CN107564085A - Scalloping processing method, device, computing device and computer-readable storage medium - Google Patents
Scalloping processing method, device, computing device and computer-readable storage medium Download PDFInfo
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Abstract
The invention discloses a kind of scalloping processing method, device, computing device and computer-readable storage medium, wherein, scalloping processing method includes：Obtain pending image and the first noise data；For each pixel in pending image, according to the first noise data, the first twisting grain data are determined；Using the first twisting grain data, the color component value of pixel is handled；Obtain scalloping data corresponding with pending image；According to scalloping data, distortion effects image is obtained；The shooting triggered according to user instructs, and preserves distortion effects image.Present invention employs deep learning method, complete scene cut processing with realizing the high accuracy of high efficiency, and according to technical scheme provided by the invention, the color component value of pixel in image is handled using noise data, distortion effects image can be readily obtained, scalloping processing mode is optimized, improves scalloping effect.
Description
Technical field
The present invention relates to image processing field, and in particular to a kind of scalloping processing method, device, computing device and meter
Calculation machine storage medium.
Background technology
With the development of science and technology, the technology of image capture device also increasingly improves.The image collected becomes apparent from, differentiated
Rate, display effect also greatly improve.But existing image possibly can not meet the needs of user, user is wished to image progress
Propertyization processing, such as, it is desirable to by the contents processing in image into the distortion effects looked over through steam.In prior art
In, it is to be handled using function pair images such as sinusoidal or cosine and obtain distortion effects image mostly, but use this side
The distortion effects of image obtained by after formula processing are bad, very stiff, not natural enough.
The content of the invention
In view of the above problems, it is proposed that the present invention so as to provide one kind overcome above mentioned problem or at least in part solve on
State scalloping processing method, device, computing device and the computer-readable storage medium of problem.
According to an aspect of the invention, there is provided a kind of scalloping processing method, this method include：
Obtain pending image and the first noise data；
For each pixel in pending image, according to the first noise data, the first twisting grain data are determined；
Using the first twisting grain data, the color component value of pixel is handled；
Obtain scalloping data corresponding with pending image；
According to scalloping data, distortion effects image is obtained.
Further, the first noise data includes multiple first color data；
According to the first noise data, determine that the first twisting grain data further comprise：
The first color data is extracted from the first noise data；
According to the first color data extracted, the first twisting grain data are determined；
Further, the first color data is extracted from the first noise data to further comprise：According to time parameter, from
The first color data is extracted in first noise data.
Further, using the first twisting grain data, processing is carried out to the color component value of pixel and further comprised：
Using the first twisting grain data, it is determined that the first distortion offset corresponding with pixel；
According to the first distortion offset and pixel, it is determined that pixel corresponding with the first distortion offset；
The color component value of pixel is assigned to pixel corresponding with the first distortion offset.
Further, using the first twisting grain data, it is determined that the first distortion offset corresponding with pixel is further
Including：Using the first twisting grain data and default torsion resistance coefficient, it is determined that the first distortion offset corresponding with pixel.
Further, according to scalloping data, obtain distortion effects image and further comprise：According to scalloping number
According to, it is determined that basic effect image, and basic effect image is defined as distortion effects image.
Further, after pending image and the first noise data is obtained, this method also includes：
Obtain the second noise data；
Using the first noise data, the second noise data is handled, generates top layer haze effect textures；
According to scalloping data, obtaining distortion effects image is specially：According to scalloping data, it is determined that basic effect
Image；Based on effect image addition top layer haze effect textures, obtain distortion effects image.
Further, the second noise data includes multiple second color data；
Using the first noise data, the second noise data is handled, generation top layer haze effect textures further wrap
Include：
For each second color data in the second noise data, according to the first noise data, the second distortion is determined
Data texturing；Using the second twisting grain data, it is determined that the second distortion offset corresponding with the second color data；According to second
Offset and the second color data are distorted, offset is corresponding to offset object it is determined that being distorted with second；Second color data is assigned
Value gives skew object corresponding to the second distortion offset；
Obtain noise twisting data corresponding with the second noise data；
According to noise twisting data, top layer haze effect textures are generated.
Further, further comprised according to noise twisting data, generation top layer haze effect textures：According to preset function
And/or default addition color value and noise twisting data, translucent processing is carried out, generates top layer haze effect textures.
Further, according to the second distortion offset and the second color data, it is determined that corresponding with the second distortion offset
Skew object further comprises：
According to the second distortion offset and the second color data, skew object to be determined is obtained；
Judge whether skew object to be determined exceedes default object range；If so, then according to preset algorithm and it is to be determined partially
Object is moved, is calculated that offset is corresponding offsets object with the second distortion；If it is not, then by it is to be determined skew object be defined as with
Skew object corresponding to second distortion offset.
Further, the first noise data is discrete chromatic noise data.
Further, the second noise data is continuous black and white noise data.
Further, this method also includes：
Scene cut processing is carried out to pending image, obtains scene cut result corresponding with pending image；Wherein,
Pending image includes special object；
According to scene cut result corresponding with pending image, the profile information of special object is determined；
According to profile information, pending image and the distortion effects image of special object, bird caging effect image is obtained.
Further, profile information, pending image and the distortion effects image according to special object, obtains bird caging
Effect image further comprises：
According to the profile information of special object, topography is extracted from distortion effects image；
Fusion treatment is carried out to pending image and topography, obtains bird caging effect image.
Further, pending image is obtained to further comprise：The pending figure that real-time image acquisition collecting device is caught
Picture.
Further, after distortion effects image is obtained, this method also includes：Show distortion effects image.
