used to convert the original image into a visually friendly image based on the visual features of the original image; An image encoding module is used to process the original image to generate an access information encoded image; An image synthesis module is used to synthesize the visually friendly image and the access information encoded image to obtain a target sample image.

[0011] Thirdly, embodiments of the present invention also provide a mobile terminal, the mobile terminal comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the image processing method for fecal health detection provided in any embodiment of the present invention.

[0012] Fourthly, embodiments of the present invention also provide a method for detecting fecal health, the method comprising: Obtain the original image of the fecal sample and convert the original image into a visually friendly image; The original image is encrypted and then uploaded to the encrypted storage server for storage, and an access token is received from the encrypted storage server. An access information encoded image is generated based on the access token, and the visually friendly image is combined with the access information encoded image to obtain a target sample image; In response to the user's detection request, the target sample image is parsed to obtain the access token; The cloud security gateway uses the access token to request the original image data from the encrypted storage server, and then requests the intelligent analysis server to perform analysis based on the original image data. Receive the fecal sample analysis results returned by the cloud security gateway.

[0013] Fifthly, embodiments of the present invention also provide an image processing system for fecal health detection, the system comprising: a mobile terminal, a cloud security gateway, an encrypted storage server, and an intelligent analysis server, wherein the mobile terminal is used to execute the image processing method for fecal health detection provided in any embodiment of the present invention.

[0014] This invention provides an image processing method for fecal health detection. First, the original image of the fecal sample is acquired. Then, based on the visual features of the original image, it is converted into a visually friendly image. Simultaneously, the original image is processed to generate an access information encoded image. Finally, the visually friendly image and the access information encoded image are combined to obtain the target sample image. This image processing method for fecal health detection, by converting the original image into a visually friendly one, can replace the original image in the user interface, fundamentally solving the psychological barrier to home fecal detection and improving the user experience. Simultaneously, through parallel dual-path processing, the access information encoded image of the original image is generated based on chain-based encryption. This image is used to request the original image data from an encrypted storage server, enabling subsequent health analysis based on the encrypted original image, ensuring the accuracy of the analysis results and the security of user information. Attached Figure Description

[0015] Figure 1 This is a flowchart of an image processing method for fecal health detection provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the image processing device for fecal health detection provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a mobile terminal provided in Embodiment 3 of the present invention. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0017] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. The process can be terminated when its operation is complete, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0018] Example 1 Figure 1 This is a flowchart of an image processing method for fecal health detection provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where users conduct home fecal health assessments using mobile terminals. The method can be executed by the image processing device for fecal health detection provided in this embodiment of the invention. This device can be implemented in hardware and / or software, and is generally integrated into a mobile terminal, specifically a mobile phone, tablet computer, etc. Figure 1 As shown, the method specifically includes the following steps: S11. Obtain the original image of the fecal sample.

[0019] S12. Based on the visual features of the original image, the original image is converted into a visually friendly image.

[0020] S13. Process the original image to generate an access information encoded image.

[0021] S14. Combine the visually friendly image with the access information encoded image to obtain the target sample image.

[0022] Specifically, when fecal images need to be collected for health assessment, users can capture the original image of the current fecal sample using their mobile device's camera. A dual-path parallel processing can then be initiated. On one hand, the original image is converted into a visually friendly image. On the other hand, the original image is chain-encrypted and encapsulated to generate an access information encoded image used to request the original image data from an encrypted storage server. The original image data can then be stored in encrypted form on the encrypted storage server, and the access information encoded image is also generated using encryption, effectively protecting user information security. Simultaneously, this path enables the acquisition of the original image data, which can then be used for fecal health analysis, thus ensuring analytical accuracy. Optionally, the access information encoded image is a QR code.

[0023] In an optional implementation, the step of converting the original image into a visually friendly image based on its visual features includes: employing a neural style transfer model to convert the original image into a cartoon-style image, a watercolor-style image, or an abstract color block image according to the visual features, thereby achieving visual friendliness. The processing can be algorithmically constrained to ensure that key visual features such as the color distribution histogram and the outline of the main subject remain highly consistent with the original image while stripping away realistic textures and details.

[0024] After processing the visually friendly image and the access information encoded image, the visually friendly image and the access information encoded image are combined to obtain a target sample image, which can be saved to the local album of the mobile terminal. The access information encoded image only stores directional access control information and does not contain any image data, so the combined target sample image achieves good local desensitization display. Optionally, the step of combining the visually friendly image and the access information encoded image to obtain the target sample image includes: embedding the access information encoded image as a watermark in the corner of the visually friendly image to obtain the target sample image.

