256results about How to "Improve visual quality" patented technology

The invention relates to a television box intelligent interaction control unit image processing method and device. The method comprises the following steps: carrying out feature analysis on an input video stream of the television box to obtain an enhanced video stream; the method comprises the following steps: collecting visual and voice interaction information of a user based on an intelligent interaction control unit, and inputting the visual and voice interaction information into a double-branch Transform model for multi-modal information fusion to obtain a fusion feature vector; performing conjoint analysis to obtain a user interaction intention and an interaction response strategy; performing local contrast enhancement and motion compensation to obtain a high-quality video frame sequence; and performing scene adaptive color space mapping and dynamic color temperature adjustment on the high-quality video frame sequence according to the environment light information acquired in real time and the pre-stored color characteristics of the display device to obtain an output video stream. According to the implementation of the method, limited computing resources can be fully utilized, efficient video processing and natural man-machine interaction are achieved, and meanwhile the display effect is dynamically optimized according to environmental changes.

The invention relates to the technical field of geological surveying and mapping, in particular to a multi-source data fusion geological surveying and mapping system which comprises a multi-dimensional data collection module, a data calibration and synchronization module, a geological image enhancement module, a data fusion and model generation module and a visualization and interaction module. The data calibration and synchronization module carries out timestamp calibration and geographic position processing on data, the geological image enhancement module enhances geological images, the data fusion and model generation module fuses the processed data and constructs a geological model by using a modeling technology, and the visualization and interaction module provides a multi-dimensional data interaction platform. Through the geological texture recovery sub-module, visual quality of images is improved, reliability and accuracy during geological data and resource evaluation are enhanced, polarized light data are collected through the polarized image processing sub-module, key information such as surface material roughness is revealed, and surface and underground geological processes can be better understood and explained.

The invention discloses a mobile terminal multi-size webpage adaptive display method based on a browser viewport, which belongs to the field of browsers and comprises the following steps of: firstly, selecting iPhoneX (375px width) as a reference viewport size; and then, calculating a zoom factor according to the proportion of the viewport width of the current equipment to the reference viewport width, and dynamically adjusting the webpage content. And then, zooming the page root element by using the zooming factor. In order to adapt to screen widths and resolutions of different devices, breakpoints are set, a compact layout is applied to a narrower device, and more blanks and spaces are reserved on a wider device. And finally, recalculating the scaling in real time and updating the page. The method has the beneficial effects that a finer self-adaptive design is realized by dynamically calculating the zoom factor, adjusting the page layout in real time and optimizing display according to the breakpoint of the equipment.

The invention belongs to the field of computer vision, and particularly relates to a consistent identity picture generation method based on a diffusion model, which comprises the following steps of: generating optimal paired data by adopting a multi-modal large language model, the paired data comprising text data and corresponding image data; carrying out encoding processing on the text data by adopting a text encoder to obtain a text embedding vector; processing the image data by adopting an image encoder to obtain an image embedding vector; inputting the text embedding vector and the image embedding vector into an MLP module for fusion to obtain a fusion embedding vector; stacking all the fusion embedding vectors, and processing the stacked fusion embedding vectors by adopting a cross attention layer to obtain deep-level features; performing multi-stage adjustment on the deep feature to obtain a consistent identity picture; according to the invention, a coarse-to-fine multi-stage fine adjustment method is adopted, and the visual quality of the generated image is gradually optimized.

The invention provides a large model-based text generation and information recovery method and system. The method comprises the following steps of: converting complex situation information into simple and accurate text description by utilizing a text generation model; converting the text description of the situation information into a semantic vector by using a text encoder of a pre-trained CLIP model, and capturing semantic information of the text; encoding the noise image into a hidden space representation by using an encoder of a variational auto-encoder, and learning a compressed representation of the image; fusing the semantic vector generated by the pre-trained CLIP model with the image hidden space representation generated by the encoder of the variational auto-encoder, and optimizing the fused hidden space representation by using the trained de-noising Unet network; the optimized hidden space representation is decoded into a high quality situational information image using a decoder of a variational auto-encoder. By applying the technical scheme of the invention, the technical problem of incomplete received information caused by inevitable information loss in the data compression process in the prior art is solved.

