Customized Recognition and Printing Method and System for Plant Leaf Veins Based on Image Recognition

Abstract

The present invention relates to the field of plant vein printing. Fabrics and papers can produce delicate color textures using traditional plant dyeing methods, but traditional dyeing processes are difficult to meet the needs of industrial production, and industrial pattern printing lacks natural textures. The low degree of freedom in pattern variation is also one of the main factors restricting its artistic and personalized development. The present invention proposes a method and system for custom recognition and printing of plant veins based on image recognition, aiming to improve production efficiency while retaining the natural beauty and delicate texture of the veins. By extracting vein images from multiple positions, more comprehensive vein information can be obtained, ensuring that no important vein features are missed when synthesizing the vein image of the whole leaf. By obtaining high-quality microscopic images in real time, the high definition and details of the veins can be retained, so that the real vein texture can be replicated during printing. Users can customize vein images according to their own needs, and through an automated processing process, batch replication can be achieved.

Description

Technical Field

[0001] This application relates to the technical field of plant vein printing, and particularly to a method and system for custom recognition and printing of plant veins based on image recognition. Background Art

[0002] Traditional hand printing and dyeing is an ancient art form that creates unique patterns and colors by directly applying plant leaves, flowers, or other natural materials to fabrics. Traditional hand printing and dyeing not only showcases the creativity of artists but also inherits rich culture and history. However, due to its time-consuming and laborious nature and difficulty in large-scale production, it appears relatively inefficient in the face of modern industrial demands.

[0003] Plant veins are a complex network structure inside leaves, responsible for transporting water and nutrients from the roots of plants to the leaves and also for carrying out photosynthesis on the leaves. Veins usually consist of xylem and phloem. Xylem is responsible for the transportation of water and minerals, while phloem is responsible for the transportation of organic substances. The morphology and structure of veins are unique in different plant species, which makes veins an important feature in plant taxonomy. Characteristics such as the shape, size, and branching pattern of veins can be used to distinguish different plant species. In addition, the development and structure of veins are also affected by environmental factors such as light, water, and temperature. Therefore, vein research is of great significance for understanding the growth and adaptation of plants to the environment.

[0004] When dealing with the replication of plant veins, the existing technology faces a series of challenges. Since the veins of each leaf are unique and have certain artistic and educational significance, although existing digital printing technologies can print clear images, they often fail to achieve the ideal accuracy in terms of structures with complex textures and details such as veins, especially for fine branches and reticular structures. It is difficult to capture fine branches and reticular structures, and it is easy to ignore some tiny or broken veins or include parts that do not belong to veins, resulting in incomplete replicated images or misjudgment of features.

[0005] Therefore, there are defects in the existing technology and it needs to be improved. Summary of the Invention

[0006] In order to solve one or several problems in the existing technology, the main purpose of this application is to provide a method and system for custom recognition and printing of plant veins based on image recognition.

[0007] To achieve the above invention purpose, this application proposes a method for custom recognition and printing of plant veins based on image recognition, and the method includes:

[0008] Obtain the microscopic image of the leaf in real time and determine whether the microscopic image meets the output conditions;

[0009] When the microscopic image meets the output conditions, extract the vein image set in the microscopic image from multiple positions according to the preset acquisition criteria;

[0010] Receive the printing instruction from the client in real time;

[0011] When receiving the printing instruction from the client, parse the printing instruction to determine the printing requirements of the vein image;

[0012] According to the printing requirements, screen out the vein images that meet the printing requirements from the vein image sets at multiple positions;

[0013] According to the screening results, fuse each screened vein image, and convert the fused vein image into a vector image, where the vein image fusion is used to synthesize the vein image of the whole leaf;

[0014] Generate a printing pattern file from the vector image.

[0015] The embodiment of the present application also provides a plant vein custom recognition and printing system based on image recognition, including:

[0016] An acquisition module, configured to obtain the microscopic image of the leaf in real time and determine whether the microscopic image meets the output conditions;

[0017] An extraction module, configured to, when the microscopic image meets the output conditions, extract the vein image set in the microscopic image from multiple positions according to the preset acquisition criteria;

[0018] A receiving module, configured to receive the printing instruction from the client in real time;

[0019] An analysis module, configured to, when receiving the printing instruction from the client, parse the printing instruction to determine the printing requirements of the vein image;

[0020] A screening module, configured to screen out the vein images that meet the printing requirements from the vein image sets at multiple positions according to the printing requirements;

[0021] A fusion module, configured to, according to the screening results, fuse each screened vein image, and convert the fused vein image into a vector image, where the vein image fusion is used to synthesize the vein image of the whole leaf;

[0022] A generation module, configured to generate a printing pattern file from the vector image.

[0023] The present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of any of the above-mentioned methods are implemented.