[0025] In an optional implementation, processing the original image to generate an access information encoded image includes: generating a data fingerprint of the original image; encrypting the original image based on a randomly generated symmetric encryption key to obtain image ciphertext; uploading the image ciphertext to the encrypted storage server for storage and receiving an access token returned by the encrypted storage server; and generating the access information encoded image based on the data fingerprint, the access token, and the public key encryption result of the symmetric encryption key.

[0026] Specifically, the data fingerprint can be a hash value calculated from the original image, such as SHA-256, which can serve as a unique identifier and integrity check code for the original image. Each time an original image is acquired, a random symmetric encryption key Ks is generated for it, and the original image is encrypted using the symmetric encryption key Ks to obtain image ciphertext C. Then, image ciphertext C is uploaded to an encrypted storage server, which stores image ciphertext C and returns a corresponding access token to the mobile terminal. This access token can specifically be a storage index ID. Based on the access token, the mobile terminal can request the corresponding encrypted form of the original image data, i.e., image ciphertext, from the encrypted storage server. The data fingerprint, access token, and the public key encryption result E_Public(Ks) of the symmetric encryption key are then serialized into a structured data packet, and this data packet is encoded to generate an access information encoded image.

[0027] In an optional implementation, before generating the data fingerprint of the original image, the method further includes compressing the original image using a lossless compression algorithm to reduce the data size. The aforementioned process of generating the access information encoded image can then be performed based on the compressed original image.

[0028] In an optional implementation, before converting the original image into a visually friendly image based on its visual features, the method further includes: identifying and cropping the target fecal region in the original image; and / or performing white balance and illumination compensation on the original image. Specifically, after acquiring the original image, it can be preprocessed to improve the accuracy of subsequent analysis. Specifically, the target fecal region of interest (ROI) in the original image can be automatically identified based on a semantic segmentation model and cropped to remove irrelevant background, resulting in a standardized original image. Then, through white balance and illumination compensation, ambient light interference can be eliminated, establishing a color analysis benchmark. The aforementioned parallel processing can then be performed based on the preprocessed original image.

[0029] Based on the above technical solution, optionally, the method further includes: responding to a user's detection request, parsing the target sample image to obtain the data fingerprint, the access token, and the public key encryption result; sending the access token and the public key encryption result to a cloud security gateway, requesting the image ciphertext from the encrypted storage server after the cloud security gateway verifies its legitimacy, decrypting the public key encryption result using a private key to obtain the symmetric encryption key, decrypting the image ciphertext using the symmetric encryption key to obtain the original image, and sending the original image to an intelligent analysis server for analysis after verifying its legitimacy based on the data fingerprint; and receiving the fecal sample analysis result returned by the cloud security gateway.

[0030] Specifically, users can select a target sample image from their local photo album at any time for fecal health analysis. This target sample image can be newly generated or previously analyzed. Upon receiving a user's corresponding detection request, the system can automatically parse the target sample image, specifically identifying and parsing the access information encoded image to extract the corresponding data fingerprint, access token, and public key encryption result. The obtained access token and public key encryption result are then sent to the cloud security gateway through a secure channel to achieve secure access to cloud data. The cloud security gateway first verifies the legitimacy of the request, such as verifying the identity and permissions of the requesting device. After successful legitimacy verification, it can request the encrypted image from the encrypted storage server based on the access token, and the encrypted storage server can then return the encrypted image corresponding to the access token. The cloud security gateway can also use the private key corresponding to the user terminal to decrypt the public key encryption result, obtaining the corresponding symmetric encryption key. This symmetric encryption key can then be used to decrypt the ciphertext of the image returned by the encrypted storage server, obtaining the original image. Integrity verification of the original image can be performed based on the corresponding data fingerprint, such as calculating the hash value of the original image and comparing it with the data fingerprint. This data fingerprint can be provided to the cloud security gateway by the user terminal in response to a detection request, or it can be uploaded to the encrypted storage server during the generation of the access information encoded image, and returned along with the image ciphertext when requesting it from the encrypted storage server. After successful verification, the original image is sent to the intelligent analysis server for analysis. Specifically, an artificial intelligence (AI) model can be used for analysis, and the analysis process can employ any existing method, without limitation. The analysis can obtain characteristics such as type (e.g., Bristol classification), color, and visible components, and can generate an analysis report containing health scores, abnormal alerts, and health suggestions. This report is then returned from the intelligent analysis server to the cloud security gateway, and then from the cloud security gateway to the user terminal, presented to the user in clear chart and text format. Throughout the entire process, the user terminal does not touch or display the original image.

