CN110059623B - Method and apparatus for generating information - Google Patents
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- CN110059623B CN110059623B CN201910312192.2A CN201910312192A CN110059623B CN 110059623 B CN110059623 B CN 110059623B CN 201910312192 A CN201910312192 A CN 201910312192A CN 110059623 B CN110059623 B CN 110059623B
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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Abstract
The embodiment of the disclosure discloses a method and a device for generating information. One embodiment of the method comprises: acquiring a target vehicle body image; inputting a target vehicle body image into a pre-trained vehicle body type recognition model to obtain type information, wherein the vehicle body type recognition model corresponds to a preset number of vehicle body postures, and the type information is used for indicating the vehicle body postures corresponding to the target vehicle body image in the preset number of vehicle body postures; and performing key point detection on the target vehicle body image based on the acquired category information to generate vehicle body key point information. The embodiment is beneficial to processing the vehicle body image by utilizing the identified key points, and the image processing efficiency can be improved; in addition, the key point detection is carried out on the vehicle body image based on the vehicle body posture corresponding to the vehicle body image, so that the accuracy of the generated vehicle body key point information can be improved, and the accuracy of processing the vehicle body image is improved.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a method and an apparatus for generating information.
Background
The vehicle body can be a part used for loading people on the vehicle or the whole vehicle. At present, people can shoot a vehicle body by using a camera to obtain an image containing a vehicle body area. When one needs to process a vehicle body region in an obtained image (for example, add a special effect, three-dimensional modeling, or the like), it is necessary to artificially identify the vehicle body region from the image and further process the identified vehicle body region.
Disclosure of Invention
Embodiments of the present disclosure propose methods and apparatuses for generating information.
In a first aspect, an embodiment of the present disclosure provides a method for generating information, the method including: acquiring a target vehicle body image; inputting a target vehicle body image into a pre-trained vehicle body type recognition model to obtain type information, wherein the vehicle body type recognition model corresponds to a preset number of vehicle body postures, and the type information is used for indicating the vehicle body postures corresponding to the target vehicle body image in the preset number of vehicle body postures; and performing key point detection on the target vehicle body image based on the acquired category information to generate vehicle body key point information.
In some embodiments, performing keypoint detection on the target vehicle body image based on the obtained category information, generating the vehicle body keypoint information comprises: and in response to the fact that the vehicle body posture indicated by the category information meets the preset condition, inputting the target vehicle body image into a pre-trained vehicle body key point recognition model, and obtaining vehicle body key point information used for representing the position of the vehicle body key point in the target vehicle body image.
In some embodiments, the method further comprises: and processing an image area corresponding to the key points of the vehicle body represented by the key point information of the vehicle body in the target vehicle body image to obtain a result vehicle body image.
In some embodiments, the method further comprises: and displaying the resulting vehicle body image.
In some embodiments, the method further comprises: and sending the result vehicle body image to a user terminal in communication connection, and controlling the user terminal to display the result vehicle body image.
In some embodiments, performing keypoint detection on the target vehicle body image based on the obtained category information, generating the vehicle body keypoint information further comprises: and generating body key point information for representing that the target body image does not comprise the body key point in response to determining that the body posture indicated by the category information does not meet the preset condition.
In a second aspect, an embodiment of the present disclosure provides an apparatus for generating information, the apparatus including: an image acquisition unit configured to acquire a target vehicle body image; the image input unit is configured to input a target vehicle body image into a pre-trained vehicle body type recognition model to obtain type information, wherein the vehicle body type recognition model corresponds to a preset number of vehicle body postures, and the type information is used for indicating the vehicle body posture corresponding to the target vehicle body image in the preset number of vehicle body postures; and an information generating unit configured to perform key point detection on the target vehicle body image based on the obtained category information, and generate vehicle body key point information.
In some embodiments, the information generating unit is further configured to: and in response to the fact that the vehicle body posture indicated by the category information meets the preset condition, inputting the target vehicle body image into a pre-trained vehicle body key point recognition model, and obtaining vehicle body key point information used for representing the position of the vehicle body key point in the target vehicle body image.