Further, display distortion effects image further comprises：Real-time display distortion effects image.
Further, after distortion effects image is obtained, this method also includes：The shooting triggered according to user instructs,
Preserve distortion effects image.
Further, after distortion effects image is obtained, this method also includes：The record command triggered according to user,
Preserve by distortion effects image as group of picture into video.
Further, after bird caging effect image is obtained, this method also includes：Show bird caging design sketch
Picture.
Further, display bird caging effect image further comprises：Real-time display bird caging effect image.
Further, after bird caging effect image is obtained, this method also includes：Referred to according to the shooting that user triggers
Order, preserve bird caging effect image.
Further, after bird caging effect image is obtained, this method also includes：Referred to according to the recording that user triggers
Order, preserve by bird caging effect image as group of picture into video.
According to another aspect of the present invention, there is provided a kind of scalloping processing unit, the device include：
Acquisition module, suitable for obtaining pending image and the first noise data；
First processing module, suitable for for each pixel in pending image, according to the first noise data, it is determined that
First twisting grain data；Using the first twisting grain data, the color component value of pixel is handled；
First generation module, suitable for obtaining scalloping data corresponding with pending image；
Second generation module, suitable for according to scalloping data, obtaining distortion effects image.
Further, the first noise data includes multiple first color data；
First processing module is further adapted for：
The first color data is extracted from the first noise data；
According to the first color data extracted, the first twisting grain data are determined；
Further, first processing module is further adapted for：According to time parameter, is extracted from the first noise data
One color data.
Further, first processing module is further adapted for：
Using the first twisting grain data, it is determined that the first distortion offset corresponding with pixel；
According to the first distortion offset and pixel, it is determined that pixel corresponding with the first distortion offset；
The color component value of pixel is assigned to pixel corresponding with the first distortion offset.
Further, first processing module is further adapted for：Using the first twisting grain data and default torsion resistance coefficient,
It is determined that the first distortion offset corresponding with pixel.
Further, the second generation module is further adapted for：According to scalloping data, it is determined that basic effect image, and
Basic effect image is defined as distortion effects image.
Further, acquisition module is further adapted for：Obtain the second noise data；
The device also includes：Second processing module, suitable for utilizing the first noise data, at the second noise data
Reason, generate top layer haze effect textures；
Second generation module is further adapted for：According to scalloping data, it is determined that basic effect image；Based on design sketch
As addition top layer haze effect textures, distortion effects image is obtained.
Further, the second noise data includes multiple second color data；
Second processing module is further adapted for：
For each second color data in the second noise data, according to the first noise data, the second distortion is determined
Data texturing；Using the second twisting grain data, it is determined that the second distortion offset corresponding with the second color data；According to second
Offset and the second color data are distorted, offset is corresponding to offset object it is determined that being distorted with second；Second color data is assigned
Value gives skew object corresponding to the second distortion offset；
Obtain noise twisting data corresponding with the second noise data；
According to noise twisting data, top layer haze effect textures are generated.
Further, Second processing module is further adapted for：According to preset function and/or default addition color value and make an uproar
Acoustic warping data, translucent processing is carried out, generate top layer haze effect textures.
Further, Second processing module is further adapted for：
According to the second distortion offset and the second color data, skew object to be determined is obtained；
Judge whether skew object to be determined exceedes default object range；If so, then according to preset algorithm and it is to be determined partially
Object is moved, is calculated that offset is corresponding offsets object with the second distortion；If it is not, then by it is to be determined skew object be defined as with
Skew object corresponding to second distortion offset.
Further, the first noise data is discrete chromatic noise data.
Further, the second noise data is continuous black and white noise data.
Further, the device also includes：
Split module, suitable for carrying out scene cut processing to pending image, obtain scene corresponding with pending image
Segmentation result；Wherein, pending image includes special object；
Determining module, suitable for according to scene cut result corresponding with pending image, determining that the profile of special object is believed
Breath；
3rd generation module, suitable for profile information, pending image and the distortion effects image according to special object, obtain
Bird caging effect image.
Further, the 3rd generation module is further adapted for：According to the profile information of special object, from distortion effects image
In extract topography；Fusion treatment is carried out to pending image and topography, obtains bird caging effect image.
Further, acquisition module is further adapted for：The pending image that real-time image acquisition collecting device is caught.
Further, the device also includes：Display module, suitable for showing distortion effects image.
Further, display module is further adapted for：Real-time display distortion effects image.
Further, the device also includes：First preserving module, suitable for the shooting instruction triggered according to user, preserve and turn round
Bent effect image.
Further, the device also includes：Second preserving module, suitable for the record command triggered according to user, preserve by
Distortion effects image as group of picture into video.
Further, the device also includes：Display module, suitable for showing bird caging effect image.
Further, display module is further adapted for：Real-time display bird caging effect image.
Further, the device also includes：First preserving module, suitable for the shooting instruction triggered according to user, preservation office
Portion's distortion effects image.
Further, the device also includes：Second preserving module, suitable for the record command triggered according to user, preserve by
Bird caging effect image as group of picture into video.
According to another aspect of the invention, there is provided a kind of computing device, including：Processor, memory, communication interface and
Communication bus, processor, memory and communication interface complete mutual communication by communication bus；
Memory is used to deposit an at least executable instruction, and executable instruction makes the above-mentioned scalloping processing of computing device
Operated corresponding to method.
In accordance with a further aspect of the present invention, there is provided a kind of computer-readable storage medium, be stored with least one in storage medium
Executable instruction, executable instruction make computing device be operated as corresponding to above-mentioned scalloping processing method.