[0031] By employing chain-based encryption and an image indexing mechanism with access information encoding, the security of the original image is ensured during storage (cloud encryption), transmission (ciphertext), and retrieval (requiring double decryption). Even if the access information-encoded image is intercepted, attackers cannot directly obtain the original image data. This forms a closed-loop privacy protection system of "local de-identified display, cloud encrypted storage, and on-demand authenticated retrieval," giving users greater control over their sensitive personal data. Furthermore, users no longer need to remember complex medical record numbers; they can simply manage target sample images in the album using user-friendly views and retrieve the corresponding original images for analysis or follow-up examinations at any time via the embedded access information-encoded image, greatly facilitating continuous health monitoring. This systematically and uncompromisingly resolves the contradiction between visual friendliness, professional analytical accuracy, and end-to-end data security, constructing a user-friendly, data-secure, and accurately analytical fecal health self-monitoring closed loop.

[0032] The technical solution provided in this invention first acquires the original image of the fecal sample. Then, based on the visual features of the original image, it is converted into a visually friendly image. Simultaneously, the original image is processed to generate an access information encoded image. Finally, the visually friendly image and the access information encoded image are combined to obtain the target sample image. By converting to a visually friendly image, the original image can be replaced in the user interface, fundamentally solving the psychological barrier to home fecal testing and improving the user experience. Simultaneously, through parallel dual-path processing, an access information encoded image of the original image is generated based on chain-based encryption. This image is used to request the original image data from an encrypted storage server, enabling subsequent health analysis based on the encrypted transmission of the original image, ensuring the accuracy of the analysis results and the security of user information.

[0033] Example 2 Figure 2 This is a schematic diagram of the image processing device for fecal health detection provided in Embodiment 2 of the present invention. This device can be implemented in hardware and / or software, and is generally integrated into a mobile terminal to execute the image processing method for fecal health detection provided in any embodiment of the present invention. Figure 2 As shown, the device includes: Image acquisition module 21 is used to acquire the original image of the fecal sample; Image conversion module 22 is used to convert the original image into a visually friendly image based on the visual features of the original image; Image encoding module 23 is used to process the original image to generate an access information encoded image; The image synthesis module 24 is used to synthesize the visually friendly image and the access information encoded image to obtain a target sample image.

[0034] The technical solution provided in this invention first acquires the original image of the fecal sample. Then, based on the visual features of the original image, it is converted into a visually friendly image. Simultaneously, the original image is processed to generate an access information encoded image. Finally, the visually friendly image and the access information encoded image are combined to obtain the target sample image. By converting to a visually friendly image, the original image can be replaced in the user interface, fundamentally solving the psychological barrier to home fecal testing and improving the user experience. Simultaneously, through parallel dual-path processing, an access information encoded image of the original image is generated based on chain-based encryption. This image is used to request the original image data from an encrypted storage server, enabling subsequent health analysis based on the encrypted transmission of the original image, ensuring the accuracy of the analysis results and the security of user information.

[0035] Based on the above technical solution, optionally, the image conversion module 22 is specifically used for: A neural style transfer model is used to convert the original image into a cartoon-style image, a watercolor-style image, or an abstract color block image based on the visual features. Optionally, a pre-trained lightweight neural style transfer model is used to determine the target art style (cartoon / watercolor / abstract color block) through a dual-mode matching strategy (user interactive selection or automatic recommendation based on histogram similarity). During the generation process, the content structure is constrained by Laplacian loss and perceptual loss, and edge overlay strategies with bilateral filtering and adaptive threshold extraction are used to enhance contour fidelity. At the same time, histogram loss, Lab space brightness locking, and AdaIN statistical alignment are used to maintain color stability, thereby achieving a balance between stylized rendering and the recognizability of the original image, ensuring that the final output "friendly view" has both a distinct artistic effect and retains key visual features.

