CN116258694A - Image processing method, electronic equipment and human body posture correction system - Google Patents
Image processing method, electronic equipment and human body posture correction system Download PDFInfo
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Abstract
The invention discloses an image processing method, which comprises the following steps: acquiring a first image, wherein a first gesture of a target object is displayed in the first image; processing the first image to obtain first key point information, wherein the first key point information comprises at least one key point position; comparing the first key point information with preset key point position information to generate second key point information, wherein the second key point information comprises at least one key point position to be corrected; and generating a second image according to the second key point information, wherein the second image displays a second gesture of the target object. The image processing method provided by the invention enables the online user to know the substandard part of the current action at a glance after receiving the action correction image, and complete modification on the basis of the feedback image part by part, thereby providing technical possibility for supervision and interaction of the current exercise course on the Internet, and enabling students or online users to enjoy private exercise experience at home.
Description
Technical Field
The present invention relates to the field of image processing technology, and in particular, to an image processing method, an electronic device, a non-transitory computer readable storage medium, and a human body posture correction system.
Background
With the development of internet technology and AI technology, more and more people choose to follow network courses or online video home fitness exercises, professional fitness coaches in the network courses do action demonstration, and students or off-line users only need to keep up with action rhythms, imitate actions, and achieve the actions or postures to achieve effective fitness effects. However, online training and guidance of one-to-one/one-to-many online training and guidance of online courses or online videos are difficult, and online training and guidance of each student or online user cannot be effectively completed by a recording and broadcasting mode or an AI teacher for teaching or watching by a large number of students or online users even if a real person training exists. Therefore, the action of a student or an off-line user is not standard, so that the training is not up to standard, the exercise is not effective, and an interaction scheme for effective posture guidance is lacked in the prior art.
Disclosure of Invention
In the prior art, there is a lack of effective solutions for giving action corrections to a learner or an online user who performs home exercise following a network course or online video, and in view of this, according to a first aspect of the present invention, there is provided an image processing method, including:
acquiring a first image, wherein a first gesture of a target object is displayed in the first image;
processing the first image to obtain first key point information, wherein the first key point information comprises at least one key point position;
comparing the first key point information with preset key point position information to generate second key point information, wherein the second key point information comprises at least one key point position to be corrected;
and generating a second image according to the second key point information, wherein the second image displays a second gesture of the target object.
According to a first aspect of the invention, the method further comprises:
and generating text information based on the second key point information, and displaying the text information on the second image.
According to a first aspect of the present invention, the processing the first image to obtain first keypoint information includes:
and obtaining the first key point information through key point detection.
According to a first aspect of the present invention, the first image includes a motion image uploaded by a user or a motion image frame acquired from a video uploaded by the user; the preset key point position information comprises the key point position of the standard action image.
According to a first aspect of the present invention, the generating a second image according to the second keypoint information includes:
determining the at least one keypoint location to be corrected;
the first image is modified by a machine learning component based on the keypoint location to be corrected, generating the second image.
According to the first aspect of the present invention, wherein the second image includes a multi-frame dynamic image, and the generating the second image according to the second keypoint information includes:
determining a first to-be-corrected keypoint location of the at least one to-be-corrected keypoint location;
and modifying the first image by a machine learning component based on the first key point position to be corrected to generate a frame image of the second image.
According to a first aspect of the present invention, wherein the generating a second image according to the second keypoint information further comprises:
and displaying the modification process of the key points on the second image.
According to a first aspect of the present invention, wherein the generating a second image according to the second keypoint information further comprises:
an indication mark is added in the second image to indicate the positions of the key points before and after modification.
According to a first aspect of the present invention, the generating a second image according to the second keypoint information includes:
and rendering and generating the second image according to the at least one key point position to be corrected and the first image, wherein the background in the second image is the same as the background in the first image, and the target object in the second image is the same as the target object in the first image.
In a second aspect, the present invention also provides a human body posture correction system, including one or more clients and a server, including:
the client is configured to receive a first image uploaded by a user, and a first gesture of a target object is displayed in the first image; transmitting an instruction for requesting to acquire correction information to a server, and uploading the first image;
the server is configured to execute the image processing method according to the first aspect of the present invention and return the second image to the client;
the client is configured to send a correction information configuration completion instruction to the user to enable the user to download the second image.