According to technical scheme provided by the invention, pending image and the first noise data are obtained, then for pending
Each pixel in image, according to the first noise data, the first twisting grain data are determined, utilize the first twisting grain number
According to handling the color component value of pixel, scalloping data corresponding with pending image obtained, then according to figure
As twisting data, distortion effects image is obtained.It is with realizing the high accuracy of high efficiency complete present invention employs deep learning method
Into scene cut processing, and according to technical scheme provided by the invention, the color point using noise data to pixel in image
Value is handled, and can readily obtain distortion effects image, is optimized scalloping processing mode, is improved scalloping
Effect.
Described above is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention,
And can be practiced according to the content of specification, and in order to allow above and other objects of the present invention, feature and advantage can
Become apparent, below especially exemplified by the embodiment of the present invention.
Brief description of the drawings
By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this area
Technical staff will be clear understanding.Accompanying drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention
Limitation.And in whole accompanying drawing, identical part is denoted by the same reference numerals.In the accompanying drawings：
Fig. 1 shows the schematic flow sheet of scalloping processing method according to an embodiment of the invention；
Fig. 2 shows the schematic flow sheet of scalloping processing method in accordance with another embodiment of the present invention；
Fig. 3 shows the structured flowchart of scalloping processing unit according to an embodiment of the invention；
Fig. 4 shows the structured flowchart of scalloping processing unit in accordance with another embodiment of the present invention；
Fig. 5 shows a kind of structural representation of computing device according to embodiments of the present invention.
Embodiment
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in accompanying drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
Limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
Completely it is communicated to those skilled in the art.
Fig. 1 shows the schematic flow sheet of scalloping processing method according to an embodiment of the invention, such as Fig. 1 institutes
Show, this method comprises the following steps：
Step S100, obtain pending image and the first noise data.
Specifically, pending image can be user oneself shooting image or website in image, can be with
It is the image that other users are shared, does not limit herein.When user is wanted pending image procossing into distortion effects
During image, such as, it is desirable to, can be in step S100 by the contents processing in image into the distortion effects looked over transmission steam
Middle the acquisition pending image and the first noise data.Wherein, the first noise data includes multiple first color data, specifically
Ground, the first noise data can be discrete chromatic noise data, using the first discrete noise data, be favorably improved image torsion
Qu Xiaoguo, make the image after processing that there are nature, preferable distortion effects.
Step S101, for each pixel in pending image, according to the first noise data, determine the first distortion
Data texturing.
In step S101, it is necessary to for each pixel in pending image, according to the first noise data, it is determined that
The first twisting grain data corresponding with the pixel.Specifically, in the pixel and the first noise data in pending image
The first color data it is corresponding, then for each pixel in pending image, according in the first noise data with
First color data corresponding to the pixel, it is determined that the first twisting grain data corresponding with the pixel, so as to for waiting to locate
Pixel in reason image determines the first different twisting grain data, and identical twisting grain is all used with all pixels point
Data are compared, and the image after processing is had nature, preferable distortion effects.
Step S102, using the first twisting grain data, the color component value of pixel is handled.
For each pixel in pending image, using the first twisting grain data corresponding with the pixel,
The processing such as assignment is carried out to the color component value of the pixel.When pending image is coloured image, with pending image
Exemplified by color mode uses rgb color pattern, the color component value of the pixel in pending image includes red, green, blue three
The color component value of Color Channel, then in a particular application, those skilled in the art can be from three colors corresponding to pixel
The color component value of suitable Color Channel is selected to be handled or to all colours passage in the color component value of passage
Color component value is all handled, and is not limited herein.For example, can be only to the color component value of the red color passage of pixel
Handled with the color component value of green color channel.
Step S103, obtain scalloping data corresponding with pending image.
Data obtained by after the color component value of each pixel in pending image is handled are i.e.
For scalloping data corresponding with pending image.
Step S104, according to scalloping data, obtain distortion effects image.
After step S103 obtains scalloping data, in step S104, according to scalloping data, distorted
Effect image.For example, user wants for the full content in pending image to be processed into the torsion with looking over through steam
Qu Xiaoguo, then can be according to scalloping data, it is determined that basic effect image, the basic effect image is in pending image
Full content all have distortion effects image, basic effect image is then defined as distortion effects image.
The scalloping processing method provided according to the present embodiment, obtains pending image and the first noise data, then
For each pixel in pending image, according to the first noise data, the first twisting grain data are determined, utilize first
Twisting grain data, the color component value of pixel is handled, obtains scalloping data corresponding with pending image,
Then according to scalloping data, distortion effects image is obtained.According to technical scheme provided by the invention, noise data pair is utilized
The color component value of pixel is handled in image, can be readily obtained distortion effects image, be optimized at scalloping
Reason mode, improve scalloping effect.
Fig. 2 shows the schematic flow sheet of scalloping processing method in accordance with another embodiment of the present invention, such as Fig. 2 institutes
Show, this method comprises the following steps：
Step S200, obtain pending image, the first noise data and the second noise data.
Wherein, the first noise data includes multiple first color data, and the second noise data includes multiple second number of colours
According to.Specifically, the first noise data is discrete chromatic noise data, and the second noise data is continuous black and white noise data,
Using the first noise data, the second noise data is handled, generates top layer haze effect textures.Using discrete first
Noise data, scalloping effect is favorably improved, makes the image after processing that there are nature, preferable distortion effects；Using even
The second continuous noise data, the top layer haze effect textures with continuous haze effect can be generated, make the image after processing
Effect is curled up with natural smog.
