CN113947548A - Animation fast fuzzy processing method and system and intelligent terminal - Google Patents
Animation fast fuzzy processing method and system and intelligent terminal Download PDFInfo
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Abstract
The application relates to the field of image fuzzy processing, in particular to a method, a system and an intelligent terminal for fast fuzzy processing of animation, which comprises the following steps: determining a target image based on the original image; determining a target point in the target image, and determining a mapping point from the original image based on the fuzzy range parameter and the target point; based on the mapped points and the target image, a blurred image is generated. And processing each target point in the target image through each mapping point, so that each pixel point in the target image is influenced by the mapping point in the blurred image to carry out gray value conversion, and the blurred image is obtained. In the process of image blurring processing, the calculation of the gray value of a pixel point is not involved, the data calculation amount is less, the occupation of memory resources can be reduced, and the blurring algorithm is applied to an animation blurring scene in which a plurality of images are continuously blurred and played, so that the situation of unsmooth and discontinuous generation in the process of image blurring processing can be reduced, and the smooth and stable image playing effect is achieved.
Description
Technical Field
The present application relates to the field of image blur processing, and in particular, to a method and a system for fast blur processing of an animation, and an intelligent terminal.
Background
The image blurring is a visual effect in the application of the image processing field, is also a basic operation in a plurality of visual special effects, and can enable an input image to generate the blurring effect of ground glass through a series of calculations. The image blurring technique is widely applied to screen display and video processing. In the deep learning field, image blurring is also used for data enhancement, and more sample data is generated, so that the robustness of a learning model to different definition data is improved.
At present, the most widely used image blurring is gaussian blurring, which is to selectively blur image data by adjusting pixel color values according to a gaussian curve. Gaussian blur can count the color values of pixels around a certain point according to a Gaussian curve, and a mathematical weighted average calculation method is adopted to obtain the color value of the curve, so that the outline of an image is finally left. However, the gaussian fuzzy algorithm involves a large amount of data calculation, and with the enhancement of the gaussian fuzzy visual effect, the amount of data calculation increases in a power series manner, and if the algorithm is run on embedded devices such as intelligent terminal devices, the image operation is not smooth due to the problem of large memory occupation.
In the prior art, as disclosed in chinese patent application with application publication No. CN105139356A, in the method, processing operations such as blurring processing on image data according to a gaussian blurring algorithm are all completed in a GPU, and then the GPU transmits the blurred image to an intelligent terminal device.
Disclosure of Invention
The image blurring processing method has the characteristic of improving the smoothness of image operation.
The above object of the present invention is achieved by the following technical solutions:
an image blur processing method includes:
determining a target image based on the original image;
determining a target point in the target image, and determining a mapping point from the original image based on the fuzzy range parameter and the target point; the gray value of the mapping point can reflect the gray value of a pixel point around the target point, and the fuzzy range parameter can reflect the distance between the target point and the mapping point;
based on the mapped points and the target image, a blurred image is generated.
By adopting the technical scheme, the mapping points are directly extracted from the original image by utilizing the fuzzy range parameters and the target points, and the mapping points can reflect the gray level distribution of the pixel points around the target points. And processing each target point in the target image through each mapping point, so that each pixel point in the target image is influenced by the mapping point in the blurred image to carry out gray value conversion, and the blurred image is obtained. In the process of image blurring processing, the calculation of the gray value of a pixel point is not involved, the data calculation amount is less, the occupation of memory resources can be reduced, and the blurring algorithm is applied to an animation blurring scene in which a plurality of images are continuously blurred and played, so that the situation of unsmooth and discontinuous generation in the process of image blurring processing can be reduced, and the smooth and stable image playing effect is achieved.
Optionally, in a specific method for determining a target point in a target image and determining a mapping point from an original image based on the blur range parameter and the target point, the method includes:
determining a target point in a target image;
determining a target coordinate based on the target point and the reference coordinate system; the original image and the target image can be overlapped in the reference coordinate system, and the target coordinate is the coordinate of the target point in the reference coordinate system;
determining a sampling area based on the fuzzy range parameter and the target coordinate;
determining mapping coordinates based on the sampling area; wherein the mapping coordinates are coordinates of the mapping points in the reference coordinate system;
based on the mapping coordinates, mapping points are determined from the original image.