[0036] Based on the above technical solution, optionally, the image encoding module 23 is specifically used for: Generate the data fingerprint of the original image; The original image is encrypted using a randomly generated symmetric encryption key to obtain the image ciphertext; The encrypted image is uploaded to the encrypted storage server for storage, and an access token is received from the encrypted storage server. The access information encoded image is generated based on the data fingerprint, the access token, and the public key encryption result of the symmetric encryption key.

[0037] Based on the above technical solution, optionally, the device further includes: An image compression module is used to compress the original image using a lossless compression algorithm before generating the data fingerprint of the original image.

[0038] Based on the above technical solution, optionally, the device further includes: The image parsing module is used to parse the target sample image in response to the user's detection request, and obtain the data fingerprint, the access token and the public key encryption result; The image analysis module is used to send the access token and the public key encryption result to the cloud security gateway. After the cloud security gateway verifies the legitimacy, it requests the image ciphertext from the encrypted storage server, decrypts the public key encryption result using the private key to obtain the symmetric encryption key, and then decrypts the image ciphertext using the symmetric encryption key to obtain the original image. After the original image is verified based on the data fingerprint, it is sent to the intelligent analysis server for analysis. The result acquisition module is used to receive the fecal sample analysis results returned by the cloud security gateway.

[0039] Based on the above technical solution, optionally, the image synthesis module 24 is specifically used for: The access information encoded image is embedded as a watermark in the corner of the visually friendly image to obtain the target sample image.

[0040] Based on the above technical solution, optionally, the device further includes: An image preprocessing module is used to identify and crop the target fecal body region in the original image before converting the original image into a visually friendly image based on the visual features of the original image; and / or to perform white balance and illumination compensation on the original image.

[0041] The image processing device for fecal health detection provided in the embodiments of the present invention can execute the image processing method for fecal health detection provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0042] It is worth noting that in the above embodiments of the image processing device for fecal health detection, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0043] Example 3 Figure 3 This is a schematic diagram of the structure of a mobile terminal provided in Embodiment 3 of the present invention, showing a block diagram of an exemplary mobile terminal suitable for implementing the embodiments of the present invention. Figure 3 The mobile terminal shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention. Figure 3 As shown, the mobile terminal includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the mobile terminal can be one or more. Figure 3 Taking a processor 31 as an example, the processor 31, memory 32, input device 33, and output device 34 in the mobile terminal can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0044] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the image processing method for fecal health detection in this embodiment of the invention (e.g., the image acquisition module 21, image conversion module 22, image encoding module 23, and image synthesis module 24 in the image processing device for fecal health detection). The processor 31 executes various functional applications and data processing of the mobile terminal by running the software programs, instructions, and modules stored in the memory 32, thereby implementing the aforementioned image processing method for fecal health detection.

[0045] The memory 32 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on the use of the mobile terminal. Furthermore, the memory 32 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 32 may further include memory remotely located relative to the processor 31, which can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0046] Input device 33 can be used to acquire raw images of fecal samples and generate key signal inputs related to user settings and function control of the mobile terminal. Output device 34 may include a display screen, which can be used to display fecal health analysis results to the user, etc.

[0047] Example 4 Embodiment 4 of the present invention also provides a method for fecal health testing, the method comprising: Obtain the original image of the fecal sample and convert the original image into a visually friendly image; The original image is encrypted and then uploaded to the encrypted storage server for storage, and an access token is received from the encrypted storage server. An access information encoded image is generated based on the access token, and the visually friendly image is combined with the access information encoded image to obtain a target sample image; In response to the user's detection request, the target sample image is parsed to obtain the access token; The cloud security gateway uses the access token to request the original image data from the encrypted storage server, and then requests the intelligent analysis server to perform analysis based on the original image data. Receive the fecal sample analysis results returned by the cloud security gateway.

[0048] For details, please refer to the above explanation, which will not be repeated here.

[0049] Example 5 Embodiment 5 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform an image processing method for fecal health detection, the method comprising: Obtain the raw image of the fecal sample; Based on the visual features of the original image, the original image is converted into a visually friendly image; The original image is processed to generate an access information encoded image; The visually friendly image and the access information encoded image are combined to obtain the target sample image.

