WO2025007685A1 - 一种数据处理方法、装置、电子设备、计算机可读介质 - Google Patents

一种数据处理方法、装置、电子设备、计算机可读介质 Download PDF

Info

Publication number
WO2025007685A1
WO2025007685A1 PCT/CN2024/096307 CN2024096307W WO2025007685A1 WO 2025007685 A1 WO2025007685 A1 WO 2025007685A1 CN 2024096307 W CN2024096307 W CN 2024096307W WO 2025007685 A1 WO2025007685 A1 WO 2025007685A1
Authority
WO
WIPO (PCT)
Prior art keywords
point
adjusted
key
description data
image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2024/096307
Other languages
English (en)
French (fr)
Inventor
陈光启
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Zitiao Network Technology Co Ltd
Original Assignee
Beijing Zitiao Network Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Zitiao Network Technology Co Ltd filed Critical Beijing Zitiao Network Technology Co Ltd
Publication of WO2025007685A1 publication Critical patent/WO2025007685A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions

Definitions

  • the present disclosure relates to a data processing method, device, electronic device, and computer-readable medium.
  • Key point prediction is a computer vision task, and key point prediction is used to locate certain key points in an image data (for example, the key point of the corner of the eye, the key point of the finger, etc.).
  • the present disclosure provides a data processing method, the method comprising:
  • the key point prediction result includes position description data of a plurality of key points; the plurality of key points include points to be adjusted and reference points corresponding to the points to be adjusted;
  • the position description data of the point to be adjusted in the key point prediction result is adjusted according to the position description data of the reference point.
  • the adjustment process of the position description data of the point to be adjusted includes:
  • the offset between the points is corrected to obtain a corrected offset;
  • the historical offset is determined based on a historical image corresponding to the image to be processed;
  • the method before updating the position description data of the point to be adjusted in the key point prediction result according to the corrected offset and the position description data of the reference point, the method further includes:
  • the position description data of the reference point is corrected to obtain the corrected position data of the reference point; the historical position data is determined based on the historical image corresponding to the image to be processed;
  • the updating of the position description data of the point to be adjusted in the key point prediction result according to the corrected offset and the position description data of the reference point comprises:
  • the position description data of the point to be adjusted in the key point prediction result is updated by using the corrected offset and the corrected position data of the reference point.
  • the method further includes:
  • the historical position data of the reference point Using the historical position data of the reference point, adjusting the position description data of the reference point in the key point prediction result; the historical position data is determined based on the historical image corresponding to the image to be processed;
  • the adjusted key point prediction result includes the adjusted position description data of the reference point and the adjusted position description data of the point to be adjusted.
  • the to-be-processed image and the historical image both belong to the same image sequence
  • the time sequence corresponding to the historical image in the image sequence is earlier than the time sequence corresponding to the image to be processed in the image sequence.
  • the method before adjusting the position description data of the point to be adjusted in the key point prediction result based on the position description data of the reference point, the method further includes:
  • the key point prediction result is divided into at least one group, wherein the group includes the position description data of at least one key point, the key points in the same group correspond to the same structural division area, and the key points in different groups correspond to different structural division areas;
  • the points to be adjusted and reference points corresponding to the points to be adjusted are determined according to key point pairs having a structural constraint relationship in the group.
  • the at least one structural division area includes one or more of an eye description area, an eyebrow description area, a nose description area, a mouth description area, and a face contour description area.
  • the point to be adjusted corresponds to at least two reference points
  • the step of adjusting the position description data of the point to be adjusted in the key point prediction result according to the position description data of the reference point includes:
  • the position description data of each reference point and the weight corresponding to each reference point is adjusted.
  • the weight corresponding to the reference point is determined based on the distance between the reference point and the point to be adjusted, and the distance is determined based on the position description data of the reference point and the position description data of the point to be adjusted.
  • the present disclosure provides a data processing device, comprising:
  • An acquisition unit is configured to acquire a key point prediction result of the image to be processed, wherein the key point prediction result includes position description data of a plurality of key points; the plurality of key points include points to be adjusted and reference points corresponding to the points to be adjusted;
  • the adjustment unit is configured to adjust the position description data of the point to be adjusted in the key point prediction result according to the position description data of the reference point.
  • the present disclosure provides an electronic device, the device comprising: a processor and a memory;
  • the memory is configured to store instructions or computer programs
  • the processor is configured to execute the instructions or computer programs in the memory so that the electronic device performs the data processing method provided by the present disclosure.
  • the present disclosure provides a computer-readable medium, in which instructions or computer programs are stored.
  • the instructions or computer programs are executed on a device, the device executes the data processing method provided by the present disclosure.
  • the present disclosure provides a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, wherein the computer program contains program codes for executing the data processing method provided by the present disclosure.
  • FIG1 is a flow chart of a data processing method provided by an embodiment of the present disclosure.
  • FIG2 is a schematic diagram of the structure of a data processing device provided by an embodiment of the present disclosure.
  • FIG. 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure.
  • the present disclosure provides a data processing method, which includes: for an image to be processed (for example, any frame of video image in a certain video data), obtaining a key point prediction result of the image to be processed, so that the key point prediction result includes position description data of multiple key points, so that when it is determined that the multiple key points include a reference point and a point to be adjusted with a structural constraint relationship, the position description data of the point to be adjusted is adjusted according to the position description data of the reference point, so that the adjusted position description data can better describe the position of the point to be adjusted, so as to effectively avoid the prediction result for the point to be adjusted. It can eliminate the visual jitter caused by fluctuations, thereby improving the stability of key point position determination.
  • the present disclosure does not limit the execution subject of the above data processing method.
  • the data processing method provided in the embodiment of the present disclosure can be applied to a device with data processing function such as a terminal device or a server.
  • the data processing method provided in the embodiment of the present disclosure can also be implemented with the help of a data communication process between different devices (for example, a terminal device and a server, two terminal devices, or two servers).
  • the terminal device can be a smart phone, a computer, a personal digital assistant (PDA) or a tablet computer.
  • PDA personal digital assistant
  • the server can be an independent server, a cluster server or a cloud server.
  • Figure 1 is a flow chart of a data processing method provided by the embodiment of the present disclosure.
  • S1 Obtain key point prediction results of an image to be processed, where the key point prediction results include position description data of multiple key points; the multiple key points include points to be adjusted and reference points corresponding to the points to be adjusted.
  • the image to be processed refers to image data that needs to be processed for key point determination; and the present disclosure does not limit the image to be processed.
  • it can be image data that needs to be processed for key point determination in any application field.
  • the image to be processed can refer to any image data existing in the image sequence (for example, any frame of video image existing in a video data). It can be seen that in one possible implementation, the image to be processed refers to image data extracted from an image sequence.
  • the key point prediction result of the image to be processed is used to describe the position distribution state of some key points corresponding to the target object in the image to be processed.
  • the target object refers to the object described by the image to be processed; and the present disclosure does not limit the target object.
  • the target object can be a face.
  • the target object can be the body of a certain creature.
  • the target object can be So is an object.
  • the key point prediction results may include position description data of multiple key points, so that the key point prediction results can describe the predicted positions of these key points in the image to be processed.
  • the position description data of the key point is used to describe the predicted position of the key point in the image to be processed; and the present disclosure does not limit the implementation method of the position description data of the key point. For example, it can be implemented using any existing or future data that can represent the position of a key point (such as position coordinates, etc.).
  • the key point prediction results of the above image to be processed may include not only the position description data of multiple key points, but also the point identification data corresponding to the multiple key points, so that the key point prediction results can not only indicate the predicted positions of these key points in the image to be processed, but also indicate which key point each key point belongs to corresponding to the above target object.
  • the point identification data corresponding to the key point is used to uniquely identify the key point, so that the point identification data corresponding to the key point can indicate which key point the key point belongs to corresponding to the target object (for example, a key point with a serial number of 2).
  • the present disclosure does not limit the implementation method of the point identification data. For example, it can be implemented using any existing or future identification information (for example, serial numbers, etc.) that can distinguish different key points corresponding to the target object.
  • the present disclosure does not limit the method for obtaining the key point prediction results of the above-mentioned image to be processed.
  • it can be specifically: after obtaining the image to be processed, perform key point prediction processing on the image to be processed to obtain the key point prediction results of the image to be processed, so that the key point prediction results can describe the distribution of key points presented in the image to be processed.
  • the present disclosure does not limit the implementation method of the key point prediction processing.
  • it can be implemented using any existing or future method that can perform key point detection processing on an image data (for example, a pre-built machine learning model with key point detection function, etc.).
  • the point to be adjusted refers to a key point without obvious visual features (that is, a key point with low recognition), so that the point to be adjusted can indicate the key point that needs to be adjusted for predicted position; and the present disclosure does not limit the determination process of the point to be adjusted.
  • the key point prediction results above include prediction results of N key points (for example, position description data + point identification data)
  • the nth key point can be regarded as the point to be adjusted.
  • n is a positive integer, n ⁇ N.
  • the preset adjustment conditions are used to describe the conditions met by the key points without obvious visual features; and the preset adjustment conditions can be set according to the actual application scenario.
  • the preset adjustment condition is disclosed without limitation.
  • the preset adjustment condition may specifically be: the position described by the position description data of the nth key point is located in a preset adjustment area; the preset adjustment area refers to an area that is determined in advance for the above-mentioned image to be processed and needs to be processed for predicted position adjustment.
  • the preset adjustment condition may also be: the point identification data corresponding to the nth key point belongs to a preset non-anchor point identification set; the non-anchor point identification set is used to record the identification of key points without obvious visual features; and the non-anchor point identification set may be pre-set according to the application scenario.
  • the reference point corresponding to the point to be adjusted refers to the key point required to be referenced when the predicted position adjustment processing is performed on the point to be adjusted; and the reference point has obvious visual features (that is, has a high degree of recognition).
  • the present disclosure does not limit the determination process of the reference point. For example, it can be specifically: searching for a key point having a corresponding relationship with the point to be adjusted from a pre-constructed mapping relationship as the reference point corresponding to the point to be adjusted.
  • the mapping relationship is used to record the structural constraint relationship between some key points without obvious visual features and some key points with obvious visual features, so that the mapping relationship can represent one or more key points with obvious visual features (for example, reference points) corresponding to each key point without obvious visual features (for example, each point to be adjusted).
  • the reference point corresponding to the point to be adjusted may include at least one key point. That is, in some possible implementations, the point to be adjusted corresponds to one reference point. In other possible implementations, the point to be adjusted corresponds to at least two reference points.
  • the present disclosure also provides a possible implementation method of the process of determining the above-mentioned points to be adjusted and their corresponding reference points, which may specifically include the following steps 11-12.
  • Step 11 Divide the key point prediction results of the above image to be processed to obtain at least one group, which includes the position description data of at least one key point.
  • the structural division areas corresponding to the key points in the same group are the same, and the structural division areas corresponding to the key points in different groups are different.
  • the division process is used to divide the position description data of the key points with structural association in the key point prediction results into the same group, so that one group can be used to record the position description data of the key points appearing in a certain structural division area, so that different groups It can show the distribution of key points in different structural division areas.
  • the structural division area refers to the area in the above-mentioned image to be processed, which is used to aggregate and display multiple key points with structural associations. It should be noted that the structural association can be set in advance according to the actual application scenario.
  • each group can include location description data of at least one key point; the structural division areas corresponding to the key points in the same group are the same (that is, for any group, all the key points in the group belong to the same structural division area); the structural division areas corresponding to the key points in different groups are different.
  • At least one structural division area corresponding to the target object may include at least one part description area of the target object (for example, a head description area, an arm description area, a leg description area, etc.).
  • the key point prediction result of the image to be processed can be used to describe some key points corresponding to the face (that is, the multiple key points described by the key point prediction result all belong to the face), and the at least one structural division area corresponding to the face can include one or more of the eye description area, the eyebrow description area, the nose description area, the mouth description area, and the face contour description area.
  • the multiple groups determined based on the at least one structural division area corresponding to the face can include one or more of the group corresponding to the eye description area, the group corresponding to the eyebrow description area, the group corresponding to the nose description area, the group corresponding to the mouth description area, and the group corresponding to the face contour description area.
  • the group corresponding to the eye description area is used to record the distribution of key points presented in the eye description area, so that the key points in the group corresponding to the eye description area are all located in the eye description area;
  • the group corresponding to the eyebrow description area is used to record the distribution of key points presented in the eyebrow description area, so that the key points in the group corresponding to the eyebrow description area are all located in the eyebrow description area;
  • the group corresponding to the nose description area is used to record the distribution of key points presented in the nose description area, so that the key points in the group corresponding to the nose description area are all located in the nose description area;
  • the group corresponding to the mouth description area is used to record the distribution of key points presented in the mouth description area, so that the mouth description area
  • the key points in the corresponding group are all located in the mouth description area;
  • the group corresponding to the facial contour description area is used to record the distribution of the key points presented in the facial contour description area, so that the key points in the group corresponding to the facial contour description area are all located in the
  • the key point prediction result can be divided and processed to obtain at least one group, so that each group includes the location description data of at least one key point, and different groups are used to describe the distribution of key points in different structural division areas, so that key point pairs with structural association relationships can be determined based on the grouping later.
  • Step 12 For any group, determine the point to be adjusted and the reference point corresponding to the point to be adjusted according to the key point pairs with structural constraint relationships in the group.
  • the key point in the key point pair that does not meet the preset reference condition can be determined as the point to be adjusted, and the key point in the key point pair that meets the preset reference condition can be determined as the reference point corresponding to the point to be adjusted, so that the predicted position adjustment processing for one or two key points in the key point pair can be completed with the help of the adjustment process shown below (for example, the adjustment process shown in S2 below or the adjustment process shown in steps 52-53 below).
  • m is a positive integer, m ⁇ the number of groups in the above "at least one group"; j is a positive integer, j ⁇ the number of key point pairs with a structural constraint relationship in the m-th group.
  • the preset reference condition refers to the condition met by the key point that can be used as a reference point; and the preset reference condition can be set in advance according to the application scenario.
  • the preset reference condition can be: a key point with obvious visual features that exists in a key point pair with a structural constraint relationship (for example, a key point that exists in the key point pair with a structural constraint relationship and whose point identification data belongs to a preset anchor point identification set, etc.); the anchor point identification set is used to record the identification of key points with obvious visual features; and the anchor point identification set can be set in advance according to the application scenario.
  • the mth group above may include the point to be adjusted and a reference point corresponding to the point to be adjusted, so that the point to be adjusted can be subsequently adjusted according to the position information of the reference point.
  • the mth group above may include the point to be adjusted and multiple reference points corresponding to the point to be adjusted (for example, 2 reference points, or 3 reference points, ...), so that the point to be adjusted can be subsequently adjusted according to the position information of the reference points and the weights corresponding to the reference points. Perform position adjustment processing.
  • the key point prediction results can be divided and processed to obtain at least one group (for example, a group corresponding to the eye description area, a group corresponding to the eyebrow description area, a group corresponding to the nose description area, a group corresponding to the mouth description area, and a group corresponding to the facial contour description area, etc.), so that different groups are used to represent the distribution of key points in different structural areas; and then the key point pairs with structural constraint relationships existing in each group are used to determine the points to be adjusted and the reference points with corresponding relationships, so that the predicted position adjustment processing of some or all key points in the key point prediction results can be realized based on the points to be adjusted and the reference points with corresponding relationships.
  • the independence between different structural areas can be effectively ensured, so that each structural area can be ensured to have sufficient deformation ability, and the mutual influence between different structural areas can be effectively avoided, which