In some embodiments, the apparatus further comprises: and the image processing unit is configured to process an image area corresponding to the key points of the vehicle body represented by the key point information of the vehicle body in the target vehicle body image to obtain a result vehicle body image.
In some embodiments, the apparatus further comprises: an image display unit configured to display the resultant body image.
In some embodiments, the apparatus further comprises: and the image transmitting unit is configured to transmit the result vehicle body image to a user terminal in communication connection and control the user terminal to display the result vehicle body image.
In some embodiments, the information generating unit is further configured to: and generating body key point information for representing that the target body image does not comprise the body key point in response to determining that the body posture indicated by the category information does not meet the preset condition.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method of any of the embodiments of the method for generating information described above.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, which when executed by a processor, implements the method of any of the above-described methods for generating information.
According to the method and the device for generating information, the target vehicle body image is obtained, then the target vehicle body image is input into a pre-trained vehicle body type recognition model, and type information is obtained, wherein the vehicle body type recognition model corresponds to a preset number of vehicle body postures, the type information is used for indicating the vehicle body postures corresponding to the target vehicle body image in the preset number of vehicle body postures, and finally, key point detection is performed on the target vehicle body image based on the obtained type information to generate the key point information of the vehicle body, so that the key points of the vehicle body can be recognized, the vehicle body image can be processed by using the recognized key points, compared with the method of artificially selecting the vehicle body area from the vehicle body image and processing the vehicle body area, the method and the device for generating information can improve the efficiency of image processing; in addition, the key point detection is carried out on the vehicle body image based on the vehicle body posture corresponding to the vehicle body image, so that the accuracy of the generated vehicle body key point information can be improved, the accuracy of processing the vehicle body image is improved, and the more accurate and processed vehicle body image is generated and displayed.
Drawings
Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;
FIG. 2 is a flow diagram for one embodiment of a method for generating information, according to the present disclosure;
FIG. 3 is a schematic diagram of one application scenario of a method for generating information in accordance with an embodiment of the present disclosure;
FIG. 4 is a flow diagram of yet another embodiment of a method for generating information according to the present disclosure;
FIG. 5 is a schematic block diagram illustrating one embodiment of an apparatus for generating information according to the present disclosure;
FIG. 6 is a schematic block diagram of a computer system suitable for use with an electronic device implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the disclosed method for generating information or apparatus for generating information may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as an image processing application, a graphic software application, a web browser application, a search application, an instant messaging tool, a mailbox client, a social platform software, and the like, may be installed on the terminal devices 101, 102, and 103.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, and 103 are hardware, they may be various electronic devices with cameras, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, such as an image processing server that processes a target body image captured by the terminal apparatuses 101, 102, 103. The image processing server may perform processing such as analysis on the received data such as the target vehicle body image, and obtain a processing result (for example, vehicle body key point information).
It should be noted that the method for generating information provided by the embodiment of the present disclosure may be executed by the terminal devices 101, 102, and 103, or may be executed by the server 105, and accordingly, the apparatus for generating information may be disposed in the terminal devices 101, 102, and 103, or may be disposed in the server 105.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules used to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. In the case where data used in the process of generating the body key point information does not need to be acquired from a remote place, the above system architecture may not include a network but only include a terminal device or a server.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for generating information in accordance with the present disclosure is shown. The method for generating information comprises the following steps:
In the present embodiment, the execution subject of the method for generating information (e.g., the server 105 shown in fig. 1) may acquire the target body image from a remote or local place by a wired connection manner or a wireless connection manner. The target vehicle body image is a vehicle body image to be detected. Specifically, the target vehicle body image may be an image obtained by photographing a vehicle body.
