WO2023184746A1 - 数据处理方法及装置、电子设备和存储介质 - Google Patents

数据处理方法及装置、电子设备和存储介质 Download PDF

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Publication number
WO2023184746A1
WO2023184746A1 PCT/CN2022/101254 CN2022101254W WO2023184746A1 WO 2023184746 A1 WO2023184746 A1 WO 2023184746A1 CN 2022101254 W CN2022101254 W CN 2022101254W WO 2023184746 A1 WO2023184746 A1 WO 2023184746A1
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image data
processing
data processing
data set
processed
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PCT/CN2022/101254
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English (en)
French (fr)
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徐淼珺
黎承志
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上海商汤智能科技有限公司
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Publication of WO2023184746A1 publication Critical patent/WO2023184746A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/60—Rotation of whole images or parts thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning

Definitions

  • the present disclosure relates to the field of computer technology, and in particular, to a data processing method and device, electronic equipment and storage media.
  • the field of artificial intelligence requires a large amount of annotated data for model training.
  • at least one kind of data processing can usually be performed on the data set, such as rotation, cropping, mirroring, dissimilarity, deblurring, etc. , after data processing, the labeling efficiency of the data set and the production efficiency of the model can be greatly improved.
  • This disclosure proposes a data processing technical solution.
  • a data processing method including: acquiring an image data set to be processed; in response to receiving a setting instruction for a data processing flow of the image data set, performing the processing according to the setting instruction.
  • the data processing method and the execution order of the data processing method determine the data processing flow corresponding to the image data set; according to the execution order of each data processing method in the data processing flow, the image data in the image data set is Perform processing to obtain the processed image data set.
  • the data processing method includes at least one of rotation processing, cropping processing, mirroring processing, dissimilarity processing, and deblurring processing; the setting instructions are also used to indicate various data processing methods.
  • the processing degree includes at least one of the rotation angle of rotation processing, the cropping range of cropping processing, the mirroring type of mirroring processing, the similarity threshold of desimilarity processing, and the sharpness threshold of deblurring processing;
  • the mirroring type includes at least one of horizontal mirroring, vertical mirroring and center mirroring, and the cropping range is determined by drawing a cropping frame on the preview image.
  • the method further includes: displaying a preview image in a designated area of the graphical interactive interface, where the preview image is any one in the image data set.
  • Image data wherein, the method further includes: when the data processing method has been set with a processing level, processing the preview image according to the data processing method and the set processing level to obtain the processed Preview the image and display it, and the processed preview image has a processing effect that matches the processing level of the data processing method.
  • processing the image data in the image data set according to the execution order of each data processing method in the data processing flow to obtain a processed image data set includes:
  • the data processing flow includes dissimilarity processing, and based on the execution sequence, when it is determined that the dissimilarity processing is currently executed, the similarity between each image data in the current image data set is determined; according to the data processing flow, similarity threshold, determine at least one image group, and retain one image data in each image group to obtain a processed image data set, where the similarity between each image data in the image group exceeds the similarity threshold .
  • the image data in the image data set are processed according to the execution order of each data processing method in the data processing flow to obtain a processed image data set, including:
  • the data processing flow includes deblurring processing, and when it is determined that the deblurring processing is currently executed based on the execution sequence, the clarity of each image data in the current image data set is determined; according to the clarity in the data processing flow degree threshold, filter out the image data in the current image data set that has a definition smaller than the definition threshold, and obtain a processed image data set.
  • the image data in the image data set are processed according to the execution order of each data processing method in the data processing flow to obtain a processed image data set, including:
  • the data processing flow includes rotation processing, and if it is determined that the rotation processing is currently executed based on the execution sequence, the image data in the current image data set is rotated according to the rotation angle in the data processing flow, Obtain the processed image data set; and/or, if the data processing flow includes mirroring processing, and the current execution of mirroring processing is determined based on the execution sequence, according to the mirroring type in the data processing flow, Mirror the image data in the current image data set to obtain a processed image data set; and/or include cropping processing in the data processing flow, and determine the current execution to cropping processing based on the execution sequence.
  • the method further includes: providing example diagrams corresponding to each data processing method.
  • the example diagrams include example diagrams with different rotation angles, example diagrams with different mirror types, and example diagrams with different definitions. , at least one of the example images with different degrees of similarity.
  • a data processing device including: an acquisition module for acquiring an image data set to be processed; a process determination module for responding to receiving a data processing process for the image data set
  • the setting instruction is used to determine the data processing flow corresponding to the image data set according to the data processing mode indicated by the setting instruction and the execution order of the data processing mode; the processing module is used to determine the data processing flow corresponding to the image data set according to each step in the data processing flow.
  • the execution sequence of the data processing method is to process the image data in the image data set to obtain a processed image data set.
  • the data processing method includes at least one of rotation processing, cropping processing, mirroring processing, dissimilarity processing, and deblurring processing; the setting instructions are also used to indicate various data processing methods.
  • the processing degree includes at least one of the rotation angle of rotation processing, the cropping range of cropping processing, the mirroring type of mirroring processing, the similarity threshold of desimilarity processing, and the sharpness threshold of deblurring processing;
  • the mirroring type includes at least one of horizontal mirroring, vertical mirroring and center mirroring, and the cropping range is determined by drawing a cropping frame on the preview image.
  • the device further includes: a preview module, configured to display a preview image in a designated area of the graphical interactive interface, where the preview image is the image Any image data in the data set; wherein the device further includes: a preview image processing module, configured to perform, according to the data processing method and the set processing level, when the data processing method has been set with a processing level.
  • the preview image is processed to obtain a processed preview image and displayed.
  • the processed preview image has a processing effect that matches the processing level of the data processing method.
  • the processing module includes: a similarity determination submodule, configured to include dissimilarity processing in the data processing flow, and determine the current execution to the dissimilarity processing based on the execution sequence. In this case, determine the similarity between each image data in the current image data set; the similarity removal processing submodule is used to determine at least one image group according to the similarity threshold in the data processing flow, and retain the images in each image group. One image data is obtained to obtain a processed image data set, and the similarity between each image data in the image group exceeds the similarity threshold.
  • the processing module includes: a sharpness determination submodule, configured to include deblurring processing in the data processing flow, and determine the current execution to deblurring processing based on the execution sequence. In this case, determine the clarity of each image data in the current image data set; the deblurring processing submodule is used to filter out the clarity of the current image data set that is smaller than the definition according to the clarity threshold in the data processing flow. The image data of the sharpness threshold is obtained to obtain the processed image data set.
  • a sharpness determination submodule configured to include deblurring processing in the data processing flow, and determine the current execution to deblurring processing based on the execution sequence. In this case, determine the clarity of each image data in the current image data set; the deblurring processing submodule is used to filter out the clarity of the current image data set that is smaller than the definition according to the clarity threshold in the data processing flow. The image data of the sharpness threshold is obtained to obtain the processed image data set.
  • the processing module includes: a rotation processing submodule, configured to include rotation processing in the data processing flow, and determine that the rotation processing is currently executed based on the execution sequence, According to the rotation angle in the data processing flow, the image data in the current image data set is rotated to obtain a processed image data set; and/or, a mirror processing submodule is used in the data processing flow Including mirror processing, and when it is determined that the current execution of mirror processing is based on the execution sequence, mirror processing is performed on the image data in the current image data set according to the mirror type in the data processing flow to obtain processed image data.
  • a rotation processing submodule configured to include rotation processing in the data processing flow, and determine that the rotation processing is currently executed based on the execution sequence, According to the rotation angle in the data processing flow, the image data in the current image data set is rotated to obtain a processed image data set
  • a mirror processing submodule is used in the data processing flow Including mirror processing, and when it is determined that the current execution of mirror processing is based on the execution sequence, mirror
  • a clipping processing submodule configured to include clipping processing in the data processing flow, and determine the current execution of the clipping process based on the execution order, according to the clipping range in the data processing flow , perform cropping processing on the image data in the current image data set to obtain the processed image data set.
  • the device further includes: an example module, used to provide example diagrams corresponding to each data processing method.
  • the example diagrams include example diagrams of different rotation angles, different mirror types, different At least one of example images with different degrees of clarity and example images with different degrees of similarity.
  • an electronic device including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to call instructions stored in the memory to execute the above method.
  • a computer-readable storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above method is implemented.
  • a computer program product including computer readable code, or a non-volatile computer readable storage medium carrying the computer readable code, when the computer readable code is stored in an electronic device
  • the processor in the electronic device executes the above method.
  • a customized data processing flow is generated by customizing the data processing method and the execution sequence of the data processing method, so that after the image data in the image data set is processed according to the customized data processing flow, The processed image data set that meets the user's needs can be obtained, and the convenience of customized data processing of the image data set can be improved.
  • FIG. 1 shows a flowchart of a data processing method according to an embodiment of the present disclosure.
  • Figure 2a shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • Figure 2b is a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • Figure 2c shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • Figure 3a shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • Figure 3b shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • FIG. 4 shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure.