  • the key point prediction result of the image to be processed can be obtained first, so that the key point prediction result can describe the distribution of key points presented in the image to be processed; then, the key point prediction result is divided into at least one group, so that different groups are used to describe the distribution of key points in different structural division areas; then, using the key point pairs with structural constraint relationships in each group, some corresponding points to be adjusted and reference points are determined, so that the predicted position adjustment processing of some or all key points in the key point prediction result can be realized based on these corresponding points to be adjusted and reference points.
  • S2 may specifically be: directly adjust the position description data of the point to be adjusted in the key point prediction result of the image to be processed above according to the position description data of the reference point.
  • S2 may specifically be: adjust the position description data of the point to be adjusted in the key point prediction result according to the position description data of each reference point corresponding to the point to be adjusted and the weight corresponding to each reference point. Data.
  • the weight corresponding to the reference point is used to characterize the degree of influence presented by the reference point in the process of position adjustment of the point to be adjusted; and the present disclosure does not limit the method for obtaining the weight corresponding to the reference point.
  • it can be set in advance according to the application scenario.
  • the weight corresponding to the reference point can be determined according to the distance between the reference point and the point to be adjusted, and the distance is determined according to the position description data of the reference point and the position description data of the point to be adjusted, so that the distance can represent the relative distance between the position described by the position description data of the reference point and the position described by the position description data of the point to be adjusted.
  • Case 1 In some application scenarios (for example, when the target object is an object), the relative offset between the key point pairs with structural constraints may remain unchanged (for example, the relative offset between all key points in the handlebar description area of a bicycle usually does not change over time).
  • the present disclosure provides a possible implementation method of the adjustment process of the position description data of the point to be adjusted above.
  • the adjustment process of the position description data of the point to be adjusted can specifically include the following steps 21-22.
  • Step 21 Determine the inter-point offset between the point to be adjusted and the reference point based on the position description data of the reference point and the position description data of the point to be adjusted.
  • the inter-point offset refers to the relative offset between the predicted position of the point to be adjusted and the predicted position of the reference point, so that the inter-point offset can represent the predicted offset of the point to be adjusted relative to the reference point.
  • the present disclosure does not limit the implementation method of the offset.
  • the offset can be implemented using the distance between the two position coordinates, so that the offset can represent the relative distance between the two key points.
  • the offset can also be implemented using the coordinate difference between the two position coordinates, so that the offset can represent the relative offset of the two key points on different coordinate axes.
  • the present disclosure does not limit the implementation of the above step 21.
  • the step 21 may specifically be: according to the position description data of the kth reference point corresponding to the point to be adjusted and The position description data of the point to be adjusted determines the inter-point offset between the point to be adjusted and the k-th reference point, where k is a positive integer and k ⁇ K.
  • Step 22 Determine offset difference characterization data using the standard offset between the point to be adjusted and the reference point, and the inter-point offset between the point to be adjusted and the reference point.
  • the standard offset between the point to be adjusted and the reference point refers to the offset pre-set for the point to be adjusted and the reference point, so that the standard offset can represent the positional offset between the point to be adjusted and the reference point under standard conditions.
  • the offset difference characterization data is used to represent the difference between the above standard offset and the above inter-point offset; and the present disclosure does not limit the determination process of the offset difference characterization data. For example, it can be specifically: subtracting the standard offset from the inter-point offset to obtain the offset difference characterization data.
  • the present disclosure does not limit the implementation method of the above step 22.
  • the step 22 can be specifically: using the standard offset between the point to be adjusted and the kth reference point corresponding to the point to be adjusted, and the inter-point offset between the point to be adjusted and the kth reference point corresponding to the point to be adjusted, determine the kth offset difference characterization data corresponding to the point to be adjusted, k is a positive integer, k ⁇ K.
  • Step 23 Based on the above offset difference characterization data, update the position description data of the point to be adjusted in the key point prediction result of the above image to be processed.
  • the step 23 can be specifically as follows: first, the position description data of the above point to be adjusted and the offset difference characterization data are added and processed to obtain the adjusted position description data of the point to be adjusted, so that the relative offset between the adjusted position description data of the point to be adjusted and the position description data of the above reference point reaches the relative offset required under standard conditions; and then, the adjusted position description data of the point to be adjusted is used to replace the position description data of the point to be adjusted in the key point prediction result of the above processed image to obtain an updated key point prediction result, so that the updated key point prediction result includes the adjusted position description data of the point to be adjusted, thereby making the updated key point prediction result have better key point position determination stability.
  • the above step 23 can be specifically: first perform weighted averaging processing on the first offset difference characterization data corresponding to the point to be adjusted, the second offset difference characterization data corresponding to the point to be adjusted, ...
  • the weight that has a corresponding relationship with the kth offset difference characterization data corresponding to the point to be adjusted is the weight corresponding to the kth reference point, k is a positive integer, k ⁇ K.
  • the final offset difference characterization data and the position description data of the point to be adjusted are added together to obtain the adjusted position description data of the point to be adjusted; and then the adjusted position description data of the point to be adjusted is used to replace the position description data of the point to be adjusted in the key point prediction result of the above image to be processed to obtain an updated key point prediction result, so that the updated key point prediction result includes the adjusted position description data of the point to be adjusted.
  • the position description data of the point to be adjusted can be adjusted based on the position description data of the reference point and the standard offset between the point to be adjusted and the reference point, so that the relative offset between the adjusted position description data of the point to be adjusted and the position description data of the reference point reaches the relative offset described by the standard offset.
  • Case 2 In some application scenarios, the relative offset between some key point pairs with structural constraint relationships may not change significantly in a short period of time, so that the relative offset between the key pairs described by some adjacent image data in the image sequence is almost unchanged.
  • the present disclosure provides a possible implementation method of the adjustment process of the position description data of the above-mentioned point to be adjusted.
  • the adjustment process of the position description data of the point to be adjusted may specifically include the following steps 31 to 33.
  • Step 31 Determine the inter-point offset between the point to be adjusted and the reference point based on the position description data of the reference point and the position description data of the point to be adjusted.
  • step 31 please refer to the relevant content of step 21 above, and for the sake of brevity, it will not be repeated here.
  • Step 32 Using the historical offset between the point to be adjusted and the reference point, the offset between the above points is corrected to obtain a corrected offset; the historical offset is determined based on the historical image corresponding to the image to be processed.
  • the historical offset between the point to be adjusted and the reference point is used to describe the relative offset between the point to be adjusted and the reference point within the historical time period.
  • the historical time period may include the time point corresponding to the previous frame image corresponding to the image to be processed; and the previous frame image and the image to be processed belong to the same image sequence, and the time sequence corresponding to the previous frame image in the image sequence is adjacent to the time sequence corresponding to the image to be processed in the image sequence, and the time sequence corresponding to the previous frame image in the image sequence is earlier than the time sequence corresponding to the image to be processed in the image sequence.
  • the historical time period may include the time points corresponding to the first few frames of images corresponding to the image to be processed.
  • the historical offset between the above-mentioned point to be adjusted and the reference point is determined according to the historical image corresponding to the image to be processed.
  • the historical image is used to describe the position distribution of multiple key points in the above-mentioned image to be processed within the historical time period; and the present disclosure does not limit the implementation method of the historical image.
  • the image to be processed refers to image data extracted from an image sequence (for example, video data)
  • the historical image corresponding to the image to be processed may include one or more image data existing in the image sequence and having a time sequence earlier than that of the image to be processed.
  • the historical image corresponding to the image to be processed may include one or more of the t-1-th frame of video image, the t-2-th frame of video image, ... (and so on), and the 1st frame of video image in the video data.
  • t is a positive integer.
  • the historical image and the image to be processed belong to the same image sequence; and the corresponding time sequence of the historical image in the image sequence is earlier than the corresponding time sequence of the image to be processed in the image sequence.
  • the present disclosure does not limit the number of images in the historical images corresponding to the image to be processed in the above paragraph.
  • the present disclosure does not limit the determination process of the historical offset between the above-mentioned point to be adjusted and the reference point.
  • the determination process of the historical offset may include: firstly performing key point prediction processing on the historical image corresponding to the image to be processed to obtain the key point prediction result of the historical image, so that the key point prediction result can represent the predicted position distribution of the above-mentioned multiple key points in the historical time period; then extracting the position prediction result of the above-mentioned reference point from the key point prediction result of the historical image. Then, the relative offset between the position prediction result of the point to be adjusted and the position prediction result of the reference point is regarded as the historical offset between the point to be adjusted and the reference point (for example, ).
  • the position prediction result of the reference point refers to the position information predicted for the reference point within the historical time period; the position prediction result of the point to be adjusted refers to the position information predicted for the point to be adjusted within the historical time period.
  • the process of determining the historical offset between the above-mentioned point to be adjusted and the reference point may include: firstly performing key point prediction processing on the previous frame image corresponding to the image to be processed (for example, the t-1th frame of video image) to obtain the key point prediction result of the previous frame image, so that the key point prediction result can represent the position distribution of the above multiple key points predicted in the previous frame image; then extracting the position prediction result of the above reference point and the position prediction result of the above-mentioned point to be adjusted from the key point prediction result of the previous frame image; secondly, calculating the relative offset between the position prediction result of the point to be adjusted and the position prediction result of the reference point, so that the relative offset can represent the offset of the point to be adjusted relative to the reference point prediction in the previous frame image; then, using the historical offset corresponding to the relative offset, performing correction processing on the relative offset to obtain the “historical offset between the point to be adjusted and the reference point” (for example, the following ).
  • the historical offset corresponding to the relative offset
  • the determination process of the "historical offset between the point to be adjusted and the reference point" in the above step 32 includes: first using the data processing method provided by the present disclosure (for example, S1-S2 or the following steps 51-53), a key point position determination process is performed on the t-1th frame video image to obtain position description information of multiple key points in the t-1th frame video image, so that these position description information can indicate the positions of these key points in the t-1th frame video image; then extracting the position description information of the above reference point and the position description information of the above point to be adjusted from these position description information; finally, determining the "historical offset between the point to be adjusted and the reference point” based on the relative offset between the position description information of the point to be adjusted and the
  • the corrected offset refers to the result obtained by correcting the "inter-point offset between the point to be adjusted and the reference point" mentioned above; and the present disclosure does not limit the determination process of the corrected offset.
  • It can be implemented by using the following formulas (1)-(2), or by using the following formulas (1) and (3).
  • an inter-point offset for example, ) can then be used to determine the offset based on the historical frame (e.g., similar to etc. based on the offset between points determined by the historical frame or similar
  • the offset between points determined based on the current frame is smoothed to obtain the offset after correction between the point to be adjusted and the reference point corresponding to the point to be adjusted.
  • the present disclosure does not limit the implementation method of the above step 32.
  • the step 32 can be specifically: using the historical offset between the point to be adjusted and the kth reference point corresponding to the point to be adjusted, the point-to-point offset between the point to be adjusted and the kth reference point is corrected to obtain the kth corrected offset corresponding to the point to be adjusted, k is a positive integer, k ⁇ K.
  • Step 33 Based on the corrected offset and the position description data of the reference point, update the position description data of the point to be adjusted in the key point prediction result of the image to be processed.
  • the step 33 may specifically be: The position description data of the reference point above are added together (as shown in formula (4) below) to obtain the adjusted position description data of the point to be adjusted above; and the adjusted position description data of the point to be adjusted is then used to replace the position description data of the point to be adjusted in the key point prediction result of the image to be processed above, so that the updated key point prediction result includes the adjusted position description data of the point to be adjusted.
  • the above step 33 may specifically be: first, the k-th corrected offset corresponding to the point to be adjusted is added to the position description data of the k-th reference point corresponding to the point to be adjusted to obtain the k-th adjusted position description data corresponding to the point to be adjusted, where k is a positive integer, k ⁇ K; then, the first adjusted position description data corresponding to the point to be adjusted, the second adjusted position description data corresponding to the point to be adjusted, ...
  • the K-th adjusted position description data corresponding to the point to be adjusted are weighted averaged to obtain the final adjusted position description data of the point to be adjusted, wherein, for the weighted average processing, the weight corresponding to the k-th adjusted position description data corresponding to the point to be adjusted is the weight corresponding to the k-th reference point, where k is a positive integer, k ⁇ K; and then, the final adjusted position description data of the point to be adjusted is used to replace the position description data of the point to be adjusted in the key point prediction result of the image to be processed above, so that the updated key point prediction result includes the final adjusted position description data of the point to be adjusted.
  • the relative offset between the position description data of the point to be adjusted and the position description data of the reference point can be calculated first; then, based on the historical offset between the two points, the relative offset is smoothed to obtain the corrected offset, so that the corrected offset can better represent the relative offset of the two points in the above image to be processed; finally, the sum of the corrected offset and the position description data of the reference point is used as the adjusted position description data of the point to be adjusted, so that the adjusted position description data can better represent the position of the point to be adjusted in the image to be processed.
  • Case 3 In some application scenarios, in order to better improve the stability of key point position determination, the key point information determined for the above historical image data can be used to constrain the position description data of the above reference point, so that the position description data of the reference point can better represent the position of the reference point in the image to be processed, so that the position description data of the above point to be adjusted can be better adjusted based on the position description data of the reference point.
  • the present disclosure provides a possible implementation method of the adjustment process of the position description data of the above-mentioned point to be adjusted.
  • the adjustment process of the position description data of the point to be adjusted can specifically include the following steps 41 to 44.
  • Step 41 Determine the inter-point offset between the point to be adjusted and the reference point based on the position description data of the reference point and the position description data of the point to be adjusted.
  • step 41 please refer to the relevant contents of step 21 above, and for the sake of brevity, they will not be repeated here.
  • Step 42 using the historical offset between the point to be adjusted and the reference point, the offset between the above points is corrected to obtain a corrected offset; the historical offset is determined based on the historical image corresponding to the image to be processed.
  • step 42 refers to the relevant contents of step 32 above, and for the sake of brevity, they will not be repeated here.
  • Step 43 using the historical position data of the reference point, correct the position description data of the reference point to obtain the corrected position data of the reference point; the historical position data is determined based on the historical image corresponding to the image to be processed.
  • the historical position data of the reference point is used to describe the position of the reference point in the historical time period; and the historical position data of the reference point is determined according to the historical image corresponding to the image to be processed above. It should be noted that the relevant content of the historical image corresponding to the image to be processed can be found above.
  • the present disclosure does not limit the process of determining the historical position data of the above reference point.
  • it can be specifically as follows: first, a key point prediction process is performed on the historical image corresponding to the image to be processed to obtain a key point prediction result of the historical image, so that the key point prediction result can represent the predicted position distribution of the above multiple key points in the historical time period; then, the position prediction result of the above reference point is extracted from the key point prediction result of the historical image as the historical position data of the reference point, so that the historical position data can represent the predicted position distribution of the reference point in the historical time period.
  • the location information obtained.
  • the process of determining the historical position data of the above reference point includes: firstly performing key point prediction processing on the previous frame of image to obtain the key point prediction result of the previous frame of image, so that the key point prediction result can represent the position distribution of the above multiple key points predicted in the previous frame of image; then extracting the position prediction result of the above reference point from the key point prediction result of the previous frame of image, so that the position prediction result can represent the position predicted for the reference point when data processing is performed on the previous frame of image; then, using the historical position data corresponding to the position prediction result of the reference point, the position prediction result of the reference point is corrected to obtain the historical position data of the reference point, so that the historical position data of the reference point can better represent the position of the reference point in the historical time period.
  • key point prediction processing can be performed on the t-th image data first to obtain the key point prediction result of the t-th image data, so that the key point prediction result includes the position description data of some reference points; then, for any reference point, the historical position data of the reference point can be used to correct the position description data of the reference point to obtain the corrected position data of the reference point, so that the corrected position data of the reference point can better represent the position of the reference point in the t-th image data.