In this embodiment, based on the target body image obtained in step 201, the executing body may input the target body image into a pre-trained body type recognition model to obtain type information. The vehicle body type recognition model corresponds to a preset number of vehicle body postures. The body posture refers to the direction of the body displayed in the body image. In practice, the direction of the vehicle body displayed in the vehicle body image is determined by the shooting angle of view when the vehicle body image is obtained by shooting, and further, the preset number of vehicle body postures can correspond to the preset number of shooting angles of view. As an example, the vehicle body type recognition model may correspond to 5 vehicle body postures determined in advance, and the 5 vehicle body postures correspond to 5 shooting angles, wherein the 5 shooting angles may be determined in advance, such as respectively straight ahead, side ahead, straight behind, side behind, and straight sideways.
It should be noted that the correspondence between the vehicle body type recognition model and the preset number of vehicle body postures may be determined during training to obtain the vehicle body type recognition model. Specifically, the body posture corresponding to the sample body image used for training the body type recognition model determines the body posture corresponding to the body type recognition model.
In this embodiment, the category information may be used to indicate a body posture corresponding to the target body image in a preset number of body postures, and may include, but is not limited to, at least one of the following: characters, numbers, symbols, images. As an example, the preset number of body postures corresponding to the body kind identification model may respectively correspond to predetermined numbers (for example, the body kind identification model corresponds to 5 body postures, and the 5 body postures respectively correspond to the following numbers: 1; 2; 3; 4; 5), and the obtained kind information may be the number (for example, "1") of the body posture corresponding to the target body image.
In this embodiment, the vehicle body type identification model may be used to represent the correspondence between the vehicle body image and the type information corresponding to the vehicle body image. Specifically, as an example, the vehicle body type identification model may be a correspondence table in which a plurality of vehicle body images and corresponding type information are stored, the correspondence table being previously prepared by a technician based on statistics of a large number of vehicle body images and type information corresponding to the vehicle body images; the model may be a model obtained by training an initial model (e.g., a neural network) by a machine learning method based on a preset training sample.
And step 203, performing key point detection on the target vehicle body image based on the obtained category information to generate vehicle body key point information.
In this embodiment, based on the category information obtained in step 202, the executing entity may perform key point detection on the target vehicle body image to generate vehicle body key point information. Wherein, the vehicle body key point information is used for indicating the vehicle body key point in the target vehicle body image, and may include but is not limited to at least one of the following: numbers, words, symbols, images.
In practice, the body key points may be key points in the body, and in particular, may be points that affect the body contour or characterize the structure of the body. As an example, the key points of the vehicle body may be points corresponding to windows, lights, and the like.
Specifically, the executing body may perform key point detection on the target vehicle body image by using various methods based on the obtained category information, and generate vehicle body key point information.
As an example, a key point detection model may be trained in advance for each of a preset number of body poses corresponding to the body type recognition model, and finally, the preset number of key point detection models may be obtained. Furthermore, the execution main body may first select a key point detection model, which has a corresponding body posture matched (same or similar) with the body posture indicated by the obtained category information, from a preset number of key point detection models. Then, the execution main body can input the target vehicle body image into the selected key point detection model to obtain the vehicle body key point information.
In this example, the keypoint detection model may be used to characterize the correspondence of the vehicle body image to the vehicle body keypoint information. For each key point detection model in a preset number of key point detection models, the key point detection model can be used for detecting key points of the corresponding vehicle body image with the vehicle body posture matched with the vehicle body posture corresponding to the key point detection model, so that the accuracy of key point detection can be improved, and more accurate vehicle body key point information can be generated.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for generating information according to the present embodiment. In the application scenario of fig. 3, the server 301 may first acquire a target body image 302. Then, the server 301 may input the target body image 302 into a pre-trained body type recognition model 303, and obtain type information 304, where the body type recognition model 303 corresponds to five (i.e., a preset number) body postures which are predetermined, and the type information 304 is used to indicate the body posture corresponding to the target body image 302 in the five body postures. Finally, the server 301 may perform key point detection on the target vehicle body image 302 based on the obtained category information 304, and generate vehicle body key point information 305.