Alternatively, in step s 200, can real-time image acquisition collecting device catch pending image.Specifically, scheme
Picture collecting device can be mobile terminal etc., so that image capture device is mobile terminal as an example, obtain mobile terminal camera in real time
The pending image captured, wherein, pending image can be arbitrary image, for example includes the image of landscape or include
Image of human body etc., is not limited herein.
Step S201, for each pixel in pending image, the first face is extracted from the first noise data
Chromatic number evidence；According to the first color data extracted, the first twisting grain data are determined.
The first color data in pixel and the first noise data in pending image is corresponding, then for waiting to locate
Each pixel in image is managed, the first color data corresponding with the pixel is extracted from the first noise data.From
And the first different twisting grain data are determined for the pixel in pending image, with all pixels point all using identical
Twisting grain data compare, contribute to obtain nature, preferable distortion effects.
In order to further improve scalloping effect, first can be extracted from the first noise data according to time parameter
Color data.Specifically, for same pixel in pending image, when time parameter changes, from the first noise
Extracting data goes out the first different color data as the first color data corresponding with the pixel.
In actual applications, the first noise data can be chromatic noise figure, then each pixel in chromatic noise figure
The corresponding color component value of point is first color data.Let it be assumed, for the purpose of illustration, that the pixel in pending image
Point is respectively A1, A2, A3 etc., and the pixel in chromatic noise figure is respectively B1, B2, B3 etc., for the picture in pending image
Vegetarian refreshments A1, when time parameter is time 1, the color component value conduct corresponding to pixel B1 is extracted from chromatic noise figure
The first color data corresponding with pixel A1；When time parameter is time 2, pixel B3 is extracted from chromatic noise figure
Corresponding color component value is as the first color data corresponding with pixel A1.
Specifically, can be corresponding with the pixel according to being extracted for each pixel in pending image
First color data and default first calculates function, and the first twisting grain data are calculated, wherein, those skilled in the art can
Set default first to calculate function according to being actually needed, do not limit herein.
Step S202, using the first twisting grain data, it is determined that the first distortion offset corresponding with pixel.
Specifically, using the first twisting grain data and default torsion resistance coefficient, it is determined that corresponding with pixel first
Distort offset.Those skilled in the art can realize the regulation to scalloping degree by the regulation to torsion resistance coefficient.
Step S203, according to the first distortion offset and pixel, it is determined that pixel corresponding with the first distortion offset.
After the first distortion offset corresponding with pixel is determined, so that it may according to the first distortion offset and pixel
Point, it is determined that pixel corresponding with the first distortion offset.
Step S204, the color component value of pixel is assigned to pixel corresponding with the first distortion offset.
Determine with after the first corresponding pixel of distortion offset, by the color component value of pixel be assigned to
Pixel corresponding to first distortion offset.For example, for the pixel A1 in pending image, with the first distortion offset pair
The pixel answered is pixel A2, then pixel A1 color component value is assigned into pixel A2, has pixel A2
Pixel A1 color component value, so as to reach the effect of scalloping.
Assuming that the color component value of the pixel in pending image includes the color point of three Color Channels of red, green, blue
Value, then in a particular application, those skilled in the art can be from the color component value of three Color Channels corresponding to pixel
The color component value of the middle suitable Color Channel of selection is assigned to pixel corresponding with the first distortion offset, or will be all
The color component value of Color Channel is all assigned to pixel corresponding with the first distortion offset, does not limit herein.For example, can
Only the color component value of the color component value of the red color passage of pixel and green color channel is correspondingly assigned to and the
Pixel corresponding to one distortion offset.
Step S205, obtain scalloping data corresponding with pending image.
Number obtained by the color component value of each pixel in pending image carries out after assignment processing
According to as scalloping data corresponding with pending image.
Step S206, according to scalloping data, it is determined that basic effect image.
Wherein, basic effect image is full content all images with distortion effects in pending image.
Step S207, for each second color data in the second noise data, according to the first noise data, it is determined that
Second twisting grain data.
The first color data in the second color data and the first noise data in second noise data is corresponding, then
For each second color data in the second noise data, extracted from the first noise data and second color data
Corresponding first color data, then according to the first color data corresponding with second color data for being extracted and preset the
Two calculate function, and the second twisting grain data are calculated, so as to be determined for the second color data in the second noise data
Different the second twisting grain data, compared with all second color data all use identical twisting grain data, help
Effect is curled up in acquisition nature, preferable smog.
Wherein, those skilled in the art can set default second to calculate function according to being actually needed, and preset second and calculate letter
Number can be identical with default first calculating function, also can be different from default first calculating function, does not limit herein.
Step S208, using the second twisting grain data, it is determined that the second distortion offset corresponding with the second color data.
Specifically, using the second twisting grain data and default smog torsion resistance coefficient, it is determined that with the second color data
Corresponding second distortion offset.Those skilled in the art can be realized and smog is turned round by the regulation to smog torsion resistance coefficient
Qu Chengdu regulation.
Step S209, according to the second distortion offset and the second color data, it is determined that corresponding with the second distortion offset
Offset object.
Specifically, according to the second distortion offset and the second color data, skew object to be determined is obtained, then judges to treat
It is determined that whether skew object exceedes default object range；If so, then it is calculated according to preset algorithm and skew object to be determined
Offset is corresponding offsets object with the second distortion；If it is not, then skew object to be determined is defined as and the second distortion offset
Corresponding skew object.