[0024] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0025] The method and system for custom recognition and printing of plant leaf veins based on image recognition according to the embodiments of the present application can obtain more comprehensive leaf vein information by extracting leaf vein images from multiple positions, ensuring that no important leaf vein features are missed when synthesizing the leaf vein image of the whole leaf. By obtaining high-quality microscopic images in real time, the high definition and details of the leaf veins can be retained, so that the real leaf vein texture can be replicated during printing. The function of receiving the printing instruction from the user terminal in real time is provided, enabling the user to customize the leaf vein image according to their own needs, increasing the flexibility of the printing process and the user experience. By screening and fusing the leaf vein images from multiple positions, a complete and coherent leaf vein image can be generated, improving the accuracy and artistic value of the printed pattern. Converting the fused leaf vein image into a vector image supports lossless magnification and editing, ensuring the quality and customizability of the printed pattern. Through the automated processing flow, batch replication can be achieved, improving the printing efficiency, while ensuring the consistency and accuracy of each printed pattern, and realizing batch printing of the leaf veins of the same leaf. Description of the Drawings

[0026] Figure 1 It is a schematic flowchart of the method for custom recognition and printing of plant leaf veins based on image recognition according to an embodiment of the present application;

[0027] Figure 2 It is a schematic flowchart of the method for custom recognition and printing of plant leaf veins based on image recognition according to an embodiment of the present application;

[0028] Figure 3 It is a schematic block diagram of the structure of the system for custom recognition and printing of plant leaf veins based on image recognition according to an embodiment of the present application;

[0029] Figure 4 It is a schematic block diagram of the structure of the computer device according to an embodiment of the present application.

[0030] The realization, functional features and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0031] To make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0032] Referring to Figure 1 , a method for custom recognition and printing of plant leaf veins based on image recognition is provided in an embodiment of the present application. The method includes:

[0033] S1. Obtain a microscopic image of a leaf in real time and determine whether the microscopic image meets the output conditions;

[0034] S2. When the microscopic image meets the output conditions, extract a set of leaf vein images from the microscopic image at multiple positions according to a preset acquisition standard;

[0035] S3. Receive a printing instruction from the client in real time;

[0036] S4. When the printing instruction from the client is received, parse the printing instruction to determine the printing requirements for the leaf vein image;

[0037] S5. According to the printing requirements, screen out the leaf vein images that meet the printing requirements from the set of leaf vein images at multiple positions;

[0038] S6. According to the screening results, fuse each screened leaf vein image, and convert the fused leaf vein image into a vector image, where the leaf vein image fusion is used to synthesize the leaf vein image of the whole leaf;

[0039] S7. Generate a printing pattern file from the vector image.

[0040] As described in the above steps S1 - S2, microscopic images of the leaf are obtained in real time through a microscope, and then image processing techniques are used to evaluate the quality of the images, such as sharpness, contrast, etc. This step ensures that only high-quality microscopic images can be used for subsequent processing, thus improving the quality of the final printed pattern. Among the microscopic images that meet the output conditions, according to the preset acquisition criteria, vein images are extracted from multiple positions. These criteria include the sharpness, integrity, and diversity of the veins. By extracting vein images from multiple positions, more comprehensive vein information can be obtained, thus improving the diversity and accuracy of the printed pattern. Under the microscope, due to the limitation of the field of view, a single shot may not cover all the characteristics of the veins on the leaf. Therefore, by extracting vein images from multiple positions, more comprehensive vein information can be ensured. The purpose of this is to ensure that the vein image of the entire leaf can be synthesized in the final printed pattern, rather than relying solely on the image of a single position. The vein network of a leaf may be very complex, and shooting from different angles and positions can capture different branches and details of the veins. By fusing the vein images from these different positions, a more complete and accurate vein image can be generated, which helps to present the complete structure and details of the veins during printing.

[0041] As described in the above steps S3 - S5, the user can send printing instructions in real time through the interface, such as selecting specific vein images, adjusting printing parameters, etc. Receiving printing instructions in real time enables the user to flexibly customize printing requirements, improving the flexibility of the printing process and the user experience. Analyze the received printing instructions to understand the user's needs, such as the selection of vein images, printing size, color, etc. By analyzing the printing instructions, it can be ensured that subsequent processing can meet the user's needs, improving the accuracy of printing. According to the user's printing requirements, select the vein images that meet the requirements from the vein image sets at multiple positions. Through screening, it can be ensured that the finally printed vein images meet the specific needs of the user, improving the pertinence of printing.

[0042] As described in the above steps S6 - S7, the selected vein images are fused to synthesize the vein image of the entire leaf, and then the fused image is converted into a vector format. Through fusion and vectorization, the coherence and integrity of the vein image can be ensured, while supporting lossless magnification and editing, improving the quality of the printed pattern. Convert the vector image into a format suitable for printing, such as PDF, EPS, etc., for use by the printer. Generating a printed pattern file enables the vein image to be accurately printed by the printer, realizing batch printing of the veins of the same leaf, and also ensuring the quality and efficiency of printing.