The method provided by the embodiment of the disclosure can identify the key points of the vehicle body, is beneficial to processing the vehicle body image by utilizing the identified key points, and can improve the efficiency of image processing compared with artificially selecting the vehicle body area from the vehicle body image and processing the vehicle body area; in addition, the key point detection is carried out on the vehicle body image based on the vehicle body posture corresponding to the vehicle body image, so that the accuracy of the generated vehicle body key point information can be improved, the accuracy of processing the vehicle body image is improved, and the more accurate and processed vehicle body image is generated and displayed.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for generating information is shown. The flow 400 of the method for generating information comprises the steps of:
In the present embodiment, the execution subject of the method for generating information (e.g., the server 105 shown in fig. 1) may acquire the target body image from a remote or local place by a wired connection manner or a wireless connection manner. The target vehicle body image is a vehicle body image to be detected. Specifically, the target vehicle body image may be an image obtained by photographing a vehicle body.
In this embodiment, based on the target body image obtained in step 401, the execution subject may input the target body image into a body type recognition model trained in advance to obtain type information. The vehicle body type recognition model corresponds to a preset number of vehicle body postures. The body posture refers to the direction of the body displayed in the body image. In practice, the direction of the vehicle body displayed in the vehicle body image is determined by the shooting angle of view when the vehicle body image is obtained by shooting, and further, the preset number of vehicle body postures can correspond to the preset number of shooting angles of view. The category information may be used to indicate a body posture corresponding to the target body image in a preset number of body postures, and may include, but is not limited to, at least one of the following: characters, numbers, symbols, images. The vehicle body type identification model can be used for representing the corresponding relation between the vehicle body image and the type information corresponding to the vehicle body image.
The steps 401 and 402 are respectively the same as the steps 201 and 202 in the foregoing embodiment, and the above description for the steps 201 and 202 also applies to the steps 401 and 402, which is not described herein again.
And step 403, in response to the fact that the vehicle body posture indicated by the category information meets the preset condition, inputting the target vehicle body image into a pre-trained vehicle body key point recognition model, and obtaining vehicle body key point information used for representing the position of the vehicle body key point in the target vehicle body image.
In this embodiment, based on the category information obtained in step 402, the executing entity may input the target body image into a pre-trained body key point recognition model in response to determining that the body posture indicated by the category information satisfies a preset condition, and obtain body key point information for representing the position of the body key point in the target body image. Wherein, the key point information of the vehicle body can include but is not limited to at least one of the following: numbers, words, symbols, images. As an example, the body key point information may be coordinates of the body key point on the target body image.
In practice, the body key points may be key points in the body, and in particular, may be points that affect the body contour or characterize the structure of the body.
In the present embodiment, the preset condition may be various conditions for limiting the posture of the vehicle body, for example, the preset condition may be that the photographing angle of view corresponding to the posture of the vehicle body is a front side, or may be that the photographing angle of view corresponding to the posture of the vehicle body is an angle of view other than a front side. Specifically, the preset condition may be a condition preset for the vehicle body key point identification model. When the vehicle body posture corresponding to the vehicle body image meets the preset condition, the vehicle body key point recognition model can recognize the vehicle body key point from the vehicle body image.
It can be understood that, since the vehicle body is of a three-dimensional structure and the captured vehicle body image is of a two-dimensional structure, when the vehicle body key point of the vehicle body image is identified by the vehicle body key point identification model, the vehicle body key point cannot be identified for all the vehicle body images due to the occlusion of the vehicle body (for example, when the vehicle body key point identification model is used for identifying the key point corresponding to the front window, the vehicle body image with the capturing view angle right side (i.e. the vehicle body image which does not satisfy the preset condition) corresponding to the vehicle body posture cannot be identified for the vehicle body key point identification model), at this time, if the vehicle body image which does not satisfy the preset condition is still input into the vehicle body key point identification model for identification, the resources consumed in the image identification process are wasted, and therefore, the present embodiment determines whether the vehicle body posture corresponding to the target vehicle body image satisfies the preset condition, in response to the fact that the preset conditions are met, the target vehicle body image is input into a pre-trained vehicle body key point recognition model, resource waste in the image recognition process can be reduced, and the utilization rate of resources is improved; in addition, the vehicle body images meeting the preset conditions are identified in a targeted manner, and more accurate vehicle body key point information can be obtained.