  • FIG. 5 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.
  • exemplary means "serving as an example, example, or illustrative.” Any embodiment described herein as “exemplary” is not necessarily to be construed as superior or superior to other embodiments.
  • a and/or B can mean: A exists alone, A and B exist simultaneously, and they exist alone. B these three situations.
  • at least one herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, and C, which can mean including from A, Any one or more elements selected from the set composed of B and C.
  • Figure 1 shows a flow chart of a data processing method according to an embodiment of the present disclosure.
  • the data processing method can be executed by an electronic device such as a terminal device or a server.
  • the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user Terminal, terminal, cellular phone, cordless phone, personal digital assistant (Personal Digital Assistant, PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc.
  • the method can call the computer-readable data stored in the memory through the processor
  • the method can be implemented by means of instructions, or the method can be executed by the server.
  • the data processing method includes:
  • step S11 the image data set to be processed is obtained.
  • the image data set may include image data to be processed, and the image data may be images directly captured by an image acquisition device, or may be image frames extracted from video data, to which embodiments of the present disclosure are not limited. It should be understood that the image data set can be a data set imported and uploaded locally, or it can also be a data set selected from a list of imported and uploaded data sets. The embodiment of the present disclosure does not limit the method of obtaining the image data set. .
  • step S12 in response to receiving a setting instruction for the data processing flow of the image data set, the data processing flow corresponding to the image data set is determined according to the data processing method indicated by the setting instruction and the execution order of the data processing method.
  • At least one data processing method can be set for the image data set, and corresponding execution orders and processing levels can be set for different data processing methods, so that a customized data processing flow corresponding to the image data set can be obtained.
  • the data processing method may include but is not limited to at least one of rotation processing, cropping processing, mirror processing, dissimilarity processing, and deblurring processing; the setting instructions are also used to indicate various data processing methods.
  • the degree of processing includes at least one of the rotation angle of rotation processing, the cropping range of cropping processing, the mirroring type of mirroring processing, the similarity threshold of desimilarity processing, and the sharpness threshold of deblurring processing; the mirroring type includes horizontal mirroring , at least one of vertical mirroring and center mirroring.
  • horizontal mirroring can be understood as mirroring the pixels in the left and right parts of the image with the vertical central axis as the center.
  • Vertical mirroring can be understood as mirroring the pixels in the upper and lower parts of the image with the horizontal central axis as the center.
  • Center mirroring It can be understood that mirroring the image centered on the intersection of the horizontal central axis and the vertical central axis is equivalent to mirroring the image horizontally and vertically.
  • the rotation angle of the rotation process may include, for example, at least 90 degrees clockwise, 180 degrees clockwise, 270 degrees clockwise, 90 degrees counterclockwise, 180 degrees counterclockwise, 270 degrees counterclockwise, or a custom rotation angle.
  • the similarity threshold of de-similarity processing can be used to filter out image data in the image data set with a similarity higher than the similarity threshold
  • the sharpness threshold of de-blurring processing can be used to filter out image data in the image data set that is lower than the sharpness threshold.
  • the graphical interactive interface can provide various settings for setting The operation control of the data processing process allows users to issue setting instructions through the operation control to set the data processing process of the image data set.
  • Figure 2a shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure
  • Figure 2b shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • the user can add the required data processing method by clicking the selection button "+” at the various data processing methods shown in the "Processing Method” at the top of Figure 2a.
  • the data processing methods selected by the user will be displayed in Figure 2a in turn.
  • a preview image can be displayed at the "Preview Image" on the right side of Figure 2a.
  • the preview image can be any image data in the image data set, so that the user can be shown at least one data processing based on the preview image. The actual processing effect of the method.
  • the graphical interactive interface shown in Figure 2b can be obtained.
  • the "Processing Flow” of the graphical interactive interface displays setting cards for data processing methods such as “removing similarity”, “removing model” and “mirror”.
  • Each setting card can provide settings for setting the degree of processing.
  • Related controls to facilitate users to set the degree of processing of different data processing methods such as setting the similarity threshold through the "slider bar” on the “Desimilarity” setting card, and selecting mirroring through the "drop-down box” on the "Mirroring” setting card Type etc.
  • the "first step”, “second step” and “third step” shown in Figure 2b can represent the execution sequence of each data processing method.
  • the user can drag and drop the card to set the "processing flow”. Up and down order to adjust the execution order of data processing methods.
  • the setting card of the unselected data processing method will disappear in the "Processing Process” , and the selection button for the unselected data processing method is displayed in the "Processing Method" at the top.
  • the setting instruction is also used to indicate the cropping range of the cropping process.
  • the cropping range can be determined by drawing a cropping box on the preview image.
  • the cropping range can include at least two sides of the cropping box. The vertex coordinates of the vertices. In this way, users can set the cropping range conveniently and intuitively.
  • Figure 2c shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • the setting card for the cutting process can be displayed at the "Processing Flow”.
  • the prompt message “Please adjust the cropping frame on the right” can be displayed to instruct the user to draw or adjust the cropping frame on the preview image displayed at "Preview Image”.
  • the user selects "Rotate” and sets the rotation angle to " "Rotate 180° clockwise”
  • the preview image is displayed as the preview image rotated 180°.
  • the method further includes: providing example diagrams corresponding to each data processing method, and the example diagrams include different rotation angles. At least one of example images, example images of different image types, example images of different definitions, and example images of different degrees of similarity. In this way, it is convenient for users to understand the processing effects of different data processing methods in advance, so that users can set up the data processing process.
  • the example pictures with different rotation angles include the same image at different rotation angles
  • the example pictures with different mirror types include the same image with different mirror types
  • the example pictures with different definitions include the same image with different definitions.
  • images with different degrees of similarity include images with different degrees of similarity to the same image.
  • Figure 3a shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • Figure 3b shows a schematic diagram of a graphical interactive interface according to an embodiment of the present disclosure.
  • Figure 3a shows "original image” and "original image” In the example images of different rotation angles such as “rotate 90° clockwise”, “rotate 180° clockwise” and “rotate 270° clockwise”
  • Figure 3b shows "clarity 0.95", “clarity 0.65", " Examples of different resolutions for "resolution 0.35" and "resolution 0.15".
  • the user can click the control at "View Processing Example” in Figure 2a, Figure 2b or Figure 2c to display the graphical interactive interface shown in Figure 3a or Figure 3b to view example diagrams corresponding to various data processing methods.
  • step S13 the image data in the image data set are processed according to the execution order of each data processing method in the data processing flow to obtain a processed image data set.
  • the setting instruction is also used to indicate the degree of processing of the data processing method.
  • the image data in the image data set is processed to obtain the processed image data set. Including: processing the image data in the image data set according to the execution order and processing degree of each data processing method in the data processing flow to obtain a processed image data set.
  • the data processing methods of the embodiments of the present disclosure can be applied to scenarios such as machine learning platforms, model production platforms, data annotation platforms, label production platforms, etc., which can improve machine learning, model production, data annotation, and Efficiency of label production.
  • the set data processing process can be displayed in the form of a task list, so that users can check the processing status of the image data set at any time.
  • data annotation can be performed on the processed image data set, and subsequent model training and model production processes can be completed.
  • a customized data processing flow is generated by customizing the data processing method and the execution sequence of the data processing method, so that after the image data in the image data set is processed according to the customized data processing flow, The processed image data set that meets the user's needs can be obtained, and the convenience and universality of data processing of the image data set can be improved.
  • the processing effect of at least one data processing method can be displayed to the user based on the preview image.
  • the method further includes: in the graphic interaction A preview image is displayed in the specified area of the interface, and the preview image is any image data in the image data set.
  • the preview image can be any image data randomly selected from the image data set.
  • the user can also specify any image data from the image data set as the preview image, or can also switch the displayed preview image.
  • the present disclosure The embodiment is not limited. For example, you can click the "switch” button below the preview image in Figure 2a, Figure 2b or Figure 2c to switch the displayed preview image, you can also click the "+” button to enlarge the preview image, or click "-" button to reduce the preview image, "100%" represents the display ratio of the preview image.
  • the preview image can be displayed in a designated area of the graphical interactive interface, such as the area where the "preview image" is located in the above-mentioned Figure 2a, Figure 2b or Figure 2c.
  • a canvas can be set in the designated area of the graphical interactive interface, and can be displayed according to the preview The aspect ratio of the image and the aspect ratio of the specified area.
  • Drawing the preview image on the canvas can facilitate the rotation of the preview image. Processing operations such as mirroring, cropping, and moving are used to show the processing effects of various data processing methods.
  • the method further includes: when the data processing method has been set with a processing level, processing the preview image according to the data processing method and the set processing level to obtain a processed preview image And displayed, the processed preview image has a processing effect that matches the processing level of the data processing method. In this way, the processing effects of different data processing methods can be displayed to users in a friendly manner.
  • the preview image when the data processing method includes rotation processing and the rotation angle has been set, the preview image can be rotated according to the set rotation angle, and the rotated preview image can be displayed horizontally and centered in the designated area.