By adopting the technical scheme, a common reference coordinate system is established based on the original image and the target image, the target coordinate is taken as a base point, the fuzzy range parameter is taken as a distance, and in the reference coordinate system, the sampling area is determined, and all pixel points in the sampling area can be determined to be pixel points distributed around the target point, so that the mapping points capable of reflecting the gray value around the target point can be extracted more quickly and accurately.
Optionally, the sampling region comprises a lateral deviation interval and a longitudinal deviation interval,
in a specific method for determining a sampling region based on a blur range parameter and target coordinates, the method comprises the following steps:
determining a transverse deviation interval based on the fuzzy range parameter and the transverse target coordinate; wherein the lateral target coordinates are capable of reflecting x-axis coordinates of the target coordinates;
determining a longitudinal deviation interval based on the fuzzy range parameter and the longitudinal target coordinate; wherein the longitudinal target coordinates are capable of reflecting y-axis coordinates of the target coordinates;
in a specific method for determining mapping coordinates based on a sampling region, the method comprises the following steps:
randomly extracting based on the transverse deviation interval, and determining a transverse deviation coordinate; wherein the laterally offset coordinate is capable of reflecting an x-axis coordinate of the mapping coordinate;
randomly extracting based on the longitudinal deviation interval, and determining a longitudinal deviation coordinate; wherein the longitudinal deviation coordinate is capable of reflecting a y-axis coordinate of the mapping coordinate;
based on the laterally offset coordinates, the longitudinally offset coordinates, and the target coordinates, mapping coordinates are determined.
By adopting the technical scheme, the transverse deviation interval can reflect each pixel point distributed around the target point in the x-axis direction, one transverse deviation coordinate is randomly selected from the transverse deviation interval, the x-axis coordinate of the mapping coordinate can be determined, and the transverse deviation interval is equivalent to the selection range of the x-axis coordinate of the mapping coordinate; the longitudinal deviation interval can reflect each pixel point distributed around the target point in the y-axis direction, a longitudinal deviation coordinate is randomly selected from the longitudinal deviation interval, the y-axis coordinate of the mapping coordinate can be determined, and the longitudinal deviation interval is equivalent to the selection range of the y-axis coordinate of the mapping coordinate. By randomly extracting the mapping coordinates in the transverse deviation interval and the longitudinal deviation interval, the randomness of image blurring processing can be increased, and mapping points can reflect the gray values around the target point more randomly. On the other hand, the transverse deviation interval and the longitudinal deviation interval can limit the value range of the mapping coordinates, and the larger the value range of the mapping coordinates is, the stronger the blurring effect is, and the strong degree of the image blurring processing can be conveniently adjusted.
Optionally, the blur range parameter is set according to an intensity setting of an image blur effect.
By adopting the technical scheme, the value of the fuzzy range parameter can change the value range of the mapping coordinate, so that the image fuzzy effect strength is changed.
Optionally, in a specific method for generating a blurred image based on the mapping points and the target image, the method includes: and carrying out gray value replacement on the target point in the target image based on the mapping point to generate a blurred image.
By adopting the technical scheme, the mapping points can randomly reflect the gray values around the target point, and the gray values of the target point can be directly changed into the gray values of the mapping points, so that each pixel point in the target image can be influenced by the surrounding pixel points to change the gray values, and the image blurring effect can be achieved more directly and quickly.
In a specific method for determining a target image based on an original image, the method comprises the following steps: and performing image copying based on the original image to determine a target image.
By adopting the technical scheme, the original image is directly copied to obtain the target image, the resolution of the original image does not need to be zoomed, the data processing amount is reduced, and the fuzzy result can be obtained more quickly.
The second purpose of the application is to provide a rapid animation fuzzy processing method which has the characteristic of improving the smoothness of image operation.