The invention discloses an ancient building protection display method and system based on virtual reality, and the method comprises the steps: obtaining user position coordinates and orientation angle data collected by a high-precision position tracking sensor in real time, mapping the data to a corresponding position of a virtual scene, dynamically updating a visual angle in the virtual scene according to the movement and steering of a user, and displaying the visual angle in the virtual scene. The immersive experience of being personally on the scene is realized; a physical rendering technology is adopted to carry out vivid simulation on illumination, materials, textures and the like of the virtual scene, and through technologies of pre-calculation of an illumination map, environment shielding and the like, the real-time rendering calculation amount is reduced, and the rendering efficiency and the visual quality of the virtual scene are improved; various displacement interaction modes including traditional rocker movement, gesture control, body inclination and the like are designed, operation habits of different users are met, user interaction behaviors are analyzed and modeled through a machine learning algorithm, interaction parameters and response modes are adjusted in a self-adaptive mode, and personalized displacement interaction experience is provided.

The invention discloses a speaking digital human generation method based on a large model learnable text latent code, which comprises the following steps: acquiring a plurality of face voice videos according to actual needs and extracting training samples to form a training sample set, each training sample comprising a reference video and a corresponding Mel spectrogram sequence and mouth reference image; constructing a general speaking digital human generation model based on learnable text latent codes, and training by adopting the training sample set; and when a speaking digital person video needs to be generated, framing is carried out on to-be-generated voice data, a Mel spectrogram sequence is extracted, a mouth reference image and a reference video are set according to actual conditions, the reference image and the reference video are input into the trained general speaking digital person generation model together, and the speaking digital person video corresponding to the to-be-generated voice data is generated. According to the method, robust text latent codes are continuously learned from mouth reference images of different speakers, the method is used for guiding the synthesis process of a general speaking digital human video, and the quality of a speaking digital human generation model is improved.

The invention discloses a low-illumination image enhancement algorithm based on deep convolution, which is suitable for the field of image processing, and the core of the algorithm lies in an efficient noise reduction process and accurate decomposition of an illumination image and a reflection image. Comprising the following steps that firstly, a channel noise reduction module works cooperatively through multiple noise reduction strategies, noise in an image is effectively removed, and a foundation is laid for follow-up processing; then, the illumination image decomposition network and the reflection image decomposition network accurately separate the brightness information, texture and color information of the image based on a deep learning structure. And finally, by ingeniously fusing the illumination image and the reflection image, reconstructing a high-quality enhanced image, remarkably improving the brightness, contrast and details of the image, and meanwhile, retaining the natural color of the image. The algorithm has the advantages of being high in noise reduction capacity, accurate in illumination image decomposition, efficient in reflection image decomposition, good in enhancement effect and the like, and an effective and practical solution is provided for image enhancement under the low-illumination condition.

The invention discloses a video generation method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of inputting a to-be-processed image into an image processing model to obtain a first image feature; inputting the to-be-processed image into a global encoder to obtain a second image feature; combining the first image feature and the second image feature to obtain a combined image feature; based on the combined image features, using a first video generation model to generate an initial video corresponding to the to-be-processed image; obtaining video text description, and encoding the video text description to obtain text encoding features; and inputting the initial video and the text coding feature into a second video generation model to obtain a target video corresponding to the to-be-processed image. The invention also relates to the technical field of block chains, and the to-be-processed image is stored in a block chain network. According to the method, visual details can be reproduced, global consistency can be kept, and the visual quality and semantic consistency of the video are effectively improved.

The invention provides a model data processing method and device, and the method comprises the steps: obtaining original model data of a target model; determining repeated vertexes, deleting vertex data corresponding to the repeated vertexes to obtain de-duplicated vertex data, and performing quantization processing on the de-duplicated vertex data to obtain processed vertex data; according to the data type of the map data, compressing the map data to obtain processed map data; according to the material data, model components with the same material attribute are determined and combined, combined model components are obtained, and the material data of the combined model components serve as processed material data; and compressing and uploading the processed vertex data, the processed mapping data and the processed material data as new model data. By reducing redundant data, optimizing a data structure and adopting an efficient compression algorithm, light weight of model data is realized, so that the rendering efficiency and the transmission rate are improved.