[0043] As described above, by extracting vein images from multiple positions, more comprehensive vein information can be obtained, ensuring that no important vein features are missed when synthesizing the vein image of the whole leaf. By obtaining high-quality microscopic images in real time, the high definition and details of the veins can be retained, so that the real vein texture can be replicated during printing. Providing the function of receiving printing instructions from the user terminal in real time enables users to customize vein images according to their own needs, increasing the flexibility of the printing process and the user experience. By screening and fusing vein images from multiple positions, a complete and coherent vein image can be generated, improving the accuracy and artistic value of the printed pattern. Converting the fused vein image into a vector image supports lossless magnification and editing, ensuring the quality and customizability of the printed pattern. Through an automated processing flow, batch replication can be achieved, improving the printing efficiency and ensuring the consistency and accuracy of each printed pattern, realizing batch printing of the veins of the same leaf.

[0044] In one embodiment, traditional plant dyeing methods can be used to produce delicate color textures on fabrics and papers. However, the traditional dyeing process is inefficient and difficult to meet the needs of industrial production. Moreover, the lack of natural texture in industrial pattern printing and the low degree of freedom in pattern changes are also one of the main factors restricting its artistic and personalized development. In the process of modern textile and paper production, traditional plant dyeing methods are favored for their natural and environmental protection characteristics. However, their production efficiency is relatively low and difficult to meet the demands of large-scale production. This embodiment proposes an innovative solution that combines traditional plant dyeing with modern digital printing technology to improve dyeing efficiency and artistic expressiveness. To further meet the market demand for personalization, this embodiment may also introduce a customizable design platform where consumers can choose colors and patterns according to their personal preferences or even participate in the design. For this reason, the method of the present invention proposes a method and system for custom recognition printing of plant veins based on image recognition, aiming to improve production efficiency while retaining the natural beauty and delicate texture of the veins. By extracting vein images from multiple positions, more comprehensive vein information can be obtained, ensuring that no important vein features are missed when synthesizing the vein image of the whole leaf. By obtaining high-quality microscopic images in real time, the high definition and details of the veins can be retained, so that the real vein texture can be replicated during printing. Providing the function of receiving printing instructions from the user terminal in real time enables users to customize vein images according to their own needs, and batch replication can be achieved through an automated processing flow.

[0045] Referring to Figure 2 , in one embodiment, before the step of fusing each screened vein image, the method includes:

[0046] S51. Identify each screened vein image and determine whether there are image defects in the vein image;

[0047] S52. When there are image defects in the vein image, mark the defect positions in the vein image and analyze the types of defects that occur in the vein image;

[0048] S53. When the defect type is that the adjacent veins are too close and cause image blurring;

[0049] S54. Input the defect type and the vein image into a preset segmentation model respectively. Through the segmentation model, segment the adjacent veins and identify the target features of each vein, where the target features include the branch points, textures, colors, and orientations of the veins;

[0050] S55. Through the segmentation model, fill the boundaries of the segmented veins according to the target features and output the processed vein image;

[0051] S56. Perform fusion based on the processed vein image.

[0052] As described in the above steps, each vein image is analyzed by image processing technology to find possible defects such as blurring, breaks, noise, etc. Since vein images may be affected by factors such as shooting conditions and microscope imaging quality, identifying defects is to ensure the quality of the fused image. Defects are discovered and marked in a timely manner, providing a basis for subsequent repair and improving the quality of the final fused image. The positions of the discovered defects are marked, and the types of defects are analyzed based on their appearance characteristics. Precise defect position marking and type analysis help to select appropriate repair methods, thereby improving the repair effect. Through precise marking and analysis, corresponding repair measures can be taken for different types of defects, ensuring the accuracy of repair and the integrity of the image. Identify the problem of image blurring caused by adjacent veins being too close, and take repair measures for this specific type of defect. Adjacent veins being too close in vein images is a common type of defect that directly affects the printing quality, so it needs to be specifically processed. By specifically processing this type of defect, the clarity of the vein image can be restored, improving the visual effect of the printed pattern. Use a pre-trained segmentation model to segment adjacent veins and extract the target features of each vein. The segmentation model can accurately identify the boundaries of veins and extract key features, which helps with subsequent repair and fusion. Through segmentation and feature extraction, the blurred area caused by adjacent veins being too close can be accurately repaired, restoring the details of the vein image. According to the segmented vein boundaries and target features, the boundaries are filled to repair the blurred area. The filling process can fill the blank areas after segmentation, restoring the integrity and continuity of the vein image. Through the filling process, the blurred area caused by adjacent veins being too close can be repaired, improving the clarity of the image and the printing quality. Use fusion technology to synthesize the repaired vein images into a whole to obtain a complete vein image. After repair, the vein images may have discontinuous boundaries or overlapping areas, and fusion can solve these problems, generating a high-quality printed pattern. Through the fusion process, a complete and coherent vein image can be synthesized, improving the integrity and artistic value of the printed pattern.

[0053] In one embodiment, after the step of marking the defect position in the vein image and analyzing the type of defect that appears in the vein image when an image defect appears in the vein image, the method further includes:

[0054] When the defect type is that the vein has a shadow and / or reflection, identify the illumination information of the defect position and the reflection characteristics of the vein surface;

[0055] Determine the light radiation parameters of the vein surface according to the illumination information and the reflection characteristics;

[0056] Input the light radiation parameters and the illumination information into a preset illumination correction model, and generate illumination adjustment parameters through the illumination correction model;

[0057] Divide the area where the leaf veins appear shaded and / or reflective, adjust the lighting information based on the lighting adjustment parameters, and recalibrate the leaf vein image at the defect location with the adjusted lighting information;

[0058] Perform fusion based on the recalibrated leaf vein image.