  • the historical position data of the reference point can be the position description data predicted for the reference point when data processing is performed on the t-1th image data in the image sequence; or the historical position data of the reference point can also be the corrected position data obtained by correcting the position description data predicted for the reference point when data processing is performed on the t-1th image data in the image sequence.
  • the present disclosure does not limit the implementation method of the "correction processing" in the above step 43.
  • it can be implemented by any existing or future method that can perform smoothing processing on multiple data.
  • the present disclosure does not limit the above step 43.
  • the step 43 is specifically It can be: using the historical position data of the kth reference point corresponding to the point to be adjusted, the position description data of the kth reference point is corrected to obtain the corrected position data of the kth reference point, where k is a positive integer and k ⁇ K.
  • step 43 it can be known that for the image to be processed, after obtaining the key point prediction result of the image to be processed, the position description data of the reference point can be first extracted from the key point prediction result; then, using the historical position data of the reference point, the position description data of the reference point is corrected (for example, smoothed) to obtain the corrected position data of the reference point, so that the corrected position data can better represent the position of the reference point in the image to be processed, which is conducive to improving the stability of the position determination of the reference point.
  • the position description data of the reference point can be first extracted from the key point prediction result; then, using the historical position data of the reference point, the position description data of the reference point is corrected (for example, smoothed) to obtain the corrected position data of the reference point, so that the corrected position data can better represent the position of the reference point in the image to be processed, which is conducive to improving the stability of the position determination of the reference point.
  • the present disclosure does not limit the execution time of the above step 43. It only needs to ensure that the execution time of the step 43 is earlier than the execution time of the following step 44 and later than the execution time of the above step S1.
  • Step 44 using the above corrected offset and the above corrected position data of the reference point, update the position description data of the point to be adjusted in the key point prediction result of the above image to be processed.
  • the step 44 can be specifically as follows: summing the above corrected offset and the above corrected position data of the reference point (as shown in formula (5) below) to obtain the adjusted position description data of the above point to be adjusted; and then using the adjusted position description data of the point to be adjusted to replace the position description data of the point to be adjusted in the key point prediction result of the above image to be processed, so that the updated key point prediction result includes the adjusted position description data of the point to be adjusted.
  • the above step 44 may specifically be: summing up the k-th corrected offset corresponding to the point to be adjusted and the corrected position data of the k-th reference point corresponding to the point to be adjusted to obtain the k-th adjusted position description data corresponding to the point to be adjusted, where k is a positive integer, k ⁇ K; then, summing up the first adjusted position description data corresponding to the point to be adjusted, the second adjusted position description data corresponding to the point to be adjusted, ...
  • the K-th adjusted position description data corresponding to the point to be adjusted are weighted averaged to obtain the final adjusted position description data of the point to be adjusted, wherein, for the weighted average processing, the weight corresponding to the k-th adjusted position description data corresponding to the point to be adjusted is the weight corresponding to the k-th reference point, k is a positive integer, k ⁇ K; the final adjusted position description data of the point to be adjusted replaces the position description data of the point to be adjusted in the key point prediction result of the image to be processed above, so that the updated key point prediction result includes the final adjusted position description data of the point to be adjusted.
  • the relative offset between the position description data of the point to be adjusted and the position description data of the reference point can be calculated first; then, based on the historical offset between the two points, the relative offset is smoothed to obtain a corrected offset, so that the corrected offset can better represent the relative offset of the two points in the above image to be processed, so that after the position description data of the reference point is corrected by using the historical position data of the reference point to obtain the corrected position data of the reference point, the sum of the corrected offset and the corrected position data of the reference point is used as the adjusted position description data of the point to be adjusted, so that the adjusted position description data can better represent the position of the point to be adjusted in the image to be processed.
  • the key point prediction result of the image to be processed (for example, any frame of video image in a certain video data) is obtained so that the key point prediction result includes position description data of multiple key points, so that when it is determined that the multiple key points include a reference point and a point to be adjusted with a structural constraint relationship, the position description data of the point to be adjusted is adjusted according to the position description data of the reference point, so that the adjusted position description data can better describe the position of the point to be adjusted, thereby effectively avoiding the visual jitter phenomenon caused by fluctuations in the prediction results of the point to be adjusted, thereby helping to improve the stability of key point position determination.
  • the position description data of some points to be adjusted in the key point prediction result can be adjusted so that the adjusted key point prediction result at least includes the adjusted position description data of these points to be adjusted (for example, the adjusted key point prediction result can include the unadjusted position description data of all reference points, and the adjusted position description data of all points to be adjusted, etc.).
  • the prediction position adjustment process in order to better improve the stability of key point position determination, after obtaining the key point prediction result, not only can the prediction position adjustment process be performed for some points to be adjusted in the key point prediction result, but also the prediction position adjustment process can be performed for some reference points in the key point prediction result with reference to historical information. , so that the adjusted key point prediction result includes the adjusted position description data of these points to be adjusted and the adjusted position description data of these reference points.
  • the present disclosure also provides a possible implementation of the above data processing method, under which the data processing method can include the following steps 51-53.
  • Step 51 Obtain key point prediction results of the image to be processed, where the key point prediction results include position description data of multiple key points; the multiple key points include points to be adjusted and reference points corresponding to the points to be adjusted.
  • step 51 can be found in the relevant contents of S1 above, and for the sake of brevity, they will not be repeated here.
  • Step 52 adjusting the position description data of the point to be adjusted in the key point prediction result of the image to be processed according to the position description data of the reference point, so that the adjusted key point prediction result includes the adjusted position description data of the point to be adjusted.
  • step 52 can be found in the relevant contents of S2 above, and for the sake of brevity, they will not be repeated here.
  • Step 53 Using the historical position data of the reference point above, adjust the position description data of the reference point in the key point prediction result of the image to be processed above, so that the adjusted key point prediction result includes the adjusted position description data of the reference point; the historical position data is determined based on the historical image corresponding to the image to be processed.
  • the present disclosure does not limit the implementation method of the adjustment process in the above step 53.
  • it can be implemented using the correction processing process shown in the above step 43.
  • it will not be repeated here.
  • the present disclosure does not limit the execution time of the above step 53, as long as the execution time of the above step 53 is ensured to be later than that of the above step 51.
  • step 51 to 53 it can be known that in a possible implementation manner, for any image data in a certain image sequence (for example, the t-th image data), key point prediction processing can be performed on the image data to obtain the key point prediction result of the image data.
  • the key point prediction result includes position description data of multiple key points; the position description data of these key points are grouped to obtain groups corresponding to each structural area; then, for each group corresponding to the structural area, the points to be adjusted and the reference points with corresponding relationships in the group are determined; then, for each reference point, the historical position data of the reference point are used to smooth the position description data of the reference point to obtain the smoothed position description data of the reference point (for example, the corrected position data of the reference point mentioned above), so that the smoothed position description data of the reference point can better represent the position of the reference point in the image data; and, for each point to be adjusted, the relative offset between the point to be adjusted and the reference point corresponding to the point to be adjusted is first calculated; then, the position description data of the reference point is smoothed using the historical position data of the reference point.
  • the historical offset between the point to be adjusted and the reference point corresponding to the point to be adjusted is smoothed to obtain a smoothed offset (for example, the offset after correction mentioned above); then, based on the smoothed position description data of the reference point and the smoothed offset, the adjusted position description data of the point to be adjusted is calculated, so that the adjusted position description data of the point to be adjusted can better represent the position of the point to be adjusted in the image data; finally, the smoothed position description data of all reference points and the adjusted position description data of all points to be adjusted are output as key point information determined for the image data, so that the key point information can be displayed to the user later or other data processing can be performed based on the key point information, thereby effectively improving the stability of key point position determination.
  • a smoothed offset for example, the offset after correction mentioned above
  • the data processing method has at least one of the following advantages:
  • the present invention selects key points with strong recognition (that is, key points with obvious visual features) and uses the positions of these key points with strong recognition as a reference to adjust the positions of other key points with weaker recognition, which is conducive to obtaining more accurate and stable key point information.
  • the present invention adjusts the positions of key points with weaker recognition according to the structural constraint relationship between key points, so that the key point information finally obtained can maintain better structure, which is conducive to obtaining better visual stability.
  • the present disclosure maintains the independence of various structural regions through grouping, so that each structural region has sufficient deformation ability and will not affect each other, which is conducive to obtaining better visual stability.
  • the embodiment of the present disclosure also provides a data processing device, which is explained and illustrated in conjunction with FIG2. It should be noted that for the technical details of the data processing device provided in the embodiment of the present disclosure, please refer to the relevant content of the data processing method above.
  • the data processing device 200 provided in the embodiment of the present disclosure includes:
  • An acquisition unit 201 is used to acquire a key point prediction result of an image to be processed, wherein the key point prediction result includes position description data of a plurality of key points; the plurality of key points include points to be adjusted and reference points corresponding to the points to be adjusted;
  • the adjusting unit 202 is configured to adjust the position description data of the point to be adjusted in the key point prediction result according to the position description data of the reference point.
  • the adjusting unit 202 includes:
  • a data determination subunit configured to determine an inter-point offset between the point to be adjusted and the reference point according to the position description data of the reference point and the position description data of the point to be adjusted;
  • a first correction subunit configured to correct the inter-point offset by using the historical offset between the point to be adjusted and the reference point to obtain a corrected offset; the historical offset is determined based on a historical image corresponding to the image to be processed;
  • the data updating subunit is used to update the position description data of the point to be adjusted in the key point prediction result according to the corrected offset and the position description data of the reference point.
  • the adjusting unit 202 further includes:
  • a second correction subunit is used to correct the position description data of the reference point by using the historical position data of the reference point to obtain the corrected position data of the reference point; the historical position data is determined according to the historical image corresponding to the image to be processed;
  • the data updating subunit is specifically used to update the position description data of the point to be adjusted in the key point prediction result by using the corrected offset and the corrected position data of the reference point.
  • the adjustment unit 202 is also used to: use the historical position data of the reference point to adjust the position description data of the reference point in the key point prediction result; the historical position data is determined based on the historical image corresponding to the image to be processed; the adjusted key point prediction result includes the adjusted position description data of the reference point and the adjusted position description data of the point to be adjusted.
  • the image to be processed and the historical image belong to the same image sequence; the corresponding time sequence of the historical image in the image sequence is earlier than that of the image to be processed.
  • the images correspond to the timing in the image sequence.
  • the data processing device 200 further includes:
  • a division unit used for performing division processing on the key point prediction result to obtain at least one group, wherein the group includes the position description data of at least one key point, the structural division areas corresponding to the key points in the same group are the same, and the structural division areas corresponding to the key points in different groups are different;
  • a determination unit is used to determine, for any of the groups, the points to be adjusted and reference points corresponding to the points to be adjusted according to key point pairs having a structural constraint relationship in the group.
  • the at least one structural division area corresponding to the face includes one or more of an eye description area, an eyebrow description area, a nose description area, a mouth description area, and a facial contour description area.
  • the point to be adjusted corresponds to at least two reference points
  • the adjusting unit 202 is specifically configured to adjust the position description data of the to-be-adjusted point in the key point prediction result according to the position description data of each reference point and the weight corresponding to each reference point.
  • the weight corresponding to the reference point is determined based on the distance between the reference point and the point to be adjusted, and the distance is determined based on the position description data of the reference point and the position description data of the point to be adjusted.
  • the key point prediction result of the image to be processed (for example, any frame of video image in a certain video data) is first obtained, so that the key point prediction result includes position description data of multiple key points; then, with reference to the position description data of some key points (that is, reference points) described by the key point prediction result, the position description data of another part of the key points (that is, the points to be adjusted) described by the key point prediction result is adjusted, so that the adjusted position description data can better describe the position of the other part of the key points, thereby effectively avoiding the visual jitter phenomenon caused by the fluctuation of the prediction results for the other part of the key points, thereby helping to improve the stability of key point position determination.
  • an embodiment of the present disclosure also provides an electronic device, which includes a processor and a memory: the memory is used to store instructions or computer programs; the processor is used to execute the instructions or computer programs in the memory, so that the electronic device executes any implementation of the data processing method provided by the embodiment of the present disclosure.
  • FIG3 it shows a schematic diagram of the structure of an electronic device 300 suitable for implementing the embodiment of the present disclosure.
  • the terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.
  • the electronic device shown in FIG3 is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present disclosure.
  • the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303.
  • ROM read-only memory
  • RAM random access memory
  • various programs and data required for the operation of the electronic device 300 are also stored.
  • the processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304.
  • An input/output (I/O) interface 305 is also connected to the bus 304.
  • the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309.
  • the communication device 309 may allow the electronic device 300 to communicate wirelessly or wired with other devices to exchange data.
  • FIG. 3 shows an electronic device 300 with various devices, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.
  • an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart.
  • the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302.
  • the processing device 301 the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.
  • the present disclosure also provides a computer-readable medium, wherein the computer-readable medium stores Instructions or computer programs are stored, and when the instructions or computer programs are executed on the device, the device executes any implementation of the data processing method provided in the embodiments of the present disclosure.
  • the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above.
  • Computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.
  • a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried.
  • This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above.
  • the computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device.
  • the program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.
  • the client and server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network).
  • HTTP Hyper Text Transfer Protocol
  • Examples of communication networks include a local area network ("LAN”), a wide area network ("WAN”), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.
  • the computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.
  • the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device can execute the method.
  • the programs used to perform the operations of the present disclosure may be written in one or more programming languages or a combination thereof.
  • the computer program code of the program design language includes, but is not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages.
  • the program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server.
  • the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).
  • LAN local area network
  • WAN wide area network
  • Internet service provider an Internet service provider
  • each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function.
  • the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.
  • each square box in the block diagram and/or flow chart, and the combination of the square boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit/module does not, in some cases, constitute a limitation on the unit itself.
  • exemplary types of hardware logic components include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.
  • FPGAs field programmable gate arrays
  • ASICs application specific integrated circuits
  • ASSPs application specific standard products
  • SOCs systems on chips
  • CPLDs complex programmable logic devices
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • a machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium.
  • a machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • Machine-readable storage medium More specific examples would include electrical connections based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or flash memory erasable programmable read-only memory
  • CD-ROM compact disk read-only memory
  • CD-ROM compact disk read-only memory
  • magnetic storage device or any suitable combination of the above.
  • At least one (item) means one or more, and “plurality” means two or more.
  • “And/or” is used to describe the association relationship of associated objects, indicating that three relationships may exist.
  • a and/or B can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural.
  • the character “/” generally indicates that the objects associated before and after are in an “or” relationship.
  • At least one of the following” or similar expressions refers to any combination of these items, including any combination of single or plural items.
  • At least one of a, b or c can mean: a, b, c, "a and b", “a and c", “b and c", or "a and b and c", where a, b, c can be single or multiple.
  • the steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two.
  • the software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Image Analysis (AREA)