In a third aspect, the present invention also provides an electronic device, including:
a processor; and
a memory storing a computer program which, when executed by the processor, causes the processor to perform the image processing method according to the first aspect of the present invention.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor, cause the processor to perform the image processing method according to the first aspect of the present invention.
According to the image processing method provided by the invention, the morphological image uploaded by the user is modified into the standard action image, and the modification is specifically carried out aiming at the wrong skeleton position, optionally, a frame of image is generated aiming at each wrong skeleton position, then a correction animation is formed, after receiving the action correction image, a learner or an off-line user can know the substandard part of the current action at a glance, and the modification is finished part by part based on the feedback image.
The human body posture correcting system provided by the invention utilizes a key point detection technology to compare action image frames in action pictures or videos uploaded by students or online users with standard actions to obtain information to be corrected, modifies the posture images uploaded by the users into standard action images through a machine learning component, and particularly aims at the targeted modification of wrong skeleton positions, optionally generates a frame of images for each wrong skeleton position, then forms a correction animation, enriches the correction images through one or more means such as image rendering, text prompting, indication marks and the like, so that after the students or offline users receive the action correction images, the substandard part of the current actions can be clear at a glance, and the modification is completed on a part-by-part basis of the feedback images. The human body posture correction system provided by the invention can vividly and timely feed back the action standard condition of the body-building user, provides technical possibility for supervision and interaction of the current body-building course on the Internet, and enables students or online users to enjoy private and educational body-building experience at home.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained by those skilled in the art from these drawings without departing from the scope of protection of the present application.
FIG. 1 illustrates an image processing method provided by one embodiment of the present invention;
FIG. 2 illustrates key point detection for an original image uploaded by a user in an image processing method according to an embodiment of the present invention;
FIG. 3 illustrates generating a corrective action image from the aligned keypoint information in an image processing method according to an embodiment of the present invention;
FIG. 4 shows a text message generated according to the compared key point information in the image processing method according to an embodiment of the present invention;
FIG. 5 illustrates a change in the position of a displayed bone of a corrective action image in an image processing method according to an embodiment of the present invention;
FIG. 6 illustrates a change in the position of a bone indicated by the addition of an indicator in a corrective action image in an image processing method according to an embodiment of the present invention;
FIG. 7 illustrates the generation of corrective action images, text messages, and human hand type indicators in an image processing method provided by one embodiment of the present invention;
FIG. 8 illustrates a human posture correction system provided by an embodiment of the present invention;
fig. 9 illustrates an interaction process of a user, a client side, and a server side in a human posture correction system according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The fast-paced life of the current society makes people more prone to choose to exercise at home in leisure time, and the mainstream sports software has functions of action demonstration, video teaching and the like, but students or off-line users usually have nonstandard places and can give correction prompts when imitating according to the demonstration actions.
Aiming at the trouble that the training is not up to standard due to nonstandard actions of students or off-line users, the invention provides an interaction scheme of posture guidance. Firstly, the invention provides an image processing method, which modifies a posture image uploaded by a user into a standard action image through a machine learning component, and particularly carries out targeted modification on wrong skeleton positions, optionally, generates a frame of image for each wrong skeleton position, then forms a correction animation (or dynamic diagram), and after receiving the action correction image, a learner or an off-line user can know the substandard part of the current action at a glance, and complete the modification on a part-by-part basis on the basis of a feedback image.
According to an embodiment of the present invention, as shown in fig. 1, the present invention provides an image processing method 10, including: step S101 to step S104. Wherein:
in step S101, a first image in which a first pose of a target object is displayed is acquired. The student or on-line user performing the home exercise records the images of the body building of himself or herself, for example, by photographing or shooting, and generates pictures or dynamic images (including multi-frame pictures). In the image processing method 10 provided by the present invention, the picture or the dynamic image is first acquired, optionally, a learner or an online user uploads the picture or the dynamic image through a corresponding client, as shown in fig. 2, the first image should include the action information to be corrected, and in a static situation, a frame of picture includes a gesture of the target object.
In step S102, the first image is processed to obtain first keypoint information, where the first keypoint information includes at least one keypoint location. Optionally, the client extracts skeleton information in the action picture or the dynamic image through a key point detection technology, and if a learner or an online user provides the dynamic image, the client is required to identify a frame containing a human body gesture from the dynamic image and extract the frame. Or the client sends a request to obtain an action correction information instruction to the server (located at the cloud), the action picture or the dynamic image is uploaded to the server, the server executes an algorithm, and skeleton information in the action picture or the dynamic image (effective action frame in the action picture or the dynamic image) is extracted through a key point detection technology. As shown in fig. 2, the skeleton information may be represented by key points, that is, each key point in the motion picture or the moving image and the position of the key point are identified.