It is default due to that can exceed that according to the second distortion offset and the obtained skew object to be determined of the second color data
Object range, therefore also need to judge whether skew object to be determined exceedes default object range.It is if to be determined inclined
Move object and exceed default object range, then according to preset algorithm and skew object to be determined, be calculated and offset with the second distortion
Skew object corresponding to amount, so as to realize the adjustment to offseting object, helps to be subsequently generated with continuous haze effect
Top layer haze effect textures.If skew object to be determined, can be directly by skew pair to be determined not less than default object range
As being defined as, offset is corresponding offsets object with the second distortion.
Step S210, the second color data is assigned to offset is corresponding offsets object with the second distortion.
After skew object corresponding with the second distortion offset is determined, the second color data is assigned to and second
Distort skew object corresponding to offset.
In actual applications, the first noise data can be chromatic noise figure, and the second noise data is black and white noise pattern, then
The color component value corresponding to each pixel in chromatic noise figure is first color data, in black and white noise pattern
Each pixel corresponding to color component value be second color data.Let it be assumed, for the purpose of illustration, that colour is made an uproar
Pixel in sound spectrogram is respectively B1, B2, B3 etc., and the pixel in black and white noise pattern is respectively C1, C2, C3 etc., it is assumed that some
Second color data is the color component value corresponding to the pixel C1 in black and white noise pattern, for second color data, with
Skew object corresponding to second distortion offset is pixel C3, then pixel C1 color component value is assigned into pixel
C3, make pixel C3 that there is pixel C1 color component value, so as to reach the effect that smog curls up.
Step S211, obtain noise twisting data corresponding with the second noise data.
Data obtained by after each second color data in the second noise data carries out assignment processing are i.e.
For noise twisting data corresponding with the second noise data.
Step S212, according to noise twisting data, generate top layer haze effect textures.
After noise twisting data has been obtained, according to noise twisting data, top layer haze effect textures are generated.Specifically
Ground, it can carry out translucent processing according to preset function and/or default addition color value and noise twisting data, generate top layer
Haze effect textures.Wherein, those skilled in the art can set preset function and default addition color value according to being actually needed, this
Place does not limit.For example, preset function can be SIN function or cosine function etc., it can be golden yellow corresponding to preset addition color value
Color value or red corresponding to color value etc..Add because the second noise data is black and white noise data, therefore according to default
Add color value and noise twisting data, the top layer haze effect textures with colored smoke effect can be generated.For example, add when default
Add color value for during color value, generation is the top layer haze effect textures with golden yellow haze effect corresponding to golden yellow.
Step S213, based on effect image addition top layer haze effect textures, obtain distortion effects image.
After basic effect image and top layer haze effect textures has been obtained, based on effect image addition top layer smog
Effect textures, distortion effects image being obtained, the distortion effects image not only has distortion effects, improves scalloping effect,
Also there is haze effect, be greatly enriched image effect.
Step S214, real-time display distortion effects image.
Obtained distortion effects image is shown in real time, user can directly be seen that to being obtained after pending image procossing
The distortion effects image arrived.After distortion effects image is obtained, the pending figure caught is replaced using distortion effects image at once
As being shown, typically it was replaced within 1/24 second, for a user, relatively short due to replacing the time, human eye does not have
Significantly discover, equivalent to showing distortion effects image in real time.
Step S215, the shooting triggered according to user instruct, and preserve distortion effects image.
After distortion effects image is shown, the shooting that can also be triggered according to user instructs, and preserves distortion effects image.Such as
User clicks on the shooting push button of camera, triggering shooting instruction, the distortion effects image of display is preserved.
Step S216, according to user trigger record command, preserve by distortion effects image as group of picture into regard
Frequently.
When showing distortion effects image, can also be preserved according to the record command of user's triggering by distortion effects image
As group of picture into video.As user clicks on the recording button of camera, triggering record command, by the distortion effects figure of display
As being preserved as the two field picture in video, so as to preserve multiple distortion effects images as group of picture into video.
Step S215 and step S216 is the optional step of the present embodiment, and in the absence of perform sequencing, according to
The different instruction selection of family triggering performs corresponding step.
In addition, in application scenes, pending image includes special object, such as human body, and user only wants to treat
Specific object region or nonspecific subject area in processing image are distorted, and in this case, this method may also include：
Scene cut processing is carried out to pending image, obtains scene cut result corresponding with pending image；According to it is pending
Scene cut result corresponding to image, determine the profile information of special object；Profile information, pending figure according to special object
Picture and distortion effects image, obtain bird caging effect image.
Wherein, when carrying out scene cut processing to pending image, deep learning method can be utilized.Deep learning is
It is a kind of based on the method that data are carried out with representative learning in machine learning.Observation (such as piece image) can use a variety of sides
Formula represents, such as vector of each pixel intensity value, or is more abstractively expressed as a series of sides, the region etc. of given shape.
And some specific method for expressing are used to be easier from example learning task (for example, recognition of face or human facial expression recognition).
Scene cut is carried out to pending image using the dividing method of deep learning, obtains scene corresponding with pending image point
Cut result.Specifically, scene cut network obtained using deep learning method etc. carries out scene cut to pending image
Processing, obtains scene cut result corresponding with pending image, then according to scene cut knot corresponding with pending image
Fruit, determine the profile information of special object.Assuming that special object is human body, then can according to scene cut result, it is determined that
Go out the profile information of human body, be human body so as to distinguish which region in pending image, which region is not human body.