In some optional implementation manners of this embodiment, after obtaining the vehicle body key point information, the execution subject may process an image area corresponding to the vehicle body key point represented by the vehicle body key point information in the target vehicle body image, and obtain a result vehicle body image. And the result vehicle body image is an image obtained after processing the target vehicle body image.
Here, the image area corresponding to the body key point represented by the body key point information may be an image area formed by a body contour affected by the body key point, or an image area corresponding to a structure (e.g., a window) represented by the body key point.
Specifically, the processing for the image region in this implementation may be various predetermined image processing, for example, the processing may be to adjust the color of a pixel point in the image region; or adding a preset image in the image area, etc. Here, by the limitation of the preset condition, the implementation manner can process the image area in the target vehicle body image of which the corresponding vehicle body posture meets the preset condition in a targeted manner, so that the consumption of processing resources can be reduced, and the efficiency of image processing can be improved; and moreover, by utilizing more accurate key point information of the vehicle body, the implementation mode can realize more accurate image processing and improve the accuracy of the result vehicle body image.
In some optional implementations of the embodiment, after obtaining the result vehicle body image, the execution subject may display the result vehicle body image.
In some optional implementation manners of this embodiment, after obtaining the result body image, the execution main body may further send the result body image to a user terminal connected in communication, and control the user terminal to display the result body image. The user terminal is a terminal used by the user and connected with the execution main body in a communication mode. Specifically, the execution main body may send a control signal to the user terminal, and then control the user terminal to display the resulting vehicle body image.
More accurate result vehicle body images can be obtained through the identified more accurate vehicle body key point information, and then the user terminal can be controlled by the implementation mode to display the more accurate result vehicle body images, so that the display effect of the vehicle body images is improved.
In some optional implementations of the embodiment, the executing body may further generate body key point information for representing that the target body image does not include the body key point in response to determining that the body posture indicated by the category information does not satisfy the preset condition.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the process 400 of the method for generating information in this embodiment highlights a step of inputting the target vehicle body image into a pre-trained vehicle body key point recognition model in response to that the vehicle body posture indicated by the category information satisfies a preset condition, and obtaining vehicle body key point information for representing the position of the vehicle body key point in the target vehicle body image, where the preset condition is a condition set for the vehicle body key point recognition model, and when the vehicle body posture satisfies the preset condition, the vehicle body key point recognition model can recognize the vehicle body key point. Therefore, the scheme described in the embodiment can limit the vehicle body posture corresponding to the vehicle body image used for inputting the vehicle body key point recognition model through the preset conditions, so that more accurate vehicle body key point information can be generated; moreover, for the vehicle body images which do not meet the preset conditions, the identification of the key point identification model of the vehicle body can be omitted, and compared with the identification of all kinds of vehicle body images, the scheme can save resources consumed in the process of identifying the vehicle body images which do not meet the preset conditions.
With further reference to fig. 5, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an apparatus for generating information, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable in various electronic devices.
As shown in fig. 5, the apparatus 500 for generating information of the present embodiment includes: an image acquisition unit 501, an image input unit 502, and an information generation unit 503. Wherein the image acquisition unit 501 is configured to acquire a target vehicle body image; the image input unit 502 is configured to input a target vehicle body image into a pre-trained vehicle body type recognition model, and obtain type information, wherein the vehicle body type recognition model corresponds to a pre-determined preset number of vehicle body postures, and the type information is used for indicating the vehicle body posture corresponding to the target vehicle body image in the preset number of vehicle body postures; the information generating unit 503 is configured to perform key point detection on the target vehicle body image based on the obtained category information, and generate vehicle body key point information.
In the present embodiment, the image acquisition unit 501 of the apparatus for generating information 500 may acquire the target vehicle body image from a remote or local place by a wired connection manner or a wireless connection manner. The target vehicle body image is a vehicle body image to be detected. Specifically, the target vehicle body image may be an image obtained by photographing a vehicle body.