In step S103, the first key point information is compared with preset key point position information, and second key point information is generated, where the second key point information includes at least one key point position to be corrected. Optionally, the client or the server identifies a specific action corresponding to the action frame in the action picture or the dynamic image by executing a preset algorithm, and standard action information is reserved in a database of the client or the server, wherein the standard action information comprises key point position information of the standard action. After the client or the server identifies the action and finds the corresponding standard action, the first key point information extracted from the first image, including the key point position information, is compared with the key point position information of the standard action, and the comparison includes comparing the relative position relationship between the adjacent key points, so that the key point with deviation of the position relationship is obtained. Optionally, the target object is adjusted to be the same as or similar to the object in the standard action graph, so as to judge the relative position relationship between the key points. Optionally, key points with a relative distance from the adjacent key points being greater than a preset threshold value may be extracted by setting a threshold value, where the key points correspond to nonstandard joint positions (also correspond to bone positions between two joints), and second key point information is generated according to the extracted key points with a relative distance from the adjacent key points being greater than the preset threshold value, where the second key point information includes at least one key point position to be corrected. Optionally, if the relative distance from each key point to the adjacent key point is smaller than the preset threshold value compared with the key point position of the standard action in the database, it may be determined that the action frame in the corresponding action picture or dynamic image shows a more standard action.
In step S104, a second image is generated according to the second keypoint information, and the second image displays a second pose of the target object. Optionally, the server modifies the first image according to the second key point information, or the server transmits the second key point information back to the client, and the client modifies the first image according to the second key point information, as shown in fig. 3, and moves the wrong/to-be-modified key point to the correct position (the position corresponding to the standard action diagram), and optionally, adjusts the modified key point according to the difference of the body proportions of the target object and the object in the standard action diagram.
According to the image processing method provided by the embodiment of the invention, the difference point information is generated according to the original image comprising the first gesture of the target object by comparing the original image with the preset standard action image, and the modified correction image is generated according to the difference point information, so that the correction content is clear at a glance, and the action correction information can be timely and conveniently acquired for an online body-building user.
According to an embodiment of the present invention, as shown in fig. 4, the image processing method 10 provided by the present invention further includes:
and generating text information based on the second key point information and displaying the text information on the second image.
Optionally, the database of the client or the server stores action names corresponding to bone positions (the relative positions of the two key points form the bone positions), modifies the first image into the second image according to the second key point information, displays text information, prompts a student or an online user how to adjust the bone positions (the relative positions of the key points). For example, as shown in fig. 4, correction information of the leg bone position is generated based on the key point position comparison with the standard motion, the leg bone position is corrected in the second image, and the "straightening knee" is displayed in the second image.
According to an embodiment of the present invention, in the image processing method 10 provided by the present invention, the processing the first image in step S102, the obtaining first key point information includes:
and obtaining the first key point information through key point detection.
Optionally, the client executes a preset algorithm, and performs key point detection on a first image (including an action image frame extracted from an action image or a body-building video) to obtain first key point information; or the client uploads the first image to the server, the server executes a preset algorithm, and the first image (comprising the action image frames extracted from the action picture or the body-building video) is subjected to the key point detection by adopting a key point detection technology to obtain first key point information.
According to an embodiment of the present invention, in the image processing method 10 provided by the present invention, the first image includes a motion image uploaded by a user or a motion image frame acquired from a video uploaded by the user; the preset key point position information comprises the key point position of the standard action image.
According to an embodiment of the present invention, in the image processing method 10 provided by the present invention, the generating the second image according to the second keypoint information in step S104 includes:
locking the at least one keypoint location to be corrected. Based on the comparison of the first key point information and the standard key point position information, the generated second key point information may include a plurality of key point positions to be corrected, and one of the key point positions to be corrected is locked for correction processing.
The first image is modified by a machine learning component based on the keypoint location to be corrected, generating the second image. Correcting the wrong key point position to the correct key point position based on the key point position to be corrected, wherein the method further comprises the step of adjusting the key point position according to the body proportion of the target object to generate a second image. Alternatively, as shown in fig. 5, the second image is a dynamic image, and shows a process of adjusting from the wrong key point position to the correct key point position, that is, a process of adjusting the bone position of the target object.