  • the data processing method includes mirroring and the mirroring type has been set, create a new hidden canvas, and mirror the pixels in the preview image according to the mirroring type on the hidden canvas to obtain the mirrored preview. image, and then render the mirrored preview image to the original canvas within the specified area.
  • the preview image in the designated area mainly facilitates the user to draw a cropping frame, and the cropping frame drawn on the preview image can represent the processing effect of the cropping process. It should be understood that since deblurring and dissimilarity processing cannot display the processing effects on only one preview image, when the data processing method includes deblurring or dissimilarity processing, the processing effects of deblurring and similarity processing can be Not reflected in the preview image.
  • the data processing method may include dissimilarity processing.
  • the image data in the image data set is processed according to the execution order of each data processing method in the data processing flow. , get the processed image data set, including:
  • the similarity between each image data in the current image data set is determined; according to the similarity threshold in the data processing flow, Determine at least one image group and retain one image data in each image group to obtain a processed image data set. The similarity between each image data in the image group exceeds the similarity threshold.
  • similarity calculation methods known in the art to determine the similarity between each image data in the image data set, such as using distance formulas (such as L1 distance, L2 distance, etc.) or error formulas (such as average Absolute value error, mean square error, etc.), the similarity between each image data is calculated, and the embodiment of the present disclosure does not limit this.
  • At least one image group is determined according to the similarity threshold in the data processing process, which is equivalent to clustering the image data in the image data set, and Image data whose similarity exceeds the similarity threshold are clustered into image groups.
  • Image data within the same image group are considered to be data with higher similarity, and image data between different image groups are considered to be data with lower similarity; then retain
  • One image data in each image group is equivalent to deleting part of the image data with high similarity in each image group, or deleting part of the repeated image data, leaving one image data in each image group, realizing targeting the entire image data Dissimilarity processing of sets.
  • the current image data set may be the original image data set
  • the dissimilarity processing is the nth step in the data processing flow
  • the current image data set may be the original image data set.
  • the current image data set may be the image data set processed by the n-1th step of data processing, n ⁇ 2.
  • customized dissimilarity processing for image data sets can be implemented according to the set similarity threshold, thereby improving customized data processing for image data sets. the convenience.
  • the data processing method may include deblurring processing.
  • the image data in the image data set is processed according to the execution order of each data processing method in the data processing flow. , get the processed image data set, including:
  • the data processing flow includes deblurring processing
  • the current execution to deblurring processing is determined based on the execution sequence
  • determine the clarity of each image data in the current image data set ; filter out the data based on the clarity threshold in the data processing flow.
  • the image data in the current image data set whose sharpness is less than the sharpness threshold is obtained as the processed image data set.
  • sharpness determination methods known in the art, such as Brenner gradient function, Tenengrad gradient function, Laplacian gradient function, etc., to determine the sharpness of each image data in the image data set.
  • This embodiment of the present disclosure does not make any reference to this. limit.
  • the current image data set may be the original image data set
  • the current image data set may be the original image data set
  • the current image data set may be the image data set processed by the n-1th step of data processing, n ⁇ 2.
  • customized deblurring for image data sets can be implemented according to the set sharpness threshold, thereby improving customized data processing for image data sets. the convenience.
  • the data processing method includes rotation processing.
  • the image data in the image data set is processed according to the execution order of each data processing method in the data processing flow, and we obtain
  • the processed image dataset includes:
  • the image data in the current image data set is rotated according to the rotation angle in the data processing flow to obtain the processed image. data set.
  • the current image data set may be the original image data set
  • the current image data set may be the original image data set
  • the image data set may be an image data set processed by the n-1th step of data processing, n ⁇ 2.
  • the data processing method may include mirror processing.
  • the image data in the image data set is processed according to the execution order of each data processing method in the data processing flow, Get the processed image data set, including:
  • the image data in the current image data set is mirrored according to the mirror type in the data processing flow to obtain the processed image data. set.
  • the current image data set may be the original image data set
  • the current image data set may be the original image data set
  • the image data set may be an image data set processed by the n-1th step of data processing, n ⁇ 2.
  • the data processing method may include cropping processing.
  • the image data in the current image data set is processed according to the execution order of each data processing method in the data processing flow. Process and obtain the processed image data set, including:
  • the image data in the current image data set is cropped according to the cropping range in the data processing flow to obtain the processed image data. set.
  • the vertex coordinates of at least two vertices of the cropping frame can be used to represent the cropping range, so that the current cropping range can be realized based on the vertex coordinates of at least two vertices of the cropping frame.
  • the image data in the image data set is cropped.
  • the current image data set may be the original image data set
  • the current image data set may be the original image data set
  • the image data set may be an image data set processed by the n-1th step of data processing, n ⁇ 2.
  • a step-by-step interactive form can be used to support users to select different data processing methods and customize the execution order of any data processing method to implement a customized data processing process.
  • the present disclosure also provides data processing devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the data processing methods provided by the present disclosure.
  • data processing devices electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the data processing methods provided by the present disclosure.
  • Figure 4 shows a block diagram of a data processing device according to an embodiment of the present disclosure. As shown in Figure 4, the device includes:
  • the acquisition module 101 is used to acquire the image data set to be processed
  • the process determination module 102 is configured to, in response to receiving a setting instruction for the data processing process of the image data set, determine the image data according to the data processing method indicated by the setting instruction and the execution order of the data processing method. Set the corresponding data processing process;
  • the processing module 103 is configured to process the image data in the image data set according to the execution order of each data processing method in the data processing flow to obtain a processed image data set.
  • the data processing method includes at least one of rotation processing, cropping processing, mirroring processing, dissimilarity processing, and deblurring processing; the setting instructions are also used to indicate various data processing methods.
  • the processing degree includes at least one of the rotation angle of rotation processing, the cropping range of cropping processing, the mirroring type of mirroring processing, the similarity threshold of desimilarity processing, and the sharpness threshold of deblurring processing;
  • the mirroring type includes at least one of horizontal mirroring, vertical mirroring and center mirroring, and the cropping range is determined by drawing a cropping frame on the preview image.
  • the device further includes: a preview module, configured to display a preview image in a designated area of the graphical interactive interface, where the preview image is the image Any image data in the data set; wherein the device further includes: a preview image processing module, configured to perform, according to the data processing method and the set processing level, when the data processing method has been set with a processing level.
  • the preview image is processed to obtain a processed preview image and displayed.
  • the processed preview image has a processing effect that matches the processing level of the data processing method.
  • the processing module 103 includes: a similarity determination submodule, configured to include dissimilarity processing in the data processing flow, and determine the current execution to dissimilarity processing based on the execution sequence. In the case of , determine the similarity between each image data in the current image data set; the similarity removal processing submodule is used to determine at least one image group according to the similarity threshold in the data processing flow, and retain each image group One image data in the image data is obtained to obtain a processed image data set, and the similarity between each image data in the image group exceeds the similarity threshold.
  • a similarity determination submodule configured to include dissimilarity processing in the data processing flow, and determine the current execution to dissimilarity processing based on the execution sequence. In the case of , determine the similarity between each image data in the current image data set; the similarity removal processing submodule is used to determine at least one image group according to the similarity threshold in the data processing flow, and retain each image group One image data in the image data is obtained to obtain a processed image data set, and the similar
  • the processing module 103 includes: a sharpness determination submodule, configured to include deblurring processing in the data processing flow, and determine the current execution to deblurring processing based on the execution sequence. In the case of , determine the clarity of each image data in the current image data set; the deblurring processing submodule is used to filter out the definitions in the current image data set that are smaller than the definition according to the clarity threshold in the data processing flow. The image data with the definition threshold is obtained to obtain the processed image data set.
  • a sharpness determination submodule configured to include deblurring processing in the data processing flow, and determine the current execution to deblurring processing based on the execution sequence. In the case of , determine the clarity of each image data in the current image data set; the deblurring processing submodule is used to filter out the definitions in the current image data set that are smaller than the definition according to the clarity threshold in the data processing flow. The image data with the definition threshold is obtained to obtain the processed image data set.
  • the processing module 103 includes: a rotation processing submodule, configured to include rotation processing in the data processing flow, and determine the current execution of the rotation processing based on the execution sequence. , perform rotation processing on the image data in the current image data set according to the rotation angle in the data processing flow, to obtain a processed image data set; and/or, a mirror processing submodule, used in the data processing flow includes mirroring processing, and based on the execution sequence it is determined that the current execution of mirroring processing is, based on the mirroring type in the data processing flow, perform mirroring processing on the image data in the current image data set to obtain the processed image Data set; and/or, a cropping processing submodule, configured to include cropping processing in the data processing flow, and when it is determined that cropping processing is currently executed based on the execution sequence, according to the cropping process in the data processing flow Range, crop the image data in the current image data set to obtain the processed image data set.
  • a rotation processing submodule configured to include rotation processing in the data processing flow, and determine the current
  • the device further includes: an example module, used to provide example diagrams corresponding to each data processing method.
