CN115830044B - Image segmentation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses an image segmentation method, an image segmentation device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring all original scanned images of a target area; determining an optimal image in the original scanned image according to normal vectors of the original scanned image and the target image; establishing a blank canvas according to the size and resolution of the optimal image; and filling each pixel point of the optimal image into the blank canvas to obtain the segmented image. According to the embodiment of the invention, the blank canvas is established by determining the optimal image close to the target image, and the pixel points of the optimal image are correspondingly filled on the blank canvas, so that the image can be finely divided, and the image dividing effect can be effectively improved.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to an image segmentation method, an image segmentation device, an electronic device, and a storage medium.
Background
With the development of scientific technology, the application of image segmentation technology in practical work is becoming wider and wider. For example, in the technical field of vehicles, dangerous areas on a driving road section can be extracted through image segmentation, so as to achieve the effect of safe driving.
Currently, in the conventional image segmentation method, panoramic segmentation or component segmentation is performed on an image based on an image segmentation model, and the segmented image is fused. However, the existing image segmentation method cannot segment the image finely, so that the image segmentation effect is poor.
Disclosure of Invention
The invention provides an image segmentation method, an image segmentation device and a storage medium, which are used for solving the technical problem that the existing image segmentation method cannot segment images finely, so that the image segmentation effect is poor.
An embodiment of the present invention provides an image segmentation method including:
acquiring all original scanned images of a target area;
determining an optimal image in the original scanned image according to normal vectors of the original scanned image and the target image;
establishing a blank canvas according to the size and resolution of the optimal image;
and filling each pixel point of the optimal image into the blank canvas to obtain a segmented image.
Further, the acquiring all original scanned images of the target area includes;
based on the holographic image technique, all original scanned images of the target area are acquired.
Further, the determining the optimal image in the original scanned image according to the normal vector of the original scanned image and the target image includes:
determining a normal vector of the target image according to the target characteristics;
according to the initial normal vector and the attitude angle of the original scanned image, calculating to obtain the current normal vector of the original scanned image;
and calculating an included angle between the normal vector of the target image and the current normal vector of the original scanned image, and determining the original scanned image corresponding to the minimum included angle as an optimal image.
Further, the calculating the current normal vector of the original scanned image according to the initial normal vector and the attitude angle of the original scanned image includes:
the current normal vector of the scanned image is calculated by adopting the following formula:
wherein [ x0, y0, z0]An initial normal vector of the original scanned image; [ x, y, z]The current normal vector of the original scanned image; alpha, beta and gamma are attitude angles of the original image.
Further, the calculating the included angle between the normal vector of the target image and the current normal vector of the original scanned image includes:
the included angle between the normal vector of the target image and the current normal vector of the original scanned image is calculated by adopting the following formula:
wherein [ a, b, c ] is the normal vector of the target image.
Further, the creating a blank canvas according to the size and resolution of the optimal image includes:
taking the product of the length of the long side of the optimal image and the resolution ratio as the length of the long side of the blank canvas;
taking the product of the short side length and the resolution of the optimal image as the short side length of the blank canvas;
and setting the length and width pixels of the blank canvas to be corresponding to the grid row and column numbers of the target image.
Further, the filling each pixel point of the best image into the blank canvas to obtain a segmented image includes:
according to the resolution ratio and normal vector of the optimal image, calculating to obtain the geographic coordinates corresponding to each pixel point of the optimal image;
according to the geographic coordinates, calculating to obtain original image coordinates of the pixel points corresponding to the geographic coordinates;
and filling the pixel points on the blank canvas according to the original image coordinates.
An embodiment of the present invention provides an image segmentation apparatus including:
the scanning image acquisition module is used for acquiring all original scanning images of the target area;
the optimal image determining module is used for determining an optimal image in the original scanning image according to the normal vector of the original scanning image and the target image;
the blank canvas establishing module is used for establishing a blank canvas according to the size and the resolution of the optimal image;
and the image pixel filling module is used for filling each pixel point of the optimal image into the blank canvas to obtain a segmented image.
An embodiment of the invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing an image segmentation method as described above when executing the computer program.
An embodiment of the present invention provides a computer readable storage medium, where the computer readable storage medium includes a stored computer program, where the computer program when executed controls a device in which the computer readable storage medium is located to perform an image segmentation method as described above.