[0059] As described above, by analyzing the shaded and reflective areas in the image, the lighting information and the reflective characteristics of the leaf vein surface are extracted. Shadows and reflections will interfere with the visual effect of the leaf vein image and affect subsequent recognition and fusion. Accurately identifying the lighting information at the defect location is helpful for subsequent lighting correction and improving the visual effect of the image. According to the extracted lighting information and reflective characteristics, the light radiation parameters on the leaf vein surface, such as lighting intensity, direction, etc., are calculated. The light radiation parameters are the input of the lighting correction model and are crucial for the correction effect. Determining accurate light radiation parameters can improve the accuracy of lighting correction and reduce the influence of shadows and reflections on the image. Using a pre-trained lighting correction model, the light radiation parameters and the lighting information are used as inputs to generate lighting adjustment parameters. The lighting correction model can automatically adjust the lighting parameters according to the input information to achieve image correction. Generating lighting adjustment parameters can automatically correct the shadows and reflections in the image and improve the visual effect of the image. Divide the shaded and reflective areas, and then adjust the lighting information according to the lighting adjustment parameters to achieve the recalibration of the leaf vein image at the defect location. Dividing and adjusting the lighting information can specifically correct the defect area and improve the correction effect. By recalibrating the leaf vein image at the defect location, shadows and reflections can be removed and the visual effect of the image can be improved. Use the fusion technology to synthesize the recalibrated leaf vein images into a whole to obtain a complete leaf vein image. There may be discontinuous boundaries or overlapping areas in the recalibrated leaf vein images. Fusion can solve these problems and generate high-quality printing patterns. Through the fusion process, a complete and coherent leaf vein image can be synthesized, improving the integrity and artistic value of the printing pattern.

[0060] In one embodiment, for fusing each screened leaf vein image, the method includes:

[0061] Obtain each screened leaf vein image and adapt the position of each leaf vein image;

[0062] According to the adaptation result, mark the overlapping areas between different images;

[0063] Identify the target features of the leaf veins in the overlapping area, construct an image coordinate system based on the target features, and input the target features into the image coordinate system to obtain the fusion position of the same overlapping area, where the target features include the branch points, textures, and orientations of the leaf veins;

[0064] Fuse the overlapping region according to the fusion position, and perform erosion and dilation processing on the fused vein edges;

[0065] Based on the results of the processing, complete the fusion of the vein images.

[0066] As described above, through image processing techniques, ensure that each vein image is spatially aligned with other images. Vein images at different positions need to be accurately aligned so that they can cover the entire leaf during fusion. The adapted images can be more accurately fused together to form a complete vein image. Identify and mark the overlapping regions between different images, which are the key parts of the fusion. The overlapping regions contain continuous information of the veins and need to be accurately fused together. Marking the overlapping regions helps to more precisely fuse the images and ensure the coherence of the veins. Identify the target features of the veins, such as branch points, textures, and orientations, in the overlapping regions, and construct an image coordinate system based on these features. The target features are the basis for the fusion of vein images, and constructing an image coordinate system helps to maintain the structure and coherence of the veins during the fusion process. The image coordinate system constructed based on the target features helps to more accurately fuse the overlapping regions and form a complete vein image. Fuse the overlapping region according to the fusion position, and then perform erosion and dilation processing on the fused vein edges to enhance the coherence and clarity of the image. Erosion and dilation processing can fill in the blanks in the fusion region and enhance the edge details of the veins. Through erosion and dilation processing, a clearer and more coherent vein image can be generated, improving the quality of the printed pattern. Use the fusion technology to synthesize the processed vein images into a whole to obtain a complete vein image. The fusion processing can combine multiple vein images into a continuous vein image to form a complete vein pattern. Through the fusion processing, a complete and coherent vein image can be generated.

[0067] In a specific embodiment, the leaf can be photographed at 100 magnifications using an optical microscope to ensure that the image clarity is at least 200 dpi. A high-magnification microscope can capture the fine features of the leaf veins, and high-clarity images are helpful for subsequent processing and printing. Set screening criteria, such as the clarity of the leaf veins being not less than 70%, and the image having no obvious distortion. The screening criteria ensure that only high-quality images can be used for subsequent processing, improving the quality of the final printed pattern. Use an image defect detection algorithm based on deep learning to detect defects such as blurring, breaks, and noise in the image. For the blurring caused by adjacent leaf veins being too close, set the similarity threshold of the segmentation model to 0.9 to ensure the accuracy of the leaf vein features. Through defect detection and repair, the image quality can be improved, ensuring the clarity and accuracy of the printed pattern. Use an image fusion algorithm based on multi-scale fusion, set the fusion weight to 0.8, to balance the clarity and details of the image. The fusion process can synthesize a complete and coherent leaf vein image, improving the integrity and artistic value of the printed pattern. Convert the fused leaf vein image into a vector format, set the conversion accuracy to 300 dpi, to ensure the printing quality. The vectorization process supports lossless magnification and editing, ensuring the quality and customizability of the printed pattern. Generate a printed pattern file from the vector image, set the printing size to A4, and the color mode to CMYK. Generating the printed pattern file enables the leaf vein image to be accurately printed by the printer.