  • the example diagrams include example diagrams of different rotation angles, different mirror types, different At least one of example images with different degrees of clarity and example images with different degrees of similarity.
  • a customized data processing flow is generated by customizing the data processing method and the execution sequence of the data processing method, so that after the image data in the image data set is processed according to the customized data processing flow, The processed image data set that meets the user's needs can be obtained, and the convenience of customized data processing of the image data set can be improved.
  • the functions or modules provided by the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • the functions or modules provided by the device provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
  • Embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above method is implemented.
  • Computer-readable storage media may be volatile or non-volatile computer-readable storage media.
  • An embodiment of the present disclosure also provides an electronic device, including: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to call instructions stored in the memory to execute the above method.
  • Embodiments of the present disclosure also provide a computer program product, including computer readable code, or a non-volatile computer readable storage medium carrying the computer readable code.
  • computer readable code When the computer readable code is stored in a processor of an electronic device, When running, the processor in the electronic device executes the above method.
  • the electronic device may be provided as a terminal, a server, or other forms of equipment.
  • FIG. 5 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure.
  • the electronic device 1900 may be provided as a server or terminal device.
  • electronic device 1900 includes a processing component 1922 , which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, executable by processing component 1922 .
  • the application program stored in memory 1932 may include one or more modules, each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above-described method.
  • Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input-output (I/O) interface 1958 .
  • the electronic device 1900 can operate based on an operating system stored in the memory 1932, such as a Microsoft server operating system (Windows Server TM ), a graphical user interface operating system (Mac OS X TM ) launched by Apple, a multi-user multi-process computer operating system (Unix TM ), a free and open source Unix-like operating system (Linux TM ), an open source Unix-like operating system (FreeBSD TM ), or similar.
  • Microsoft server operating system Windows Server TM
  • Mac OS X TM graphical user interface operating system
  • Unix TM multi-user multi-process computer operating system
  • Linux TM free and open source Unix-like operating system
  • FreeBSD TM open source Unix-like operating system
  • a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.
  • the present disclosure may be a system, method, and/or computer program product.
  • a computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement aspects of the present disclosure.
  • Computer-readable storage media may be tangible devices that can retain and store instructions for use by an instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or Flash memory), Static Random Access Memory (SRAM), Compact Disk Read Only Memory (CD-ROM), Digital Versatile Disk (DVD), Memory Stick, Floppy Disk, Mechanical Coding Device, such as a printer with instructions stored on it.
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • Flash memory Static Random Access Memory
  • CD-ROM Compact Disk Read Only Memory
  • DVD Digital Versatile Disk
  • Memory Stick
  • Computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or through electrical wires. transmitted electrical signals.
  • Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to various computing/processing devices, or to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage on a computer-readable storage medium in the respective computing/processing device .
  • Computer program instructions for performing operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages.
  • the computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as an Internet service provider through the Internet). connect).
  • LAN local area network
  • WAN wide area network
  • an external computer such as an Internet service provider through the Internet. connect
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA)
  • the electronic circuit can Computer readable program instructions are executed to implement various aspects of the disclosure.
  • These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, thereby producing a machine that, when executed by the processor of the computer or other programmable data processing apparatus, , resulting in an apparatus that implements the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
