WO2021027890A1 - 车牌图像生成方法、装置及计算机存储介质 - Google Patents
车牌图像生成方法、装置及计算机存储介质 Download PDFInfo
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Definitions
- the theoretical three-dimensional view of the virtual license plate is adjusted to obtain the three-dimensional model of the virtual license plate.
- the determining module is used to determine the surface attribute information of the virtual license plate
- a three-dimensional model of the virtual license plate is generated according to the shape of the frame, the background color, the plurality of characters, and the sequence of the plurality of characters.
- the building module is specifically configured to determine the shape of the frame of the virtual license plate according to the deformation of the virtual license plate.
- the surface attribute information of the license plate includes at least one or more of the following attribute information:
- the shooting parameters of the virtual shooting scene include at least one or more of the following parameters:
- the color of the light in the virtual shooting scene The color of the light in the virtual shooting scene, the brightness of the light, the exposure parameter of the virtual camera in the virtual shooting scene, the positions of the virtual camera and the virtual license plate in the virtual shooting scene.
- the device further includes:
- the marking module is used for marking the license plate recognition result on the virtual license plate in the license plate image.
- a computer-readable storage medium is provided, and instructions are stored in the computer-readable storage medium, which when run on a computer, cause the computer to execute the steps of the card image generation method provided in any one of the above aspects.
- a computer program product containing instructions which when running on a computer, causes the computer to execute the steps of the card image generation method provided by any of the above aspects.
- a virtual license plate can be simulated according to actual needs, and then a three-dimensional model of the virtual license plate can be constructed, and the license plate surface attribute information of the virtual license plate can be determined to generate a license plate image for the virtual license plate. Therefore, when determining the training samples for the neural network model for license plate recognition, the license plate image can be directly generated through the embodiment of the application according to actual needs, and the license plate image can be obtained without the need for the camera to collect the real license plate, which improves the acquisition The efficiency of license plate images.
- Fig. 1 is a flowchart of a method for generating a license plate image provided by an embodiment of the present application.
- Fig. 2 is a block diagram of a device for generating a license plate image provided by an embodiment of the present application.
- License plate recognition refers to: recognizing the characters used to indicate the license plate mark in the license plate image to obtain the license plate mark corresponding to the license plate image.
- the license plate identification includes multiple characters, and the multiple characters may be one or more of numbers, English letters, Chinese characters, and other characters.
- the license plate identification can also be called the license plate number.
- fast license plate recognition can be achieved through neural network technology.
- the diversity of training samples used to train neural network directly affects the recognition accuracy of neural network after training. Therefore, it is necessary to obtain a large number of different types of training samples to improve the recognition accuracy of the trained neural network model.
- the training samples refer to multiple license plate images, and each license plate image is marked with a label indicating the license plate identification.
- the license plate image generation method adopted in the embodiment of the present application is applied to the scene of obtaining training samples for the neural network model for license plate recognition.
- Fig. 1 is a flowchart of a method for generating a license plate image provided by an embodiment of the present application. As shown in Figure 1, the method includes the following steps:
- Step 101 Construct a three-dimensional model of a virtual license plate.
- the virtual license plate is a simulated license plate according to requirements.
- the embodiment of the present application can simulate a virtual license plate according to actual needs, and then generate a license plate image for the virtual license plate through step 101 to step 103. Therefore, when determining the training samples for the neural network model for license plate recognition, the license plate image can be directly generated through steps 101 to 103 according to actual needs, and the license plate image can be obtained without the need for the camera to collect the real license plate. The efficiency of obtaining license plate images.
- the virtual license plate is a license plate simulated according to actual needs, the license plate images corresponding to different types of virtual license plates can be generated through steps 101 to 103, which improves the diversity of license plate images in the training sample, thereby improving the subsequent basis The recognition accuracy of the neural network model trained by the training sample.
- step 101 may specifically be: determine the shape of the frame of the virtual license plate; determine the background color of the virtual license plate; determine the multiple characters and the arrangement of multiple characters on the virtual license plate for indicating the license plate identifier Sequence: According to the shape of the frame, the background color, multiple characters and the sequence of multiple characters, a three-dimensional model of the virtual license plate is generated.