[0068] In one embodiment, after the step of extracting the set of leaf vein images from the microscopic images at multiple positions, the method further includes:

[0069] Calculating a difference image between the leaf vein image and the original microscopic image corresponding to the leaf vein image;

[0070] According to the calculated result, obtaining the missing features in the difference image;

[0071] Identifying whether the missing features in the difference image meet the conditions of the leaf vein features;

[0072] Extracting the shape, texture, and size information of the missing features, and inputting the shape, texture, and size information of the missing features and the leaf vein image into a preset matching model respectively, and analyzing the similarity coefficient between the missing features and the leaf vein features through the matching model;

[0073] When the similarity coefficient is greater than a preset similarity threshold, it is determined that the missing features meet the conditions of the leaf vein features;

[0074] Based on the determined result, filling the missing features into the leaf vein image to obtain an adjusted leaf vein image.

[0075] As described above, by calculating the difference image between the vein image and its corresponding original microscopic image, the differences between the two can be found, i.e., the possibly missing features. The difference image can visually display the differences between the vein image and the original image, which helps to discover the missing features. The difference image provides a basis for identifying the missing features and a foundation for subsequent repair and fusion. By analyzing the difference image, the possibly missing features can be extracted, such as tiny vein branches or broken parts. The missing features may be important components in the vein image and need to be identified and repaired. Obtaining the missing features helps to improve the integrity and accuracy of the vein image. By analyzing information such as the shape, texture, and size of the missing features, it is determined whether they meet the conditions of vein features. Only the missing features that meet the conditions of vein features need to be repaired and fused. Identifying the missing features that meet the conditions helps to improve the integrity and accuracy of the vein image. Extract the key information of the missing features and input them together with the vein image into a matching model, and analyze the similarity coefficient between the two through the model. The matching model can automatically judge the similarity between the missing features and the vein features, which helps to accurately identify and repair the missing features. By analyzing the similarity coefficient through the matching model, it can be accurately judged whether the missing features need to be repaired and fused. Set a similarity threshold. When the similarity coefficient is greater than this threshold, it is determined that the missing features meet the conditions of vein features. The similarity threshold can be used as a judgment criterion to ensure that the similarity between the missing features and the vein features reaches a certain degree. Judging whether the missing features meet the conditions of vein features according to the similarity coefficient helps to improve the integrity and accuracy of the vein image. According to the judgment result of the similarity coefficient, fill the missing features that meet the conditions into the vein image. Filling in the missing features can repair the defects in the vein image and improve the integrity and accuracy of the image. By filling in the missing features, a more complete and accurate vein image can be obtained, providing a high-quality basis for subsequent fusion and printing.

[0076] In one embodiment, after the step of filling the missing features into the vein image, the method further includes:

[0077] Judge whether the missing features meet the conditions of the fracture features of the veins;

[0078] Extract the edge features of the missing features, and determine whether there are irregular fracture marks on the edges of the missing features;

[0079] When there are irregular fracture marks on the edges of the missing features, analyze whether the texture features of the missing features are continuous with the target veins;

[0080] When the texture features of the missing features are continuous with the target veins, obtain whether the cross-sectional width of the missing features matches the cross-sectional width of the target veins;

[0081] When the transverse width of the missing feature matches the transverse width of the target vein, analyze whether the direction of the missing feature is consistent with the trend of the target vein;

[0082] When the direction of the missing feature is consistent with the trend of the target vein, it is determined that the missing feature meets the conditions of the vein breakage feature;

[0083] Based on the fact that the missing feature meets the vein breakage feature, fill the missing feature onto the target vein to obtain an adjusted vein image.

[0084] As described above, by analyzing information such as the shape, texture, and size of the missing feature, determine whether it conforms to the vein breakage feature, such as irregular edges, texture breaks, etc. The vein breakage feature may be a part that is easily overlooked in the image processing process. Accurately judging whether the missing feature meets the conditions of the vein breakage feature helps to improve the integrity and accuracy of the vein image. By extracting the edge features of the missing feature, it can be determined whether there are irregular breakage traces, such as the irregularity and breakage of the edge. Irregular breakage traces are typical manifestations of the vein breakage feature, and extracting edge features helps to accurately judge whether the missing feature meets the conditions of the vein breakage feature. By analyzing the texture features of the missing feature, determine whether there is continuity with the target vein, such as the continuity and similarity of the texture. Texture features are important manifestations of the vein breakage feature. Analyzing texture features helps to accurately judge whether the missing feature meets the conditions of the vein breakage feature. By obtaining the transverse width of the missing feature and the transverse width of the target vein, determine whether they match. Judging whether the transverse widths match helps to accurately judge whether the missing feature meets the conditions of the vein breakage feature. By analyzing whether the direction of the missing feature is consistent with the trend of the target vein, determine whether they match. According to the above analysis, judge whether the missing feature meets the conditions of the vein breakage feature. Accurately judging whether the missing feature meets the conditions of the vein breakage feature helps to improve the integrity and accuracy of the vein image. According to the judgment result, fill the missing feature that meets the conditions onto the target vein to obtain an adjusted vein image.