In this embodiment, based on the target body image obtained by the image obtaining unit 501, the image input unit 502 may input the target body image into a body type recognition model trained in advance, and obtain type information. The vehicle body type recognition model corresponds to a preset number of vehicle body postures. The body posture means a direction of the body displayed in the body image for inputting the body type recognition model. In practice, the direction of the vehicle body displayed in the vehicle body image is determined by the shooting angle of view when the vehicle body image is obtained by shooting, and further, the preset number of vehicle body postures can correspond to the preset number of shooting angles of view.
In this embodiment, the category information may be used to indicate a body posture corresponding to the target body image in a preset number of body postures, and may include, but is not limited to, at least one of the following: characters, numbers, symbols, images.
In this embodiment, the vehicle body type identification model may be used to represent the correspondence between the vehicle body image and the type information corresponding to the vehicle body image.
In this embodiment, based on the category information obtained by the image input unit 502, the information generation unit 503 may perform key point detection on the target vehicle body image to generate vehicle body key point information. Wherein, the vehicle body key point information is used for indicating the vehicle body key point in the target vehicle body image, and may include but is not limited to at least one of the following: numbers, words, symbols, images.
In some optional implementations of this embodiment, the information generating unit 502 may be further configured to: and in response to the fact that the vehicle body posture indicated by the category information meets the preset condition, inputting the target vehicle body image into a pre-trained vehicle body key point recognition model, and obtaining vehicle body key point information used for representing the position of the vehicle body key point in the target vehicle body image.
In some optional implementations of this embodiment, the apparatus 500 may further include: and the image processing unit (not shown in the figure) is configured to process the image area corresponding to the key point of the vehicle body represented by the key point information of the vehicle body in the target vehicle body image to obtain a result vehicle body image.
In some optional implementations of this embodiment, the apparatus 500 may further include: and an image display unit (not shown in the figure) configured to display the resultant body image.
In some optional implementations of this embodiment, the apparatus 500 may further include: and an image transmitting unit (not shown in the figure) configured to transmit the resulting body image to a communicatively connected user terminal and control the user terminal to display the resulting body image.
In some optional implementations of this embodiment, the information generating unit 503 may be further configured to: and generating body key point information for representing that the target body image does not comprise the body key point in response to determining that the body posture indicated by the category information does not meet the preset condition.
It will be understood that the elements described in the apparatus 500 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 500 and the units included therein, and are not described herein again.
The device 500 provided by the above embodiment of the present disclosure can identify key points of a vehicle body, and is helpful for processing a vehicle body image by using the identified key points, and compared with artificially selecting a vehicle body region from the vehicle body image and processing the vehicle body region, the present disclosure can improve the efficiency of image processing; in addition, the key point detection is carried out on the vehicle body image based on the vehicle body posture corresponding to the vehicle body image, so that the accuracy of the generated vehicle body key point information can be improved, the accuracy of processing the vehicle body image is improved, and the more accurate and processed vehicle body image is generated and displayed.
Referring now to fig. 6, a schematic diagram of an electronic device (e.g., terminal devices 101, 102, 103 or server 105 of fig. 1) 600 suitable for implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a target vehicle body image; inputting a target vehicle body image into a pre-trained vehicle body type recognition model to obtain type information, wherein the vehicle body type recognition model corresponds to a preset number of vehicle body postures, and the type information is used for indicating the vehicle body postures corresponding to the target vehicle body image in the preset number of vehicle body postures; and performing key point detection on the target vehicle body image based on the acquired category information to generate vehicle body key point information.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may 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 the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The name of the unit does not in some cases constitute a limitation of the unit itself, and for example, the image acquisition unit may also be described as a "unit that acquires an image of a target vehicle body".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (14)
1. A method for generating information, comprising:
acquiring a target vehicle body image;
inputting the target vehicle body image into a pre-trained vehicle body type recognition model to obtain type information, wherein the vehicle body type recognition model corresponds to a preset number of vehicle body postures, and the type information is used for indicating the vehicle body posture corresponding to the target vehicle body image in the preset number of vehicle body postures;
and performing key point detection on the target vehicle body image based on the obtained category information to generate vehicle body key point information.