- the shape of the frame of the virtual license plate may include the shape of the outer frame and may also include the shape of the inner frame.
- the realization of determining the shape of the outer frame of the virtual license plate may be: a plurality of outer frame options are displayed in the current display interface, and each outer frame option indicates a kind of outer frame shape.
- the shape of the outer frame of the virtual license plate is determined based on the outer frame option corresponding to the selection operation.
- the selection operation can be triggered by the manager according to actual needs. That is, the shape of the outer frame of the virtual license plate is determined according to actual needs. For example, if it is currently necessary to generate a license plate image for a virtual license plate of a military license plate, you can select the frame option corresponding to the military license plate from a number of frame options, so that the shape of the frame of the virtual license plate is determined to have the shape of the military license plate. The shape of the frame.
- the foregoing determination of the shape of the inner frame of the license plate and the background color of the virtual license plate can refer to the foregoing implementation of determining the shape of the outer frame of the virtual license plate, which will not be elaborated here.
- the deformation of the virtual license plate can also be considered.
- the deformation conditions include deformation due to breakage, deformation due to folding, deformation due to wrinkle distortion, and so on.
- the shape of the frame of a normal license plate is a rectangle. When the license plate is damaged, a certain corner of the frame of the license plate may be worn away. At this time, the shape of the outer frame of the license plate is not a rectangle, but may be a trapezoid.
- a virtual virtual machine After determining the frame shape, background color, multiple characters, and the sequence of multiple characters by any of the above methods, a virtual virtual machine can be generated according to the frame shape, background color, multiple characters, and the sequence of multiple characters.
- Three-dimensional model of license plate The three-dimensional model of the virtual license plate is used to indicate the three-dimensional view of the virtual license plate.
- the surface attribute information of the license plate includes at least one or more of the following attribute information:
- Step 103 Generate a license plate image for the virtual license plate according to the surface attribute information of the license plate and the three-dimensional model.
- the license plate image for the virtual license plate can be generated through step 103.
- the shooting parameters of the virtual shooting scene described above include at least one or more of the following parameters: the color of the light in the virtual shooting scene, the brightness of the light, and the exposure parameters of the virtual camera in the virtual shooting scene , The position of the virtual camera and the virtual license plate in the virtual shooting scene.
- the exposure parameters of the virtual camera may include the exposure time and exposure intensity of the virtual camera.
- the shooting parameters are only used for illustration.
- the shooting parameters may include any shooting factors that can affect the license plate image collected by the camera, and the examples are not described here.
- the virtual license plate constructed by the embodiment of the present application is used for subsequent training of the recognition model. Therefore, after generating the license plate image for the virtual license plate according to the license plate attribute information and the three-dimensional model in step 103, it can also be included in the license plate image Annotate the license plate recognition result on the virtual license plate, so that the license plate recognition result and the license plate image are used as a training sample in the future.
- a virtual license plate can be simulated according to actual needs, and then a three-dimensional model of the virtual license plate is constructed, and the license plate surface attribute information of the virtual license plate is determined to generate a license plate image for the virtual license plate. Therefore, when determining the training samples for the neural network model for license plate recognition, the license plate image can be directly generated through the embodiment of the application according to actual needs, and the license plate image can be obtained without the need for the camera to collect the real license plate, which improves the acquisition The efficiency of license plate images.
- the building module is specifically used for:
- license plate attribute information and three-dimensional model generate virtual photos for virtual license plates
- a virtual license plate can be simulated according to actual needs, and then a three-dimensional model of the virtual license plate is constructed, and the license plate surface attribute information of the virtual license plate is determined to generate a license plate image for the virtual license plate. Therefore, when determining the training samples for the neural network model for license plate recognition, the license plate image can be directly generated through the embodiment of the application according to actual needs, and the license plate image can be obtained without the need for the camera to collect the real license plate, which improves the acquisition The efficiency of license plate images.
- the virtual license plate is a license plate simulated according to actual needs
- the license plate images corresponding to different types of virtual license plates can be generated through the embodiments of the present application, which improves the diversity of the license plate images in the training samples, thereby improving the subsequent training according to The recognition accuracy of the neural network model trained by the sample.