  • These computer-readable program instructions can also be stored in a computer-readable storage medium. These instructions cause the computer, programmable data processing device and/or other equipment to work in a specific manner. Therefore, the computer-readable medium storing the instructions includes An article of manufacture that includes instructions that implement aspects of the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.
  • Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other equipment, causing a series of operating steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executed on a computer, other programmable data processing apparatus, or other equipment to implement the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions that embody one or more elements for implementing the specified logical function(s).
  • Executable instructions may occur out of the order noted in the figures. For example, two consecutive blocks may actually execute substantially in parallel, or they may sometimes execute in the reverse order, depending on the functionality involved.
  • each block of the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration can be implemented by special purpose hardware-based systems that perform the specified functions or acts. , or can be implemented using a combination of specialized hardware and computer instructions.
  • the computer program product can be implemented specifically through hardware, software or a combination thereof.
  • the computer program product is embodied as a computer storage medium.
  • the computer program product is embodied as a software product, such as a Software Development Kit (SDK), etc. wait.
  • SDK Software Development Kit
  • the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process.
  • the specific execution order of each step should be based on its function and possible The internal logic is determined.
  • the products applying the technical solution of this application will clearly inform the personal information processing rules and obtain the individual's independent consent before processing personal information. If the technical solution in this application involves sensitive personal information, the product applying the technical solution in this application must obtain the individual's separate consent before processing sensitive personal information, and meet the requirement of "express consent" at the same time. For example, setting up clear and conspicuous signs on personal information collection devices such as cameras to inform them that they have entered the scope of personal information collection and that personal information will be collected.
  • personal information processing rules may include personal information processing rules.
  • Information such as information processors, purposes of processing personal information, methods of processing, and types of personal information processed.

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Abstract

一种数据处理方法及装置、电子设备和存储介质，包括：获取待处理的图像数据集（S11）；响应于接收到针对图像数据集的数据处理流程的设置指令，根据设置指令指示的数据处理方式以及数据处理方式的执行顺序，确定图像数据集对应的数据处理流程（S12）；按照数据处理流程中的各个数据处理方式的执行顺序，对图像数据集中的图像数据进行处理，得到处理后的图像数据集（S13）。

Description

数据处理方法及装置、电子设备和存储介质
本公开要求在2022年03月29日提交中国专利局、申请号为202210323280.4、申请名称为“数据处理方法及装置、电子设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及计算机技术领域，尤其涉及一种数据处理方法及装置、电子设备和存储介质。
背景技术
人工智能领域需要大量标注后的数据进行模型训练，为提供数据集的标注效率以及模型训练效果，通常可以对数据集进行至少一种数据处理，例如旋转、裁剪、镜像、去相似、去模糊等，通过数据处理后可以大幅度提高数据集的标注效率以及模型的生产效率。
发明内容
本公开提出了一种数据处理技术方案。
根据本公开的一方面，提供了一种数据处理方法，包括：获取待处理的图像数据集；响应于接收到针对所述图像数据集的数据处理流程的设置指令，根据所述设置指令指示的数据处理方式以及所述数据处理方式的执行顺序，确定所述图像数据集对应的数据处理流程；按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集。
在一种可能的实现方式中，所述数据处理方式包括旋转处理、裁剪处理、镜像处理、去相似处理以及去模糊处理中的至少一种；所述设置指令还用于指示各种数据处理方式的处理程度，所述处理程度包括旋转处理的旋转角度、裁剪处理的裁剪范围、镜像处理的镜像类型、去相似处理的相似度阈值以及去模糊处理的清晰度阈值中的至少一种；所述镜像类型包括水平镜像、垂直镜像以及中心镜像中的至少一种，所述裁剪范围是通过在预览图像上绘制裁剪框所确定的。
在一种可能的实现方式中，在获取到所述图像数据集后，所述方法还包括：在图形交互界面的指定区域处显示预览图像，所述预览图像为所述图像数据集中的任一图像数据；其中，所述方法还包括：在所述数据处理方式已设置有处理程度的情况下，根据所述数据处理方式以及设置的处理程度，对所述预览图像进行处理，得到处理后的预览图像并进行显示，所述处理后的预览图像具有与所述数据处理方式的处理程度相匹配的处理效果。
在一种可能的实现方式中，所述按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括： 在所述数据处理流程中包括去相似处理，且基于所述执行顺序确定当前执行到去相似处理的情况下，确定当前的图像数据集中各个图像数据之间的相似度；根据所述数据处理流程中的相似度阈值，确定至少一个图像组，并保留各个图像组中的一个图像数据，得到处理后的图像数据集，所述图像组中的各个图像数据之间的相似度超过所述相似度阈值。
在一种可能的实现方式中，所述按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：在所述数据处理流程中包括去模糊处理，且基于所述执行顺序确定当前执行到去模糊处理的情况下，确定当前的图像数据集中各个图像数据的清晰度；根据所述数据处理流程中的清晰度阈值，筛除所述当前的图像数据集中清晰度小于所述清晰度阈值的图像数据，得到处理后的图像数据集。
在一种可能的实现方式中，所述按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：在所述数据处理流程中包括旋转处理，且基于所述执行顺序确定当前执行到旋转处理的情况下，根据所述数据处理流程中的旋转角度，对当前的图像数据集中的图像数据进行旋转处理，得到处理后的图像数据集；和/或，在所述数据处理流程中包括镜像处理，且基于所述执行顺序确定当前执行到镜像处理的情况下，根据所述数据处理流程中的镜像类型，对当前的图像数据集中的图像数据进行镜像处理，得到处理后的图像数据集；和/或，在所述数据处理流程中包括裁剪处理，且基于所述执行顺序确定当前执行到裁剪处理的情况下，根据所述数据处理流程中的裁剪范围，对当前的图像数据集中的图像数据进行裁剪处理，得到处理后的图像数据集。
在一种可能的实现方式中，所述方法还包括：提供各个数据处理方式对应的示例图，所述示例图包括不同旋转角度的示例图、不同镜像类型的示例图、不同清晰度的示例图、不同相似度的示例图中的至少一种。
根据本公开的一方面，提供了一种数据处理装置，包括：获取模块，用于获取待处理的图像数据集；流程确定模块，用于响应于接收到针对所述图像数据集的数据处理流程的设置指令，根据所述设置指令指示的数据处理方式以及所述数据处理方式的执行顺序，确定所述图像数据集对应的数据处理流程；处理模块，用于按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集。