The invention discloses a model display control method and device based on a digital twin system, and a medium. The method comprises the following steps: acquiring comprehensive information of a model; performing polygon simplification on the components of the model according to the simplification rate of the components in the comprehensive information to obtain a simplified component set; and rendering the component set according to the attribute data of the component set, and performing display control on the component set of the rendered model. According to the model display control method and device based on the digital twin system and the medium, accurate polygon simplification is performed on model components by using the simplification rate, and the model data volume is remarkably reduced, so that the loading speed is increased, and the system performance is improved; according to the method, rendering and display control are carried out by utilizing the attribute data of the component set, so that the model can still present a high-quality visual effect after being simplified, and the problem that the loading speed of the model is difficult to improve so as to carry out effective display control on the model can be solved.

The invention belongs to the technical field of image stitching, and discloses a multi-camera image stitching method and system. The method comprises the following steps: collecting data of a plurality of real-time images shot by a multi-camera device, and carrying out image preprocessing; collecting real-time image edge information of each piece of preprocessed real-time image data, and extracting a plurality of real-time feature points; performing feature point matching on the plurality of real-time feature points by using a pre-trained feature point matching model; performing image registration on all the preprocessed real-time image data according to a plurality of feature point matching results; carrying out image fusion on the real-time overlapping region of the real-time image data after all the images are registered by using an improved multi-band fusion algorithm; and according to the real-time splicing parameters, carrying out image splicing on the real-time image data after all the images are fused to obtain real-time holographic image data. The problems of poor visual effect, low accuracy, low processing speed, poor splicing quality and poor fusion effect in the prior art are solved.

The invention discloses a high dynamic range image tone mapping method and device, a medium and a program product, and relates to the field of image processing. The method comprises the following steps: calculating a brightness logarithm and a large-scale texture layer logarithm of a high dynamic range image to enhance image details and generate a global detail enhanced image; the brightness distribution is optimized by counting the brightness histogram, cutting the abnormal brightness value and compensating the abnormal brightness value; determining the average brightness and the standard deviation of the low dynamic range image based on the optimized histogram, obtaining a normalized brightness grey-scale map, and performing local detail enhancement; and restoring the brightness of the enhanced image to an original HDR range, mapping a color channel, and merging color spaces to obtain a final LDR image. According to the method, the dynamic range of the high-dynamic-range image can be compressed, and meanwhile, the detail features and brightness distribution reasonability of the image are effectively kept, so that the subjective visual quality and user experience of the low-dynamic-range image are remarkably improved.

The embodiment of the invention discloses a low-illumination image enhancement method and device based on an improved normalized stream. The method comprises the following steps: acquiring a low-illumination image to be enhanced; performing normalization processing on the to-be-enhanced low-illumination image to obtain a normalized spectrum; performing edge extraction processing on the normalized spectrum to obtain an edge spectrum; performing feature fusion on the to-be-enhanced low-illumination image, the normalized spectrum and the edge spectrum to obtain a feature fusion image; performing feature extraction on the feature fusion image by adopting a feature extraction module to obtain a feature extraction image; performing feature mapping on the feature extraction image by adopting a feature mapping module to obtain a feature mapping image; and inputting the feature mapping image into the normalized stream reversible network, and outputting a normal illumination image. By enhancing the low-illumination image through the method, the enhancement effect can be improved.

The invention discloses an underwater image color constancy enhancement method and system, and relates to the technical field of underwater image enhancement, and the method comprises the steps: receiving underwater image data, inputting the underwater image data into a pre-established multi-level color correction model based on an adaptive correction factor, and outputting to obtain a corrected image; inputting the corrected image into a pre-established multi-scale joint restoration network model, outputting to obtain a restored image, inputting the restored image into a pre-established residual error-based detail enhancement network model, and outputting to obtain an enhanced image; and inputting the enhanced image into a pre-established underwater image enhancement network model for optimization training, outputting and obtaining a trained underwater image enhancement network model, obtaining a to-be-tested underwater image data set, inputting the to-be-tested underwater image data set into the trained underwater image enhancement network model, and obtaining a to-be-tested underwater image data set. And an underwater image enhancement result is output, so that the color reality and naturalness of the image are remarkably improved.