According to an embodiment of the present invention, in the image processing method 10 provided by the present invention, the second image includes a multi-frame dynamic image, and the generating the second image according to the second keypoint information in step S104 further includes:
binding a first to-be-corrected keypoint location of the at least one to-be-corrected keypoint location. Based on the comparison of the first key point information and the standard key point position information, the generated second key point information may include a plurality of key point positions to be corrected, and the first key point positions to be corrected are locked in sequence to carry out correction processing.
And modifying the first image by a machine learning component based on the first key point position to be corrected to generate a frame image of the second image. Correcting the wrong key point position to the correct key point position based on the first key point position to be corrected, wherein the method further comprises the step of adjusting the key point position according to the body proportion of the target object to generate a second image. Optionally, the second image is a dynamic image, and shows a process of adjusting from the wrong key point position to the correct key point position, that is, shows a process of adjusting the bone position of the target object. Because the second key point information comprises a plurality of key point positions to be corrected, the key point positions to be corrected are sequentially locked, multi-frame correction images are sequentially generated, dynamic images or videos are generated from the correction images, and a plurality of bone position adjustment processes are sequentially displayed.
According to an embodiment of the present invention, in the image processing method 10 provided by the present invention, the generating the second image according to the second keypoint information further includes:
an indication mark is added in the second image to indicate the positions of the key points before and after modification.
As shown in fig. 6, in the generated second image, an indicator is displayed at the modified bone location to facilitate adjustment of limb movements by the learner or the online user. Optionally, the second image is a dynamic image, and the bone adjustment process is displayed, and then the indication marks are displayed before and after adjustment, or the indication marks are dynamic marks, and move along with the movement of the key point (bone), so that the adjustment position and the adjustment process are clearly displayed.
According to an embodiment of the present invention, in the image processing method 10 provided by the present invention, the generating the second image according to the second keypoint information in step S104 further includes:
and rendering and generating the second image according to the at least one key point position to be corrected and the first image, wherein the background in the second image is the same as the background in the first image, and the target object in the second image is the same as the target object in the first image.
Optionally, the server transmits the correction information back to the client, and the client renders according to the correction information to generate a second image, wherein the background in the second image is the same as the background in the first image, and the target object in the second image is the same as the target object in the first image.
According to one embodiment of the present invention, a second image is generated as shown in fig. 7, wherein the background in the corrective action image (the second image) is the same as the background in the original image uploaded by the user (the first image), and the target object in the corrective action image is the same as the target object in the original image uploaded by the user.
The image processing method 10 provided in one or more embodiments of the present invention compares the action image frames in the action pictures or videos uploaded by the students or the online users with the standard actions by using the key point detection technology, obtains the information to be corrected, generates the correction image by one or more means such as image modification, rendering, text prompting, indication marks, etc., vividly and timely feeds back the action standard condition of the exercise user, and provides technical possibility for supervision and interaction of the current exercise course on the internet.
According to an embodiment of the present invention, as shown in fig. 8, the present invention further provides a human body posture correction system 100, including one or more clients 110 and a server 120, wherein:
receiving a first image uploaded by a user, wherein a first gesture of a target object is displayed in the first image;
and sending an instruction for requesting to acquire correction information to a server, and uploading the first image.
The server 120 is configured to perform the image processing method 10 as described in one or more of the embodiments above and to return the second image to the client;
the client 110 is configured to send a correction information configuration completion instruction to the user to cause the user to download the second image.
In the human body posture correcting system 100 provided by the present invention, the specific limitation of the server 120 in executing the image processing method is similar to the specific limitation of the image processing method 10 described above, and reference may be made to the description of the image processing method 10, and the description thereof will not be repeated here.
According to an embodiment of the present invention, as shown in fig. 9, in the human body posture correction system 100 provided by the present invention, an interaction process of one or more users, one or more clients, and a server includes:
uploading a motion picture to be corrected to a client by a user;
the client requests the server to acquire action correction information;
the server side performs skeleton recognition and marking on the actions in the action pictures, compares the actions with standard skeleton actions in a database, and generates difference points;
producing actions to be optimized based on the difference points;
returning the action information to be optimized to the client;
the client renders the image and displays the optimized action;
the user clicks on the point of action to be optimized.