The second objective of the present invention is achieved by the following technical solutions:
the animation fast fuzzy processing method comprises any one of the image fuzzy processing methods, and further comprises the following steps:
preparing data: determining an original image;
parameter determination: determining effect information; wherein the effect information comprises a blur range parameter;
and (3) cache allocation: distributing image cache based on the original image;
fast blurring: based on the original image and the fuzzy range parameter, carrying out fuzzy processing by using an image fuzzy processing method to generate a fuzzy image;
and (3) animation display: based on the blurred image, a blurred animation is displayed.
The third purpose of the present application is to provide an image blur processing module, which has the characteristic of improving the smoothness of image operation.
The third object of the invention is achieved by the following technical scheme:
an image blur processing module comprising:
an image acquisition unit for determining a target image based on an original image; wherein the resolution of the target image matches the resolution of the original image;
the fuzzy sampling unit is used for determining a target point in the target image and determining a mapping point from the original image based on the fuzzy range parameter and the target point; the gray value of the mapping point can reflect the gray value of a pixel point around the target point, and the fuzzy range parameter can reflect the distance between the target point and the mapping point;
and the fuzzy conversion unit is used for generating a fuzzy image based on the mapping points.
The fourth purpose of the application is to provide a rapid animation fuzzy processing system which has the characteristic of improving the smoothness of image operation.
The fourth object of the present invention is achieved by the following technical solutions:
the animation fast fuzzy processing system comprises the image fuzzy processing module, and further comprises:
a data preparation module: for determining an original image;
a parameter determination module: for determining effect information; wherein the effect information comprises a blur range parameter;
a cache allocation module: the image cache is distributed based on the original image;
an animation display module: for displaying a blurred animation based on the blurred image.
The fifth purpose of the application is to provide an intelligent terminal which has the characteristic of improving the smoothness of image operation.
The fifth object of the present invention is achieved by the following technical solutions:
an intelligent terminal comprises a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and can execute any one of the image blurring processing method and the animation fast blurring processing method.
Drawings
Fig. 1 is a flowchart illustrating an image blur processing method according to the present application.
Fig. 2 is a sub-flow diagram in the image blur processing method of the present application.
FIG. 3 is a schematic diagram of the relationship between the sampling area, the target coordinates and the mapping coordinates.
Fig. 4 is a schematic diagram of the result of the image blurring process of the present application, where (a) is an original image, (b) is a blurred image, and (c) is a blurred image, and the blur range parameter of (b) is smaller than the blur range parameter of (c).
FIG. 5 is a flowchart illustrating an animation fast blur processing method according to the present application.
FIG. 6 is a block diagram of an animation fast blur processing system of the present application.
Fig. 7 is a schematic unit diagram of an image blur processing module of the present application.
In the figure, 1, a data preparation module; 2. a parameter determination module; 3. a cache allocation module; 4. an image blur processing module; 41. an image acquisition unit; 42. a fuzzy sampling unit; 43. a fuzzy conversion unit; 5. and an animation display module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.
In addition, the reference numerals of the steps in this embodiment are only for convenience of description, and do not represent the limitation of the execution sequence of the steps, and in actual application, the execution sequence of the steps may be adjusted or performed simultaneously as needed, and these adjustments or substitutions all belong to the protection scope of the present invention.
Embodiments of the present application are described in further detail below with reference to figures 1-7 of the drawings.
The first embodiment is as follows:
the embodiment of the application provides an image blurring processing method, and the main flow of the method is described as follows.
Referring to fig. 1, S01, a target image is determined based on an original image.
The method aims to obtain the blurred image of the original image after the blurring processing more quickly. The target image is an image obtained by image copying based on the original image, and the resolution of the target image is consistent with that of the original image. In the present embodiment, the original image is a grayscale image. The distribution of the pixel points of the different gray values of the target image is consistent with the distribution of the pixel points of the different gray values of the original image.
And S02, determining a target point in the target image, and determining a mapping point from the original image based on the fuzzy range parameter and the target point.
The target point refers to a pixel point which is located in a fuzzy processing area of the target image and has not undergone gray value change. The blurring processing region refers to a region in the original image that needs to be subjected to blurring processing, and since the resolution of the original image matches the resolution of the target image, the position of the blurring processing region of the target image can reflect the region in the original image that needs to be subjected to blurring processing. In the present embodiment, the blur processing area covers 100% of the target image.