According to the embodiment of the invention, the blank canvas is established by determining the optimal image close to the target image, and the pixel points of the optimal image are correspondingly filled on the blank canvas, so that the image can be finely divided, and the image dividing effect can be effectively improved.
Drawings
Fig. 1 is a schematic flow chart of an image segmentation method according to an embodiment of the present invention;
fig. 2 is a schematic view of a panoramic point cloud superimposed image provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a newly created blank canvas according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of automatic segmentation parameter setting according to an embodiment of the present invention;
FIG. 5 is a schematic view of feature line selection according to an embodiment of the present invention;
FIG. 6 is a schematic view of a segmented image provided by an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an image segmentation apparatus 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 only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the description of the present application, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or an implicit indication of the number of technical features being indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.
Referring to fig. 1, an embodiment of the present invention provides an image segmentation method, which includes:
s1, acquiring all original scanned images of a target area;
in the embodiment of the invention, the target area can be set according to actual needs, and the target area can be a certain road section; the original scan image may be a vehicle-mounted scan image. The embodiment of the invention can synchronously acquire the image data and the point cloud data during vehicle-mounted scanning based on the holographic image technology, wherein the image data has accurate position and posture information and can form a corresponding relationship with the point cloud data.
S2, determining an optimal image in the original scanned image according to normal vectors of the original scanned image and the target image;
in the embodiment of the present invention, the target image may be an image including a preset target, and the preset target may be a person, a vehicle, a road, a building, or the like.
Because the two adjacent original scanned images have a certain overlapping rate, and some road sections can be scanned back and forth, the same point cloud data or the same characteristic object can correspond to a plurality of images. In order to improve the image segmentation effect, the embodiment of the invention determines the best image closest to the target image in all original scanned images.
S3, establishing a blank canvas according to the size and resolution of the optimal image;
in the embodiment of the invention, the newly established blank canvas needs to be related to the size and resolution of the optimal image, and the pixel points of the optimal image are filled on the blank canvas to realize the segmentation of the image.
And S4, filling each pixel point of the optimal image into the blank canvas to obtain the segmented image.
In the embodiment of the invention, each pixel point of the optimal image is filled into the corresponding position of the blank canvas, and after filling of all pixel points of the optimal image is completed, a new image is generated on the blank canvas, and the image is the image obtained by dividing the optimal image.
According to the embodiment of the invention, the blank canvas is established by determining the optimal image close to the target image, and the pixel points of the optimal image are correspondingly filled on the blank canvas, so that the image can be finely divided, the image dividing effect can be effectively improved, and the image dividing result can be widely applied to production works such as quality inspection, component investigation, three-dimensional modeling and the like.
In one embodiment, all original scanned images of the target region are acquired, including;
based on the holographic image technique, all original scanned images of the target area are acquired.
In the embodiment of the invention, the original scanned image can be obtained by scanning through the holographic image technology on the road section where the vehicle runs.
The embodiment of the invention can also adopt a vehicle-mounted laser scanning system to collect the point cloud data, and calculate the collected point cloud data and extract the object vector.
According to the embodiment of the invention, the camera and the laser scanner can be rigidly connected, the line eccentricity and the angle eccentricity between the camera and the laser scanner are calibrated, and the image acquired by the camera and the point cloud data acquired by the laser scanner are registered to obtain the superimposed original scanning image shown in fig. 2.
In one embodiment, step S2, determining the best image in the original scanned image according to the normal vector of the original scanned image and the target image, may further include the following sub-steps:
s21, determining a normal vector of the target image according to the target characteristics;
in the embodiment of the invention, the normal vector [ a, b, c ] of the target image can be determined according to the actual position of the target feature, and the size of the target image can also be determined according to the size of the target feature.
S22, calculating to obtain the current normal vector of the original scanned image according to the initial normal vector and the attitude angle of the original scanned image;
in the embodiment of the invention, when the original scanned images are acquired through the camera, the gesture information corresponding to each original scanned image is stored in the gesture file of the camera, and the gesture information comprises the position, the course angle, the pitch angle, the roll angle, the gesture angle and other data of the exposure time of the camera. According to the embodiment of the invention, the current normal vector of each original scanned image can be calculated according to the initial normal vector and the attitude angle of the original scanned image.