The human body posture correction system 100 provided by the invention utilizes a key point detection technology to compare action image frames in action pictures or videos uploaded by students or online users with standard actions to obtain information to be corrected, modifies a posture image uploaded by the users into a standard action image through a machine learning component, specifically modifies the wrong skeleton position, optionally generates a frame image for each wrong skeleton position, then forms a correction animation, enriches the correction image through one or more means such as image rendering, text prompt, indication marks and the like, so that after the students or offline users receive the action correction image, the position of the current action which does not reach the standard can be clear at a glance, and the modification is completed on a part-by-part basis of a feedback image. The human body posture correction system provided by the invention is vivid and timely feeds back the condition that the actions of the body-building user reach the standard, provides technical possibility for supervision and interaction of the current body-building course on the Internet, and enables students or online users to enjoy private and educational body-building experience at home.
The present invention also provides an electronic device including:
a processor; and
a memory storing a computer program that, when executed by the processor, causes the processor to perform the image processing method 10 as described in one or more embodiments above.
The present invention also provides a non-transitory computer readable storage medium having stored thereon computer readable instructions that, when executed by a processor, cause the processor to perform the image processing method 10 as described in one or more embodiments above.
The foregoing has outlined rather broadly the more detailed description of embodiments of the present application, wherein specific examples have been provided herein to illustrate the principles and embodiments of the present application, and wherein the above examples are provided to assist in the understanding of the methods and concepts of the present application. Meanwhile, based on the ideas of the present application, those skilled in the art can make changes or modifications on the specific embodiments and application scope of the present application, which belong to the scope of the protection of the present application. In view of the foregoing, this description should not be construed as limiting the application.
Claims (12)
1. An image processing method, comprising:
acquiring a first image, wherein a first gesture of a target object is displayed in the first image;
processing the first image to obtain first key point information, wherein the first key point information comprises at least one key point position;
comparing the first key point information with preset key point position information to generate second key point information, wherein the second key point information comprises at least one key point position to be corrected;
and generating a second image according to the second key point information, wherein the second image displays a second gesture of the target object.
2. The method of claim 1, further comprising:
and generating text information based on the second key point information, and displaying the text information on the second image.
3. The method of claim 1 or 2, wherein the processing the first image to obtain first keypoint information comprises:
and obtaining the first key point information through key point detection.
4. The method of claim 1 or 2, wherein the first image comprises a motion image uploaded by a user or a motion image frame obtained from a video uploaded by a user; the preset key point position information comprises the key point position of the standard action image.
5. The method of claim 1 or 2, wherein the generating a second image from the second keypoint information comprises:
determining the at least one keypoint location to be corrected;
the first image is modified by a machine learning component based on the keypoint location to be corrected, generating the second image.
6. The method of claim 1 or 2, wherein the second image comprises a multi-frame dynamic image, the generating a second image from the second keypoint information comprising:
determining a first to-be-corrected keypoint location of the at least one to-be-corrected keypoint location;
and modifying the first image by a machine learning component based on the first key point position to be corrected to generate a frame image of the second image.
7. The method of claim 6, wherein the generating a second image from the second keypoint information further comprises:
and displaying the modification process of the key points on the second image.
8. The method of claim 7, wherein the generating a second image from the second keypoint information further comprises:
an indication mark is added in the second image to indicate the positions of the key points before and after modification.
9. The method of claim 1 or 2, wherein the generating a second image from the second keypoint information comprises:
and rendering and generating the second image according to the at least one key point position to be corrected and the first image, wherein the background in the second image is the same as the background in the first image, and the target object in the second image is the same as the target object in the first image.
10. A human body posture correction system comprising one or more clients and a server, comprising:
the client is configured to receive a first image uploaded by a user, and a first gesture of a target object is displayed in the first image; transmitting an instruction for requesting to acquire correction information to a server, and uploading the first image;
the server being configured to perform the image processing method of any of claims 1-9 and to return the second image to the client;
the client is configured to send a correction information configuration completion instruction to the user to enable the user to download the second image.
11. An electronic device, comprising:
a processor; and
a memory storing a computer program which, when executed by the processor, causes the processor to perform the image processing method of any one of claims 1-9.
12. A non-transitory computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor, cause the processor to perform the image processing method of any of claims 1-9.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117499748A (en) * | 2023-11-02 | 2024-02-02 | 江苏濠汉信息技术有限公司 | Classroom teaching interaction method and system based on edge calculation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117499748A (en) * | 2023-11-02 | 2024-02-02 | 江苏濠汉信息技术有限公司 | Classroom teaching interaction method and system based on edge calculation |
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