The mapping points refer to the pixel points distributed around the target point within the limit range corresponding to the fuzzy range parameter, so that the gray value of the mapping points can reflect the gray value of the pixel points around the target point.
The blur range parameter can reflect the distance between the target point and the mapped point. In this embodiment, the value of the blur range parameter may change the value range of the mapping coordinate, so as to change the image blur effect strength. The stronger the set fuzzy effect is, the larger the fuzzy range parameter is; the more gradual the blurring effect is set, the smaller the blurring range parameter is.
Referring to fig. 1 and 2, in step S02, the method includes:
and S021, determining a target point in the target image, and determining a target coordinate based on the reference coordinate system.
And traversing pixel points in the fuzzy processing area, which are not subjected to gray value transformation in the current fuzzy processing, to obtain target points.
The projections of the original image and the target image in the reference coordinate system can be overlapped. The target coordinates are coordinates of the target point in the reference coordinate system.
And S022, determining a sampling area based on the fuzzy range parameter and the target coordinate.
The sampling area is an area formed by taking the target coordinate as a circle center and taking the fuzzy range parameter as a radius. The sampling area is an area formed around the target coordinates and can reflect the distribution of pixel points around the target point.
Referring to fig. 2 and 3, in step S022, the method includes:
s0221, determining a transverse deviation interval based on the fuzzy range parameter and the transverse target coordinate.
Wherein the lateral target coordinates refer to the x-axis coordinates of the target coordinates. The transverse deviation interval is a value interval generated based on the transverse target coordinate and the fuzzy range parameter, and can reflect each pixel point distributed around the target point in the x-axis direction. In this embodiment, the lateral deviation interval defines the selection range of the x-axis coordinate of the mapping point.
If the value set by the blur range parameter is d, the radius corresponding to the blur range parameter is a = d/2, and if the horizontal target coordinate is x1, the horizontal deviation interval is: (x 1-a, x1+ a).
S0222, determining a longitudinal deviation interval based on the fuzzy range parameter and the longitudinal target coordinate.
Wherein the longitudinal target coordinate refers to a y-axis coordinate of the target coordinate. The longitudinal deviation interval is a value interval generated based on the longitudinal target coordinate and the fuzzy range parameter, and can reflect each pixel point distributed around the target point in the y-axis direction. In this embodiment, the longitudinal deviation interval defines a selection range of the y-axis coordinate of the mapping point.
If the value set by the blur range parameter is d, the radius corresponding to the blur range parameter is a = d/2, and if the vertical target coordinate is y1, the vertical deviation interval is: (y 1-b, y1+ b).
S023, determining the mapping coordinates based on the sampling area.
The mapping coordinates are coordinates of the mapping points in a reference coordinate system. The mapping coordinates are pixel coordinate points extracted in the sampling area.
Referring to fig. 2 and 3, in step S023, the method includes:
s0231, performing random extraction based on the lateral deviation interval, and determining the lateral deviation coordinates.
The transverse deviation coordinate refers to an x-axis coordinate of the mapping coordinate, and a numerical value corresponding to the transverse deviation coordinate is randomly extracted from the transverse deviation interval.
Taking the lateral deviation coordinate as q1 as an example, x1-a < q1 < x1+ a.
S0232, random extraction is carried out based on the longitudinal deviation interval, and longitudinal deviation coordinates are determined.
The longitudinal deviation coordinate refers to a y-axis coordinate of the mapping coordinate, and a numerical value corresponding to the longitudinal deviation coordinate is randomly extracted from a longitudinal deviation interval.
Taking the longitudinal deviation coordinate as q2 as an example, y1-b < q2 < y1+ b.
S0233, determining mapping coordinates based on the transverse deviation coordinates, the longitudinal deviation coordinates and the target coordinates.
The transverse deviation coordinates can reflect the deviation amount of the target coordinates on the x axis, the longitudinal deviation coordinates can reflect the deviation amount of the target coordinates on the y axis, and the mapping coordinates can be determined by combining the transverse deviation coordinates, the longitudinal deviation coordinates and the target coordinates.
Taking the target coordinate as (x 1, y 1), the lateral offset coordinate as q1, and the longitudinal offset coordinate as q2 as an example, the mapping coordinates are (x 2, y 2), where x2= q1 and y2= q 2.