After the profile information of special object is determined, so that it may according to the profile information of special object, from distortion effects
Topography is extracted in image, fusion treatment then is carried out to pending image and topography, obtains bird caging effect
Image.Specifically, it can determine which region is special object area in distortion effects image according to the profile information of special object
Domain, which region are non-specific object regions, nonspecific subject area can be referred to as into background area, then from distortion effects image
In extract the image of specific object region or the image of nonspecific subject area as topography.For example, work as special object
For human body when, user want the human region in pending image is distorted, then according to human body profile information, from torsion
The image of human region is extracted in bent effect image as topography, then pending image and topography are melted
Conjunction is handled, and obtains bird caging effect image, and the bird caging effect image is that only human region has distortion effects and carried on the back
Scene area does not have the image of distortion effects；And for example, when special object is human body, user is wanted to removing people in pending image
Background area outside body region is distorted, then according to the profile information of human body, the back of the body is extracted from distortion effects image
Then the image of scene area carries out fusion treatment to pending image and topography, obtains bird caging as topography
Effect image, the bird caging effect image be only background area has distortion effects and human region does not have distortion effects
Image.
In the case where obtaining bird caging effect image, real-time display is bird caging design sketch in step S214
Picture, without being shown to distortion effects image, the shooting triggered in step S215 according to user instructs, and preserves bird caging
Effect image, the record command triggered in step S216 according to user, is preserved by bird caging effect image as two field picture
The video of composition.
The scalloping processing method provided according to the present embodiment, the face using a kind of noise data to pixel in image
Colouring component value is handled, and can readily obtain basic effect image；And using the noise data to another noise number
According to being handled, additionally it is possible to top layer haze effect textures are obtained, so as to obtain not only with distortion effects but also with haze effect
Image, scalloping processing mode is not only optimized, be effectively improved scalloping effect, be also greatly enriched image effect
Fruit；In addition, also the image with bird caging effect can be obtained according to scene cut result corresponding with pending image, this
Invention employs deep learning method, completes scene cut processing with realizing the high accuracy of high efficiency, effectively meets use
The individual demand at family.
Fig. 3 shows the structured flowchart of scalloping processing unit according to an embodiment of the invention, as shown in figure 3,
The device includes：Acquisition module 301, first processing module 302, the first generation module 303 and the second generation module 304.
Acquisition module 301 is suitable to：Obtain pending image and the first noise data.
Wherein, the first noise data includes multiple first color data, and specifically, the first noise data can be discrete coloured silk
Coloured noise data, using the first discrete noise data, scalloping effect is favorably improved, the image after processing is had certainly
So, preferable distortion effects.
First processing module 302 is suitable to：For each pixel in pending image, according to the first noise data,
Determine the first twisting grain data；Using the first twisting grain data, the color component value of pixel is handled.
First generation module 303 is suitable to：Obtain scalloping data corresponding with pending image.
Second generation module 304 is suitable to：According to scalloping data, distortion effects image is obtained.
Alternatively, the second generation module 304 is further adapted for：According to scalloping data, it is determined that basic effect image, and
Basic effect image is defined as distortion effects image.
The scalloping processing unit provided according to the present embodiment, acquisition module obtain pending image and the first noise number
According to first processing module is directed to each pixel in pending image, according to the first noise data, determines the first grai twisted
Data are managed, using the first twisting grain data, the color component value of pixel are handled, the first generation module is obtained and treated
Scalloping data corresponding to image are handled, the second generation module obtains distortion effects image according to scalloping data.According to
Technical scheme provided by the invention, the color component value of pixel in image is handled using noise data, can be facilitated
Ground obtains distortion effects image, optimizes scalloping processing mode, improves scalloping effect.
Fig. 4 shows the structured flowchart of scalloping processing unit in accordance with another embodiment of the present invention, such as Fig. 4 institutes
Show, the device includes：Acquisition module 401, first processing module 402, the first generation module 403, Second processing module 404,
Two generation modules 405, display module 406, the first preserving module 407 and the second preserving module 408.
Acquisition module 401 is suitable to：Obtain pending image, the first noise data and the second noise data.
Wherein, the first noise data includes multiple first color data, and the second noise data includes multiple second number of colours
According to.Specifically, the first noise data is discrete chromatic noise data, and the second noise data is continuous black and white noise data,
Using the first noise data, the second noise data is handled, generates top layer haze effect textures.
Alternatively, acquisition module 401 is further adapted for：The pending image that real-time image acquisition collecting device is caught.
First processing module 402 is suitable to：For each pixel in pending image, carried from the first noise data
Take out the first color data；According to the first color data extracted, the first twisting grain data are determined；Utilize the first grai twisted
Data are managed, the color component value of pixel is handled.
Wherein, first processing module 402 is further adapted for：According to time parameter, is extracted from the first noise data
One color data.
First processing module 402 is further adapted for：Using the first twisting grain data, it is determined that corresponding with pixel first
Distort offset；According to the first distortion offset and pixel, it is determined that pixel corresponding with the first distortion offset；By pixel
The color component value of point is assigned to pixel corresponding with the first distortion offset.
First processing module 402 is further adapted for：Using the first twisting grain data and default torsion resistance coefficient, it is determined that with
First distortion offset corresponding to pixel.
First generation module 403 is suitable to：Obtain scalloping data corresponding with pending image.
Second processing module 404 is suitable to：Using the first noise data, the second noise data is handled, generates top layer
Haze effect textures.
Specifically, Second processing module 404 is further adapted for：For each second number of colours in the second noise data
According to according to the first noise data, determining the second twisting grain data；Using the second twisting grain data, it is determined that with the second color
Second distortion offset corresponding to data；According to the second distortion offset and the second color data, it is determined that being offset with the second distortion
Skew object corresponding to amount；Second color data is assigned to offset is corresponding offsets object with the second distortion；Obtain and the
Noise twisting data corresponding to two noise datas；According to noise twisting data, top layer haze effect textures are generated.