2. The method of claim 1, wherein the performing key point detection on the target vehicle body image based on the obtained category information, generating vehicle body key point information comprises:
and in response to the fact that the vehicle body posture indicated by the category information meets the preset condition, inputting the target vehicle body image into a pre-trained vehicle body key point recognition model, and obtaining vehicle body key point information used for representing the position of the vehicle body key point in the target vehicle body image.
3. The method of claim 2, wherein the method further comprises:
and processing an image area corresponding to the key points of the vehicle body represented by the key point information of the vehicle body in the target vehicle body image to obtain a result vehicle body image.
4. The method of claim 3, wherein the method further comprises:
and displaying the result vehicle body image.
5. The method of claim 3, wherein the method further comprises:
and sending the result vehicle body image to a user terminal in communication connection, and controlling the user terminal to display the result vehicle body image.
6. The method according to one of claims 2 to 5, wherein the performing key point detection on the target vehicle body image based on the obtained category information, and generating vehicle body key point information further comprises:
and generating body key point information for representing that the target body image does not comprise body key points in response to determining that the body posture indicated by the category information does not meet the preset condition.
7. An apparatus for generating information, comprising:
an image acquisition unit configured to acquire a target vehicle body image;
an image input unit configured to input the target body image into a pre-trained body type recognition model, and obtain type information, wherein the body type recognition model corresponds to a pre-determined preset number of body postures, and the type information is used for indicating the body posture corresponding to the target body image in the preset number of body postures;
and an information generating unit configured to perform key point detection on the target vehicle body image based on the obtained category information, and generate vehicle body key point information.
8. The apparatus of claim 7, wherein the information generating unit is further configured to:
and in response to the fact that the vehicle body posture indicated by the category information meets the preset condition, inputting the target vehicle body image into a pre-trained vehicle body key point recognition model, and obtaining vehicle body key point information used for representing the position of the vehicle body key point in the target vehicle body image.
9. The apparatus of claim 8, wherein the apparatus further comprises:
and the image processing unit is configured to process an image area corresponding to the key points of the vehicle body represented by the key point information of the vehicle body in the target vehicle body image to obtain a result vehicle body image.
10. The apparatus of claim 9, wherein the apparatus further comprises:
an image display unit configured to display the resultant body image.
11. The apparatus of claim 9, wherein the apparatus further comprises:
an image transmitting unit configured to transmit the result body image to a communicatively connected user terminal and control the user terminal to display the result body image.
12. The apparatus according to one of claims 8-11, wherein the information generating unit is further configured to:
and generating body key point information for representing that the target body image does not comprise body key points in response to determining that the body posture indicated by the category information does not meet the preset condition.
13. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
14. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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| CN111753635B (en) * | 2020-03-31 | 2023-08-04 | 杭州海康威视数字技术股份有限公司 | Intelligent scoring method and device for chemical experiment operation and storage medium |
| CN112131935B (en) * | 2020-08-13 | 2024-07-30 | 浙江大华技术股份有限公司 | Method and device for identifying person carried in motor vehicle compartment and computer equipment |
| CN112052807B (en) * | 2020-09-10 | 2022-06-10 | 讯飞智元信息科技有限公司 | Vehicle position detection method, device, electronic equipment and storage medium |