Referring to fig. 4, by randomly extracting the mapping coordinates in the horizontal deviation interval and the vertical deviation interval, the randomness of the image blurring process can be increased, so that the mapping points can more randomly reflect the gray scale values around the target point. On the other hand, the transverse deviation interval and the longitudinal deviation interval can limit the value range of the mapping coordinates, and the larger the value range of the mapping coordinates is, the stronger the blurring effect is, and the strong degree of the image blurring processing can be conveniently adjusted.
Referring to fig. 2 and 3, S024 determines a mapping point from the original image based on the mapping coordinates.
The mapping point refers to a pixel point corresponding to the mapping coordinate in the original image.
And S03, replacing the gray value of the target point in the target image based on the mapping point to generate a blurred image.
Each target point in the target image has a corresponding mapping point, and the gray value of the target point is changed into the gray value of the mapping point corresponding to the target point, so that the gray value of each target point in the blurring processing area in the target image can be changed under the influence of the gray values of the surrounding pixel points, and the image blurring effect is achieved. Therefore, mapping points are acquired based on the target points, and gray value replacement is performed on each target point based on each mapping point, so that a blurred image can be obtained.
The implementation principle of the image blurring processing method in the first embodiment of the application is as follows: and (4) directly extracting mapping points from the original image by using the fuzzy range parameters and the target point, wherein the mapping points can reflect the gray values of pixel points around the target point. And processing each target point in the target image through each mapping point, so that each pixel point in the target image is influenced by the mapping point in the blurred image to carry out gray value conversion, and the blurred image is obtained. In the process of image blurring processing, the calculation of the gray value of a pixel point is not involved, the data calculation amount is less, the occupation of memory resources can be reduced, and the blurring algorithm is applied to an animation blurring scene in which a plurality of images are continuously blurred and played, so that the situation of unsmooth and discontinuous generation in the process of image blurring processing can be reduced, and the smooth and stable image playing effect is achieved.
Example two:
the embodiment of the application provides a rapid fuzzy processing method for animation. The main flow of the animation fast blur processing method is described as follows.
Referring to fig. 5, S1, data preparation: an original image is determined.
The original image refers to an image that needs to be blurred. In the embodiment, the image information is obtained through the intelligent terminal device, and the original image to be blurred is determined according to the image information. The user can select the original image through the intelligent terminal.
S2, parameter determination: determining effect information; wherein the effect information includes a blur range parameter.
Wherein, the effect information comprises a fuzzy range parameter and a fuzzy processing area. The fuzzy range parameter is determined by the strength of the image fuzzy processing effect set by the intelligent terminal, and the stronger the set fuzzy effect is, the larger the fuzzy range parameter is; the more gradual the blurring effect is set, the smaller the blurring range parameter is. The user can modify the fuzzy range parameter or the fuzzy processing area through the intelligent terminal.
S3, cache allocation: based on the original image, an image buffer is allocated.
And allocating a maximum buffer for the blurring operation of the original image in advance according to the width and the height of the original image.
S4, fast blur: and based on the original image and the fuzzy range parameter, carrying out fuzzy processing by using an image fuzzy processing method to generate a fuzzy image.
Wherein, the fast blurring step adopts the image blurring processing method in the above embodiment.
S5, animation display: based on the blurred image, a blurred animation is displayed.
The original image and the blurred image obtained after the blurring processing form an image sequence, and the intelligent terminal displays the image based on the image sequence to form the blurred animation. Or forming an image sequence by a plurality of blurred images obtained after the blurring treatment with different intensities, and displaying the images by the intelligent terminal based on the image sequence to form a blurred animation.
The animation fast blur processing method provided by this embodiment can implement each step of the foregoing embodiment, and therefore can achieve the same technical effect as the foregoing embodiment, and for principle analysis, reference may be made to the related description of the foregoing method steps, which will not be described herein again.
Example three:
referring to fig. 6 and 7, in one embodiment, an animation fast blurring processing system is provided, which corresponds to the animation fast blurring processing method in the second embodiment one by one, and includes a data preparation module 1, a parameter determination module 2, a buffer allocation module 3, an image blurring processing module 4, and an animation display module 5. The functional modules are explained in detail as follows:
data preparation module 1: for determining the original image and sending the original image information to the image blurring processing module 4.