[0050] Storage media can be any type of memory device or storage device. The term "storage media" is intended to include: mounting media, such as CD-ROMs, floppy disks, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disks or optical storage); registers or other similar types of memory elements. Storage media may also include other types of memory or combinations thereof. Furthermore, storage media can reside in a computer system in which a program is executed, or it can reside in a different second computer system connected to the computer system via a network (such as the Internet). The second computer system can provide program instructions to the computer for execution. The term "storage media" can include two or more storage media that can reside in different locations (e.g., in different computer systems connected via a network). Storage media can store program instructions (e.g., specifically implemented as a computer program) that can be executed by one or more processors.

[0051] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the method operations described above, but can also perform related operations in the image processing method for fecal health detection provided in any embodiment of the present invention.

[0052] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0053] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0054] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, 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 a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including 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 of the present invention.

[0055] Example 6 Embodiment 6 of the present invention also provides an image processing system for fecal health detection. The system includes a mobile terminal, a cloud security gateway, an encrypted storage server, and an intelligent analysis server. The mobile terminal is used to execute the image processing method for fecal health detection provided in any of the above embodiments and has the corresponding beneficial effects of executing the method.

[0056] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. An image processing method for fecal health detection, characterized in that, include: Obtain the raw image of the fecal sample; Based on the visual features of the original image, the original image is converted into a visually friendly image; The original image is processed to generate an access information encoded image; The visually friendly image and the access information encoded image are combined to obtain the target sample image.

2. The image processing method for fecal health detection according to claim 1, characterized in that, The process of converting the original image into a visually friendly image based on its visual features includes: A neural style transfer model is used to convert the original image into a cartoon-style image, a watercolor-style image, or an abstract color block image based on the visual features.

3. The image processing method for fecal health detection according to claim 1, characterized in that, The process of processing the original image to generate an access information encoded image includes: Generate the data fingerprint of the original image; The original image is encrypted using a randomly generated symmetric encryption key to obtain the image ciphertext; The encrypted image is uploaded to the encrypted storage server for storage, and an access token is received from the encrypted storage server. The access information encoded image is generated based on the data fingerprint, the access token, and the public key encryption result of the symmetric encryption key.

4. The image processing method for fecal health detection according to claim 2, characterized in that, The method further includes: In response to the user's detection request, the target sample image is parsed to obtain the data fingerprint, the access token, and the public key encryption result; The access token and the public key encryption result are sent to the cloud security gateway. After the cloud security gateway verifies the legitimacy, it requests the image ciphertext from the encrypted storage server. The private key is used to decrypt the public key encryption result to obtain the symmetric encryption key. The symmetric encryption key is then used to decrypt the image ciphertext to obtain the original image. After the original image is verified based on the data fingerprint, it is sent to the intelligent analysis server for analysis. Receive the fecal sample analysis results returned by the cloud security gateway.

5. The image processing method for fecal health detection according to claim 1, characterized in that, The step of synthesizing the visually friendly image and the access information encoded image to obtain the target sample image includes: The access information encoded image is embedded as a watermark in the corner of the visually friendly image to obtain the target sample image.

6. The image processing method for fecal health detection according to claim 1, characterized in that, Before converting the original image into a visually friendly image based on its visual features, the method further includes: Identify and crop the target fecal body region in the original image; and / or, White balance and illumination compensation are applied to the original image.

7. An image processing device for fecal health detection, characterized in that, include: The image acquisition module is used to acquire raw images of fecal samples; An image conversion module is used to convert the original image into a visually friendly image based on the visual features of the original image; An image encoding module is used to process the original image to generate an access information encoded image; An image synthesis module is used to synthesize the visually friendly image and the access information encoded image to obtain a target sample image.

8. A mobile terminal, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the image processing method for fecal health detection as described in any one of claims 1-6.

9. A method for detecting fecal health, characterized in that, include: Obtain the original image of the fecal sample and convert the original image into a visually friendly image; The original image is encrypted and then uploaded to the encrypted storage server for storage, and an access token is received from the encrypted storage server. An access information encoded image is generated based on the access token, and the visually friendly image is combined with the access information encoded image to obtain a target sample image; In response to the user's detection request, the target sample image is parsed to obtain the access token; The cloud security gateway uses the access token to request the original image data from the encrypted storage server, and then requests the intelligent analysis server to perform analysis based on the original image data. Receive the fecal sample analysis results returned by the cloud security gateway.

10. An image processing system for fecal health detection, characterized in that, include: The mobile terminal includes a cloud security gateway, an encrypted storage server, and an intelligent analysis server, wherein the mobile terminal is used to execute the image processing method for fecal health detection as described in any one of claims 1-6.

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