在一种可能的实现方式中，所述数据处理方式包括旋转处理、裁剪处理、镜像处理、去相似处理以及去模糊处理中的至少一种；所述设置指令还用于指示各种数据处理方式的处理程度，所述处理程度包括旋转处理的旋转角度、裁剪处理的裁剪范围、镜像处理的镜像类型、去相似处理的相似度阈值以及去模糊处理的清晰度阈值中的至少一种；所述镜像类型包括水平镜像、垂直镜像以及中心镜像中的至少一种，所述裁剪范围是通过 在预览图像上绘制裁剪框所确定的。
在一种可能的实现方式中，在获取到所述图像数据集后，所述装置还包括：预览模块，用于在图形交互界面的指定区域处显示预览图像，所述预览图像为所述图像数据集中的任一图像数据；其中，所述装置还包括：预览图像处理模块，用于在所述数据处理方式已设置有处理程度的情况下，根据所述数据处理方式以及设置的处理程度，对所述预览图像进行处理，得到处理后的预览图像并进行显示，所述处理后的预览图像具有与所述数据处理方式的处理程度相匹配的处理效果。
在一种可能的实现方式中，所述处理模块，包括：相似度确定子模块，用于在所述数据处理流程中包括去相似处理，且基于所述执行顺序确定当前执行到去相似处理的情况下，确定当前的图像数据集中各个图像数据之间的相似度；去相似处理子模块，用于根据所述数据处理流程中的相似度阈值，确定至少一个图像组，并保留各个图像组中的一个图像数据，得到处理后的图像数据集，所述图像组中的各个图像数据之间的相似度超过所述相似度阈值。
在一种可能的实现方式中，所述处理模块，包括：清晰度确定子模块，用于在所述数据处理流程中包括去模糊处理，且基于所述执行顺序确定当前执行到去模糊处理的情况下，确定当前的图像数据集中各个图像数据的清晰度；去模糊处理子模块，用于根据所述数据处理流程中的清晰度阈值，筛除所述当前的图像数据集中清晰度小于所述清晰度阈值的图像数据，得到处理后的图像数据集。
在一种可能的实现方式中，所述处理模块，包括：旋转处理子模块，用于在所述数据处理流程中包括旋转处理，且基于所述执行顺序确定当前执行到旋转处理的情况下，根据所述数据处理流程中的旋转角度，对当前的图像数据集中的图像数据进行旋转处理，得到处理后的图像数据集；和/或，镜像处理子模块，用于在所述数据处理流程中包括镜像处理，且基于所述执行顺序确定当前执行到镜像处理的情况下，根据所述数据处理流程中的镜像类型，对当前的图像数据集中的图像数据进行镜像处理，得到处理后的图像数据集；和/或，裁剪处理子模块，用于在所述数据处理流程中包括裁剪处理，且基于所述执行顺序确定当前执行到裁剪处理的情况下，根据所述数据处理流程中的裁剪范围，对当前的图像数据集中的图像数据进行裁剪处理，得到处理后的图像数据集。
在一种可能的实现方式中，所述装置还包括：示例模块，用于提供各个数据处理方式对应的示例图，所述示例图包括不同旋转角度的示例图、不同镜像类型的示例图、不同清晰度的示例图、不同相似度的示例图中的至少一种。
根据本公开的一方面，提供了一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
根据本公开的一方面，提供了一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。
根据本公开的一方面，提供了一种计算机程序产品，包括计算机可读代码，或者承载有计算机可读代码的非易失性计算机可读存储介质，当所述计算机可读代码在电子设备的处理器中运行时，所述电子设备中的处理器执行上述方法。
在本公开实施例中，通过自定义设置数据处理方式以及数据处理方式的执行顺序，生成自定义的数据处理流程，这样在按照自定义的数据处理流程对图像数据集中的图像数据进行处理后，可以得到符合用户需求的处理后的图像数据集，提高对图像数据集进行自定义数据处理的便捷性。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，而非限制本公开。根据下面参考附图对示例性实施例的详细说明，本公开的其它特征及方面将变得清楚。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，这些附图示出了符合本公开的实施例，并与说明书一起用于说明本公开的技术方案。
图1示出根据本公开实施例的数据处理方法的流程图。
图2a示出根据本公开实施例的一种图形交互界面的示意图。
图2b出根据本公开实施例的一种图形交互界面的示意图。
图2c示出根据本公开实施例的一种图形交互界面的示意图。
图3a示出根据本公开实施例的一种图形交互界面的示意图。
图3b示出根据本公开实施例的一种图形交互界面的示意图。
图4示出根据本公开实施例的数据处理装置的框图。
图5示出根据本公开实施例的一种电子设备1900的框图。
具体实施方式
以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。
本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合，例如，包括A、B、C中的至少一种，可以表示包括从A、B和C构成的集合中选择的任意一个或多个元素。
另外，为了更好地说明本公开，在下文的具体实施方式中给出了众多的具体细节。 本领域技术人员应当理解，没有某些具体细节，本公开同样可以实施。在一些实例中，对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述，以便于凸显本公开的主旨。
图1示出根据本公开实施例的数据处理方法的流程图，所述数据处理方法可以由终端设备或服务器等电子设备执行，终端设备可以为用户设备(User Equipment，UE)、移动设备、用户终端、终端、蜂窝电话、无绳电话、个人数字助理(Personal Digital Assistant，PDA)、手持设备、计算设备、车载设备、可穿戴设备等，所述方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现，或者，可通过服务器执行所述方法。如图1所示，所述数据处理方法包括：
在步骤S11中，获取待处理的图像数据集。
其中，图像数据集可以包括待处理的图像数据，图像数据可以是图像采集设备直接拍摄的图像，还可以是从视频数据中提取的图像帧，对此本公开实施例不作限制。应理解的是，图像数据集可以是从本地导入上传的数据集，或还可以是从已导入上传的数据集列表中选择的数据集，对于图像数据集的获取方式，本公开实施例不作限制。
在步骤S12中，响应于接收到针对图像数据集的数据处理流程的设置指令，根据设置指令指示的数据处理方式以及数据处理方式的执行顺序，确定图像数据集对应的数据处理流程。
应理解的是，针对图像数据集可以设置至少一种数据处理方式，不同数据处理方式可以设置对应的执行顺序以及处理程度，这样可以得到该图像数据集对应的自定义的数据处理流程。
在一种可能的实现方式中，数据处理方式可以包括不但限于旋转处理、裁剪处理、镜像处理、去相似处理以及去模糊处理中的至少一种；设置指令还用于指示各种数据处理方式的处理程度，处理程度包括旋转处理的旋转角度、裁剪处理的裁剪范围、镜像处理的镜像类型、去相似处理的相似度阈值以及去模糊处理的清晰度阈值中的至少一种；镜像类型包括水平镜像、垂直镜像以及中心镜像中的至少一种。
其中，水平镜像可以理解为将图像左右部分的像素点以垂直中轴线为中心进行镜像对换，垂直镜像可以理解为将图像上下部分的像素点以水平中轴线为中心进行镜像对换，中心镜像可以理解为将图像以水平中轴线和垂直中轴线的交点为中心进行镜像对换，相当于将图像进行水平镜像与垂直镜像。
其中，旋转处理的旋转角度例如可以至少包括顺时针90度、顺时针180度、顺时针270度、逆时针90度、逆时针180度、逆时针270度或自定义旋转角度等。去相似处理的相似度阈值可以用于筛除图像数据集中相似度高于相似度阈值的图像数据，去模糊处理的清晰度阈值可以用于筛除图像数据集中低于清晰度阈值的图像数据。
可理解的是，本领域技术人员可以采用本领域已知的软件开发技术，设计并实现本公开中数据处理方法的应用程序以及对应的图形交互界面，图形交互界面中可以提供各 种用于设置数据处理流程的操作控件，以便于用户通过操作控件发出设置指令，来设置图像数据集的数据处理流程。
图2a示出根据本公开实施例的一种图形交互界面的示意图，图2b出根据本公开实施例的一种图形交互界面的示意图。其中，用户可以通过点击图2a顶部“处理方式”处示出的各种数据处理方式处的选择按钮“+”来添加所需的数据处理方式，用户选中的数据处理方式会依次显示在图2a左侧的“处理流程”处，图2a右侧的“预览图像”处可以显示预览图像，预览图像可以为图像数据集中的任一图像数据，这样可以基于预览图像向用户展示至少一种数据处理方式实际的处理效果。
其中，当用户在图2a中依次选择了“去相似”、“去模型”以及“镜像”后，可以得到图2b示出的图形交互界面。如图2b所示的图形交互界面的“处理流程”处显示有“去相似”、“去模型”以及“镜像”等数据处理方式的设置卡片，各个设置卡片上可以提供用于设置处理程度的相关控件，来便于用户设置不同数据处理方式的处理程度，例如通过“去相似”的设置卡片上的“滑动条”设置相似度阈值，通过“镜像”的设置卡片上的“下拉框”选择镜像类型等。
其中，如图2b中示出的“第一步”、“第二步”以及“第三步”可以代表各个数据处理方式的执行顺序，用户可以通过拖拽设置卡片在“处理流程”处的上下顺序，来调整数据处理方式的执行顺序，还可以点击设置卡片上的关闭按钮“X”取消选中任一数据处理方式，被取消选中的数据处理方式的设置卡片会在“处理流程”中消失，并在顶部“处理方式”中显示该被取消选中的数据处理方式的选择按钮。
如上所述，设置指令还用于指示裁剪处理的裁剪范围，在一种可能的实现方式中，裁剪范围是可以通过在预览图像上绘制裁剪框所确定的，裁剪范围可以包括裁剪框的至少两个顶点的顶点坐标。通过该方式，可以使用户便捷直观地设置裁剪范围。
图2c示出根据本公开实施例的一种图形交互界面的示意图，如图2c所示，当用户选择了“裁剪”后，可以在“处理流程”处显示裁剪处理的设置卡片，设置卡片上可以显示有“请在右侧调整裁剪框”的提示信息，以指示用户在“预览图像”处显示的预览图像上绘制或调整裁剪框，当用户选择了“旋转”并设置了旋转角度为“顺时针旋转180°”时，预览图像显示为旋转180°后的预览图像。
其中，可以在预览图像上新建一个用于获取交互操作的canvas画布，并在该新建的canvas画布上绘制出裁剪框，这样用户可以便于调整canvas画布上的裁剪框的大小和位置，也可以单独对预览图像进行缩放和拖拽等交互操作，来调整预览图像的大小和位置等。
为了便于用户预先了解不同数据处理方式在不同处理程度下的处理效果，在一种可能的实现方式中，所述方法还包括：提供各个数据处理方式对应的示例图，示例图包括不同旋转角度的示例图、不同镜像类型的示例图、不同清晰度的示例图、不同相似度的示例图中的至少一种。通过该方式，可以便于用户预先了解不同数据处理方式的处理效果，以便于用户设置数据处理流程。
其中，不同旋转角度的示例图包括同一图像在不同旋转角度下的图像，不同镜像类型的示例图包括同一图像在不同镜像类型下的图像，不同清晰度的示例图包括同一图像在不同清晰度下的图像，不同相似度的示例图包括与同一图像具有不同相似度的图像。
图3a示出根据本公开实施例的一种图形交互界面的示意图，图3b示出根据本公开实施例的一种图形交互界面的示意图，图3a中展示了“原图”以及“原图”在“顺时针旋转90°”、“顺时针旋转180°”以及“顺时针旋转270°”等不同旋转角度的示例图，图3b中展示了“清晰度0.95”、“清晰度0.65”、“清晰度0.35”以及“清晰度0.15”不同清晰度的示例图。其中，用户可以通过点击图2a、图2b或图2c中“查看处理示例”处的控件，显示上述图3a或图3b示出的图形交互界面，以查看各种数据处理方式对应的示例图。
需要说明的是，上述图2a、图2b、图2c、图3a以及图3b示出的图形交互界面是本公开实施例提供的一种实现方式，实际上本领域技术人员可以根据实际需求设计图形交互界面的界面布局、所具备的功能控件等，对此本公开实施例不作限制。
在步骤S13中，按照数据处理流程中的各个数据处理方式的执行顺序，对图像数据集中的图像数据进行处理，得到处理后的图像数据集。
如上所述，设置指令还用于指示数据处理方式的处理程度，按照数据处理流程中的各个数据处理方式的执行顺序，对图像数据集中的图像数据进行处理，得到处理后的图像数据集，可以包括：按照数据处理流程中各个数据处理方式的执行顺序以及处理程度，对图像数据集中的图像数据进行处理，得到处理后的图像数据集。