S23, calculating an included angle between the normal vector of the target image and the current normal vector of the original scanned image, and determining the original scanned image corresponding to the minimum included angle as the optimal image.
In the embodiment of the invention, the original scanned image closest to the target image can be determined as the optimal image according to the normal vector included angle between the two images.
In one embodiment, calculating the current normal vector of the original scanned image from the initial normal vector and the pose angle of the original scanned image comprises:
the current normal vector of the scanned image is calculated by adopting the following formula:
wherein, [ x0, y0, z0] is the initial normal vector of the original scanned image; [ x, y, z ] is the current normal vector of the original scanned image; alpha, beta and gamma are attitude angles of the original image.
In the embodiment of the present invention, before the calculation is performed to obtain the current normal vector of the original scanned image, the scanned original scanned image may be further screened to obtain the original scanned image meeting the preset condition, which may specifically be:
according to the center position of the geometric figure and the position of the exposure point, an original scanning image with the distance from the target image in a preset threshold range is calculated according to a distance formula between the two points, and then the current normal vector of the original scanning image is calculated.
In one embodiment, calculating the angle between the normal vector of the target image and the current normal vector of the original scanned image includes:
the included angle between the normal vector of the target image and the current normal vector of the original scanned image is calculated by adopting the following formula:
wherein [ a, b, c ] is the normal vector of the target image.
The normal vector included angle between each original scanning image and the target image is calculated, and the original scanning image corresponding to the minimum included angle is selected as the optimal image from all normal vector included angles.
In one embodiment, step S3, creating a blank canvas according to the size and resolution of the best image, may further include the following sub-steps:
s31, taking the product of the length of the long side of the optimal image and the resolution as the length of the long side of the blank canvas;
s32, taking the product of the short side length and the resolution of the optimal image as the short side length of the blank canvas;
referring to fig. 3, in the embodiment of the present invention, when the size of the optimal image is m×n (in m) and the resolution is P, the size of the blank canvas is pm×pn (in pixels).
S33, setting the length and width pixels of the blank canvas to be corresponding to the grid row number and the grid column number of the target image.
According to the embodiment of the invention, the size of the blank canvas and the grid row number corresponding to the target image are set according to the size and resolution of the optimal image, and when the pixel points of the optimal image are sequentially filled into the blank canvas, the pixel points of the optimal image can be completely filled into the newly built blank canvas, so that a complete segmentation image can be formed.
In one embodiment, the step S4 of filling each pixel point of the best image into a blank canvas to obtain a segmented image may further include the following sub-steps:
s41, calculating to obtain the geographic coordinates corresponding to each pixel point of the optimal image according to the resolution ratio and the normal vector of the optimal image;
s42, calculating according to the geographic coordinates to obtain original image coordinates of the pixel points corresponding to the geographic coordinates;
in the embodiment of the invention, the original image coordinates corresponding to the pixel points can be obtained by adopting a collinearity equation to calculate:
wherein, (x 0, y0, z 0) is the projection center coordinate of the original image; (xA, yA, zA) is the geographic coordinates corresponding to the pixel points in the best image; f is the primary distance of the original image; wherein:
wherein,,,is the attitude angle of the original image.
S43, filling the pixel points into the blank canvas according to the original image coordinates.
In the embodiment of the invention, after the pixel points are filled on the blank canvas, the pixel points are assigned with RGB values, so that the obtained images are more accurate.
Referring to fig. 4, in one embodiment, a data processing program may be programmed according to a related algorithm, and automatic segmentation of the image corresponding to the feature information may be implemented according to the data processing program, and the generated segmented image and the feature data may be stored synchronously.
Referring to fig. 5, in one embodiment of the present invention, based on the vehicle-mounted laser point cloud and the image data, the physical object extracted by the point cloud is subjected to corresponding image segmentation, the hardware environment is configured as NVIDIA titanium Xp, the video memory of the video card is 12G, the video memory speed is 11.4Gbps, the bit width is 384 bits, and the bandwidth is 547.7GB/s. In the range of 450KM2 in Guangdong province, the image segmentation method provided by the embodiment of the invention is adopted for image segmentation. The number of the automatic extraction feature information is 718, 36 error data are deleted through man-machine interaction, and 712 effective data are finally obtained. Wherein the broken line may be used as a selected feature line in the figure.