The invention relates to the technical field of gate channel target image recognition, and particularly discloses a gate channel target recognition detection method and system, and the method comprises the steps: collecting image data, carrying out the image preprocessing steps, such as illumination compensation and noise removal, extracting the brightness features and texture features in an image, and calculating the visibility coefficient of a target region. According to the method, high identifiability of the target is kept under various illumination conditions, the final identification accuracy of the target in different environments is evaluated by comprehensively analyzing the visibility coefficient and the identification confidence of the target, so that the image preprocessing and target identification algorithms are dynamically adjusted, the target can be rapidly and accurately identified, and the identification accuracy of the target is improved. The target identification efficiency in the gate channel is improved, the high efficiency and robustness of the system under the complex illumination condition are ensured, and the method has practical value and application prospect.

The invention discloses a weak supervision defogging method based on uncertainty driving, and the method employs a weak supervision defogging network of a U-Net architecture, and the weak supervision defogging network comprises a regional dynamic attention module RDAM, a lower sampling layer, a multi-scale feature fusion module MSFFM, and an upper sampling layer. The RDAM is used for processing an input image or the output of a previous layer of RDAM, constructing a dynamic convolutional layer and obtaining a regional dynamic attention feature map through the dynamic convolutional layer; the down-sampling layer is used for down-sampling the output of the RDAM and reducing the spatial resolution; the MSFFM is used for processing input features through a window multi-head self-attention mechanism so as to capture a long-distance dependency relationship between pixels and performing multi-scale feature extraction and fusion; the upper sampling layer is used for recovering the feature map output by the MSFFM to the resolution ratio of the feature map output by the MSFFM of the upper layer and performing feature splicing with the feature map output by the MSFFM of the upper layer; and enabling the up-sampled feature map to sequentially pass through a 3 * 3 convolutional layer, a ReLU activation function and a 1 * 1 convolutional layer, and finally outputting a defogged image.

The invention provides an image encoding and decoding method, device and equipment, a medium and a computer program product. The method comprises the following steps: encoding input image data to obtain encoded image data; determining a signal-to-noise ratio of a channel for transmitting the coded image data, and fusing the coded image data and the signal-to-noise ratio to obtain fused image data; decoding the fused image data based on coding and decoding loss to obtain decoded image data; the coding and decoding loss is determined based on the signal-to-noise ratio. According to the method, the signal-to-noise ratio of the coding and decoding process and the signal-to-noise ratio of the transmission channel are fused, so that the balance between the image compression ratio and the transmission noise immunity is realized, the visual quality and the structural integrity after image transmission are optimized, and the efficient compression transmission of the image is realized.

The invention provides a drainage well lid blockage detection system and method based on machine vision, and the system comprises a data collection module, a preprocessing module, an edge detection module, a rectangular calibration module, a blockage detection module and an early warning judgment module. Whether the point is a blockage point or not is judged at the corresponding position in the grey-scale map according to the pixel value, the blockage point is summed, the ratio of the blockage point to the total pixel value serves as the standard for judging the blockage rate, and a corresponding early warning and solution scheme is provided according to the blockage degree grade, so that the blockage rate can be accurately judged even if the blockage object is cleaned. And water accumulation in rainy days is avoided to a certain extent.

The invention discloses a target segmentation vector boundary simplification method, electronic equipment and a storage medium, and relates to the technical field of remote sensing. The method comprises the following steps: acquiring a remote sensing image, and performing target segmentation on the remote sensing image to obtain raster data; converting the raster data into vector data; combining adjacent surface element vectors with common boundaries to obtain a plurality of combined surface elements; extracting an intersection point of each common boundary and the boundary of the corresponding combined surface element as an anchor point; according to the anchor points, performing segmentation simplification on the boundary and the common boundary of each combined surface element to obtain a plurality of simplified broken lines of which the head ends and the tail ends are connected; and performing line cutting on the multiple simplified broken lines to obtain a boundary simplification result. According to the target segmentation vector boundary simplification method provided by the embodiment of the invention, the condition that a plurality of broken lines in an intersection relationship exist in a vector result of target segmentation is solved, and the problems that topological errors such as overlapping and gaps occur and the topological consistency is changed are avoided.