The parameter determination module 2: for determining effect information and sending parameter setting information to the image blurring processing module 4.
The cache allocation module 3: for allocating an image buffer based on the original image.
The image blurring processing module 4: the image processing device is used for carrying out fuzzy processing by using an image fuzzy processing method based on the original image and the fuzzy range parameter, generating a fuzzy image and sending fuzzy image information to the animation display module 5.
The animation display module 5: for displaying a blurred animation based on the blurred image.
Specifically, the image blur processing module 4 includes:
an image acquisition unit 41, configured to determine a target image based on the original image, and send target image information to the blur sampling unit 42.
A blur sampling unit 42 for determining a mapping point from the target image based on the blur range parameter and the position of the target point, and sending mapping point information to the blur conversion unit 43.
And a blur conversion unit 43 for generating a blurred image based on the mapping points.
The animation fast fuzzy processing system provided by the embodiment can achieve the same technical effects as the previous embodiment because of the functions of the modules and the logical connection between the modules, and the principle analysis can refer to the related description of the method steps, which will not be described herein again. Example four:
in one embodiment, an intelligent terminal is provided and includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the memory stores training data, algorithm formulas, filtering mechanisms, and the like in a training model. The processor is used for providing calculation and control capability, and the processor realizes the following steps when executing the computer program:
and S01, determining a target image based on the original image.
S02, determining a mapping point from the original image based on the target point in the target image, and determining the mapping point from the original image based on the fuzzy range parameter and the mapping point.
In step S02, the method includes:
and S021, determining mapping coordinates based on the reference coordinate system.
And S022, determining a sampling area based on the fuzzy range parameter and the mapping coordinate.
In step S022, the method includes:
s0221, determining a transverse deviation interval based on the fuzzy range parameter and the transverse mapping coordinate.
S0222, determining a longitudinal deviation interval based on the fuzzy range parameter and the longitudinal mapping coordinate.
S023, determining the mapping coordinates based on the sampling area.
In step S023, the method includes:
s0231, performing random extraction based on the lateral deviation interval, and determining the lateral deviation coordinates.
S0232, random extraction is carried out based on the longitudinal deviation interval, and longitudinal deviation coordinates are determined.
S0233, determining the mapping coordinates based on the transverse deviation coordinates, the longitudinal deviation coordinates and the mapping coordinates.
And S024, determining mapping points from the original image based on the mapping coordinates.
And S03, replacing the gray value of the target point in the target image based on the mapping point to generate a blurred image.
In the intelligent terminal provided by this embodiment, after the computer program in the memory is run on the processor, the steps of the first embodiment are implemented, so that the same technical effects as those of the first embodiment can be achieved, and for principle analysis, reference may be made to the related description of the steps of the method, which will not be described herein again.
Example five:
in one embodiment, an intelligent terminal is provided and includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the memory stores training data, algorithm formulas, filtering mechanisms, and the like in a training model. The processor is used for providing calculation and control capability, and the processor realizes the following steps when executing the computer program:
s1, data preparation: an original image is determined.
S2, parameter determination: determining effect information; wherein the effect information includes a blur range parameter.
S3, cache allocation: based on the original image, an image buffer is allocated.
S4, fast blur: and based on the original image and the fuzzy range parameter, carrying out fuzzy processing by using an image fuzzy processing method to generate a fuzzy image.
S5, animation display: based on the blurred image, a blurred animation is displayed.
In the intelligent terminal provided by this embodiment, after the computer program in the memory is run on the processor, the steps of the second embodiment are implemented, so that the same technical effects as those of the second embodiment can be achieved, and for principle analysis, reference may be made to the related description of the steps of the method, which will not be described herein again.
The embodiments are preferred embodiments of the present application, and the scope of the present application is not limited by the embodiments, so: all equivalent variations made according to the methods and principles of the present application should be covered by the protection scope of the present application.