- the terminal 300 includes a processor 301 and a memory 302.
- the power supply 309 is used to supply power to various components in the terminal 300.
- the power source 309 may be alternating current, direct current, disposable batteries, or rechargeable batteries.
- the rechargeable battery may support wired charging or wireless charging.
- the rechargeable battery can also be used to support fast charging technology.
- the acceleration sensor 311 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 300.
- the acceleration sensor 311 may be used to detect the components of the gravitational acceleration on three coordinate axes.
- the processor 301 may control the touch screen 305 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 311.
- the acceleration sensor 311 may also be used for the collection of game or user motion data.
- the gyroscope sensor 312 can detect the body direction and rotation angle of the terminal 300, and the gyroscope sensor 312 can cooperate with the acceleration sensor 311 to collect the user's 3D actions on the terminal 300.
- the processor 301 can implement the following functions according to the data collected by the gyroscope sensor 312: motion sensing (for example, changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.
- the pressure sensor 313 may be disposed on the side frame of the terminal 300 and/or the lower layer of the touch screen 305.
- the processor 301 performs left and right hand recognition or quick operation according to the holding signal collected by the pressure sensor 313.
- the processor 301 controls the operability controls on the UI interface according to the user's pressure operation on the touch display screen 305.
- the operability control includes at least one of a button control, a scroll bar control, an icon control, and a menu control.
- the embodiment of the present application also provides a computer program product containing instructions, which when running on a terminal, causes the terminal to execute the method for generating a license plate image provided in the foregoing embodiment.
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Abstract
Description
Claims (17)
- 一种车牌图像生成方法,其特征在于,所述方法包括:构建虚拟车牌的三维模型,所述虚拟车牌为根据需求模拟出的车牌;确定所述虚拟车牌的车牌表面属性信息;根据所述车牌表面属性信息和所述三维模型,生成针对所述虚拟车牌的车牌图像。
- 如权利要求1所述的方法,其特征在于,所述构建虚拟车牌的三维模型,包括:确定所述虚拟车牌的边框的形状;确定所述虚拟车牌的底色;确定所述虚拟车牌上用于指示车牌标识的多个字符以及所述多个字符的排列顺序;根据所述边框的形状、所述底色、所述多个字符以及所述多个字符的排列顺序,生成所述虚拟车牌的三维模型。