Abstract

本公开的实施例提供一种数据处理方法、装置、电子设备、计算机可读介质，该方法包括：先获取待处理图像的关键点预测结果，以使该关键点预测结果包括多个关键点的位置描述数据；再参考该关键点预测结果所描述的部分关键点的位置描述数据，对该关键点预测结果所描述的另一部分关键点的位置描述数据进行调整处理。

Description

一种数据处理方法、装置、电子设备、计算机可读介质
本申请要求于2023年7月3日递交的中国专利申请第202310808970.3号的优先权，在此全文引用上述中国专利申请公开的内容以作为本申请的一部分。
技术领域
本公开涉及一种数据处理方法、装置、电子设备、计算机可读介质。
背景技术
关键点预测是一种计算机视觉任务，而且关键点预测用于针对一个图像数据中某些关键点(比如，眼角这一关键点、手指这一关键点等)进行定位。
发明内容
本公开提供的技术方案如下：
本公开提供一种数据处理方法，所述方法包括：
获取待处理图像的关键点预测结果，所述关键点预测结果包括多个关键点的位置描述数据；所述多个关键点包括待调整点和所述待调整点对应的参考点；
依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据。
在一种可能的实施方式下，所述待调整点的位置描述数据的调整过程，包括：
依据所述参考点的位置描述数据和所述待调整点的位置描述数据，确定所述待调整点与所述参考点之间的点间偏移量；
利用所述待调整点与所述参考点之间的历史偏移量，对所述点间偏移量进行校正处理，得到校正后偏移量；所述历史偏移量是根据所述待处理图像对应的历史图像所确定的；
依据所述校正后偏移量以及所述参考点的位置描述数据，更新所述关键 点预测结果中所述待调整点的位置描述数据。
在一种可能的实施方式下，所述依据所述校正后偏移量以及所述参考点的位置描述数据，更新所述关键点预测结果中所述待调整点的位置描述数据之前，所述方法还包括：
利用所述参考点的历史位置数据，对所述参考点的位置描述数据进行校正处理，得到所述参考点的校正后位置数据；所述历史位置数据是根据所述待处理图像对应的历史图像所确定的；
所述依据所述校正后偏移量以及所述参考点的位置描述数据，更新所述关键点预测结果中所述待调整点的位置描述数据，包括：
利用所述校正后偏移量以及所述参考点的校正后位置数据，更新所述关键点预测结果中所述待调整点的位置描述数据。
在一种可能的实施方式下，所述获取待处理图像的关键点预测结果之后，所述方法还包括：
利用所述参考点的历史位置数据，调整所述关键点预测结果中所述参考点的位置描述数据；所述历史位置数据是根据所述待处理图像对应的历史图像所确定的；
调整后的关键点预测结果包括所述参考点的调整后的位置描述数据以及所述待调整点的调整后的位置描述数据。
在一种可能的实施方式下，所述待处理图像与所述历史图像均属于同一个图像序列；
所述历史图像在所述图像序列中对应的时序早于所述待处理图像在所述图像序列中对应的时序。
在一种可能的实施方式下，所述依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据之前，所述方法还包括：
对所述关键点预测结果进行划分处理，得到至少一个分组，所述分组包括至少一个关键点的位置描述数据，同一分组中关键点所对应的结构性划分区域相同，不同分组中关键点所对应的结构性划分区域不同；
对于任一所述分组，依据该分组中具有结构约束关系的关键点对，确定所述待调整点和所述待调整点对应的参考点。
在一种可能的实施方式下，若所述多个关键点均属于脸部，则所述脸部对 应的至少一个结构性划分区域包括眼睛描述区域、眉毛描述区域、鼻子描述区域、嘴巴描述区域以及脸部轮廓描述区域中的一个或者多个。
在一种可能的实施方式下，所述待调整点对应于至少两个参考点；
所述依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据，包括：
依据各所述参考点的位置描述数据以及各所述参考点对应的权重，调整所述关键点预测结果中所述待调整点的位置描述数据。
在一种可能的实施方式下，对于所述待调整点对应的任一参考点，该参考点对应的权重是根据该参考点与所述待调整点之间的距离所确定的，所述距离是根据该参考点的位置描述数据与所述待调整点的位置描述数据所确定的。
本公开提供了一种数据处理装置，包括：
获取单元，被配置为获取待处理图像的关键点预测结果，所述关键点预测结果包括多个关键点的位置描述数据；所述多个关键点包括待调整点和所述待调整点对应的参考点；
调整单元，被配置为依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据。
本公开提供了一种电子设备，所述设备包括：处理器和存储器；
所述存储器，被配置为存储指令或计算机程序；
所述处理器，被配置为执行所述存储器中的所述指令或计算机程序，以使得所述电子设备执行本公开提供的数据处理方法。
本公开提供了一种计算机可读介质，所述计算机可读介质中存储有指令或计算机程序，当所述指令或计算机程序在设备上运行时，使得所述设备执行本公开提供的数据处理方法。
本公开提供了一种计算机程序产品，其包括承载在非暂态计算机可读介质上的计算机程序，该计算机程序包含用于执行本公开提供的数据处理方法的程序代码。
附图说明
为了更清楚地说明本公开实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描 述中的附图仅仅是本公开中记载的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。
图1为本公开实施例提供的一种数据处理方法的流程图；
图2为本公开实施例提供的一种数据处理装置的结构示意图；
图3为本公开实施例提供的一种电子设备的结构示意图。
具体实施方式
经研究发现，对于一些关键点预测方法来说，因该关键点预测方法在某些方面(比如，预测精度、标注信息的不确定性等)存在局限性，以使利用该关键点预测方法所确定的预测结果会在一定范围内波动，从而产生视觉抖动现象。
经研究还发现，对于关键点来说，不同关键点的视觉辨识度不一，其具体为：一些关键点(比如，位于眼角位置上的关键点等)在视觉上很明显，以使这些视觉特征明显的关键点具有较高的视觉辨识度，从而使得利用上段关键点预测方法针对这些视觉特征明显的关键点所确定的预测结果较为稳定；但是，另一些关键点并无明显的视觉特征，其只是作为某些关键点位(比如，位于左眼的两个眼角位置上的关键点等)之间的过渡，以使这些无明显视觉特征的关键点具有较低的视觉辨识度，从而使得利用上段关键点预测方法针对这些无明显视觉特征的关键点所确定的预测结果容易产生波动，进而导致视觉抖动现象。
经研究进一步发现，对于关键点来说，一些关键点之间存在比较稳定的结构约束(比如，某些关键点之间的相对偏移量保持不变等)，故为了更好地提高预测效果，可以基于该结构约束针对上段所示的预测结果进行校正处理。
基于上段发现可知，为了更好地提高预测效果，本公开提供了一种数据处理方法，该方法包括：对于待处理图像(比如，某个视频数据中的任意一帧视频图像)来说，获取该待处理图像的关键点预测结果，以使该关键点预测结果包括多个关键点的位置描述数据，以便在确定该多个关键点包括具有结构约束关系的参考点和待调整点时，依据该参考点的位置描述数据，对该待调整点的位置描述数据进行调整处理，以使调整后的位置描述数据能够更好地描述出该待调整点所处位置，如此能够有效地避免因针对该待调整点的预测结果 发生波动而导致的视觉抖动现象，从而有利于提高关键点位置确定稳定性。
另外，本公开不限定上文数据处理方法的执行主体，例如，本公开实施例提供的数据处理方法可以应用于终端设备或服务器等具有数据处理功能的设备。又如，本公开实施例提供的数据处理方法也可以借助不同设备(例如，终端设备与服务器、两个终端设备、或者两个服务器)之间的数据通信过程进行实现。其中，终端设备可以为智能手机、计算机、个人数字助理(Personal Digital Assitant，PDA)或平板电脑等。服务器可以为独立服务器、集群服务器或云服务器。
为了使本技术领域的人员更好地理解本公开方案，下面将结合本公开实施例中的附图，对本公开实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本公开一部分实施例，而不是全部的实施例。基于本公开中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本公开保护的范围。
为了更好地理解本公开所提供的技术方案，下面先结合一些附图对本公开提供的数据处理方法进行说明。如图1所示，本公开实施例提供的数据处理方法，包括下文S1-S2。其中，该图1为本公开实施例提供的一种数据处理方法的流程图。
S1：获取待处理图像的关键点预测结果，该关键点预测结果包括多个关键点的位置描述数据；该多个关键点包括待调整点和该待调整点对应的参考点。
其中，待处理图像是指需要进行关键点确定处理的图像数据；而且本公开不限定该待处理图像，比如，其可以任意一种应用领域下需要进行关键点确定处理的图像数据。又如，在一些图像序列相关的应用场景(比如，视频相关的应用场景)下，该待处理图像可以是指该图像序列中存在的任意一个图像数据(比如，一个视频数据中存在的任意一帧视频图像)。可见，在一种可能的实施方式下，该待处理图像是指从一个图像序列中抽取的图像数据。
待处理图像的关键点预测结果用于描述该待处理图像中目标对象对应的一些关键点在该待处理图像中所呈现的位置分布状态。其中，目标对象是指该待处理图像所描述的对象；而且本公开不限定该目标对象，比如，该目标对象可以为脸部。又如，该目标对象可以是某种生物的躯体。还如，该目标对象可 以是一个物体。
另外，对于上文待处理图像的关键点预测结果来说，该关键点预测结果可以包括多个关键点的位置描述数据，以使该关键点预测结果能够描述出这些关键点在该待处理图像中预测所处位置。其中，该关键点的位置描述数据用于描述该关键点在该待处理图像中预测所处位置；而且本公开不限定该关键点的位置描述数据的实施方式，例如，其可以采用现有的或者未来出现的任意一种能够表示出一个关键点所处位置的数据(比如，位置坐标等)进行实施。
此外，在一些应用场景下，上文待处理图像的关键点预测结果可以不仅包括多个关键点的位置描述数据，还包括该多个关键点对应的点标识数据，以使该关键点预测结果不仅能够表示出这些关键点在该待处理图像中预测所处位置，还能够表示出每个关键点分别属于上文目标对象对应的哪种关键点。其中，该关键点对应的点标识数据用于唯一标识该关键点，以使该关键点对应的点标识数据能够表示出该关键点属于该目标对象对应的哪种关键点(比如，具有2这一序号标识的关键点)。需要说明的是，本公开不限定该点标识数据的实施方式，比如，其可以采用现有的或者未来出现的任意一种能够区分该目标对象对应的不同关键点的标识信息(比如，序号等)进行实施。
还有，本公开不限定上文待处理图像的关键点预测结果的获取方式，例如，其具体可以为：在获取到待处理图像之后，针对该待处理图像进行关键点预测处理，得到该待处理图像的关键点预测结果，以使该关键点预测结果能够描述出该待处理图像中所呈现的关键点分布情况。需要说明的是，本公开不限定该关键点预测处理的实施方式，例如，其可以采用现有的或者未来出现的任意一种能够针对一个图像数据进行关键点检测处理的方法(比如，预先构建的具有关键点检测功能的机器学习模型等)进行实施。
待调整点是指无明显视觉特征的关键点(也就是，具有较小辨识度的关键点)，以使该待调整点能够表示出需要进行预测位置调整处理的关键点；而且本公开不限定该待调整点的确定过程，例如，当上文关键点预测结果包括N个关键点的预测结果(比如，位置描述数据+点标识数据)时，如果第n个关键点的预测结果满足预设调整条件，则可以将该第n个关键点视为待调整点。其中，n为正整数，n≤N。该预设调整条件用于描述无明显视觉特征的关键点所达到的条件；而且该预设调整条件可以依据实际的应用场景设定。另外，本 公开不限定该预设调整条件，例如，该预设调整条件具体可以为：由该第n个关键点的位置描述数据所描述的位置位于预设调整区域内；该预设调整区域是指预先针对上文待处理图像所确定的、需要进行预测位置调整处理的区域。又如，该预设调整条件也可以为：该第n个关键点对应的点标识数据属于预先设定的非锚点标识集合；该非锚点标识集合用于记录无明显视觉特征的关键点的标识；而且该非锚点标识集合可以预先依据应用场景所设定。
待调整点对应的参考点是指在针对该待调整点进行预测位置调整处理时所需参考的关键点；而且该参考点具有明显视觉特征(也就是，具有较高辨识度)。另外，本公开不限定该参考点的确定过程，例如，其具体可以为：从预先构建的映射关系中查找与该待调整点具有对应关系的关键点，作为该待调整点对应的参考点。其中，该映射关系用于记录一些无明显视觉特征的关键点与一些具有明显视觉特征的关键点之间所存在的结构约束关系，以使该映射关系能够表示出每个无明显视觉特征的关键点(比如，每个待调整点)所对应的一个或者多个具有明显视觉特征的关键点(比如，参考点)。可见，在一种可能的实施方式中，该待调整点对应的参考点可以包括至少一个关键点。也就是，在一些可能的实施方式下，该待调整点对应于一个参考点。在另一些可能的实施方式下，该待调整点对应于至少两个参考点。
实际上，对于上文目标对象(比如，脸部)对应的一些关键点来说，这些关键点可能会聚集在不同的区域(比如，眼睛区域、眉毛区域等)，而且在同一个区域内的关键点之间可能会存在结构约束关系，但是在不同区域内的关键点之间不存在结构约束关系，故为了更好地提高结构约束效果，本公开还提供了上文待调整点及其对应的参考点的确定过程的一种可能的实施方式，其具体可以包括下文步骤11-步骤12。
步骤11：对上文待处理图像的关键点预测结果进行划分处理，得到至少一个分组，该分组包括至少一个关键点的位置描述数据，同一分组中关键点所对应的结构性划分区域相同，不同分组中关键点所对应的结构性划分区域不同。
其中，划分处理用于将上文关键点预测结果中存在的、具有结构关联关系的关键点的位置描述数据划分到同一个分组中，以使一个分组能够用于记录某个结构性划分区域内所出现的关键点的位置描述数据，从而使得不同分组 能够表示出不同结构性划分区域内所呈现的关键点分布情况。该结构性划分区域是指上文待处理图像中存在的、用于聚集展示多个具有结构关联关系的关键点的区域。需要说明的是，该结构关联关系可以预先依据实际的应用场景所设定。
基于上段内容可知，对于划分所得的多个分组来说，其具有以下特点：每个分组可以包括至少一个关键点的位置描述数据；同一分组中关键点所对应的结构性划分区域相同(也就是，对于任意一个分组来说，该分组中所有关键点均属于同一个结构性划分区域)；不同分组中关键点所对应的结构性划分区域不同。
另外，本公开实施例不限定上述结构性划分区域的实施方式，例如，当上文待处理图像的关键点预测结果用于描述针对目标对象(比如，某个动物的躯体)对应的一些关键点时，该目标对象对应的至少一个结构性划分区域可以包括该目标对象的至少一个部位描述区域(比如，头部描述区域、胳膊描述区域、腿描述区域等)。