Claims (10)
1. An image blur processing method, comprising:
determining a target image based on the original image;
determining a target point in the target image, and determining a mapping point from the original image based on the fuzzy range parameter and the target point; the gray value of the mapping point can reflect the gray value of a pixel point around the target point, and the fuzzy range parameter can reflect the distance between the target point and the mapping point;
based on the mapped points and the target image, a blurred image is generated.
2. The image blur processing method according to claim 1, wherein a specific method of determining the target point in the target image and determining the mapping point from the original image based on the blur range parameter and the target point comprises:
determining a target point in a target image;
determining a target coordinate based on the target point and the reference coordinate system; the original image and the target image can be overlapped in the reference coordinate system, and the target coordinate is the coordinate of the target point in the reference coordinate system;
determining a sampling area based on the fuzzy range parameter and the target coordinate;
determining mapping coordinates based on the sampling area; wherein the mapping coordinates are coordinates of the mapping points in the reference coordinate system;
based on the mapping coordinates, mapping points are determined from the original image.
3. The image blur processing method according to claim 2, characterized in that: the sampling region includes a lateral offset interval and a longitudinal offset interval,
in a specific method for determining a sampling region based on a blur range parameter and target coordinates, the method comprises the following steps:
determining a transverse deviation interval based on the fuzzy range parameter and the transverse target coordinate; wherein the lateral target coordinates are capable of reflecting x-axis coordinates of the target coordinates;
determining a longitudinal deviation interval based on the fuzzy range parameter and the longitudinal target coordinate; wherein the longitudinal target coordinates are capable of reflecting y-axis coordinates of the target coordinates;
in a specific method for determining mapping coordinates based on a sampling region, the method comprises the following steps:
randomly extracting based on the transverse deviation interval, and determining a transverse deviation coordinate; wherein the laterally offset coordinate is capable of reflecting an x-axis coordinate of the mapping coordinate;
randomly extracting based on the longitudinal deviation interval, and determining a longitudinal deviation coordinate; wherein the longitudinal deviation coordinate is capable of reflecting a y-axis coordinate of the mapping coordinate;
based on the laterally offset coordinates, the longitudinally offset coordinates, and the target coordinates, mapping coordinates are determined.
4. The image blur processing method according to claim 1, characterized in that: the blurring range parameter is set according to the intensity setting of the image blurring effect.
5. The image blur processing method according to claim 1, wherein a specific method of generating a blurred image based on the mapping point and the target image includes:
and carrying out gray value replacement on the target point in the target image based on the mapping point to generate a blurred image.
6. The image blur processing method according to claim 1, wherein in a specific method of determining the target image based on the original image, the method comprises: and performing image copying based on the original image to determine a target image.
7. The animation fast fuzzy processing method is characterized by comprising the following steps: the image blur processing method according to any one of claims 1 to 6, further comprising:
preparing data: determining an original image;
parameter determination: determining effect information; wherein the effect information comprises a blur range parameter;
and (3) cache allocation: distributing image cache based on the original image;
fast blurring: based on the original image and the fuzzy range parameter, carrying out fuzzy processing by using an image fuzzy processing method to generate a fuzzy image;
and (3) animation display: based on the blurred image, a blurred animation is displayed.
8. An image blur processing module, comprising:
an image acquisition unit (41) for determining a target image based on an original image; wherein the resolution of the target image matches the resolution of the original image;
a blur sampling unit (42) for determining a target point in the target image and determining a mapping point from the original image based on the blur range parameter and the target point; the gray value of the mapping point can reflect the gray value of a pixel point around the target point, and the fuzzy range parameter can reflect the distance between the target point and the mapping point;
and a blur conversion unit (43) for generating a blurred image based on the mapping point and the target image.
9. The animation fast fuzzy processing system is characterized in that: comprising the image blur processing module (4) according to claim 8, the animation fast blur processing system further comprising:
data preparation module (1): for determining an original image;
parameter determination module (2): for determining effect information; wherein the effect information comprises a blur range parameter;
cache allocation module (3): the image cache is distributed based on the original image;
animation display module (5): for displaying a blurred animation based on the blurred image.
10. An intelligent terminal, characterized by comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and execute the image blur processing method or the animation fast blur processing method according to any one of claims 1 to 7.
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