[0085] It should be added that from the perspectives of art and leaf biology, leaf printing is not just a surface replication process, but an opportunity for in-depth research and expression. Each feature in the leaf, such as veins, stomata, and surface texture, plays an important role in the growth and adaptation of plants. Through precise printing techniques, these tiny details can be recorded, helping to understand the physiological mechanisms and ecological adaptability of plants. By observing the complex structure of leaves, artworks can better convey this beauty. Differential images can help significantly highlight details that were not captured in the vein images. Noise filtering effectively distinguishes important features from background noise, helping subsequent processing to focus on key areas. Using differential images to extract missing features helps identify those tiny or broken vein parts, ensuring the integrity of the reproduction. Integrating the missing features into the reproduced image enriches the dataset and improves accuracy. By setting clear feature conditions, the possibility of misjudgment is reduced, ensuring that the extracted features are indeed related to the veins. Allowing the system to learn and adapt to the unique structures of different leaves enhances the generalization ability of the model. Detailed feature descriptions help with comparison and classification, ensuring that the missing parts form an effective correspondence with known vein features.

[0086] In one embodiment, converting the fused vein image into a vector image, the method includes:

[0087] Identifying the branch points of the fused vein image and segmenting the vein image into multiple regions according to the branch points;

[0088] Extracting the vein features of each segmented region;

[0089] Converting each extracted vein feature into a vector format to obtain vector image blocks;

[0090] Performing a first preprocessing on each vector image block and synthesizing the vector image blocks after the first preprocessing;

[0091] Based on the synthesis result, performing a second preprocessing on the overall vector image to obtain the final vector image.

[0092] As described above, branch points in the vein image are identified through image processing techniques. These branch points are the key to segmenting the vein image. Branch points in the vein image are crucial parts of the vein structure. By identifying these points, the vein image can be segmented more accurately. Accurately segmenting the vein image helps with subsequent feature extraction and vectorization processing, ensuring the accuracy and integrity of the vectorized image. Vein features such as texture, shape, color, etc. are extracted from each segmented region. Vein features are the basis for vectorization processing. Extracting accurate features helps generate high-quality vector images. Feature extraction provides key information for vectorization, contributing to the generation of vector images that can accurately reflect the vein structure. The extracted vein features are converted into a vector format to generate vector image blocks. The vector format can be enlarged and edited without loss, ensuring print quality and allowing for more flexible resizing of the image. Vectorization processing generates vector image blocks that can be enlarged arbitrarily without distortion, providing high-quality images for subsequent synthesis and printing. Each vector image block is preprocessed, such as smoothing, sharpening, etc., and then the preprocessed image blocks are synthesized into a whole. Preprocessing can improve the image quality, while synthesis can combine multiple image blocks into a complete vein image. Through preprocessing and synthesis, a vein image with higher quality and more details can be generated, providing a high-quality image basis for final printing. Based on the synthesis, further preprocessing is performed on the entire vector image, such as adjusting color, contrast, etc. Further preprocessing can further enhance the overall quality of the image, making it more suitable for printing and display. The vector image after the second preprocessing can better meet the printing requirements, improving the artistic value and accuracy of the printing effect.

[0093] The plant vein custom recognition and printing method based on image recognition of the present application can obtain more comprehensive vein information by extracting vein images from multiple positions, ensuring that no important vein features are missed when synthesizing the vein image of the whole leaf. By obtaining high-quality microscopic images in real time, the high definition and details of the veins can be retained, so that the real vein texture can be replicated during printing. The function of receiving print instructions from the user terminal in real time is provided, enabling users to customize vein images according to their own needs, increasing the flexibility of the printing process and the user experience. By screening and fusing vein images from multiple positions, a complete and coherent vein image can be generated, improving the accuracy and artistic value of the printed pattern. Converting the fused vein image into a vector image supports lossless enlargement and editing, ensuring the quality and customizability of the printed pattern. Through an automated processing flow, batch replication can be achieved, improving the printing efficiency and ensuring the consistency and accuracy of each printed pattern, realizing batch printing of the veins of the same leaf.

[0094] Refer to Figure 3, an embodiment of the present application also provides a plant vein custom recognition and printing system based on image recognition, including:

[0095] An acquisition module 1, configured to acquire a microscopic image of a leaf in real time and determine whether the microscopic image meets the output conditions;

[0096] An extraction module 2, configured to, when the microscopic image meets the output conditions, extract a set of vein images from the microscopic image at multiple positions according to a preset acquisition standard;

[0097] A receiving module 3, configured to receive a printing instruction from a user terminal in real time;

[0098] An analysis module 4, configured to, when receiving a printing instruction from a user terminal, analyze the printing instruction to determine the printing requirements for the vein image;

[0099] A screening module 5, configured to screen out vein images that meet the printing requirements from a set of vein images at multiple positions according to the printing requirements;

[0100] A fusion module 6, configured to fuse each screened vein image according to the screening result and convert the fused vein image into a vector image, where the vein image fusion is used to synthesize a vein image of an overall leaf;

[0101] A generation module 7, configured to generate a printing pattern file from the vector image.