The invention discloses a virtual reality scene modeling method and system based on big data, and relates to the technical field of virtual reality, and the method comprises the steps: matching a model component library, a texture component library and an illumination component library according to scene demand information; constructing a three-dimensional model according to the model component library and the scene demand information; and receiving dynamic configuration parameters of the illumination component library and the texture component library, and performing scene real-time rendering on the three-dimensional model. According to the method, the model component library, the texture component library and the illumination component library are matched, and the three-dimensional model is quickly built according to the model component library and the scene demand information, so that the complexity of modeling operation is reduced; the rendering effect of the three-dimensional model is updated in real time by analyzing the dynamic configuration parameters of the illumination and texture component library, so that visual effects under different illumination conditions are simulated in a virtual environment, the sense of reality of materials and surfaces is improved, rapid feedback and immediate adjustment of the design effect are ensured, and highly-customized and immersive user experience is provided.

The invention discloses a text-based motion video generation method and device, a storage medium and equipment, and belongs to the technical field of computers. Obtaining a positive prompt word and a negative prompt word; performing text coding on the positive prompt words and the negative prompt words to obtain text features; the Animatediff model and the LCM Lora model are inserted into a Stable-Diffusion model, and a video generation model is obtained; processing the text features by using a video generation model to obtain a motion video; and performing video enhancement processing on the motion video by using the video enhancement processing model to obtain a high-definition motion video. According to the method, the LCM Lora model can be used for reasoning acceleration, the ControlNet model is used for enabling the video action to be consistent with the action in the action reference video, and the video enhancement processing model is used for generating the high-definition video.

The invention discloses a three-dimensional scene reconstruction method under a sparse view angle based on multi-modal feature guidance. The method comprises the following steps: training an initial sparse view angle image encoder, an initial multi-modal guidance module and an initial three-dimensional Gaussian parameter predictor; respectively inputting the sparse view angle image into a trained sparse view angle image encoder and an image segmentation model of the frozen parameters to obtain shallow image features and segmentation features, and inputting the sparse view angle text description into a text encoder of the frozen parameters to obtain text features; in the trained multi-modal guide module, optimizing and refining the shallow image features by using the segmentation features and the text features to obtain multi-modal fusion features; and reconstructing a three-dimensional scene according to the multi-modal fusion features by using the trained three-dimensional Gaussian parameter predictor. According to the method, the features of image and text modals are fused, under the guidance of the multi-modal features, high-quality three-dimensional scene reconstruction is realized by using the sparse view angle image, and the robustness is relatively high.

The invention relates to the technical field of image restoration, and discloses a diffusion model double attribute constraint-based image restoration network training method, which comprises the following steps of: enabling a degraded image to pass through an image restoration network to obtain a restored image; the clear image and the restored image are input into a diffusion model after noise adding, predicted noise is obtained respectively, and the predicted noise difference between the clear image and the restored image is used as a natural attribute constraint; similarly, the clear image and the restored image are input into the diffusion model after noise addition, semantic features at the intermediate layer are networked respectively, and semantic attribute constraints are constructed by using the semantic features between the clear image and the restored image; constructing a loss function by using the natural attribute constraint and the semantic attribute constraint in combination with the original reconstruction constraint, and training an existing image restoration network; therefore, the visual quality and semantic attributes of the image output by the image restoration network are improved, and an optimal restoration result is obtained.

The utility model discloses a lens for inhibiting eye axis growth by using a microstructure and point diffusion superposition technology, and belongs to the technical field of optical lenses. The lens comprises a lens body, the lens body comprises a main lens structure, point diffusion girdle structures, micro lens girdle structures and a bright vision area, the bright vision area is arranged in the center of the main lens structure, the point diffusion girdle structures and the micro lens girdle structures are concentrically and alternately arranged on the main lens structure to form a double-effect inhibition area, and the double-effect inhibition area is arranged on the outer side of the bright vision area. The objective of the utility model is to superpose a contrast reduction lens on the basis of the existing defocus lens, so that the development of myopia is effectively reduced, microstructures are exposed, thousands of light diffusion points are arranged on other parts, the exposed microstructures are closely arranged to ensure 65% of defocus area and correction area, and the defocus amount is gradually reduced from outside to inside, so that the myopia is effectively reduced. According to the technology, myopia defocus is generated while imaging is manufactured in advance, meanwhile, the light and shade contrast ratio is effectively reduced, and the purpose of delaying the myopia progress of children and adolescents is achieved.