Fig. 6 is a schematic diagram of an image segmentation result according to an embodiment of the present invention.
The embodiment of the invention has the following beneficial effects:
according to the embodiment of the invention, the blank canvas is established by determining the optimal image close to the target image, and the pixel points of the optimal image are correspondingly filled on the blank canvas, so that the image can be finely divided, the image dividing effect can be effectively improved, and the image dividing result can be widely applied to production works such as quality inspection, component investigation, three-dimensional modeling and the like.
Referring to fig. 7, based on the same inventive concept as the above embodiment, an embodiment of the present invention provides an image segmentation apparatus including:
a scanned image acquisition module 10, configured to acquire all original scanned images of the target region;
the optimal image determining module 20 is configured to determine an optimal image in the original scanned image according to normal vectors of the original scanned image and the target image;
the blank canvas creation module 30 is configured to create a blank canvas according to the size and resolution of the optimal image;
the image pixel filling module 40 is configured to fill each pixel of the best image into a blank canvas to obtain a segmented image.
In one embodiment, the scanned image acquisition module 10 is further configured to:
based on the holographic image technique, all original scanned images of the target area are acquired.
In one embodiment, the best image determination module 20 is further configured to:
determining a normal vector of the target image according to the target characteristics;
according to the initial normal vector and the attitude angle of the original scanned image, calculating to obtain the current normal vector of the original scanned image;
and calculating an included angle between the normal vector of the target image and the current normal vector of the original scanned image, and determining the original scanned image corresponding to the minimum included angle as an optimal image.
In one embodiment, calculating the current normal vector of the original scanned image from the initial normal vector and the pose angle of the original scanned image comprises:
the current normal vector of the scanned image is calculated by adopting the following formula:
wherein, [ x0, y0, z0] is the initial normal vector of the original scanned image; [ x, y, z ] is the current normal vector of the original scanned image; alpha, beta and gamma are attitude angles of the original image.
In one embodiment, calculating the angle between the normal vector of the target image and the current normal vector of the original scanned image includes:
the included angle between the normal vector of the target image and the current normal vector of the original scanned image is calculated by adopting the following formula:
wherein [ a, b, c ] is the normal vector of the target image.
In one embodiment, the blank canvas creation module 30 is further configured to:
taking the product of the length of the long side of the optimal image and the resolution as the length of the long side of the blank canvas;
taking the product of the short side length and the resolution of the optimal image as the short side length of the blank canvas;
the length and width pixels of the blank canvas are set to correspond to the grid row number of the target image.
In one embodiment, the image pixel fill module 40 is further configured to:
according to the resolution ratio and normal vector of the optimal image, calculating to obtain the geographic coordinates corresponding to each pixel point of the optimal image;
according to the geographic coordinates, calculating to obtain original image coordinates of the pixel points corresponding to the geographic coordinates;
and filling the pixel points into the blank canvas according to the original image coordinates.
An embodiment of the invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing an image segmentation method as described above when executing the computer program.
An embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, where the computer-readable storage medium is controlled to execute the image segmentation method described above by a device in which the computer program is located when the computer program is run.
The foregoing is a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention and are intended to be comprehended within the scope of the present invention.