[0102] As described above, it can be understood that each component of the plant vein custom recognition and printing system based on image recognition proposed in the present application can implement the functions of any one of the above-mentioned plant vein custom recognition and printing methods based on image recognition, and the specific structure will not be elaborated.

[0103] Refer to Figure 4 , an embodiment of the present application also provides a computer device, which may be a server, and its internal structure may be as Figure 4 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a plant vein custom recognition and printing method based on image recognition.

[0104] The above-mentioned processor executes the above-mentioned method for custom recognition and printing of plant leaf veins based on image recognition, including: obtaining a microscopic image of a leaf in real time, and determining whether the microscopic image meets the output conditions; when the microscopic image meets the output conditions, extracting a set of leaf vein images from the microscopic image at multiple positions according to a preset acquisition standard; receiving a printing instruction from a client in real time; when the printing instruction from the client is received, parsing the printing instruction to determine the printing requirements for the leaf vein images; according to the printing requirements, screening out the leaf vein images that meet the printing requirements from the set of leaf vein images at multiple positions; according to the screening results, fusing each screened leaf vein image, and converting the fused leaf vein image into a vector image, wherein the leaf vein image fusion is used to synthesize the leaf vein image of the whole leaf; generating a printing pattern file from the vector image.

[0105] The above-mentioned method for custom recognition and printing of plant leaf veins based on image recognition can obtain more comprehensive leaf vein information by extracting leaf vein images from multiple positions, ensuring that no important leaf vein features are missed when synthesizing the leaf vein image of the whole leaf. By obtaining high-quality microscopic images in real time, the high definition and details of the leaf veins can be retained, so that the real leaf vein texture can be replicated during printing. The function of receiving printing instructions from the client in real time is provided, enabling users to customize leaf vein images according to their own needs, increasing the flexibility of the printing process and the user experience. By screening and fusing leaf vein images from multiple positions, a complete and coherent leaf vein image can be generated, improving the accuracy and artistic value of the printing pattern. Converting the fused leaf vein image into a vector image supports lossless magnification and editing, ensuring the quality and customizability of the printing pattern. Through an automated processing flow, batch replication can be achieved, improving the printing efficiency, while ensuring the consistency and accuracy of each printed pattern, and realizing batch printing of the leaf veins of the same leaf.

[0106] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for custom recognition and printing of plant leaf veins based on image recognition, including the steps of: obtaining a microscopic image of a leaf in real time, and determining whether the microscopic image meets the output conditions; when the microscopic image meets the output conditions, extracting a set of leaf vein images from the microscopic image at multiple positions according to a preset acquisition standard; receiving a printing instruction from a client in real time; when the printing instruction from the client is received, parsing the printing instruction to determine the printing requirements for the leaf vein images; according to the printing requirements, screening out the leaf vein images that meet the printing requirements from the set of leaf vein images at multiple positions; according to the screening results, fusing each screened leaf vein image, and converting the fused leaf vein image into a vector image, wherein the leaf vein image fusion is used to synthesize the leaf vein image of the whole leaf; generating a printing pattern file from the vector image.

[0107] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in the present application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0108] It should be noted that in this article, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article, or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, apparatus, article, or method including that element.

[0109] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for customizing plant vein identification and printing based on image recognition, characterized in that: The method comprises: Acquire a microscopic image of the leaf in real time, and determine whether the microscopic image meets the output conditions; When the microscopic image meets the output condition, extracting a leaf vein image set in the microscopic image from multiple positions according to a preset acquisition standard; Receive printing instructions from the user in real time; When receiving a printing instruction from the user terminal, parsing the printing instruction to determine the printing requirements of the leaf vein image; According to the printing requirement, selecting a leaf vein image that meets the printing requirement from a set of leaf vein images at multiple locations; According to the screening result, each screened leaf vein image is fused, and the fused leaf vein image is converted into a vector image, wherein the leaf vein image fusion is used to synthesize the leaf vein image of the whole leaf; Generate a printing pattern file from the vector image; The method of fusing each filtered leaf vein image comprises: Obtain each filtered leaf vein image, and adapt the position of each leaf vein image; According to the adaptation results, mark the overlapping areas between different images; Identify the target features of the leaf veins in the overlapping area, construct an image coordinate system based on the target features, input the target features into the image coordinate system, and obtain a fusion position of the same overlapping area, wherein the target features include a branch point, texture, and direction of the leaf veins; The overlapping areas are merged according to the fusion positions, and the edges of the merged leaf veins are corroded and expanded; Based on the processing results, the fusion of leaf vein images is completed.