Alternatively, Second processing module 404 is further adapted for：According to preset function and/or default addition color value and
Noise twisting data, translucent processing is carried out, generate top layer haze effect textures.
Alternatively, Second processing module 404 is further adapted for：According to the second distortion offset and the second color data, obtain
To skew object to be determined；Judge whether skew object to be determined exceedes default object range；If so, then according to preset algorithm and
Skew object to be determined, is calculated that offset is corresponding offsets object with the second distortion；If it is not, then by skew object to be determined
It is defined as that offset is corresponding offsets object with the second distortion.
Second generation module 405 is suitable to：According to scalloping data, it is determined that basic effect image；Based on effect image
Top layer haze effect textures are added, obtain distortion effects image.
Display module 406 is suitable to：Show distortion effects image.
Alternatively, display module 406 is further adapted for：Real-time display distortion effects image.Display module 406 will obtain
Distortion effects image is shown that user can directly be seen that the distortion effects figure to being obtained after pending image procossing in real time
Picture.After the second generation module 405 obtains distortion effects image, display module 406 is replaced using distortion effects image catch at once
The pending image caught is shown, is typically replaced within 1/24 second, for a user, relative due to replacing the time
Short, human eye is not discovered significantly, and distortion effects image is shown in real time equivalent to display module 406.
First preserving module 407 is suitable to：The shooting triggered according to user instructs, and preserves distortion effects image.
After distortion effects image is shown, the shooting that the first preserving module 407 can trigger according to user instructs, and preserves and turns round
Bent effect image.As user clicks on the shooting push button of camera, triggering shooting instruction, the first preserving module 407 is by the distortion of display
Effect image is preserved.
Second preserving module 408 is suitable to：The record command triggered according to user, is preserved by distortion effects image as frame figure
As the video of composition.
When showing distortion effects image, the second preserving module 408 can according to user trigger record command, preserve by
Distortion effects image as group of picture into video.As user clicks on the recording button of camera, triggering record command, the second guarantor
Storing module 408 is preserved the distortion effects image of display as the two field picture in video, so as to preserve multiple distortion effects
Image as group of picture into video.
According to the first preserving module 407 and the second preserving module 408 corresponding to the different instruction execution that user triggers.
In addition, in application scenes, pending image includes special object, such as human body, and user only wants to treat
Specific object region or nonspecific subject area in processing image are distorted, and in this case, the device also includes：Point
Cut module 409, the generation module 411 of determining module 410 and the 3rd.
Segmentation module 409 is suitable to：Scene cut processing is carried out to pending image, obtains field corresponding with pending image
Scape segmentation result.
Determining module 410 is suitable to：According to scene cut result corresponding with pending image, the profile of special object is determined
Information.
3rd generation module 411 is suitable to：Profile information, pending image and the distortion effects image of foundation special object,
Obtain bird caging effect image.
Wherein, the 3rd generation module 411 is further adapted for：According to the profile information of special object, from distortion effects image
In extract topography；Fusion treatment is carried out to pending image and topography, obtains bird caging effect image.
So in this case, what display module 406 was shown is not distortion effects image, but the 3rd generation module
411 obtained bird caging effect images, for example, real-time display bird caging effect image.Similarly, the first preserving module 407
Suitable for the shooting instruction triggered according to user, bird caging effect image is preserved.Second preserving module 408 is suitable to be touched according to user
The record command of hair, preserve by bird caging effect image as group of picture into video.
The scalloping processing unit provided according to the present embodiment, the face using a kind of noise data to pixel in image
Colouring component value is handled, and can readily obtain basic effect image；And using the noise data to another noise number
According to being handled, additionally it is possible to top layer haze effect textures are obtained, so as to obtain not only with distortion effects but also with haze effect
Image, scalloping processing mode is not only optimized, be effectively improved scalloping effect, be also greatly enriched image effect
Fruit；In addition, also the image with bird caging effect can be obtained according to scene cut result corresponding with pending image, this
Invention employs deep learning method, completes scene cut processing with realizing the high accuracy of high efficiency, effectively meets use
The individual demand at family.
Present invention also offers a kind of nonvolatile computer storage media, computer-readable storage medium is stored with least one can
Execute instruction, executable instruction can perform the scalloping processing method in above-mentioned any means embodiment.
Fig. 5 shows a kind of structural representation of computing device according to embodiments of the present invention, the specific embodiment of the invention
The specific implementation to computing device does not limit.
As shown in figure 5, the computing device can include：Processor (processor) 502, communication interface
(Communications Interface) 504, memory (memory) 506 and communication bus 508.
Wherein：
Processor 502, communication interface 504 and memory 506 complete mutual communication by communication bus 508.
Communication interface 504, for being communicated with the network element of miscellaneous equipment such as client or other servers etc..
Processor 502, for configuration processor 510, it can specifically perform in above-mentioned scalloping processing method embodiment
Correlation step.
Specifically, program 510 can include program code, and the program code includes computer-managed instruction.
Processor 502 is probably central processor CPU, or specific integrated circuit ASIC (Application
Specific Integrated Circuit), or it is arranged to implement the integrated electricity of one or more of the embodiment of the present invention
Road.The one or more processors that computing device includes, can be same type of processor, such as one or more CPU；Also may be used
To be different types of processor, such as one or more CPU and one or more ASIC.