Claims (8)
1. An image segmentation method, comprising:
acquiring all original scanned images of a target area;
determining an optimal image in the original scanned image according to normal vectors of the original scanned image and the target image, wherein the method comprises the following steps: determining a normal vector of the target image according to the actual position of the target feature; according to the initial normal vector and the attitude angle of the original scanned image, calculating to obtain the current normal vector of the original scanned image; calculating an included angle between the normal vector of the target image and the current normal vector of the original scanned image, and determining the original scanned image corresponding to the minimum included angle as an optimal image;
establishing a blank canvas according to the size and resolution of the optimal image;
filling each pixel point of the optimal image into the blank canvas to obtain a segmented image, wherein the method comprises the following steps: according to the resolution ratio and normal vector of the optimal image, calculating to obtain the geographic coordinates corresponding to each pixel point of the optimal image; according to the geographic coordinates, calculating to obtain original image coordinates of the pixel points corresponding to the geographic coordinates; filling the pixel points on the blank canvas according to the original image coordinates;
the calculating according to the geographic coordinates to obtain the original image coordinates of the pixel points corresponding to the geographic coordinates comprises the following steps:
and calculating to obtain original image coordinates corresponding to the pixel points by adopting a collineation equation:
in the method, in the process of the invention,、/>is the original image coordinatesx 0 ,y 0 ,z 0 ) Projecting a center coordinate for the original image; (x A ,y A ,z A ) The geographic coordinates corresponding to the pixel points in the optimal image; f is the primary distance of the original image; wherein:
wherein,,/>,/>for the attitude angle of the original image, +.>、/>、/>、/>、/>、/>、/>、/>、/>The three-dimensional image is 9 directional cosine corresponding to the attitude angle of the original image.
2. The image segmentation method as set forth in claim 1, wherein the acquiring all original scanned images of the target region includes;
based on the holographic image technique, all original scanned images of the target area are acquired.
3. The image segmentation method as set forth in claim 1, wherein the calculating the current normal vector of the original scanned image according to the initial normal vector and the attitude angle of the original scanned image comprises:
the current normal vector of the scanned image is calculated by adopting the following formula:
wherein [ thex 0 ,y 0 ,z 0 ]An initial normal vector of the original scanned image; [ x, y, z]The current normal vector of the original scanned image; alpha, beta and gamma are attitude angles of the original image.
4. The image segmentation method as set forth in claim 1, wherein the calculating an angle between a normal vector of the target image and a current normal vector of the original scanned image comprises:
the included angle between the normal vector of the target image and the current normal vector of the original scanned image is calculated by adopting the following formula:
wherein [ a, b, c ] is the normal vector of the target image, and [ x, y, z ] is the current normal vector of the original scanned image.
5. The image segmentation method as set forth in claim 1, wherein the creating a blank canvas according to the size and resolution of the optimal image comprises:
taking the product of the length of the long side of the optimal image and the resolution ratio as the length of the long side of the blank canvas;
taking the product of the short side length and the resolution of the optimal image as the short side length of the blank canvas;
and setting the length and width pixels of the blank canvas to be corresponding to the grid row and column numbers of the target image.
6. An image dividing apparatus, comprising:
the scanning image acquisition module is used for acquiring all original scanning images of the target area;
the optimal image determining module is used for determining an optimal image in the original scanning image according to the normal vector of the original scanning image and the target image; the method is particularly used for: determining a normal vector of the target image according to the actual position of the target feature; according to the initial normal vector and the attitude angle of the original scanned image, calculating to obtain the current normal vector of the original scanned image; calculating an included angle between the normal vector of the target image and the current normal vector of the original scanned image, and determining the original scanned image corresponding to the minimum included angle as an optimal image;
the blank canvas establishing module is used for establishing a blank canvas according to the size and the resolution of the optimal image;
the image pixel filling module is used for filling each pixel point of the optimal image into the blank canvas to obtain a segmented image, and is specifically used for: according to the resolution ratio and normal vector of the optimal image, calculating to obtain the geographic coordinates corresponding to each pixel point of the optimal image; according to the geographic coordinates, calculating to obtain original image coordinates of the pixel points corresponding to the geographic coordinates; filling the pixel points on the blank canvas according to the original image coordinates; the calculating according to the geographic coordinates to obtain the original image coordinates of the pixel points corresponding to the geographic coordinates comprises the following steps:
and calculating to obtain original image coordinates corresponding to the pixel points by adopting a collineation equation:
in the method, in the process of the invention,、/>is the original image coordinatesx 0 ,y 0 ,z 0 ) Projecting a center coordinate for the original image; (x A ,y A ,z A ) The geographic coordinates corresponding to the pixel points in the optimal image; f is the primary distance of the original image; wherein:
wherein,,/>,/>for the attitude angle of the original image, +.>、/>、/>、/>、/>、/>、/>、/>、/>The three-dimensional image is 9 directional cosine corresponding to the attitude angle of the original image.
7. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the image segmentation method of any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the image segmentation method according to any one of claims 1-5.
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