基于上段内容可知，在一种可能的实施方式下，当上文目标对象为脸部时，上文待处理图像的关键点预测结果可以用于描述针对脸部对应的一些关键点(也就是，该关键点预测结果所描述的多个关键点均属于脸部)，而且该脸部对应的至少一个结构性划分区域可以包括眼睛描述区域、眉毛描述区域、鼻子描述区域、嘴巴描述区域以及脸部轮廓描述区域中的一个或者多个。可见，基于该脸部对应的至少一个结构性划分区域所确定的多个分组分别可以包括该眼睛描述区域对应的分组、该眉毛描述区域对应的分组、该鼻子描述区域对应的分组、该嘴巴描述区域对应的分组以及该脸部轮廓描述区域对应的分组中的一个或者多个。其中，该眼睛描述区域对应的分组用于记录在该眼睛描述区域内所呈现的关键点分布情况，以使该眼睛描述区域对应的分组中的关键点均位于该眼睛描述区域内；该眉毛描述区域对应的分组用于记录在该眉毛描述区域内所呈现的关键点分布情况，以使该眉毛描述区域对应的分组中的关键点均位于该眉毛描述区域内；该鼻子描述区域对应的分组用于记录在该鼻子描述区域内所呈现的关键点分布情况，以使该鼻子描述区域对应的分组中的关键点均位于该鼻子描述区域内；该嘴巴描述区域对应的分组用于记录在该嘴巴描述区域内所呈现的关键点分布情况，以使该嘴巴描述区域对 应的分组中的关键点均位于该嘴巴描述区域内；该脸部轮廓描述区域对应的分组用于记录在该脸部轮廓描述区域内所呈现的关键点分布情况，以使该脸部轮廓描述区域对应的分组中的关键点均位于该脸部轮廓描述区域内。
基于上文步骤11的相关内容可知，对于上文待处理图像来说，在获取到该待处理图像的关键点预测结果之后，可以针对该关键点预测结果进行划分处理，得到至少一个分组，以使每个分组均包括至少一个关键点的位置描述数据，并使得不同分组分别用于描述不同结构性划分区域内的关键点分布情况，以便后续能够基于该分组确定具有结构关联关系的关键点对。
步骤12：对于任一分组，依据该分组中具有结构约束关系的关键点对，确定待调整点和该待调整点对应的参考点。
本公开中，对于第m个分组中存在的第j个具有结构约束关系的关键点对来说，可以将该关键点对中不满足预设参考条件的关键点确定为待调整点，并将该关键点对中满足预设参考条件的关键点，确定为该待调整点对应的参考点，以便后续能够借助下文所示的调整过程(比如，下文S2所示的调整过程或者下文步骤52-步骤53所示的调整过程)完成针对该关键点对中一个或者两个关键点的预测位置调整处理。其中，m为正整数，m≤上文“至少一个分组”中的分组个数；j为正整数，j≤该第m个分组中存在的具有结构约束关系的关键点对的个数。该预设参考条件是指能够作为参考点进行使用的关键点所达到的条件；而且该预设参考条件可以预先依据应用场景设定，例如，该预设参考条件可以为：在具有结构约束关系的关键点对中存在的、具有明显视觉特征的关键点(比如，该具有结构约束关系的关键点对中存在的、点标识数据属于预先设定的锚点标识集合的关键点等)；该锚点标识集合用于记录具有明显视觉特征的关键点的标识；而且该锚点标识集合可以预先依据应用场景所设定。
基于上段内容可知，在一些可能的实施方式下，上文第m个分组可以包括待调整点以及该待调整点对应的一个参考点，以便后续能够依据这个参考点所具有的位置信息，对该待调整点进行位置调整处理。另外，在另一些可能的实施方式下，上文第m个分组可以包括该待调整点以及该待调整点对应的多个参考点(比如，2个参考点、或者3个参考点、……)，以便后续能够依据这些参考点所具有的位置信息以及这些参考点对应的权重，对该待调整点 进行位置调整处理。
基于上文步骤11至步骤12的相关内容可知，在一种可能的实施方式下，在获取到待处理图像的关键点预测结果之后，可以针对该关键点预测结果进行划分处理，得到至少一个分组(比如，眼睛描述区域对应的分组、眉毛描述区域对应的分组、鼻子描述区域对应的分组、嘴巴描述区域对应的分组以及脸部轮廓描述区域对应的分组等)，以使不同分组用于表示不同结构性区域内的关键点分布情况；再利用每个分组中存在的具有结构约束关系的关键点对，确定具有对应关系的待调整点以及参考点，以便后续能够基于该具有对应关系的待调整点以及参考点，实现针对该关键点预测结果中部分关键点或者全部关键点的预测位置调整处理，如此能够有效地确保不同结构性区域之间的独立性，从而能够确保每个结构性区域具有足够的形变能力，进而能够有效地避免不同结构性区域之间的相互影响，如此有利于提高结构约束效果。
基于上文S1的相关内容可知，在一些应用场景下，对于待处理图像(比如，某个视频数据中的任意一帧视频图像)来说，可以先获取该待处理图像的关键点预测结果，以使该关键点预测结果能够描述出该待处理图像中所呈现的关键点分布情况；再将该关键点预测结果划分成至少一个分组，以使不同分组分别用于描述不同结构性划分区域内的关键点分布情况；然后，利用每个分组中存在的具有结构约束关系的关键点对，确定一些具有对应关系的待调整点以及参考点，以便后续能够基于这些具有对应关系的待调整点以及参考点，实现针对该关键点预测结果中部分关键点或者全部关键点的预测位置调整处理。
S2：依据参考点的位置描述数据，调整待处理图像的关键点预测结果中待调整点的位置描述数据。
需要说明的是，本公开不限定上文S2中所涉及的待调整点的位置描述数据的调整过程的实施方式，比如，在一些可能的实施方式下，当上文一个待调整点对应于1个参考点时，该S2具体可以为：直接依据该参考点的位置描述数据，调整上文待处理图像的关键点预测结果中待调整点的位置描述数据即可。又如，在另一些可能的实施方式下，当上文一个待调整点对应于至少两个参考点时，该S2具体可以为：依据该待调整点对应的各参考点的位置描述数据以及各参考点对应的权重，调整关键点预测结果中该待调整点的位置描述 数据。其中，该参考点对应的权重用于表征在针对该待调整点的位置调整过程中该参考点所呈现的影响程度；而且本公开不限定该参考点对应的权重的获取方式，比如，可以预先依据应用场景设定。又如，该参考点对应的权重可以根据该参考点与该待调整点之间的距离所确定的，该距离是根据该参考点的位置描述数据与该待调整点的位置描述数据所确定的，以使该距离能够表示出由该参考点的位置描述数据所描述的位置与由该待调整点的位置描述数据所描述的位置之间的相对距离。
为了便于理解上段内容，下面结合一些情况进行说明。
情况1，在一些应用场景下(比如，当上文目标对象为物体时)，上文具有结构约束关系的关键点对之间的相对偏移量可能会一直保持不变(比如，一个自行车的车把描述区域内所有关键点之间的相对偏移量通常不会随着时间发生变化)。
基于上述情况1，本公开提供了上文待调整点的位置描述数据的调整过程的一种可能的实施方式，在该实施方式下，对于具有对应关系的待调整点与参考点来说，该待调整点的位置描述数据的调整过程具体可以包括下文步骤21-步骤22。
步骤21：依据参考点的位置描述数据与待调整点的位置描述数据，确定待调整点与参考点之间的点间偏移量。
其中，点间偏移量是指待调整点的预测位置与参考点的预测位置之间的相对偏移量，以使该点间偏移量能够表示出该待调整点相对于该参考点预测发生的偏移情况。
需要说明的是，本公开不限定偏移量的实施方式，比如，当一个关键点的位置描述数据采用位置坐标进行实施时，该偏移量可以采用两个位置坐标之间的距离进行实施，以使该偏移量能够表示出两个关键点之间的相对距离。又如，当一个关键点的位置描述数据采用位置坐标进行实施时，该偏移量也可以采用两个位置坐标之间的坐标差值进行实施，以使该偏移量能够表示出两个关键点在不同坐标轴上所呈现的相对偏移情况。
另外，本公开不限定上文步骤21的实施方式，比如，当一个待调整点对应于K个参考点，K为正整数(比如，K＝1，或者2，或者3，……)时，该步骤21具体可以为：依据该待调整点对应的第k个参考点的位置描述数据与 该待调整点的位置描述数据，确定该待调整点与该第k个参考点之间的点间偏移量，k为正整数，k≤K。
步骤22：利用待调整点与参考点之间的标准偏移量、以及待调整点与参考点之间的点间偏移量，确定偏移量差异表征数据。
其中，待调整点与参考点之间的标准偏移量是指预先针对该待调整点与该参考点所设定的偏移量，以使该标准偏移量能够表示出在标准情况下该待调整点与参考点之间在位置上所呈现的偏移情况。
偏移量差异表征数据用于表示上文标准偏移量与上文点间偏移量之间所呈现的差异性；而且本公开不限定该偏移量差异表征数据的确定过程，比如，其具体可以为：将该标准偏移量与该点间偏移量进行作差处理，得到该偏移量差异表征数据。
另外，本公开不限定上文步骤22的实施方式，比如，当一个待调整点对应于K个参考点，K为正整数(比如，K＝1，或者2，或者3，……)时，该步骤22具体可以为：利用该待调整点与该待调整点对应的第k个参考点之间的标准偏移量、以及该待调整点与该待调整点对应的第k个参考点之间的点间偏移量，确定该待调整点对应的第k个偏移量差异表征数据，k为正整数，k≤K。
步骤23：依据上文偏移量差异表征数据，更新上文待处理图像的关键点预测结果中待调整点的位置描述数据。
需要说明的是，本公开不限定上文步骤23的实施方式，例如，当一个待调整点对应于1个参考点，而且上文偏移量差异表征数据是指上文标准偏移量与上文点间偏移量之间的差值时，该步骤23具体可以为：先将上文待调整点的位置描述数据与该偏移量差异表征数据进行加和处理，得到该待调整点的调整后的位置描述数据，以使该待调整点的调整后的位置描述数据与上文参考点的位置描述数据之间的相对偏移情况达到标准情况下所需达到的相对偏移情况；再利用该待调整点的调整后的位置描述数据，替换上文待处理图像的关键点预测结果中待调整点的位置描述数据，得到更新后的关键点预测结果，以使该更新后的关键点预测结果包括该待调整点的调整后的位置描述数据，从而使得该更新后的关键点预测结果具有更好地关键点位置确定稳定性。
又如，当一个待调整点对应于K个参考点，而且利用上文步骤22获取到 该待调整点对应的第1个偏移量差异表征数据、该待调整点对应的第2个偏移量差异表征数据、……(以此类推)、以及该待调整点对应的第K个偏移量差异表征数据时，上文步骤23具体可以为：先将该待调整点对应的第1个偏移量差异表征数据、该待调整点对应的第2个偏移量差异表征数据、……(以此类推)、以及该待调整点对应的第K个偏移量差异表征数据进行加权平均处理，得到最终的偏移量差异表征数据，其中，对于该加权平均处理来说，与该待调整点对应的第k个偏移量差异表征数据具有对应关系的权重就是该第k个参考点对应的权重，k为正整数，k≤K。然后，将该最终的偏移量差异表征数据与该待调整点的位置描述数据进行加和处理，得到该待调整点的调整后的位置描述数据；再利用该待调整点的调整后的位置描述数据，替换上文待处理图像的关键点预测结果中待调整点的位置描述数据，得到更新后的关键点预测结果，以使该更新后的关键点预测结果包括该待调整点的调整后的位置描述数据。
基于上文步骤21至步骤23的相关内容可知，在一种可能的实施方式下，对于任意一对具有对应关系的待调整点以及参考点来说，可以依据该参考点的位置描述数据、以及该待调整点与该参考点之间的标准偏移量，针对该待调整点的位置描述数据进行调整处理，以使该待调整点的调整后的位置描述数据与该参考点的位置描述数据之间的相对偏移情况达到该标准偏移量所描述的相对偏移情况。
情况2，在一些应用场景下，一些具有结构约束关系的关键点对之间的相对偏移量在短时间内可能不会发生较大变化，从而使得由图像序列中一些位置相邻的图像数据所描述的该关键对之间的相对偏移量几乎是不变的。
基于上述情况2，本公开提供了上文待调整点的位置描述数据的调整过程的一种可能的实施方式，在该实施方式下，当上文待处理图像是指从图像序列中所抽取的图像数据时，对于具有对应关系的待调整点与参考点来说，该待调整点的位置描述数据的调整过程其具体可以包括下文步骤31-步骤33。
步骤31：依据参考点的位置描述数据和待调整点的位置描述数据，确定待调整点与参考点之间的点间偏移量。
需要说明的是，步骤31的相关内容请参见上文步骤21的相关内容，为了简要起见，在此不再赘述。
步骤32：利用待调整点与参考点之间的历史偏移量，对上文点间偏移量进行校正处理，得到校正后偏移量；该历史偏移量是根据待处理图像对应的历史图像所确定的。
其中，待调整点与参考点之间的历史偏移量用于描述在历史时间段内该待调整点与该参考点之间的相对偏移情况。需要说明的是，本公开不限定该历史时间段，比如，该历史时间段可以包括该待处理图像对应的上一帧图像所对应的时间点；而且该上一帧图像与该待处理图像属于同一个图像序列，该上一帧图像在该图像序列中对应的时序与该待处理图像在该图像序列中对应的时序相邻，该上一帧图像在该图像序列中对应的时序早于该待处理图像在该图像序列中对应的时序。又如，该历史时间段可以包括该待处理图像对应的前几帧图像所对应的时间点。
另外，上文待调整点与参考点之间的历史偏移量是根据待处理图像对应的历史图像所确定的。其中，该历史图像用于描述上文待处理图像中多个关键点在历史时间段内所呈现的位置分布情况；而且本公开不限定该历史图像的实施方式，例如，当该待处理图像是指从一个图像序列(比如，视频数据)中所抽取的图像数据时，该待处理图像对应的历史图像可以包括在该图像序列中存在的、时序比该待处理图像的时序靠前的一个或者多个图像数据。可见，在一种可能的实施方式下，当该待处理图像是指一个视频数据中的第t帧视频图像时，该待处理图像对应的历史图像可以包括该视频数据中的第t-1帧视频图像、第t-2帧视频图像、……(以此类推)、以及第1帧视频图像中的一个或者多个。其中，t为正整数。
基于上段内容可知，对于上文待处理图像对应的历史图像来说，该历史图像与该待处理图像均属于同一个图像序列；而且该历史图像在该图像序列中对应的时序早于该待处理图像在该图像序列中对应的时序。
此外，本公开不限定上段中待处理图像对应的历史图像中的图像个数。
还有，本公开不限定上文待调整点与参考点之间的历史偏移量的确定过程，例如，该历史偏移量的确定过程可以包括：先对该待处理图像对应的历史图像进行关键点预测处理，得到该历史图像的关键点预测结果，以使该关键点预测结果能够表示出上文多个关键点在历史时间段内所呈现的预测位置分布情况；再从该历史图像的关键点预测结果中提取出上文参考点的位置预测结 果以及上文待调整点的位置预测结果；然后，将该待调整点的位置预测结果与该参考点的位置预测结果之间的相对偏移量，视为该待调整点与该参考点之间的历史偏移量(比如，下文)。其中，该参考点的位置预测结果是指在历史时间段内针对该参考点预测所得的位置信息；该待调整点的位置预测结果是指在历史时间段内针对该待调整点预测所得的位置信息。
又如，上文待调整点与参考点之间的历史偏移量的确定过程可以包括：先对该待处理图像对应的上一帧图像(比如，第t-1帧视频图像)进行关键点预测处理，得到该上一帧图像的关键点预测结果，以使该关键点预测结果能够表示出上文多个关键点在该上一帧图像内预测所得的位置分布情况；再从该上一帧图像的关键点预测结果中提取出上文参考点的位置预测结果以及上文待调整点的位置预测结果；其次，计算该待调整点的位置预测结果与该参考点的位置预测结果之间的相对偏移量，以使该相对偏移量能够表示出在上一帧图像中该待调整点相对于该参考点预测所发生的偏移情况；然后，利用该相对偏移量对应的历史偏移量，针对该相对偏移量进行校正处理，得到该“待调整点与参考点之间的历史偏移量”(比如，下文)。其中，该相对偏移量对应的历史偏移量是根据该上一帧图像对应的历史图像(比如，第t-2帧视频图像)所确定的。
基于上述两段内容可知，在一种可能的实施方式下，当上文待处理图像是指一个视频数据中的第t帧视频图像，而且该待处理图像对应的历史图像包括该视频数据中的第t-1帧视频图像时，上文步骤32中“待调整点与参考点之间的历史偏移量”的确定过程包括：先利用本公开所提供的数据处理方法(比如，S1-S2或者下文步骤51-53)，针对该第t-1帧视频图像进行关键点位置确定处理，得到该第t-1帧视频图像中多个关键点的位置描述信息，以使这些位置描述信息能够表示出这些关键点在该第t-1帧视频图像中所处位置；再从这些位置描述信息中提取上文参考点的位置描述信息以及上文待调整点的位置描述信息；最后，依据该待调整点的位置描述信息与该参考点的位置描述信息之间的相对偏移量(及其对应的历史偏移量)，确定该“待调整点与参考点之间的历史偏移量”。
校正后偏移量是指针对上文“待调整点与参考点之间的点间偏移量”进行校正处理所得结果；而且本公开不限定该校正后偏移量的确定过程，比如，其 可以采用下文公式(1)-(2)进行实施，或者采用下文(1)和(3)进行实施。