2. The method for customizing plant vein identification and printing based on image recognition according to claim 1, characterized in that: Before the step of fusing each filtered leaf vein image, the method comprises: identifying each screened leaf vein image, and determining whether there is an image defect in the leaf vein image; When an image defect appears in the leaf vein image, marking the defect position in the leaf vein image, and analyzing the type of the defect appearing in the leaf vein image; When the defect type is that adjacent leaf veins are too close to each other, resulting in blurred images; Inputting the defect type and the leaf vein image into a preset segmentation model respectively, segmenting adjacent leaf veins through the segmentation model, and identifying target features of each leaf vein, wherein the target features include the branch point, texture, color and direction of the leaf vein; Filling the boundaries of the segmented leaf veins according to the target features through the segmentation model, and outputting the processed leaf vein image; Fusion is performed based on the processed leaf vein images.

3. The method for customizing plant vein identification and printing based on image recognition according to claim 2, characterized in that: After the steps of marking the defect position in the leaf vein image when an image defect appears in the leaf vein image, and analyzing the type of the defect appearing in the leaf vein image, the method further comprises: When the defect type is shadow and / or reflection on the leaf vein, identifying the illumination information of the defect position and the reflection characteristics of the leaf vein surface; Determining light radiation parameters on the surface of the leaf veins according to the illumination information and the reflection characteristics; Inputting the light radiation parameters and the light information into a preset light correction model, and generating light adjustment parameters through the light correction model; Segmenting the area where the leaf veins appear shadows and / or reflections, adjusting the illumination information based on illumination adjustment parameters, and re-correcting the leaf vein image at the defective position using the adjusted illumination information; Fusion is performed based on the corrected leaf vein images.

4. The method for customizing plant vein identification and printing based on image recognition according to claim 1, characterized in that: After the step of extracting the leaf vein image set in the microscopic image from multiple positions, the method further comprises: Calculating a differential image between the leaf vein image and an original microscopic image corresponding to the leaf vein image; According to the calculation result, obtaining the missing features in the difference image; Identifying whether the missing features in the differential image meet the condition of leaf vein features; Extracting the shape, texture and size information of the missing feature, inputting the shape, texture and size information of the missing feature and the leaf vein image into a preset matching model respectively, and analyzing the similarity coefficient between the missing feature and the leaf vein feature through the matching model; When the similarity coefficient is greater than a preset similarity threshold, it is determined that the missing feature meets the condition of the leaf vein feature; Based on the determination result, the missing features are filled into the leaf vein image to obtain an adjusted leaf vein image.

5. The method for customizing plant vein identification and printing based on image recognition according to claim 4, characterized in that: After the step of filling the missing features into the leaf vein image, the method further comprises: Determining whether the missing feature satisfies the condition of the broken feature of the leaf vein; Extracting edge features of the missing features, based on whether there are irregular fracture traces on the edges of the missing features; When there are irregular fracture marks on the edge of the missing feature, analyzing whether the texture feature of the missing feature is continuous with the target leaf vein; When the texture feature of the omitted feature is continuous with the target leaf vein, obtaining whether the cross-sectional width of the omitted feature matches the cross-sectional width of the target leaf vein; When the cross-sectional width of the omitted feature matches the cross-sectional width of the target leaf vein, analyzing whether the direction of the omitted feature is consistent with the direction of the target leaf vein; When the direction of the missing feature is consistent with the direction of the target leaf vein, it is determined that the missing feature meets the condition of the broken feature of the leaf vein; Based on the fact that the missing features satisfy the broken features of leaf veins, the missing features are filled onto the target leaf veins to obtain an adjusted leaf vein image.

6. The method for customizing plant vein identification and printing based on image recognition according to claim 1, characterized in that: The method for converting the fused leaf vein image into a vector image comprises: Identifying branch points of the fused leaf vein image, and dividing the leaf vein image into a plurality of regions according to the branch points; Extract the vein features of each segmented area; Convert each extracted leaf vein feature into a vector format to obtain a vector image block; Performing a first preprocessing on each vector image block, and synthesizing the first preprocessed vector image blocks; Based on the synthesis result, the vector image is subjected to a second preprocessing as a whole to obtain a final vector image.

7. A plant vein custom recognition and printing system based on image recognition, used in the method according to any one of claims 1 to 6, characterized in that: include: An acquisition module, used for acquiring a microscopic image of the leaf in real time and determining whether the microscopic image meets the output conditions; An extraction module, used for extracting a leaf vein image set in the microscopic image from multiple positions according to a preset acquisition standard when the microscopic image meets the output condition; A receiving module, used for receiving a printing instruction from a user terminal in real time; A parsing module, for parsing the printing instruction and determining the printing requirements of the leaf vein image when receiving the printing instruction from the user terminal; A screening module, used for screening leaf vein images that meet the printing requirements from leaf vein images at multiple locations according to the printing requirements; A fusion module, used for fusing each filtered leaf vein image according to the filtering result, and converting the fused leaf vein image into a vector image, wherein the leaf vein image fusion is used to synthesize the leaf vein image of the whole leaf; A generating module is used to generate a printing pattern file from the vector image.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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