The invention discloses a white balance correction method and system based on eye white region segmentation and color compensation, electronic equipment and a storage medium, and relates to the field of computer vision, image processing and color correction, and the method comprises the steps: obtaining a portrait image of a user, and carrying out the detection of the portrait image through a face detection model obtained through pre-training, cutting out a face image; through a deep learning eye white segmentation model obtained through pre-training, performing eye white feature learning processing on the human face image cut from the human face image pair, and extracting an eye white region; using an RGB mode and a gray mode to sort pixels in the eye white area, taking out a pixel block closest to white according to a preset middle section percentage, and calculating related parameters of RGB three channels; and generating a white balance image of the final original image according to the RGB three-channel related parameters. According to the method, the visual quality of the image is improved, the color authenticity of the face image is enhanced, and the generated image has a more natural and accurate white balance effect.

The invention provides a computer vision-based craniocerebral CT image tumor identification method and system, and the method comprises the steps: obtaining a craniocerebral CT image set of a to-be-detected patient, and recognizing a brain tissue region image in the CT image set according to a connected domain matching method; performing image saliency enhancement on the brain tissue region image to obtain an enhanced image; taking a neighborhood window by taking any point on the enhanced image as a center, and calculating a gray average value of all pixel points in the neighborhood window; taking the gray average value of the corresponding pixel points as the output of a central pixel point to obtain a mean value image; obtaining an optimal segmentation threshold value according to the relevancy between the mean value image and the enhanced image; and segmenting the enhanced image by using the optimal segmentation threshold to obtain a segmented image, and inputting the segmented image into a preset tumor recognition model to obtain a recognition result. According to the invention, through saliency enhancement processing, the visual quality of the image is improved, the tumor features are easier to identify, and thus the performance of a subsequent classification model is improved.

The invention relates to the technical field of image generation, and discloses an arc-shaped screen multi-view 3D image generation method and device, and the method comprises the steps: collecting a view parameter set of an arc-shaped screen, and carrying out the residual feature processing of an original image sequence, and obtaining a multi-dimensional space feature map; performing tensor product operation on the multi-dimensional spatial feature map and the view angle parameter set to obtain view angle conversion delay data; performing graph convolution operation to obtain space-time dependence features; performing multi-head self-attention calculation on the space-time dependency features to obtain delay compensation features, and performing comparative learning on the view angle features based on the delay compensation features to obtain view angle mapping parameters; the view angle mapping parameters, the delay compensation features and the original image sequence are input into a U-Net network for 3D image reconstruction, and a 3D image sequence with continuous view angles is output, the feature consistency in the view angle conversion process is improved, image distortion during view angle switching is reduced, and therefore the visual quality of 3D images is improved.

The invention discloses an image fusion method based on a double-branch feature decoupling auto-encoder, and belongs to the field of computer vision and image processing. According to the method, for the problems of modal pollution and structural distortion in infrared and visible light image fusion, structural semantic information and high-frequency texture details of a source image are extracted respectively by constructing a content feature coding module and a detail feature coding module, and feature decoupling is achieved. And performing deep fusion on the decoupled features by using an adaptive feature weighting mechanism, and reconstructing a fused image with infrared target saliency and visible light detail definition through a shared decoder. According to the method, an end-to-end two-stage training strategy is adopted for optimization, complex prior or post-processing is not needed, the effects of improving the fused image contrast, edge preservation and target recognition performance are remarkable, and the method is suitable for the fields of weak light monitoring, intelligent perception, unmanned system navigation and the like.

The invention discloses a grinding wheel abrasive particle image segmentation method based on machine vision, and the method comprises the following steps: inputting a grinding wheel abrasive particle sample image, converting the sample image into a gray-scale image, and carrying out the preprocessing of image enhancement on the gray-scale image; performing operation on the gray-scale map line by line and column by column by two-dimensional Gaussian filtering; calculating the partial derivative of each pixel point in the x-axis direction and the y-axis direction, and obtaining the gradient of each pixel point according to the partial derivative; performing non-maximum suppression according to the gradient; acquiring a global threshold by using an Otsu algorithm, and introducing the global threshold into an adaptive function for setting image segmentation by high and low thresholds of a Canny algorithm to realize edge connection; lag compensation is carried out, and middle edges are accepted and rejected; and morphologically processing irrelevant areas and filling holes. According to the method, edge connection is achieved by setting the adaptive function of image threshold segmentation, the problem of noise interference caused by the grinding wheel base body area and the environment is effectively solved, the boundary of the grinding wheel abrasive particle image contour can be accurately divided, and good flexibility is achieved.