应理解的是，不同数据处理方式、不同数据处理方式的不同执行顺序以及不同数据处理方式的不同处理程度的排列组合，可以得到不同的数据处理流程，不同数据处理流程对同一图像数据集进行处理，得到不同的处理后的图像数据集。例如，可以先执行去相似处理，再执行裁剪处理；或还可以先执行裁剪处理，再执行去相似处理，两种数据处理流程得到的处理后的图像数据集可能是不同的。
在一种可能的实现方式中，本公开实施例的数据处理方式可以应用于机器学***台、数据标注平台、标签生产平台等场景中，可以提高机器学习、模型生产、数据标注以及标签生产的效率。
在一种可能的实现方式中，设置好的数据处理流程可以以任务列表的形式进行展示，以便于用户随时查看针对图像数据集的处理状态。在得到处理后的图像数据集后，可以对处理后的图像数据集进行数据标注，并完成后续的模型训练以及模型生产等过程。
在本公开实施例中，通过自定义设置数据处理方式以及数据处理方式的执行顺序，生成自定义的数据处理流程，这样在按照自定义的数据处理流程对图像数据集中的图像数据进行处理后，可以得到符合用户需求的处理后的图像数据集，提高对图像数据集进行数据处理的便捷性与普适性。
如上所述，可以基于预览图像向用户展示至少一种数据处理方式的处理效果，在一种可能的实现方式中，在通过步骤S11获取到图像数据集后，所述方法还包括：在图形交 互界面的指定区域处显示预览图像，预览图像为图像数据集中的任一图像数据。
其中，预览图像可以为从图像数据集中随机选取的任一图像数据，当然可以用户也可以从图像数据集中指定任一图像数据作为预览图像，或还可以切换已显示的预览图像，对此本公开实施例不作限制，例如可以通过点击图2a、图2b或图2c中预览图像下面的“切换”按钮来切换显示的预览图像，还可以点击“+”按钮来放大预览图像，或点击“-”按钮来缩小预览图像，“100％”代表预览图像的显示比例。
其中，预览图像可以显示在图形交互界面的指定区域，例如上述图2a、图2b或图2c中“预览图像”所在的区域，图形交互界面的指定区域中可以设置有canvas画布，并可以根据预览图像的长宽比以及指定区域的长宽比，在该canvas画布上绘制预览图像，这样可以使预览图像在指定区域内水平居中显示，在canvas画布上绘制预览图像可以便于对预览图像进行旋转、镜像、裁剪以及移动等处理操作，来展现各种数据处理方式的处理效果。
在一种可能的实现方式中，所述方法还包括：在数据处理方式已设置有处理程度的情况下，根据数据处理方式以及设置的处理程度，对预览图像进行处理，得到处理后的预览图像并进行显示，处理后的预览图像具有与数据处理方式的处理程度相匹配的处理效果。通过该方式，可以友好地向用户展示不同数据处理方式的处理效果。
其中，在数据处理方式包括旋转处理且已设置旋转角度的情况下，可以根据设置的旋转角度，对预览图像进行旋转处理，并在指定区域内水平居中显示旋转后的预览图像。在数据处理方式包括镜像处理且已设置镜像类型的情况下，新建一个隐藏的canvas画布，并在该隐藏的canvas画布上按照镜像类型对预览图像中的像素点进行镜像处理，得到镜像后的预览图像，然后将镜像后的预览图像渲染到指定区域内原始的canvas画布上。
如上所述，当数据处理方式包括裁剪处理时，指定区域内的预览图像主要便于用户绘制裁剪框，在预览图像上绘制的裁剪框可以表征裁剪处理的处理效果。应理解的是，由于去模糊处理与去相似处理无法仅在一张预览图像上显示处理效果，当数据处理方式包括去模糊处理或去相似处理时，去模糊处理与相似度处理的处理效果可以不体现在预览图像上。
如上所述，数据处理方式可以包括去相似处理，在一种可能的实现方式中，在步骤S13中，按照数据处理流程中的各个数据处理方式的执行顺序，对图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：
在数据处理流程中包括去相似处理，且基于执行顺序确定当前执行到去相似处理的情况下，确定当前的图像数据集中各个图像数据之间的相似度；根据数据处理流程中的相似度阈值，确定至少一个图像组，并保留各个图像组中的一个图像数据，得到处理后的图像数据集，图像组中的各个图像数据之间的相似度超过相似度阈值。
其中，本领域技术人员可以采用本领域已知的相似度计算方式，确定图像数据集中各个图像数据之间的相似度，例如利用距离公式(如L1距离、L2距离等)或误差公式(如平均绝对值误差、均方误差等)，计算各个图像数据之间的相似度，对此本公开实施例不 作限制。
其中，由于图像组中的各个图像数据之间的相似度超过相似度阈值，根据数据处理流程中的相似度阈值，确定至少一个图像组，相当于将图像数据集中的图像数据进行聚类，将相似度超过相似度阈值的图像数据聚类为图像组，同一图像组内的图像数据认为是相似度较高的数据，不同图像组之间的图像数据认为是相似度较低的数据；再保留各个图像组中的一个图像数据，相当于删除各个图像组中部分相似度较高的图像数据，或者说删除部分重复的图像数据，使每个图像组中剩余一个图像数据，实现针对整个图像数据集的去相似处理。
应理解的是，当去相似处理是数据处理流程中的第1步时，该当前的图像数据集可以是原始的图像数据集，当去相似处理是数据处流程中的第n步时，该当前的图像数据集可以是经过第n-1步数据处理方式所处理后的图像数据集，n≥2。
在本公开实施例中，能够在数据处理流程中包括去相似处理的情况下，根据设置的相似度阈值，实现针对图像数据集的自定义去相似处理，提高针对图像数据集进行自定义数据处理的便捷性。
如上所述，数据处理方式可以包括去模糊处理，在一种可能的实现方式中，在步骤S13中，按照数据处理流程中的各个数据处理方式的执行顺序，对图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：
在数据处理流程中包括去模糊处理，且基于执行顺序确定当前执行到去模糊处理的情况下，确定当前的图像数据集中各个图像数据的清晰度；根据数据处理流程中的清晰度阈值，筛除当前的图像数据集中清晰度小于清晰度阈值的图像数据，得到处理后的图像数据集。
其中，本领域技术人员可以采用本领域已知的清晰度确定方式，例如Brenner梯度函数、Tenengrad梯度函数、Laplacian梯度函数等，确定图像数据集中各个图像数据的清晰度，对此本公开实施例不作限制。
应理解的是，当去模糊处理是数据处理流程中的第1步时，该当前的图像数据集可以是原始的图像数据集，当去模糊处理是数据处流程中的第n步时，该当前的图像数据集可以是经过第n-1步数据处理方式所处理后的图像数据集，n≥2。
在本公开实施例中，能够在数据处理流程中包括去模糊处理的情况下，根据设置的清晰度阈值，实现针对图像数据集的自定义去模糊处理，提高针对图像数据集进行自定义数据处理的便捷性。
如上所述，数据处理方式包括旋转处理，在一种可能的实现方式中，在步骤S13中，按照数据处理流程中的各个数据处理方式的执行顺序，对图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：
在数据处理流程中包括旋转处理，且基于执行顺序确定当前执行到旋转处理的情况下，根据数据处理流程中的旋转角度，对当前的图像数据集中的图像数据进行旋转处理， 得到处理后的图像数据集。通过该方式，能够在数据处理流程中包括旋转处理的情况下，根据设置的旋转角度，实现针对图像数据集的自定义旋转处理，提高针对图像数据集进行自定义数据处理的便捷性。
应理解的是，当旋转处理是数据处理流程中的第1步时，该当前的图像数据集可以是原始的图像数据集，当旋转处理是数据处流程中的第n步时，该当前的图像数据集可以是经过第n-1步数据处理方式所处理后的图像数据集，n≥2。
如上所述，数据处理方式可以包括镜像处理，在一种可能的实现方式中，在步骤S13中，按照数据处理流程中的各个数据处理方式的执行顺序，对图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：
在数据处理流程中包括镜像处理，且基于执行顺序确定当前执行到镜像处理的情况下，根据数据处理流程中的镜像类型，对当前图像数据集中的图像数据进行镜像处理，得到处理后的图像数据集。通过该方式，能够在数据处理流程中包括镜像处理的情况下，根据设置的镜像类型，实现针对图像数据集的自定义镜像处理，提高针对图像数据集进行自定义数据处理的便捷性。
应理解的是，当镜像处理是数据处理流程中的第1步时，该当前的图像数据集可以是原始的图像数据集，当镜像处理是数据处流程中的第n步时，该当前的图像数据集可以是经过第n-1步数据处理方式所处理后的图像数据集，n≥2。
如上所述，数据处理方式可以包括裁剪处理，在一种可能的实现方式中，在步骤S13中，按照数据处理流程中的各个数据处理方式的执行顺序，对当前的图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：
在数据处理流程中包括裁剪处理，且基于执行顺序确定当前执行到裁剪处理的情况下，根据数据处理流程中的裁剪范围，对当前的图像数据集中的图像数据进行裁剪，得到处理后的图像数据集。通过该方式，能够在数据处理流程中包括裁剪处理的情况下，根据设置的裁剪范围，实现针对图像数据集的自定义裁剪处理，提高针对图像数据集进行自定义数据处理的便捷性。
如上所述，在通过步骤S12设置裁剪处理的裁剪范围时，可以采用裁剪框的至少两个顶点的顶点坐标表征裁剪范围，由此可以根据裁剪框的至少两个顶点的顶点坐标，实现对当前的图像数据集中的图像数据进行裁剪处理。
应理解的是，当裁剪处理是数据处理流程中的第1步时，该当前的图像数据集可以是原始的图像数据集，当裁剪处理是数据处流程中的第n步时，该当前的图像数据集可以是经过第n-1步数据处理方式所处理后的图像数据集，n≥2。
根据本公开的实施例，能够以步骤流程式的交互形式，支持用户选择不同数据处理方式以及自定义设置任意数据处理方式的执行顺序，来实现自定义数据处理流程。
可以理解，本公开提及的上述各个方法实施例，在不违背原理逻辑的情况下，均可以彼此相互结合形成结合后的实施例，限于篇幅，本公开不再赘述。本领域技术人员可 以理解，在具体实施方式的上述方法中，各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。
此外，本公开还提供了数据处理装置、电子设备、计算机可读存储介质、程序，上述均可用来实现本公开提供的任一种数据处理方法，相应技术方案和描述和参见方法部分的相应记载，不再赘述。
图4示出根据本公开实施例的数据处理装置的框图，如图4所示，所述装置包括：
获取模块101，用于获取待处理的图像数据集；
流程确定模块102，用于响应于接收到针对所述图像数据集的数据处理流程的设置指令，根据所述设置指令指示的数据处理方式以及所述数据处理方式的执行顺序，确定所述图像数据集对应的数据处理流程；
处理模块103，用于按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集。
在一种可能的实现方式中，所述数据处理方式包括旋转处理、裁剪处理、镜像处理、去相似处理以及去模糊处理中的至少一种；所述设置指令还用于指示各种数据处理方式的处理程度，所述处理程度包括旋转处理的旋转角度、裁剪处理的裁剪范围、镜像处理的镜像类型、去相似处理的相似度阈值以及去模糊处理的清晰度阈值中的至少一种；所述镜像类型包括水平镜像、垂直镜像以及中心镜像中的至少一种，所述裁剪范围是通过在预览图像上绘制裁剪框所确定的。
在一种可能的实现方式中，在获取到所述图像数据集后，所述装置还包括：预览模块，用于在图形交互界面的指定区域处显示预览图像，所述预览图像为所述图像数据集中的任一图像数据；其中，所述装置还包括：预览图像处理模块，用于在所述数据处理方式已设置有处理程度的情况下，根据所述数据处理方式以及设置的处理程度，对所述预览图像进行处理，得到处理后的预览图像并进行显示，所述处理后的预览图像具有与所述数据处理方式的处理程度相匹配的处理效果。
在一种可能的实现方式中，所述处理模块103，包括：相似度确定子模块，用于在所述数据处理流程中包括去相似处理，且基于所述执行顺序确定当前执行到去相似处理的情况下，确定当前的图像数据集中各个图像数据之间的相似度；去相似处理子模块，用于根据所述数据处理流程中的相似度阈值，确定至少一个图像组，并保留各个图像组中的一个图像数据，得到处理后的图像数据集，所述图像组中的各个图像数据之间的相似度超过所述相似度阈值。
在一种可能的实现方式中，所述处理模块103，包括：清晰度确定子模块，用于在所述数据处理流程中包括去模糊处理，且基于所述执行顺序确定当前执行到去模糊处理的情况下，确定当前的图像数据集中各个图像数据的清晰度；去模糊处理子模块，用于根据所述数据处理流程中的清晰度阈值，筛除所述当前的图像数据集中清晰度小于所述清晰度阈值的图像数据，得到处理后的图像数据集。
在一种可能的实现方式中，所述处理模块103，包括：旋转处理子模块，用于在所述数据处理流程中包括旋转处理，且基于所述执行顺序确定当前执行到旋转处理的情况下，根据所述数据处理流程中的旋转角度，对当前的图像数据集中的图像数据进行旋转处理，得到处理后的图像数据集；和/或，镜像处理子模块，用于在所述数据处理流程中包括镜像处理，且基于所述执行顺序确定当前执行到镜像处理的情况下，根据所述数据处理流程中的镜像类型，对当前的图像数据集中的图像数据进行镜像处理，得到处理后的图像数据集；和/或，裁剪处理子模块，用于在所述数据处理流程中包括裁剪处理，且基于所述执行顺序确定当前执行到裁剪处理的情况下，根据所述数据处理流程中的裁剪范围，对当前的图像数据集中的图像数据进行裁剪处理，得到处理后的图像数据集。
在一种可能的实现方式中，所述装置还包括：示例模块，用于提供各个数据处理方式对应的示例图，所述示例图包括不同旋转角度的示例图、不同镜像类型的示例图、不同清晰度的示例图、不同相似度的示例图中的至少一种。
在本公开实施例中，通过自定义设置数据处理方式以及数据处理方式的执行顺序，生成自定义的数据处理流程，这样在按照自定义的数据处理流程对图像数据集中的图像数据进行处理后，可以得到符合用户需求的处理后的图像数据集，提高对图像数据集进行自定义数据处理的便捷性。
在一些实施例中，本公开实施例提供的装置具有的功能或包含的模块可以用于执行上文方法实施例描述的方法，其具体实现可以参照上文方法实施例的描述，为了简洁，这里不再赘述。
本公开实施例还提出一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。计算机可读存储介质可以是易失性或非易失性计算机可读存储介质。
本公开实施例还提出一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
本公开实施例还提供了一种计算机程序产品，包括计算机可读代码，或者承载有计算机可读代码的非易失性计算机可读存储介质，当所述计算机可读代码在电子设备的处理器中运行时，所述电子设备中的处理器执行上述方法。
电子设备可以被提供为终端、服务器或其它形态的设备。
图5示出根据本公开实施例的一种电子设备1900的框图。例如，电子设备1900可以被提供为一服务器或终端设备。参照图5，电子设备1900包括处理组件1922，其进一步包括一个或多个处理器，以及由存储器1932所代表的存储器资源，用于存储可由处理组件1922的执行的指令，例如应用程序。存储器1932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1922被配置为执行指令，以执行上述方法。
电子设备1900还可以包括一个电源组件1926被配置为执行电子设备1900的电源管理， 一个有线或无线网络接口1950被配置为将电子设备1900连接到网络，和一个输入输出(I/O)接口1958。电子设备1900可以操作基于存储在存储器1932的操作***，例如微软服务器操作***(Windows Server TM)，苹果公司推出的基于图形用户界面操作***(Mac OS X TM)，多用户多进程的计算机操作***(Unix TM),自由和开放原代码的类Unix操作***(Linux TM)，开放原代码的类Unix操作***(FreeBSD TM)或类似。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器1932，上述计算机程序指令可由电子设备1900的处理组件1922执行以完成上述方法。
本公开可以是***、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是(但不限于)电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用 计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(***)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的***、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的***来实现，或者可以用专用硬件与计算机指令的组合来实现。
该计算机程序产品可以具体通过硬件、软件或其结合的方式实现。在一个可选实施例中，所述计算机程序产品具体体现为计算机存储介质，在另一个可选实施例中，计算机程序产品具体体现为软件产品，例如软件开发包(Software Development Kit，SDK)等等。
上文对各个实施例的描述倾向于强调各个实施例之间的不同之处，其相同或相似之处可以互相参考，为了简洁，本文不再赘述。
本领域技术人员可以理解，在具体实施方式的上述方法中，各步骤的撰写顺序并不意味着严格的执行顺序而对实施过程构成任何限定，各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。
若本申请技术方案涉及个人信息，应用本申请技术方案的产品在处理个人信息前， 已明确告知个人信息处理规则，并取得个人自主同意。若本申请技术方案涉及敏感个人信息，应用本申请技术方案的产品在处理敏感个人信息前，已取得个人单独同意，并且同时满足“明示同意”的要求。例如，在摄像头等个人信息采集装置处，设置明确显著的标识告知已进入个人信息采集范围，将会对个人信息进行采集，若个人自愿进入采集范围即视为同意对其个人信息进行采集；或者在个人信息处理的装置上，利用明显的标识/信息告知个人信息处理规则的情况下，通过弹窗信息或请个人自行上传其个人信息等方式获得个人授权；其中，个人信息处理规则可包括个人信息处理者、个人信息处理目的、处理方式以及处理的个人信息种类等信息。
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术的改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Claims (11)

  1. 一种数据处理方法，其特征在于，包括：
    获取待处理的图像数据集；
    响应于接收到针对所述图像数据集的数据处理流程的设置指令，根据所述设置指令指示的数据处理方式以及所述数据处理方式的执行顺序，确定所述图像数据集对应的数据处理流程；
    按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集。
  2. 根据权利要求1所述的方法，其特征在于，所述数据处理方式包括旋转处理、裁剪处理、镜像处理、去相似处理以及去模糊处理中的至少一种；
    所述设置指令还用于指示至少一种数据处理方式的处理程度，所述处理程度包括旋转处理的旋转角度、裁剪处理的裁剪范围、镜像处理的镜像类型、去相似处理的相似度阈值以及去模糊处理的清晰度阈值中的至少一种；
    所述镜像类型包括水平镜像、垂直镜像以及中心镜像中的至少一种，所述裁剪范围是通过在预览图像上绘制裁剪框所确定的。
  3. 根据权利要求1或2所述的方法，其特征在于，在获取到所述图像数据集后，所述方法还包括：在图形交互界面的指定区域处显示预览图像，所述预览图像为所述图像数据集中的任一图像数据；
    其中，所述方法还包括：
    在所述数据处理方式已设置有处理程度的情况下，根据所述数据处理方式以及设置的处理程度，对所述预览图像进行处理，得到处理后的预览图像并进行显示，所述处理后的预览图像具有与所述数据处理方式的处理程度相匹配的处理效果。
  4. 根据权利要求1-3任一项所述的方法，所述按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：
    在所述数据处理流程中包括去相似处理，且基于所述执行顺序确定当前执行到去相似处理的情况下，确定当前的图像数据集中各个图像数据之间的相似度；
    根据所述数据处理流程中的相似度阈值，确定至少一个图像组，并保留各个图像组中的一个图像数据，得到处理后的图像数据集，所述图像组中的各个图像数据之间的相似度超过所述相似度阈值。
  5. 根据权利要求1-4任一项所述的方法，其特征在于，所述按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处 理后的图像数据集，包括：
    在所述数据处理流程中包括去模糊处理，且基于所述执行顺序确定当前执行到去模糊处理的情况下，确定当前的图像数据集中各个图像数据的清晰度；
    根据所述数据处理流程中的清晰度阈值，筛除所述当前的图像数据集中清晰度小于所述清晰度阈值的图像数据，得到处理后的图像数据集。
  6. 根据权利要求1-5任一项所述的方法，其特征在于，所述按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集，包括：
    在所述数据处理流程中包括旋转处理，且基于所述执行顺序确定当前执行到旋转处理的情况下，根据所述数据处理流程中的旋转角度，对当前的图像数据集中的图像数据进行旋转处理，得到处理后的图像数据集；和/或，
    在所述数据处理流程中包括镜像处理，且基于所述执行顺序确定当前执行到镜像处理的情况下，根据所述数据处理流程中的镜像类型，对当前的图像数据集中的图像数据进行镜像处理，得到处理后的图像数据集；和/或，
    在所述数据处理流程中包括裁剪处理，且基于所述执行顺序确定当前执行到裁剪处理的情况下，根据所述数据处理流程中的裁剪范围，对当前的图像数据集中的图像数据进行裁剪处理，得到处理后的图像数据集。
  7. 根据权利要求1-6任一项所述的方法，其特征在于，所述方法还包括：
    提供至少一种数据处理方式对应的示例图，所述示例图包括不同旋转角度的示例图、不同镜像类型的示例图、不同清晰度的示例图、不同相似度的示例图中的至少一种。
  8. 一种数据处理装置，其特征在于，包括：
    获取模块，用于获取待处理的图像数据集；
    流程确定模块，用于响应于接收到针对所述图像数据集的数据处理流程的设置指令，根据所述设置指令指示的数据处理方式以及所述数据处理方式的执行顺序，确定所述图像数据集对应的数据处理流程；
    处理模块，用于按照所述数据处理流程中的各个数据处理方式的执行顺序，对所述图像数据集中的图像数据进行处理，得到处理后的图像数据集。
  9. 一种电子设备，其特征在于，包括：
    处理器；
    用于存储处理器可执行指令的存储器；
    其中，所述处理器被配置为调用所述存储器存储的指令，以执行权利要求1至7中任 意一项所述的方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序指令，其特征在于，所述计算机程序指令被处理器执行时实现权利要求1至7中任意一项所述的方法。
  11. 一种计算机程序产品，包括计算机可读代码，或者承载有计算机可读代码的非易失性计算机可读存储介质，当所述计算机可读代码在电子设备的处理器中运行时，所述电子设备中的处理器执行用于实现权利要求1至7中的任意一项所述的方法。
PCT/CN2022/101254 2022-03-29 2022-06-24 数据处理方法及装置、电子设备和存储介质 WO2023184746A1 (zh)

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