式中，表示第t个视频图像中第i个待调整点的位置描述数据(比如，预测所得的位置坐标)；表示该第i个待调整点对应的参考点的位置描述数据(比如，预测所得的位置坐标)；表示基于该第t个视频图像所确定的、该第i个待调整点与该第i个待调整点对应的参考点之间的点间偏移量；表示基于该第t-1个视频图像所确定的、该第i个待调整点与该第i个待调整点对应的参考点之间的点间偏移量；表示基于该第t个视频图像所确定的、该第i个待调整点与该第i个待调整点对应的参考点之间的校正后偏移量；表示基于该第t-1个视频图像所确定的、该第i个待调整点与该第i个待调整点对应的参考点之间的校正后偏移量；μ表示预先设定的平滑系数。
基于上文步骤32的相关内容可知，在一些应用场景下，对于当前帧(比如，上文待处理图像或者上文第t帧视频图像等)来说，在基于该当前帧的图像数据确定出一个待调整点与该待调整点对应的参考点之间的点间偏移量(比如，)之后，可以利用基于历史帧所确定的偏移量(比如，类似于等基于历史帧所确定的点间偏移量或者类似于等基于历史帧所确定的校正后偏移量等)，对基于当前帧所确定的点间偏移量进行平滑处理，得到该待调整点与该待调整点对应的参考点之间的校正后偏移量。
另外，本公开不限定上文步骤32的实施方式，比如，当一个待调整点对应于K个参考点，K为正整数(比如，K＝1，或者2，或者3，……)时，该步骤32具体可以为：利用该待调整点与该待调整点对应的第k个参考点之间的历史偏移量，对该待调整点与该第k个参考点之间的点间偏移量进行校正处理，得到该待调整点对应的第k个校正后偏移量，k为正整数，k≤K。
步骤33：依据上文校正后偏移量以及上文参考点的位置描述数据，更新上文待处理图像的关键点预测结果中待调整点的位置描述数据。
需要说明的是，本公开不限定上文步骤33的实施方式，例如，当一个待调整点对应于1个参考点时，该步骤33具体可以为：将上文校正后偏移量以 及上文参考点的位置描述数据进行加和处理(如下文公式(4)所示)，得到上文待调整点的调整后的位置描述数据；再利用该待调整点的调整后的位置描述数据，替换上文待处理图像的关键点预测结果中待调整点的位置描述数据，以使更新后的关键点预测结果包括该待调整点的调整后的位置描述数据。
式中，表示第t个视频图像中第i个待调整点的调整后的位置描述数据；表示基于该第t个视频图像所确定的、该第i个待调整点与该第i个待调整点对应的参考点之间的校正后偏移量；表示该第i个待调整点对应的参考点的位置描述数据(比如，预测所得的位置坐标)。
又如，当一个待调整点对应于K个参考点时，上文步骤33具体可以为：先将该待调整点对应的第k个校正后偏移量与该待调整点对应的第k个参考点的位置描述数据进行加和处理，得到该待调整点对应的第k个调整后的位置描述数据，k为正整数，k≤K；然后，将该待调整点对应的第1个调整后的位置描述数据、该待调整点对应的第2个调整后的位置描述数据、……(以此类推)、以及该待调整点对应的第K个调整后的位置描述数据进行加权平均处理，得到该待调整点的最终调整后的位置描述数据，其中，对于该加权平均处理来说，与该待调整点对应的第k个调整后的位置描述数据具有对应关系的权重就是该第k个参考点对应的权重，k为正整数，k≤K；再利用该待调整点的最终调整后的位置描述数据，替换上文待处理图像的关键点预测结果中待调整点的位置描述数据，以使更新后的关键点预测结果包括该待调整点的最终调整后的位置描述数据。
基于上文步骤31至步骤33的相关内容可知，在一种可能的实施方式下，对于任意一对具有对应关系的待调整点以及参考点来说，可以先计算该待调整点的位置描述数据与该参考点的位置描述数据之间的相对偏移量；再依据这两个点之间的历史偏移量，针对该相对偏移量进行平滑处理，得到校正后偏移量，以使该校正后偏移量能够更好地表示出这两个点在上文待处理图像下所呈现的相对偏移情况；最后，将该校正后偏移量以及该参考点的位置描述数据之间的和值，作为该待调整点的调整后的位置描述数据，以使该调整后的位置描述数据能够更好地表示出该待调整点在该待处理图像中所处位置。
情况3，在一些应用场景下，为了更好地提高关键点位置确定稳定性，还 可以利用针对上文历史图像数据所确定的关键点信息约束上文参考点的位置描述数据，以使该参考点的位置描述数据能够更好地表示出该参考点在该待处理图像中所处位置，以便后续能够基于该参考点的位置描述数据，更好地调整上文待调整点的位置描述数据。
基于上述情况3，本公开提供了上文待调整点的位置描述数据的调整过程的一种可能的实施方式，在该实施方式下，当上文待处理图像是指从图像序列中所抽取的图像数据时，对于具有对应关系的待调整点与参考点来说，该待调整点的位置描述数据的调整过程具体可以包括下文步骤41-步骤44。
步骤41：依据参考点的位置描述数据和待调整点的位置描述数据，确定待调整点与参考点之间的点间偏移量。
需要说明的是，步骤41的相关内容请参见上文步骤21的相关内容，为了简要起见，在此不再赘述。
步骤42：利用待调整点与参考点之间的历史偏移量，对上文点间偏移量进行校正处理，得到校正后偏移量；该历史偏移量是根据待处理图像对应的历史图像所确定的。
需要说明的是，步骤42的相关内容请参见上文步骤32的相关内容，为了简要起见，在此不再赘述。
步骤43：利用参考点的历史位置数据，对该参考点的位置描述数据进行校正处理，得到该参考点的校正后位置数据；该历史位置数据是根据待处理图像对应的历史图像所确定的。
其中，参考点的历史位置数据用于描述该参考点在历史时间段内所处位置；而且该参考点的历史位置数据是根据上文待处理图像对应的历史图像所确定的。需要说明的是，该待处理图像对应的历史图像的相关内容请参见上文。
另外，本公开不限定上文参考点的历史位置数据的确定过程，例如，其具体可以为：先对该待处理图像对应的历史图像进行关键点预测处理，得到该历史图像的关键点预测结果，以使该关键点预测结果能够表示出上文多个关键点在历史时间段内所呈现的预测位置分布情况；再将从该历史图像的关键点预测结果中提取出的上文参考点的位置预测结果，作为该参考点的历史位置数据，以使该历史位置数据能够表示出在历史时间段内针对该参考点预测所 得的位置信息。
又如，当上文待处理图像(比如，第t帧视频图像)对应的历史图像包括该待处理图像对应的上一帧图像(比如，第t-1帧视频图像)时，上文参考点的历史位置数据的确定过程包括：先针对该上一帧图像进行关键点预测处理，得到该上一帧图像的关键点预测结果，以使该关键点预测结果能够表示出上文多个关键点在该上一帧图像内预测所得的位置分布情况；再从该上一帧图像的关键点预测结果中提取上文参考点的位置预测结果，以使该位置预测结果能够表示出在针对该上一帧图像进行数据处理时针对该参考点预测所得的位置；然后，利用该参考点的位置预测结果对应的历史位置数据，对该参考点的位置预测结果进行校正处理，得到该参考点的历史位置数据，以使该参考点的历史位置数据能够更好地表示出在历史时间段内该参考点所处位置。其中，该参考点的位置预测结果对应的历史位置数据是依据该上一帧图像对应的历史图像(比如，第t-2帧视频图像)所确定的。
基于上述两段内容可知，在一些应用场景下，对于一个图像序列(比如，视频数据等)来说，当正在针对该图像序列中第t个图像数据进行数据处理时，可以先针对该第t个图像数据进行关键点预测处理，得到该第t个图像数据的关键点预测结果，以使该关键点预测结果包括一些参考点的位置描述数据；然后，对于任一参考点来说，可以利用该参考点的历史位置数据，对该参考点的位置描述数据进行校正处理，得到该参考点的校正后位置数据，以使该参考点的校正后位置数据能够更好的表示出该参考点在该第t个图像数据中所处位置。其中，该参考点的历史位置数据可以为在针对该图像序列中第t-1个图像数据进行数据处理时针对该参考点预测所得的位置描述数据；或者该参考点的历史位置数据也可以为在针对该图像序列中第t-1个图像数据进行数据处理时，通过针对该参考点预测所得的位置描述数据进行校正处理所得到的校正后位置数据。
需要说明的是，本公开不限定上文步骤43中“校正处理”的实施方式，例如，其可以采用现有的或者未来出现的任意一种能够针对多个数据进行平滑处理的方法进行实施。
另外，本公开不限定上文步骤43，比如，当一个待调整点对应于K个参考点，K为正整数(比如，K＝1，或者2，或者3，……)时，该步骤43具体 可以为：利用该待调整点对应的第k个参考点的历史位置数据，对该第k个参考点的位置描述数据进行校正处理，得到该第k个参考点的校正后位置数据，k为正整数，k≤K。
基于上文步骤43的相关内容可知，对于待处理图像来说，在获取到该待处理图像的关键点预测结果之后，可以先从该关键点预测结果提取出参考点的位置描述数据；再利用该参考点的历史位置数据，对该参考点的位置描述数据进行校正处理(比如，平滑处理)，得到该参考点的校正后位置数据，以使该校正后位置数据能够更好地表示出该参考点在该待处理图像中所处位置，如此有利于提高针对该参考点的位置确定稳定性。
需要说明的是，本公开不限定上文步骤43的执行时间，只需确保该步骤43的执行时间早于下文步骤44的执行时间，并晚于上文S1的执行时间即可。
步骤44：利用上文校正后偏移量以及上文参考点的校正后位置数据，更新上文待处理图像的关键点预测结果中待调整点的位置描述数据。
需要说明的是，本公开不限定上文步骤33的实施方式，例如，当一个待调整点对应于1个参考点时，该步骤44具体可以为：将上文校正后偏移量以及上文参考点的校正后位置数据进行加和处理(如下文公式(5)所示)，得到上文待调整点的调整后的位置描述数据；再利用该待调整点的调整后的位置描述数据，替换上文待处理图像的关键点预测结果中待调整点的位置描述数据，以使更新后的关键点预测结果包括该待调整点的调整后的位置描述数据。
式中，表示第t个视频图像中第i个待调整点的调整后的位置描述数据；表示基于该第t个视频图像所确定的、该第i个待调整点与该第i个待调整点对应的参考点之间的校正后偏移量；表示该第i个待调整点对应的参考点的校正后位置数据(比如，预测所得的位置坐标)。
又如，当一个待调整点对应于K个参考点时，上文步骤44具体可以为：将该待调整点对应的第k个校正后偏移量以及该待调整点对应的第k个参考点的校正后位置数据进行加和处理，得到该待调整点对应的第k个调整后的位置描述数据，k为正整数，k≤K；然后，将该待调整点对应的第1个调整后的位置描述数据、该待调整点对应的第2个调整后的位置描述数据、……(以 此类推)、以及该待调整点对应的第K个调整后的位置描述数据进行加权平均处理，得到该待调整点的最终调整后的位置描述数据，其中，对于该加权平均处理来说，与该待调整点对应的第k个调整后的位置描述数据具有对应关系的权重就是该第k个参考点对应的权重，k为正整数，k≤K；将该待调整点的最终调整后的位置描述数据，替换上文待处理图像的关键点预测结果中待调整点的位置描述数据，以使更新后的关键点预测结果包括该待调整点的最终调整后的位置描述数据。
基于上文步骤41至步骤44的相关内容可知，在一种可能的实施方式下，对于任意一对具有对应关系的待调整点以及参考点来说，可以先计算该待调整点的位置描述数据与该参考点的位置描述数据之间的相对偏移量；再依据这两个点之间的历史偏移量，针对该相对偏移量进行平滑处理，得到校正后偏移量，以使该校正后偏移量能够更好地表示出这两个点在上文待处理图像下所呈现的相对偏移情况，以便在利用该参考点的历史位置数据，对该参考点的位置描述数据进行校正处理，得到该参考点的校正后位置数据之后，将该校正后偏移量以及该参考点的校正后位置数据之间的和值，作为该待调整点的调整后的位置描述数据，以使该调整后的位置描述数据能够更好地表示出该待调整点在该待处理图像中所处位置。
基于上文S1至S2的相关内容可知，对于本公开实施例提供的数据处理方法来说，获取待处理图像(比如，某个视频数据中的任意一帧视频图像)的关键点预测结果，以使该关键点预测结果包括多个关键点的位置描述数据，以便在确定该多个关键点包括具有结构约束关系的参考点和待调整点时，依据该参考点的位置描述数据，对该待调整点的位置描述数据进行调整处理，以使调整后的位置描述数据能够更好地描述出该待调整点所处位置，如此能够有效地避免因针对该待调整点的预测结果发生波动而导致的视觉抖动现象，从而有利于提高关键点位置确定稳定性。
另外，基于上文S1至S2的相关内容可知，在一些应用场景下，为了更好地提高关键点位置确定稳定性，在获取到关键点预测结果之后，可以针对该关键点预测结果中的一些待调整点的位置描述数据进行调整处理，以使调整后的关键点预测结果至少包括这些待调整点的调整后的位置描述数据(比如，调整后的关键点预测结果可以包括所有参考点的没有调整的位置描述数据、 以及所有待调整点的调整后的位置描述数据等)。
实际上，在另一些应用场景下，为了更好地提高关键点位置确定稳定性，在获取到关键点预测结果之后，不仅可以针对该关键点预测结果中的一些待调整点进行预测位置调整处理，还可以参考历史信息针对该关键点预测结果中的一些参考点进行预测位置调整处理。，以使调整后的关键点预测结果包括这些待调整点的调整后的位置描述数据以及这些参考点的调整后的位置描述数据。基于此，本公开还提供了上文数据处理方法的一种可能的实施方式，在该实施方式下，该数据处理方法可以包括下文步骤51-步骤53。
步骤51：获取待处理图像的关键点预测结果，该关键点预测结果包括多个关键点的位置描述数据；该多个关键点包括待调整点和该待调整点对应的参考点。
需要说明的是，步骤51的相关内容请参见上文S1的相关内容，为了简要起见，在此不再赘述。
步骤52：依据上文参考点的位置描述数据，调整上文待处理图像的关键点预测结果中待调整点的位置描述数据，以使调整后的关键点预测结果包括该待调整点的调整后的位置描述数据。
需要说明的是，步骤52的相关内容请参见上文S2的相关内容，为了简要起见，在此不再赘述。
步骤53：利用上文参考点的历史位置数据，调整上文待处理图像的关键点预测结果中该参考点的位置描述数据，以使调整后的关键点预测结果包括该参考点的调整后的位置描述数据；该历史位置数据是根据待处理图像对应的历史图像所确定的。
需要说明的是，本公开不限定上文步骤53中调整过程的实施方式，例如，其可以采用上文步骤43所示的校正处理过程进行实施，为了简要起见，在此不再赘述。
还需要说明的是，本公开也不限定上文步骤53的执行时间，只要确保该步骤53的执行时间晚于上文步骤51即可。