Memory 506, for depositing program 510.Memory 506 may include high-speed RAM memory, it is also possible to also include
Nonvolatile memory (non-volatile memory), for example, at least a magnetic disk storage.
Program 510 specifically can be used for so that processor 502 is performed at the scalloping in above-mentioned any means embodiment
Reason method.In program 510 specific implementation of each step may refer to corresponding steps in above-mentioned scalloping Processing Example and
Corresponding description, will not be described here in unit.It is apparent to those skilled in the art that for description convenience and
Succinctly, the specific work process of the equipment of foregoing description and module, the corresponding process that may be referred in preceding method embodiment are retouched
State, will not be repeated here.
Algorithm and display be not inherently related to any certain computer, virtual system or miscellaneous equipment provided herein.
Various general-purpose systems can also be used together with teaching based on this.As described above, required by constructing this kind of system
Structure be obvious.In addition, the present invention is not also directed to any certain programmed language.It should be understood that it can utilize various
Programming language realizes the content of invention described herein, and the description done above to language-specific is to disclose this hair
Bright preferred forms.
In the specification that this place provides, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention
Example can be put into practice in the case of these no details.In some instances, known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this description.
Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect,
Above in the description to the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：I.e. required guarantor
The application claims of shield features more more than the feature being expressly recited in each claim.It is more precisely, such as following
Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself
Separate embodiments all as the present invention.
Those skilled in the art, which are appreciated that, to be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment
Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or
Sub-component.In addition at least some in such feature and/or process or unit exclude each other, it can use any
Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so to appoint
Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power
Profit requires, summary and accompanying drawing) disclosed in each feature can be by providing the alternative features of identical, equivalent or similar purpose come generation
Replace.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included some features rather than further feature, but the combination of the feature of different embodiments means in of the invention
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
One of meaning mode can use in any combination.
The all parts embodiment of the present invention can be realized with hardware, or to be run on one or more processor
Software module realize, or realized with combinations thereof.It will be understood by those of skill in the art that it can use in practice
Microprocessor or digital signal processor (DSP) are come one of some or all parts in realizing according to embodiments of the present invention
A little or repertoire.The present invention is also implemented as setting for performing some or all of method as described herein
Standby or program of device (for example, computer program and computer program product).Such program for realizing the present invention can deposit
Storage on a computer-readable medium, or can have the form of one or more signal.Such signal can be from because of spy
Download and obtain on net website, either provide on carrier signal or provided in the form of any other.
It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between bracket should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" before element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of some different elements and being come by means of properly programmed computer real
It is existing.In if the unit claim of equipment for drying is listed, several in these devices can be by same hardware branch
To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and run after fame
Claim.
Claims (10)
1. a kind of scalloping processing method, methods described include：
Obtain pending image and the first noise data；
For each pixel in the pending image, according to first noise data, the first twisting grain is determined
Data；Using the first twisting grain data, the color component value of the pixel is handled；
Obtain scalloping data corresponding with pending image；
According to described image twisting data, distortion effects image is obtained.
2. according to the method for claim 1, wherein, first noise data includes multiple first color data；
It is described according to first noise data, determine that the first twisting grain data further comprise：
The first color data is extracted from first noise data；
According to the first color data extracted, the first twisting grain data are determined.
3. the method according to claim 11, wherein, it is described to extract the first color data from first noise data
Further comprise：According to time parameter, the first color data is extracted from first noise data.
4. according to the method described in claim any one of 1-3, wherein, it is described to utilize the first twisting grain data, to institute
The color component value for stating pixel carries out processing and further comprised：
Using the first twisting grain data, it is determined that the first distortion offset corresponding with the pixel；
According to the described first distortion offset and the pixel, it is determined that pixel corresponding with the described first distortion offset；
The color component value of the pixel is assigned to pixel corresponding with the described first distortion offset.
5. according to the method for claim 4, wherein, it is described utilize the first twisting grain data, it is determined that with the picture
The first distortion offset further comprises corresponding to vegetarian refreshments：Using the first twisting grain data and default torsion resistance coefficient,
It is determined that the first distortion offset corresponding with the pixel.
6. according to the method described in claim any one of 1-5, wherein, it is described according to described image twisting data, distorted
Effect image further comprises：According to described image twisting data, it is determined that basic effect image, and by the basic effect image
It is defined as distortion effects image.
7. according to the method described in claim any one of 1-5, wherein, obtain pending image and the first noise data described
Afterwards, methods described also includes：
Obtain the second noise data；
Using first noise data, second noise data is handled, generates top layer haze effect textures；
It is described according to described image twisting data, obtaining distortion effects image is specially：According to described image twisting data, it is determined that
Basic effect image；The top layer haze effect textures are added for the basic effect image, obtain distortion effects image.
8. a kind of scalloping processing unit, described device include：
Acquisition module, suitable for obtaining pending image and the first noise data；
First processing module, suitable for for each pixel in the pending image, according to first noise data,
Determine the first twisting grain data；Using the first twisting grain data, at the color component value of the pixel
Reason；
First generation module, suitable for obtaining scalloping data corresponding with pending image；
Second generation module, suitable for according to described image twisting data, obtaining distortion effects image.
9. a kind of computing device, including：Processor, memory, communication interface and communication bus, the processor, the storage
Device and the communication interface complete mutual communication by the communication bus；
The memory is used to deposit an at least executable instruction, and the executable instruction makes the computing device such as right will
Ask and operated corresponding to the scalloping processing method any one of 1-7.
10. a kind of computer-readable storage medium, an at least executable instruction, the executable instruction are stored with the storage medium
Make operation corresponding to scalloping processing method of the computing device as any one of claim 1-7.
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