| CN112200183A (en) * | 2020-09-30 | 2021-01-08 | 北京字节跳动网络技术有限公司 | Image processing method, device, equipment and computer readable medium |
| CN112967339B (en) * | 2020-12-28 | 2023-07-25 | 北京市商汤科技开发有限公司 | Vehicle pose determining method, vehicle control method and device and vehicle |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102456127A (en) * | 2010-10-21 | 2012-05-16 | 三星电子株式会社 | Head posture estimation equipment and head posture estimation method |
| CN103824049A (en) * | 2014-02-17 | 2014-05-28 | 北京旷视科技有限公司 | Cascaded neural network-based face key point detection method |
| CN104268573A (en) * | 2014-09-24 | 2015-01-07 | 深圳市华尊科技有限公司 | Vehicle detecting method and device |
| CN104992148A (en) * | 2015-06-18 | 2015-10-21 | 江南大学 | ATM terminal human face key points partially shielding detection method based on random forest |
| CN105447490A (en) * | 2015-11-19 | 2016-03-30 | 浙江宇视科技有限公司 | Vehicle key point detection method based on gradient regression tree and apparatus thereof |
| CN106971187A (en) * | 2017-04-12 | 2017-07-21 | 华中科技大学 | A kind of vehicle part detection method and system based on vehicle characteristics point |
| CN107506702A (en) * | 2017-08-08 | 2017-12-22 | 江西高创保安服务技术有限公司 | Human face recognition model training and test system and method based on multi-angle |
| CN108319952A (en) * | 2017-01-16 | 2018-07-24 | 浙江宇视科技有限公司 | A kind of vehicle characteristics extracting method and device |
| CN108319907A (en) * | 2018-01-26 | 2018-07-24 | 腾讯科技（深圳）有限公司 | A kind of vehicle identification method, device and storage medium |
| CN108416321A (en) * | 2018-03-23 | 2018-08-17 | 北京市商汤科技开发有限公司 | For predicting that target object moves method, control method for vehicle and the device of direction |
| CN109063768A (en) * | 2018-08-01 | 2018-12-21 | 北京旷视科技有限公司 | Vehicle recognition methods, apparatus and system again |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108182376B (en) * | 2017-11-15 | 2020-12-08 | 浙江工业大学 | Vehicle window corner detection-based vehicle window positioning method |
| CN108108656B (en) * | 2017-11-15 | 2020-07-07 | 浙江工业大学 | Vehicle window corner detection and multidirectional projection-based vehicle window accurate positioning method |
-
2019
- 2019-04-18 CN CN201910312192.2A patent/CN110059623B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102456127A (en) * | 2010-10-21 | 2012-05-16 | 三星电子株式会社 | Head posture estimation equipment and head posture estimation method |
| CN103824049A (en) * | 2014-02-17 | 2014-05-28 | 北京旷视科技有限公司 | Cascaded neural network-based face key point detection method |
| CN104268573A (en) * | 2014-09-24 | 2015-01-07 | 深圳市华尊科技有限公司 | Vehicle detecting method and device |
| CN104992148A (en) * | 2015-06-18 | 2015-10-21 | 江南大学 | ATM terminal human face key points partially shielding detection method based on random forest |
| CN105447490A (en) * | 2015-11-19 | 2016-03-30 | 浙江宇视科技有限公司 | Vehicle key point detection method based on gradient regression tree and apparatus thereof |
| CN108319952A (en) * | 2017-01-16 | 2018-07-24 | 浙江宇视科技有限公司 | A kind of vehicle characteristics extracting method and device |
| CN106971187A (en) * | 2017-04-12 | 2017-07-21 | 华中科技大学 | A kind of vehicle part detection method and system based on vehicle characteristics point |
| CN107506702A (en) * | 2017-08-08 | 2017-12-22 | 江西高创保安服务技术有限公司 | Human face recognition model training and test system and method based on multi-angle |
| CN108319907A (en) * | 2018-01-26 | 2018-07-24 | 腾讯科技（深圳）有限公司 | A kind of vehicle identification method, device and storage medium |
| CN108416321A (en) * | 2018-03-23 | 2018-08-17 | 北京市商汤科技开发有限公司 | For predicting that target object moves method, control method for vehicle and the device of direction |
| CN109063768A (en) * | 2018-08-01 | 2018-12-21 | 北京旷视科技有限公司 | Vehicle recognition methods, apparatus and system again |
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|---|---|
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Address after: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Patentee after: Douyin Vision Co.,Ltd. Address before: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Patentee before: Tiktok vision (Beijing) Co.,Ltd. Address after: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Patentee after: Tiktok vision (Beijing) Co.,Ltd. Address before: 100041 B-0035, 2 floor, 3 building, 30 Shixing street, Shijingshan District, Beijing. Patentee before: BEIJING BYTEDANCE NETWORK TECHNOLOGY Co.,Ltd. |