- 如权利要求2所述的方法,其特征在于,所述确定所述虚拟车牌的边框的形状,包括:根据所述虚拟车牌的形变情况,确定所述虚拟车牌的边框的形状。
- 如权利要求2所述的方法,其特征在于,所述根据所述边框的形状、所述底色、所述多个字符以及所述多个字符的排列顺序,生成所述虚拟车牌的三维模型,包括:根据所述边框的形状、所述底色、所述多个字符以及所述多个字符的排列顺序,生成所述虚拟车牌的理论三维视图;根据所述虚拟车牌的折痕情况,对所述虚拟车牌的理论三维视图进行调整,得到所述虚拟车牌的三维模型。
- 如权利要求1所述的方法,其特征在于,所述车牌表面属性信息至少包括以下属性信息中的一种或多种:所述虚拟车牌的金属属性;所述虚拟车牌的漫反射属性;所述虚拟车牌的平整度;所述虚拟车牌的镜面反射属性;所述虚拟车牌上的灰尘情况;所述虚拟车牌上的污渍情况、覆盖情况以及遮挡情况。
- 如权利要求1所述的方法,其特征在于,所述根据所述车牌属性信息和所述三维模型,生成针对所述虚拟车牌的车牌图像,包括:确定虚拟拍摄场景的拍摄参数;根据所述拍摄参数、所述车牌属性信息和所述三维模型,生成针对所述虚拟车牌的虚拟照片;对所述虚拟照片进行后处理,得到所述车牌图像。
- 如权利要求6所述的方法,其特征在于,所述虚拟拍摄场景的拍摄参数至少包括以下参数中的一种或多种:所述虚拟拍摄场景中的灯光的颜色、所述灯光的亮度、所述虚拟拍摄场景中虚拟摄像机的曝光参数、在所述虚拟拍摄场景中所述虚拟摄像机和所述虚拟车牌的位置。
- 如权利要求1所述的方法,其特征在于,所述根据所述车牌属性信息和所述三维模型,生成针对所述虚拟车牌的车牌图像之后,还包括:在所述车牌图像中标注所述虚拟车牌上的车牌识别结果。
- 一种车牌图像生成装置,其特征在于,所述装置包括:构建模块,用于构建虚拟车牌的三维模型,所述虚拟车牌为根据需求模拟出的车牌;确定模块,用于确定所述虚拟车牌的车牌表面属性信息;生成模块,用于根据所述车牌表面属性信息和所述三维模型,生成针对所述虚拟车牌的车牌图像。
- 如权利要求9所述的装置,其特征在于,所述构建模块,具体用于:确定所述虚拟车牌的边框的形状;确定所述虚拟车牌的底色;确定所述虚拟车牌上用于指示车牌标识的多个字符以及所述多个字符的排列顺序;根据所述边框的形状、所述底色、所述多个字符以及所述多个字符的排列顺序,生成所述虚拟车牌的三维模型。
- 如权利要求10所述的装置,其特征在于,所述构建模块,具体用于:根据所述虚拟车牌的形变情况,确定所述虚拟车牌的边框的形状。
- 如权利要求10所述的装置,其特征在于,所述构建模块,具体用于:根据所述边框的形状、所述底色、所述多个字符以及所述多个字符的排列顺序,生成所述虚拟车牌的理论三维视图;根据所述虚拟车牌的折痕情况,对所述虚拟车牌的理论三维视图进行调整,得到所述虚拟车牌的三维模型。
- 如权利要求9所述的装置,其特征在于,所述车牌表面属性信息至少包括以下属性信息中的一种或多种:所述虚拟车牌的金属属性;所述虚拟车牌的漫反射属性;所述虚拟车牌的平整度;所述虚拟车牌的镜面反射属性;所述虚拟车牌上的灰尘情况;所述虚拟车牌上的污渍情况、覆盖情况以及遮挡情况。
- 如权利要求9所述的装置,其特征在于,所述生成模块,具体用于:确定虚拟拍摄场景的拍摄参数;根据所述拍摄参数、所述车牌属性信息和所述三维模型,生成针对所述虚 拟车牌的虚拟照片;对所述虚拟照片进行后处理,得到所述车牌图像。
- 如权利要求14所述的装置,其特征在于,所述虚拟拍摄场景的拍摄参数至少包括以下参数中的一种或多种:所述虚拟拍摄场景中的灯光的颜色、所述灯光的亮度、所述虚拟拍摄场景中虚拟摄像机的曝光参数、在所述虚拟拍摄场景中所述虚拟摄像机和所述虚拟车牌的位置。
- 如权利要求9所述的装置,其特征在于,所述装置还包括:标注模块,用于在所述车牌图像中标注所述虚拟车牌上的车牌识别结果。
- 一种车牌图像生成装置,其特征在于,所述装置包括:处理器;用于存储处理器可执行指令的存储器;其中,所述处理器被配置为执行上述权利要求1至权利要求8中的任一项权利要求所述的方法的步骤。
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CN115063785A (zh) * | 2022-08-17 | 2022-09-16 | 深圳联友科技有限公司 | 高速公路场景使用目标识别模型定位车牌的方法及装置 |
CN115497084A (zh) * | 2022-11-14 | 2022-12-20 | 深圳天海宸光科技有限公司 | 一种仿真车牌图片和字符级别标注的生成方法 |
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CN115063785A (zh) * | 2022-08-17 | 2022-09-16 | 深圳联友科技有限公司 | 高速公路场景使用目标识别模型定位车牌的方法及装置 |
CN115497084A (zh) * | 2022-11-14 | 2022-12-20 | 深圳天海宸光科技有限公司 | 一种仿真车牌图片和字符级别标注的生成方法 |
CN115497084B (zh) * | 2022-11-14 | 2023-03-31 | 深圳天海宸光科技有限公司 | 一种仿真车牌图片和字符级别标注的生成方法 |
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