基于上文步骤51至步骤53的相关内容可知，在一种可能的实施方式下，对于某个图像序列中的任一图像数据(比如，第t个图像数据)来说，可以先针对该图像数据进行关键点预测处理，得到该图像数据的关键点预测结果，以 使该关键点预测结果包括多个关键点的位置描述数据；再将这些关键点的位置描述数据进行分组，得到各个结构性区域所对应的分组；然后，对于每个结构性区域所对应的分组，确定该分组中存在的具有对应关系的待调整点以及参考点；随后，对于每个参考点，利用该参考点的历史位置数据，针对该参考点的位置描述数据进行平滑处理，得到该参考点的平滑后的位置描述数据(比如，上文参考点的校正后位置数据)，以使该参考点的平滑后的位置描述数据能够更好地表示出该参考点在该图像数据中所处位置；并且，对于每个待调整点，先计算该待调整点与该待调整点对应的参考点之间的相对偏移量；再利用该待调整点与该待调整点对应的参考点之间的历史偏移量，对该相对偏移量进行平滑处理，得到平滑后偏移量(比如，上文校正后偏移量)；然后，依据该参考点的平滑后的位置描述数据以及该平滑后偏移量，计算该待调整点的调整后的位置描述数据，以使该待调整点的调整后的位置描述数据能够更好地表示出该待调整点在该图像数据中所处位置；最终，将所有参考点的平滑后的位置描述数据以及所有待调整点的调整后的位置描述数据，作为针对该图像数据所确定的关键点信息进行输出，以便后续能够向用户展示该关键点信息或者基于该关键点信息进行其他数据处理，如此能够有效地提高关键点位置确定稳定性。
基于本公开提供的数据处理方法的相关内容可知，该数据处理方法具有以下至少一项优点：
①本公开通过选取具有较强辨识度的关键点(也就是，具有明显视觉特征的关键点)，并以这些具有较强辨识度的关键点所处位置作为基准调整其他具有较弱辨识度的关键点所处位置，如此有利于获得更准确稳定的关键点信息。
②本公开通过依据关键点之间的结构约束关系，调整哪些具有较弱辨识度的关键点所处位置，如此使得最终获得的关键点信息能够保持更好的结构性，从而有利于获得更好的视觉稳定性。
③本公开通过分组方式保持了各个结构性区域之间的独立性，使得每个结构性区域区域具有足够的形变能力，不会相互影响，如此有利于获得更好的视觉稳定性。
基于本公开实施例提供的数据处理方法，本公开实施例还提供了一种数据处理装置，下面结合图2进行解释和说明。其中，图2为本公开实施例提 供的一种数据处理装置的结构示意图。需要说明的是，本公开实施例提供的数据处理装置的技术详情，请参照上文数据处理方法的相关内容。
如图2所示，本公开实施例提供的数据处理装置200，包括：
获取单元201，用于获取待处理图像的关键点预测结果，所述关键点预测结果包括多个关键点的位置描述数据；所述多个关键点包括待调整点和所述待调整点对应的参考点；
调整单元202，用于依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据。
在一种可能的实施方式下，所述调整单元202，包括：
数据确定子单元，用于依据所述参考点的位置描述数据和所述待调整点的位置描述数据，确定所述待调整点与所述参考点之间的点间偏移量；
第一校正子单元，用于利用所述待调整点与所述参考点之间的历史偏移量，对所述点间偏移量进行校正处理，得到校正后偏移量；所述历史偏移量是根据所述待处理图像对应的历史图像所确定的；
数据更新子单元，用于依据所述校正后偏移量以及所述参考点的位置描述数据，更新所述关键点预测结果中所述待调整点的位置描述数据。
在一种可能的实施方式下，所述调整单元202，还包括：
第二校正子单元，用于利用所述参考点的历史位置数据，对所述参考点的位置描述数据进行校正处理，得到所述参考点的校正后位置数据；所述历史位置数据是根据所述待处理图像对应的历史图像所确定的；
所述数据更新子单元，具体用于：利用所述校正后偏移量以及所述参考点的校正后位置数据，更新所述关键点预测结果中所述待调整点的位置描述数据。
在一种可能的实施方式下，所述调整单元202，还用于：利用所述参考点的历史位置数据，调整所述关键点预测结果中所述参考点的位置描述数据；所述历史位置数据是根据所述待处理图像对应的历史图像所确定的；调整后的关键点预测结果包括所述参考点的调整后的位置描述数据以及所述待调整点的调整后的位置描述数据。
在一种可能的实施方式下，所述待处理图像与所述历史图像均属于同一个图像序列；所述历史图像在所述图像序列中对应的时序早于所述待处理图 像在所述图像序列中对应的时序。
在一种可能的实施方式下，所述数据处理装置200，还包括：
划分单元，用于对所述关键点预测结果进行划分处理，得到至少一个分组，所述分组包括至少一个关键点的位置描述数据，同一分组中关键点所对应的结构性划分区域相同，不同分组中关键点所对应的结构性划分区域不同；
确定单元，用于对于任一所述分组，依据该分组中具有结构约束关系的关键点对，确定所述待调整点和所述待调整点对应的参考点。
在一种可能的实施方式下，若所述多个关键点均属于脸部，则所述脸部对应的至少一个结构性划分区域包括眼睛描述区域、眉毛描述区域、鼻子描述区域、嘴巴描述区域以及脸部轮廓描述区域中的一个或者多个。
在一种可能的实施方式下，所述待调整点对应于至少两个参考点；
所述调整单元202，具体用于：依据各所述参考点的位置描述数据以及各所述参考点对应的权重，调整所述关键点预测结果中所述待调整点的位置描述数据。
在一种可能的实施方式下，对于所述待调整点对应的任一参考点，该参考点对应的权重是根据该参考点与所述待调整点之间的距离所确定的，所述距离是根据该参考点的位置描述数据与所述待调整点的位置描述数据所确定的。
基于上述数据处理装置200的相关内容可知，对于本公开实施例提供的数据处理装置200来说，先获取待处理图像(比如，某个视频数据中的任意一帧视频图像)的关键点预测结果，以使该关键点预测结果包括多个关键点的位置描述数据；再参考该关键点预测结果所描述的部分关键点(也就是，参考点)的位置描述数据，对该关键点预测结果所描述的另一部分关键点(也就是，待调整点)的位置描述数据进行调整处理，以使调整后的位置描述数据能够更好地描述出该另一部分关键点所处位置，如此能够有效地避免因针对该另一部分关键点的预测结果发生波动而导致的视觉抖动现象，从而有利于提高关键点位置确定稳定性。
另外，本公开实施例还提供了一种电子设备，所述设备包括处理器以及存储器：所述存储器，用于存储指令或计算机程序；所述处理器，用于执行所述存储器中的所述指令或计算机程序，以使得所述电子设备执行本公开实施例提供的数据处理方法的任一实施方式。
参见图3，其示出了适于用来实现本公开实施例的电子设备300的结构示意图。本公开实施例中的终端设备可以包括但不限于诸如移动电话、笔记本电脑、数字广播接收器、PDA(个人数字助理)、PAD(平板电脑)、PMP(便携式多媒体播放器)、车载终端(例如车载导航终端)等等的移动终端以及诸如数字TV、台式计算机等等的固定终端。图3示出的电子设备仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图3所示，电子设备300可以包括处理装置(例如中央处理器、图形处理器等)301，其可以根据存储在只读存储器(ROM)302中的程序或者从存储装置308加载到随机访问存储器(RAM)303中的程序而执行各种适当的动作和处理。在RAM303中，还存储有电子设备300操作所需的各种程序和数据。处理装置301、ROM 302以及RAM 303通过总线304彼此相连。输入/输出(I/O)接口305也连接至总线304。
通常，以下装置可以连接至I/O接口305：包括例如触摸屏、触摸板、键盘、鼠标、摄像头、麦克风、加速度计、陀螺仪等的输入装置306；包括例如液晶显示器(LCD)、扬声器、振动器等的输出装置307；包括例如磁带、硬盘等的存储装置308；以及通信装置309。通信装置309可以允许电子设备300与其他设备进行无线或有线通信以交换数据。虽然图3示出了具有各种装置的电子设备300，但是应理解的是，并不要求实施或具备所有示出的装置。可以替代地实施或具备更多或更少的装置。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在非暂态计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信装置309从网络上被下载和安装，或者从存储装置308被安装，或者从ROM302被安装。在该计算机程序被处理装置301执行时，执行本公开实施例的方法中限定的上述功能。
本公开实施例提供的电子设备与上述实施例提供的方法属于同一发明构思，未在本实施例中详尽描述的技术细节可参见上述实施例，并且本实施例与上述实施例具有相同的有益效果。
本公开实施例还提供了一种计算机可读介质，所述计算机可读介质中存 储有指令或计算机程序，当所述指令或计算机程序在设备上运行时，使得所述设备执行本公开实施例提供的数据处理方法的任一实施方式。
需要说明的是，本公开上述的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读信号介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、RF(射频)等等，或者上述的任意合适的组合。
在一些实施方式中，客户端、服务器可以利用诸如HTTP(Hyper Text Transfer Protocol，超文本传输协议)之类的任何当前已知或未来研发的网络协议进行通信，并且可以与任意形式或介质的数字数据通信(例如，通信网络)互连。通信网络的示例包括局域网(“LAN”)，广域网(“WAN”)，网际网(例如，互联网)以及端对端网络(例如，ad hoc端对端网络)，以及任何当前已知或未来研发的网络。
上述计算机可读介质可以是上述电子设备中所包含的；也可以是单独存在，而未装配入该电子设备中。
上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被该电子设备执行时，使得该电子设备可以执行上述方法。
可以以一种或多种程序设计语言或其组合来编写用于执行本公开的操作 的计算机程序代码，上述程序设计语言包括但不限于面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码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)。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、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、或代码的一部分，该模块、程序段、或代码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。也应当注意，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，两个接连地表示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操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的单元可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，单元/模块的名称在某种情况下并不构成对该单元本身的限定。
本文中以上描述的功能可以至少部分地由一个或多个硬件逻辑部件来执行。例如，非限制性地，可以使用的示范类型的硬件逻辑部件包括：现场可编程门阵列(FPGA)、专用集成电路(ASIC)、专用标准产品(ASSP)、片上系统(SOC)、复杂可编程逻辑设备(CPLD)等等。
在本公开的上下文中，机器可读介质可以是有形的介质，其可以包含或存储以供指令执行系统、装置或设备使用或与指令执行系统、装置或设备结合地使用的程序。机器可读介质可以是机器可读信号介质或机器可读储存介质。机器可读介质可以包括但不限于电子的、磁性的、光学的、电磁的、红外的、或半导体系统、装置或设备，或者上述内容的任何合适组合。机器可读存储介质 的更具体示例会包括基于一个或多个线的电气连接、便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM或快闪存储器)、光纤、便捷式紧凑盘只读存储器(CD-ROM)、光学储存设备、磁储存设备、或上述内容的任何合适组合。
需要说明的是，本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的系统或装置而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。
应当理解，在本公开中，“至少一个(项)”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，用于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：只存在A，只存在B以及同时存在A和B三种情况，其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项(个)”或其类似表达，是指这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a，b或c中的至少一项(个)，可以表示：a，b，c，“a和b”，“a和c”，“b和c”，或“a和b和c”，其中a，b，c可以是单个，也可以是多个。
还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
结合本文中所公开的实施例描述的方法或算法的步骤可以直接用硬件、处理器执行的软件模块，或者二者的结合来实施。软件模块可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用 本公开。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本公开的精神或范围的情况下，在其它实施例中实现。因此，本公开将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。

Claims (12)

  1. 一种数据处理方法，包括：
    获取待处理图像的关键点预测结果，所述关键点预测结果包括多个关键点的位置描述数据；所述多个关键点包括待调整点和所述待调整点对应的参考点；
    依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据。
  2. 根据权利要求1所述的方法，其中，所述待调整点的位置描述数据的调整过程，包括：
    依据所述参考点的位置描述数据和所述待调整点的位置描述数据，确定所述待调整点与所述参考点之间的点间偏移量；
    利用所述待调整点与所述参考点之间的历史偏移量，对所述点间偏移量进行校正处理，得到校正后偏移量；所述历史偏移量是根据所述待处理图像对应的历史图像所确定的；
    依据所述校正后偏移量以及所述参考点的位置描述数据，更新所述关键点预测结果中所述待调整点的位置描述数据。
  3. 根据权利要求2所述的方法，其中，所述依据所述校正后偏移量以及所述参考点的位置描述数据，更新所述关键点预测结果中所述待调整点的位置描述数据之前，所述方法还包括：
    利用所述参考点的历史位置数据，对所述参考点的位置描述数据进行校正处理，得到所述参考点的校正后位置数据；所述历史位置数据是根据所述待处理图像对应的历史图像所确定的；
    所述依据所述校正后偏移量以及所述参考点的位置描述数据，更新所述关键点预测结果中所述待调整点的位置描述数据，包括：
    利用所述校正后偏移量以及所述参考点的校正后位置数据，更新所述关键点预测结果中所述待调整点的位置描述数据。
  4. 根据权利要求1所述的方法，其中，所述获取待处理图像的关键点预测结果之后，所述方法还包括：
    利用所述参考点的历史位置数据，调整所述关键点预测结果中所述参考 点的位置描述数据；所述历史位置数据是根据所述待处理图像对应的历史图像所确定的；
    调整后的关键点预测结果包括所述参考点的调整后的位置描述数据以及所述待调整点的调整后的位置描述数据。
  5. 根据权利要求2-4任一项所述的方法，其中，所述待处理图像与所述历史图像均属于同一个图像序列；
    所述历史图像在所述图像序列中对应的时序早于所述待处理图像在所述图像序列中对应的时序。
  6. 根据权利要求1所述的方法，其中，所述依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据之前，所述方法还包括：
    对所述关键点预测结果进行划分处理，得到至少一个分组，所述分组包括至少一个关键点的位置描述数据，同一分组中关键点所对应的结构性划分区域相同，不同分组中关键点所对应的结构性划分区域不同；
    对于任一所述分组，依据该分组中具有结构约束关系的关键点对，确定所述待调整点和所述待调整点对应的参考点。
  7. 根据权利要求6所述的方法，其中，若所述多个关键点均属于脸部，则所述脸部对应的至少一个结构性划分区域包括眼睛描述区域、眉毛描述区域、鼻子描述区域、嘴巴描述区域以及脸部轮廓描述区域中的一个或者多个。
  8. 根据权利要求1所述的方法，其中，所述待调整点对应于至少两个参考点；
    所述依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据，包括：
    依据各所述参考点的位置描述数据以及各所述参考点对应的权重，调整所述关键点预测结果中所述待调整点的位置描述数据。
  9. 根据权利要求8所述的方法，其中，对于所述待调整点对应的任一参考点，该参考点对应的权重是根据该参考点与所述待调整点之间的距离所确定的，所述距离是根据该参考点的位置描述数据与所述待调整点的位置描述数据所确定的。
  10. 一种数据处理装置，包括：
    获取单元，被配置为获取待处理图像的关键点预测结果，所述关键点预测结果包括多个关键点的位置描述数据；所述多个关键点包括待调整点和所述待调整点对应的参考点；
    调整单元，被配置为依据所述参考点的位置描述数据，调整所述关键点预测结果中所述待调整点的位置描述数据。
  11. 一种电子设备，包括：处理器和存储器；其中，
    所述存储器，被配置为存储指令或计算机程序；
    所述处理器，被配置为执行所述存储器中的所述指令或计算机程序，以使得所述电子设备执行权利要求1-9任一项所述的方法。
  12. 一种计算机可读介质，其中，所述计算机可读介质中存储有指令或计算机程序，当所述指令或计算机程序在设备上运行时，使得所述设备执行权利要求1-9任一项所述的方法。
PCT/CN2024/096307 2023-07-03 2024-05-30 一种数据处理方法、装置、电子设备、计算机可读介质 Ceased WO2025007685A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202310808970.3 2023-07-03
CN202310808970.3A CN119251517A (zh) 2023-07-03 2023-07-03 一种数据处理方法、装置、电子设备、计算机可读介质

Publications (1)

Publication Number Publication Date
WO2025007685A1 true WO2025007685A1 (zh) 2025-01-09

Family

ID=94015418

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2024/096307 Ceased WO2025007685A1 (zh) 2023-07-03 2024-05-30 一种数据处理方法、装置、电子设备、计算机可读介质

Country Status (2)

Country Link
CN (1) CN119251517A (zh)
WO (1) WO2025007685A1 (zh)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108470322A (zh) * 2018-03-09 2018-08-31 北京小米移动软件有限公司 处理人脸图像的方法、装置及可读存储介质
CN112132895A (zh) * 2020-09-10 2020-12-25 湖北亿咖通科技有限公司 基于图像的位置确定方法、电子设备及存储介质
CN113012039A (zh) * 2021-03-30 2021-06-22 北京市商汤科技开发有限公司 图像处理方法、装置、电子设备及存储介质
CN113610966A (zh) * 2021-08-13 2021-11-05 北京市商汤科技开发有限公司 三维姿态调整的方法、装置、电子设备及存储介质
CN115273172A (zh) * 2022-06-17 2022-11-01 芜湖云从科技有限公司 三维人脸关键点标注方法、设备、系统和存储介质
WO2023202434A1 (zh) * 2022-04-19 2023-10-26 华为技术有限公司 3d模型匹配方法、电子设备及相关装置
CN117934603A (zh) * 2023-12-14 2024-04-26 广东省新黄埔中医药联合创新研究院 一种穴位标定方法、装置、电子设备和可读存储介质

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108470322A (zh) * 2018-03-09 2018-08-31 北京小米移动软件有限公司 处理人脸图像的方法、装置及可读存储介质
CN112132895A (zh) * 2020-09-10 2020-12-25 湖北亿咖通科技有限公司 基于图像的位置确定方法、电子设备及存储介质
CN113012039A (zh) * 2021-03-30 2021-06-22 北京市商汤科技开发有限公司 图像处理方法、装置、电子设备及存储介质
CN113610966A (zh) * 2021-08-13 2021-11-05 北京市商汤科技开发有限公司 三维姿态调整的方法、装置、电子设备及存储介质
WO2023202434A1 (zh) * 2022-04-19 2023-10-26 华为技术有限公司 3d模型匹配方法、电子设备及相关装置
CN115273172A (zh) * 2022-06-17 2022-11-01 芜湖云从科技有限公司 三维人脸关键点标注方法、设备、系统和存储介质
CN117934603A (zh) * 2023-12-14 2024-04-26 广东省新黄埔中医药联合创新研究院 一种穴位标定方法、装置、电子设备和可读存储介质

Also Published As

Publication number Publication date
CN119251517A (zh) 2025-01-03

Similar Documents

Publication Publication Date Title
WO2023273985A1 (zh) 一种语音识别模型的训练方法、装置及设备
CN112364860B (zh) 字符识别模型的训练方法、装置和电子设备
CN113033377B (zh) 字符位置修正方法、装置、电子设备和存储介质
WO2024109268A1 (zh) 一种数字模型比对方法、装置、设备及介质
WO2024109860A1 (zh) 一种交互方法、装置、电子设备、计算机可读介质
CN111369461A (zh) 美颜参数调整方法、装置及电子设备
CN110717467A (zh) 头部姿势的估计方法、装置、设备及存储介质
CN112418233B (zh) 图像处理方法、装置、可读介质及电子设备
CN111680754B (zh) 图像分类方法、装置、电子设备及计算机可读存储介质
EP4538965A1 (en) Loop closure detection method and apparatus, and electronic device and medium
WO2024087910A1 (zh) 正畸治疗监测方法、装置、设备及存储介质
US20220230343A1 (en) Stereo matching method, model training method, relevant electronic devices
WO2023151348A1 (zh) 一种图像中关键点的处理方法和相关装置
CN116071527A (zh) 一种对象处理方法、装置、存储介质及电子设备
CN115239996A (zh) 物品位置校正方法、装置、电子设备和计算机可读介质
WO2025247205A1 (zh) 三维人脸模型的摆正处理方法、装置、设备及介质
WO2025007685A1 (zh) 一种数据处理方法、装置、电子设备、计算机可读介质
CN114359963A (zh) 姿态识别方法及通信系统
WO2025021169A1 (zh) 图像处理方法、设备、存储介质及程序产品
WO2025045144A1 (zh) 图像检测方法、装置、电子设备和存储介质
US20230186599A1 (en) Image processing method and apparatus, device, medium and program product
WO2024159982A1 (zh) 车位检测效果的评价方法、装置及相关设备
WO2024152797A1 (zh) 视频补全方法、装置、介质和电子设备
CN111583139B (zh) 腮红调整方法、装置、电子设备及计算机可读介质
CN118485819A (zh) 一种关键点检测方法、装置、设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24835168

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE