CN107967667A - Generation method, device, terminal device and the storage medium of sketch - Google Patents
Generation method, device, terminal device and the storage medium of sketch Download PDFInfo
- Publication number
- CN107967667A CN107967667A CN201711392583.7A CN201711392583A CN107967667A CN 107967667 A CN107967667 A CN 107967667A CN 201711392583 A CN201711392583 A CN 201711392583A CN 107967667 A CN107967667 A CN 107967667A
- Authority
- CN
- China
- Prior art keywords
- face
- sketch
- facial image
- picture
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000001815 facial effect Effects 0.000 claims abstract description 135
- 238000010801 machine learning Methods 0.000 claims abstract description 18
- 230000015654 memory Effects 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 8
- 238000002372 labelling Methods 0.000 claims description 8
- 210000001508 eye Anatomy 0.000 description 31
- 210000004709 eyebrow Anatomy 0.000 description 25
- 230000002093 peripheral effect Effects 0.000 description 9
- 238000012545 processing Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 7
- 238000013461 design Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 230000008901 benefit Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000011218 segmentation Effects 0.000 description 3
- 241000233805 Phoenix Species 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 210000000887 face Anatomy 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000005291 magnetic effect Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000010897 surface acoustic wave method Methods 0.000 description 2
- 244000144730 Amygdalus persica Species 0.000 description 1
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 240000001008 Dimocarpus longan Species 0.000 description 1
- 235000000235 Euphoria longan Nutrition 0.000 description 1
- 241001272720 Medialuna californiensis Species 0.000 description 1
- 244000018633 Prunus armeniaca Species 0.000 description 1
- 235000009827 Prunus armeniaca Nutrition 0.000 description 1
- 235000006040 Prunus persica var persica Nutrition 0.000 description 1
- 241000124033 Salix Species 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000005252 bulbus oculi Anatomy 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 210000000088 lip Anatomy 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 210000001331 nose Anatomy 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/04—Context-preserving transformations, e.g. by using an importance map
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The embodiment of the present application discloses a kind of generation method of sketch, device, terminal device and storage medium.This method includes：Obtain the face feature of facial image；The facial image is labeled according to the face feature；Facial image after mark is inputted to sketch and generates model, obtains the corresponding sketch picture of the facial image, wherein, the sketch generation model includes the model based on machine learning algorithm generation.The generation method of sketch provided by the embodiments of the present application, the facial image for carrying out face feature mark is inputted to sketch and generates model, obtains the corresponding sketch picture of facial image, can improve the convenience of generation sketch.
Description
Technical field
The invention relates to technical field of image processing, more particularly to a kind of generation method of sketch, device, terminal
Equipment and storage medium.
Background technology
With the development of mobile communication technology and the popularization of intelligent terminal, intelligent mobile terminal has become people's life
One of indispensable instrument in work.Terminal device not only has the function of to take pictures, and also has the function of picture processing, for example,
Picture is converted into the picture of the different-styles such as cartoon, ink and wash.
Sketch is one kind of image stylization, and real-life sketch is showed mainly using pencil as instrument with lines
Personage or the art form of landscape.If user want obtain sketch picture, it is necessary to specialty personage manually complete, not only into
This height, and it is extremely inconvenient.
The content of the invention
The embodiment of the present application provides a kind of generation method of sketch, device, terminal device and storage medium, can improve element
Trace designs the convenience that piece generates.
In a first aspect, the embodiment of the present application provides a kind of generation method of sketch, this method includes：
Obtain the face feature of facial image；
The facial image is labeled according to the face feature；
Facial image after mark is inputted to sketch and generates model, obtains the corresponding sketch picture of the facial image,
Wherein, the sketch generation model includes the model based on machine learning algorithm generation.
Second aspect, the embodiment of the present application additionally provide a kind of generating means of sketch, which includes：
Face feature acquisition module, for obtaining the face feature of facial image；
Facial image labeling module, for being labeled according to the face feature to the facial image；
Sketch picture acquisition module, generating model for inputting the facial image after mark to sketch, obtaining the people
The corresponding sketch picture of face image, wherein, the sketch generation model includes the model based on machine learning algorithm generation.
The third aspect, the embodiment of the present application additionally provide a kind of terminal device, including：Processor, memory and storage
On a memory and the computer program that can run on a processor, the processor are realized such as when performing the computer program
Generation method described in first aspect.
Fourth aspect, the embodiment of the present application additionally provide a kind of storage medium, are stored thereon with computer program, the program
Generation method as described in relation to the first aspect is realized when being executed by processor.
The embodiment of the present application, first obtain facial image face feature, then according to face feature to facial image into
Rower is noted, and is finally inputted the facial image after mark to sketch and is generated model, obtains the corresponding sketch picture of facial image, its
In, sketch generation model includes the model based on machine learning algorithm generation.The generation side of sketch provided by the embodiments of the present application
Method, the facial image for carrying out face feature mark is inputted to sketch and generates model, obtains the corresponding sketch picture of facial image,
The convenience of generation sketch can be improved.
Brief description of the drawings
Fig. 1 is a kind of flow chart of the generation method of sketch in the embodiment of the present application；
Fig. 2 is the flow chart of the generation method of another sketch in the embodiment of the present application；
Fig. 3 is the flow chart of the generation method of another sketch in the embodiment of the present application；
Fig. 4 is a kind of structure diagram of the generating means of sketch in the embodiment of the present application；
Fig. 5 is a kind of structure diagram of terminal device in the embodiment of the present application；
Fig. 6 is the structure diagram of another terminal device in the embodiment of the present application.
Embodiment
The application is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the application, rather than the restriction to the application.It also should be noted that in order to just
It illustrate only part relevant with the application rather than entire infrastructure in description, attached drawing.
Fig. 1 is a kind of flow chart of the generation method of sketch provided by the embodiments of the present application, the present embodiment be applicable to by
Normal picture generates the situation of corresponding sketch picture, and this method can be performed by the generating means of sketch, which can collect
In the terminal devices such as Cheng Yuru mobile phones, tablet computer.As shown in Figure 1, this method comprises the following steps.
Step 110, the face feature of facial image is obtained.
Wherein, facial image can be the picture for including face, can be the photo that user utilizes terminal device shooting, or
Photo sent by other-end equipment that person is received by social software etc..Face can refer to each position of face, including
Ear, eyebrow, eyes, nose and lip, face feature can include face position and face attribute, can under this application scene
To only focus on the eyebrow in face, eyes, nose and lip, to the feature of ear without considering.Face position can be five
Official is located at the position of face, and face attribute can characterize the type of face.Wherein, the type of face can be with existing classification side
Formula is determined.For example, eyebrow type can include crescent or half moon eyebrow, synophrys, triangle eyebrow, arched eyebrows, slanted eyebrows and straight eyebrows slanting upwards and outwards etc.；Ocular form can
With including peach blossom eye, auspicious phoenix eyes, sleep phoenix eyes, willow leaf eye, apricot eye, fox eye, copper bell eye, longan, slim eye and fawn eye etc.；Nose
Type can include ultra-narrow nose, narrow nose, middle nose, platyrrhiny and super platyrrhiny etc.；Lip can be included under thin slim, roomy type, the corners of the mouth
Incline type, cola type, doll type and cupid's type etc..
In the present embodiment, obtaining the mode of face position can be, using the location algorithm in correlation technique to face into
Row positioning, location algorithm can be supervision descending method (Supervised Descent Method, SDM), active list item model
(Active Appearance Model, AAM) algorithm or active shape model (Active Shape Model, ASM) algorithm
Deng.The mode for obtaining face attribute can be that the face of facial image are compared with standard form respectively, obtain face
Attribute, wherein, standard form can be the face graphic template made according to the different type of each face.Exemplary, it is right
After facial image analysis, the face attribute of acquisition is arched eyebrows, fawn eye, middle nose and cola type lip.
Specifically, it can carry out recognition of face to facial image first to obtain the process of the face feature of facial image,
Extraction includes the region of face, and then the face in face are positioned using the location algorithm in correlation technique, obtains five
Official position, subsequently respectively analyzes the type of face, or the face in facial image and standard form are compared
It is right, face attribute is obtained, so as to obtain the face feature of facial image.
Step 120, facial image is labeled according to face feature.
Wherein, facial image being labeled can mark face feature in facial image.To facial image into
The mode of the mark of row face position can be, using the location algorithm of correlation technique to the facial feature localization of facial image after, root
The mark of face position is completed according to the characteristic point of positioning result.The mode being labeled to the face attribute of facial image can be with
It is to be added to the type of each face in corresponding face in the form of a flag respectively.
Step 130, the facial image after mark is inputted to sketch and generates model, obtain the corresponding sketch map of facial image
Piece.
Wherein, sketch generation model includes the model based on machine learning algorithm generation, can be carried out based on sample set
The model that constantly training obtains.Sketch generation model can be the model that normal picture can be converted into sketch picture.Sketch
Picture can a kind of show the stylized picture of personage by the way of lines.
In the present embodiment, the facial image for carrying out face feature mark is inputted after generating model to sketch, sketch generation
Model carries out facial image sketch processing, the corresponding sketch map of generation facial image according to the face feature of facial image
Piece.Under this application scene, the process that facial image is converted into sketch picture by sketch generation model can be, according to default face
Each face region is converted into sketch image by order successively, exemplary, it is assumed that default face order is eyebrow, eye
Eyeball, nose and lip, after the facial image input sketch generation model after mark, are first handled eyebrow region, root
Be converted into corresponding sketch image according to eyebrow type, then eyes region handled, subsequently to nose region at
Reason, is finally handled lip region, so as to obtain the corresponding sketch picture of whole facial image；Alternatively, according to five
Official's feature carries out sketch conversion to each face region at the same time.
Optionally, facial image can be converted into the element of different-style information by the sketch generation model in the present embodiment
Trace designs piece.Wherein, stylized information can include common style, cartoon style and distorting mirror style etc..People after by mark
Before face image input sketch generation model, user can select stylized information, sketch generation model according to the hobby of oneself
Facial image is converted into by corresponding stylized sketch picture according to the stylized information that user selects.The advantage of doing so is that
The diversity of sketch picture can be improved.
The technical solution of the present embodiment, obtains the face feature of facial image, then according to face feature to face first
Image is labeled, and is finally inputted the facial image after mark to sketch and is generated model, obtains the corresponding sketch of facial image
Picture, wherein, sketch generation model includes the model based on machine learning algorithm generation.Sketch provided by the embodiments of the present application
Generation method, the facial image for carrying out face feature mark is inputted to sketch and generates model, obtains the corresponding element of facial image
Trace designs piece, can improve the convenience of generation sketch.
Optionally, face feature includes face position and face attribute.Facial image is labeled according to face feature,
It can also be implemented by following manner：
First, facial image is split according to face position, obtains multiple face subgraphs.
Wherein, the criterion split to facial image can be face sub-picture pack after segmentation containing at least one complete
Face.For example, facial image can be divided into respectively comprising six left eye, right eye, Zuo Mei, right eyebrow, nose, lip faces
Subgraph, or be divided into respectively comprising three left eyebrow left eye, right eyebrow right eye, nose lip people's face images etc..The present embodiment
In, on the basis of ensureing that everyone face image includes at least one complete face, facial image can be carried out any
The segmentation of form, does not limit herein.
Specifically, after the face position of facial image is obtained, facial image is split according to face position, is obtained
Multiple face subgraphs.
Then, at least one target person face image is chosen from multiple face subgraphs.
After segmentation is carried out to facial image and obtains multiple face subgraphs, at least one is chosen from multiple face subgraphs
A target person face image.Exemplary, it is assumed that facial image is divided into respectively comprising left eye, right eye, Zuo Mei, right eyebrow, nose
Son, after six people's face images of lip, choose comprising nose, lip the two face subgraphs as target person face image.
In the present embodiment, choosing the mode of at least one target person face image can be, be chosen according to preset rules, or
Person is chosen according to the manual operation of user.Wherein preset rules can choose the face subgraph or bag for including eyes
Face subgraph containing eyebrow or the face subgraph comprising nose etc..It can be that user works as according to itself that user chooses manually
Preceding operation is intended to be chosen, such as user currently wants that by the regioinvertions comprising lip be sketch image, then can select
Face subgraph comprising lip.
Finally, the target person face image of selection is labeled according to face attribute.
In this implementation, can be according to the mode that face attribute is labeled the target person face image of selection, according to
The attribute for the face that target person face image includes is labeled target person face image.Exemplary, it is assumed that facial image
Face attribute be arched eyebrows, fawn eye, middle nose and cola type lip, if the target person face image chosen is includes left eye
Face subgraph, then be labeled target person face image according to the attribute of eyes, and addition is labeled as " fawn eye ".
Optionally, the facial image after mark is inputted to sketch and generates model, obtain the corresponding sketch map of facial image
Piece, can be implemented by following manner：Target person face image after mark is inputted to sketch and generates model, obtains target face
The corresponding first sketch picture of subgraph.
In the present embodiment, the target person face image for carrying out face attribute labeling is inputted after generating model to sketch, element
The attribute for the face that generation model is included according to target person face image is retouched, sketch processing is carried out to target person face image,
Generate the corresponding first sketch picture of target person face image.Exemplary, it is assumed that included in the target person face image of selection
Two face of nose and lip, after the nose of target person face image and lip are carried out face attribute labeling, input sketch life
Into model, nose and the corresponding sketch image of lip are obtained.
Optionally, after the corresponding first sketch picture of target person face image is obtained, following steps are further included：By
One sketch picture is merged with remaining face subgraph, obtains the second sketch picture.
Wherein, remaining face subgraph is the face subgraph in addition to target person face image in multiple face subgraphs
Picture.Specifically, after the corresponding first sketch picture of target subgraph is obtained, by the first sketch picture and remaining face subgraph
Merge, obtain the second sketch picture.Exemplary, it is assumed that facial image is divided into respectively comprising left eyebrow left eye, right eyebrow
Three right eye, nose lip people's face images, wherein the face subgraph comprising right eyebrow right eye is chosen for target person face figure
As being converted into sketch picture, by the sketch picture and remaining two people's face image, that is, the face subgraph of left eyebrow left eye is included
Picture and face subgraph comprising nose lip merge, and obtain the second sketch image, and the second sketch picture of acquisition only has
Right eyebrow right eye region is converted into sketch, remaining region also keeps the style of original image.
The technical solution of the present embodiment, first splits facial image according to face position, obtains multiple people's faces
Image, then chooses at least one target person face image from multiple face subgraphs, subsequently according to face attribute to choosing
The target person face image taken is labeled, and is subsequently inputted the target person face image after mark to sketch and is generated model,
The corresponding first sketch picture of target person face image is obtained, is finally closed the first sketch picture and remaining face subgraph
And obtain the second sketch picture.The subregion of facial image is converted into sketch image, improves the diversity of sketch conversion.
Optionally, facial image is split according to face position, obtains multiple face subgraphs, following sides can be passed through
Formula is implemented：Split according to the connectivity pair facial image of face position region, obtain multiple face subgraphs.
Wherein, the connectedness in region can be the continuity of face region.Such as：Region where left eyebrow and left eye
There are connectedness, and connectedness is then not present due to being separated by nose in left eyebrow and right eye region.In the present embodiment, according to
The connectivity pair target facial image segmentation of face position region, is to ensure that face are not divided.
Optionally, at least one target person face image is chosen from multiple face subgraphs, can be real by following manner
Apply：At least one target person face image is chosen from multiple face subgraphs, target person face image includes at least one
Face.
Split according to the connectivity pair facial image of face position region, after obtaining multiple face subgraphs, from more
At least one target person face image is chosen in personal face image so that target person face image includes at least one five
Official.
The technical solution of the present embodiment, splits according to the connectivity pair target facial image of face position region, obtains
To multiple face subgraphs, at least one target person face image, target person face image are chosen from multiple face subgraphs
Include at least one face.So that including at least one complete face in face subgraph, face can be prevented to be divided,
So as to improve the accuracy of sketch conversion.
Fig. 2 is the flow chart of the generation method of another sketch provided by the embodiments of the present application.As shown in Fig. 2, this method
Include the following steps.
Step 210, human face sketch pictures are obtained, and obtain the face feature of human face sketch pictures.
Wherein, human face sketch pictures can be searched for from network data base and obtained.Obtain human face sketch pictures
The process of face feature can be that recognition of face is carried out to every pictures in human face sketch pictures, utilizes correlation technique
In location algorithm the face in face are positioned, obtain face position, then the type of face is analyzed, obtain
Face attribute, so as to obtain the face feature of human face sketch pictures.
Step 220, the gray value of each pixel in human face sketch pictures is obtained.
In the present embodiment, obtaining the mode of the gray value of pixel can be, be read using existing picture handling implement
After human face sketch picture, the corresponding gray matrix of sketch picture is obtained, the ash of each pixel is achieved with from gray matrix
Angle value.
Step 230, human face sketch pictures are labeled according to the face feature of human face sketch pictures and gray value,
Obtain human face sketch picture sample collection.
In the present embodiment, human face sketch pictures are labeled according to the face feature of human face sketch pictures mode
Similar with the above-mentioned mode being labeled according to face feature to facial image, details are not described herein again.According to gray value to face
The mode that sketch pictures are labeled can be, by the corresponding gray value mark of each pixel in corresponding pixel.
Step 240, according to human face sketch picture sample collection, preset model is instructed based on setting machine learning algorithm
Practice, obtain sketch generation model.
Specifically, after human face sketch picture sample collection is obtained, setting machine is based on using human face sketch picture sample collection
Learning algorithm is trained preset model, preset model is constantly learnt face feature and the pass before pixel gray value
System, makes preset model have the ability that facial image is converted into sketch picture, so as to obtain sketch generation model.
Step 250, the face feature of facial image is obtained.
Step 260, facial image is labeled according to face feature.
Step 270, the facial image after mark is inputted to sketch and generates model, obtain the corresponding sketch map of facial image
Piece.
Optionally, human face sketch pictures are obtained, can also be implemented by following manner：According to default style information to people
Face sketch pictures are classified, and obtain human face sketch picture subset.
Wherein, default style information includes cartoon style, common style and distorting mirror style etc..Specifically, obtaining
After substantial amounts of human face sketch picture, the style of every sketch picture is analyzed, is then classified to affiliated default style
Change in the corresponding human face sketch picture subset of information.Exemplary, it is assumed that include 1000 sketches in human face sketch pictures
Picture, wherein 300 belong to cartoon style, 400 belong to common style, and 300 belong to distorting mirror style.
Optionally, human face sketch pictures are labeled according to the face feature of human face sketch pictures and gray value,
Human face sketch picture sample collection is obtained, can be implemented by following manner：According to the face feature and gray scale of human face sketch pictures
Value is labeled human face sketch picture subset, obtains human face sketch picture sample subset.
In the present embodiment, human face sketch picture subset is carried out according to the face feature of human face sketch pictures and gray value
The mode of mark, is labeled human face sketch pictures with above-mentioned face feature and gray value according to human face sketch pictures
Mode it is similar, details are not described herein again.After obtaining human face sketch picture sample subset, according to human face sketch picture sample subset,
Preset model is trained based on setting machine learning algorithm, obtains sketch generation model so that sketch generation model has
Generate the ability of different-style sketch picture.The advantage of doing so is that the diversity of generation sketch picture can be improved.
The technical solution of the present embodiment, according to the face feature of human face sketch pictures and gray value to human face sketch picture
Collection is labeled, and obtains human face sketch picture sample collection, according to human face sketch picture sample collection, based on setting machine learning algorithm
Preset model is trained, obtains sketch generation model.Utilize the human face sketch picture sample set pair preset model after mark
It is trained, the accuracy of sketch generation model generation sketch picture can be improved.
Optionally, the facial image after mark is inputted to sketch and generates model, obtain the corresponding element of the facial image
Trace designs piece, can be implemented by following manner：Selection target default style information, the facial image after mark is inputted to sketch
Model is generated, obtains the corresponding target default style sketch picture of facial image.
Specifically, after user can select the target default style information of the sketch picture of desired generation, after mark
Facial image input to sketch generate model, so as to obtain the corresponding target default style sketch map of target facial image
Piece.Exemplary, user selects distorting mirror style, the facial image after mark is inputted after generating model to sketch, so that it may obtain
Obtain distorting mirror style sketch picture.The advantage of doing so is that user can like selection sketch style according to oneself, so as to carry
High user experience.
Fig. 3 is the flow chart of the generation method of another sketch provided by the embodiments of the present application.As to above-described embodiment
Be explained further, as shown in figure 3, this method comprises the following steps.
Step 301, classified according to default style information to human face sketch pictures, obtain human face sketch picture
Collection.
Step 302, human face sketch picture subset is carried out according to the face feature of human face sketch picture subset and gray value
Mark, obtains human face sketch picture sample subset.
Step 303, according to this collection of human face sketch picture appearance, preset model is instructed based on setting machine learning algorithm
Practice, obtain sketch generation model.
Step 304, the face feature of facial image is obtained, face feature includes face position and face attribute.
Step 305, facial image is split according to face position, obtains multiple face subgraphs.
Step 306, at least one target person face image is chosen from multiple face subgraphs.
Step 307, the target person face image of selection is labeled according to face attribute.
Step 308, selection target default style information.
Step 309, the target person face image after mark is inputted to sketch and generates model, obtain target person face image
Corresponding stylized first sketch picture.
Step 310, the first sketch picture of stylization is merged with remaining face subgraph, obtains the second sketch map
Piece.
The technical solution of the present embodiment, the target person face image after mark is inputted to sketch and generates model, obtains mesh
The corresponding stylized first sketch picture of face subgraph is marked, stylized first sketch picture is carried out with remaining face subgraph
Merge, obtain the second sketch picture.Not only the subregion of face picture can be made to be converted into sketch image, part can also be made
Sketch image is shown with default style, improves the diversity of sketch picture.
Fig. 4 is a kind of structure diagram of the generating means of sketch provided by the embodiments of the present application.As shown in figure 4, the dress
Put including：Face feature acquisition module 410, facial image labeling module 420 and sketch picture acquisition module 430.
Face feature acquisition module 410, for obtaining the face feature of facial image；
Facial image labeling module 420, for being labeled according to the face feature to the facial image；
Sketch picture acquisition module 430, generates model, described in acquisition for inputting the facial image after mark to sketch
The corresponding sketch picture of facial image, wherein, the sketch generation model includes the model based on machine learning algorithm generation.
Optionally, face feature includes face position and face attribute, and facial image labeling module 420, is additionally operable to：
Facial image is split according to face position, obtains multiple face subgraphs；
At least one target person face image is chosen from multiple face subgraphs；
The target person face image of selection is labeled according to face attribute；
Correspondingly, sketch picture acquisition module 430, is additionally operable to：
Target person face image after mark is inputted to sketch and generates model, obtains target person face image corresponding the
One sketch picture.
Optionally, sketch picture acquisition module 430, is additionally operable to：
First sketch picture and remaining face subgraph are merged, obtain the second sketch picture, remaining face subgraph
As being the face subgraph in multiple face subgraphs in addition to target person face image.
Optionally, facial image labeling module 420, is additionally operable to：
Split according to the connectivity pair facial image of face position region, obtain multiple face subgraphs；
At least one target person face image is chosen from multiple face subgraphs, target person face image is included at least
One face.
Optionally, including：
Human face sketch pictures acquisition module, for obtaining human face sketch pictures, and obtains human face sketch pictures
Face feature；
Gray value acquisition module, for obtaining the gray value of each pixel in human face sketch pictures；
Human face sketch picture sample collection acquisition module, for the face feature and gray value pair according to human face sketch pictures
Human face sketch pictures are labeled, and obtain human face sketch picture sample collection；
Sketch generates model acquisition module, for according to human face sketch picture sample collection, based on setting machine learning algorithm
Preset model is trained, obtains sketch generation model.
Optionally, human face sketch pictures acquisition module, is additionally operable to：
Classified according to default style information to human face sketch pictures, obtain human face sketch picture subset；Wherein,
Default style information includes cartoon style, common style and distorting mirror style；
Correspondingly, human face sketch picture sample collection acquisition module, is additionally operable to：
Human face sketch picture subset is labeled according to the face feature of human face sketch pictures and gray value, obtains people
Face sketch picture sample set.
Optionally, sketch picture acquisition module 430, is additionally operable to：
Selection target default style information；
Facial image after mark is inputted to sketch and generates model, obtains the corresponding target default style of facial image
Sketch picture.
Above device can perform the method that the foregoing all embodiments of the application are provided, and it is corresponding to possess the execution above method
Function module and beneficial effect.Not ins and outs of detailed description in the present embodiment, reference can be made to the foregoing all implementations of the application
The method that example is provided.
Fig. 5 is a kind of structure diagram of terminal device provided by the embodiments of the present application.As shown in figure 5, terminal device 500
Including memory 501 and processor 502, wherein processor 502 is used to perform following steps：
Obtain the face feature of facial image；
The facial image is labeled according to the face feature；
Facial image after mark is inputted to sketch and generates model, obtains the corresponding sketch picture of the facial image,
Wherein, the sketch generation model includes the model based on machine learning algorithm generation.
Fig. 6 is the structure diagram of another terminal device provided by the embodiments of the present application.As shown in fig. 6, the terminal can
With including：Housing (not shown), memory 601, central processing unit (Central Processing Unit, CPU) 602
(also known as processor, hereinafter referred to as CPU), the computer program that is stored on memory 601 and can be run on processor 602,
Circuit board (not shown) and power circuit (not shown).The circuit board is placed in the space that the housing surrounds
Portion；The CPU602 and the memory 601 are arranged on the circuit board；The power circuit, for for the terminal
Each circuit or device power supply；The memory 601, for storing executable program code；The CPU602 is by reading
The executable program code that is stored in memory 601 is stated to run program corresponding with the executable program code.
The terminal further includes：Peripheral Interface 603, RF (Radio Frequency, radio frequency) circuit 605, voicefrequency circuit
606th, loudspeaker 611, power management chip 608, input/output (I/O) subsystem 609, touch-screen 612, other input/controls
Equipment 610 and outside port 604, these components are communicated by one or more communication bus or signal wire 607.
It should be understood that graphic terminal 600 is only an example of terminal, and terminal device 600 can be with
With than more or less components shown in figure, two or more components can be combined, or can have
Different component configurations.Various parts shown in figure can be including one or more signal processings and/or special integrated
Hardware, software including circuit are realized in the combination of hardware and software.
Below just it is provided in this embodiment for sketch generation terminal device be described in detail, the terminal device with
Exemplified by smart mobile phone.
Memory 601, the memory 601 can be accessed by CPU602, Peripheral Interface 603 etc., and the memory 601 can
Including high-speed random access memory, can also include nonvolatile memory, such as one or more disk memories,
Flush memory device or other volatile solid-state parts.
The peripheral hardware that outputs and inputs of equipment can be connected to CPU602 and deposited by Peripheral Interface 603, the Peripheral Interface 603
Reservoir 601.
I/O subsystems 609, the I/O subsystems 609 can be by the input/output peripherals in equipment, such as touch-screen 612
With other input/control devicess 610, Peripheral Interface 603 is connected to.I/O subsystems 609 can include 6091 He of display controller
For controlling one or more input controllers 6092 of other input/control devicess 610.Wherein, one or more input controls
Device 6092 processed receives electric signal from other input/control devicess 610 or sends electric signal to other input/control devicess 610,
Other input/control devicess 610 can include physical button (pressing button, rocker buttons etc.), dial, slide switch, behaviour
Vertical pole, click on roller.What deserves to be explained is input controller 6092 can with it is following any one be connected：Keyboard, infrared port,
The instruction equipment of USB interface and such as mouse.
Wherein, according to touch-screen operation principle and transmission information medium classification, touch-screen 612 can be resistance-type,
Capacitor induction type, infrared-type or surface acoustic wave type.Classify according to mounting means, touch-screen 612 can be：It is external hanging type, built-in
Formula or monoblock type.Classify according to technical principle, touch-screen 612 can be：Vector pressure sensing technology touch-screen, resistive technologies are touched
Touch screen, capacitance technology touch-screen, infrared technology touch-screen or surface acoustic wave technique touch-screen.
Touch-screen 612, the touch-screen 612 are the input interface and output interface between user terminal and user, can
User is shown to depending on output, visual output can include figure, text, icon, video etc..Optionally, touch-screen 612 is by user
The electric signal (electric signal of such as contact surface) triggered on touch screen curtain, is sent to processor 602.
Display controller 6091 in I/O subsystems 609 receives electric signal from touch-screen 612 or is sent out to touch-screen 612
Electric signals.Touch-screen 612 detects the contact on touch-screen, and the contact detected is converted to and shown by display controller 6091
The interaction of user interface object on touch-screen 612, that is, realize human-computer interaction, the user interface being shown on touch-screen 612
Icon that object can be the icon of running game, be networked to corresponding network etc..What deserves to be explained is equipment can also include light
Mouse, light mouse is not show the touch sensitive surface visually exported, or the extension of the touch sensitive surface formed by touch-screen.
RF circuits 605, are mainly used for establishing the communication of intelligent sound box and wireless network (i.e. network side), realize intelligent sound box
Data receiver and transmission with wireless network.Such as transmitting-receiving short message, Email etc..
Voicefrequency circuit 606, is mainly used for receiving voice data from Peripheral Interface 603, which is converted to telecommunications
Number, and the electric signal is sent to loudspeaker 611.
Loudspeaker 611, for the voice signal for receiving intelligent sound box from wireless network by RF circuits 605, is reduced to
Sound simultaneously plays the sound to user.
Power management chip 608, the hardware for being connected by CPU602, I/O subsystem and Peripheral Interface are powered
And power management.
In the present embodiment, central processing unit 602 is used for：
Obtain the face feature of facial image；
The facial image is labeled according to the face feature；
Facial image after mark is inputted to sketch and generates model, obtains the corresponding sketch picture of the facial image,
Wherein, the sketch generation model includes the model based on machine learning algorithm generation.
Further, the face feature includes face position and face attribute, it is described according to the face feature to institute
Facial image is stated to be labeled, including：
The facial image is split according to the face position, obtains multiple face subgraphs；
At least one target person face image is chosen from the multiple face subgraph；
The target person face image of selection is labeled according to the face attribute；
Model is generated correspondingly, inputting the facial image after mark to sketch, obtains the corresponding element of the facial image
Trace designs piece, including：
Target person face image after mark is inputted to sketch and generates model, the target person face image is obtained and corresponds to
The first sketch picture.
Further, after the corresponding first sketch picture of the target person face image is obtained, further include：
The first sketch picture and remaining face subgraph are merged, obtain the second sketch picture, it is described remaining
Face subgraph is the face subgraph in addition to the target person face image in the multiple face subgraph.
Further, it is described that the facial image is split according to the face position, obtain multiple face subgraphs
Picture, including：
Split according to facial image described in the connectivity pair of the face position region, obtain multiple face subgraphs
Picture；
It is described to choose at least one target person face image from the multiple face subgraph, including：
At least one target person face image is chosen from the multiple face subgraph, in the target person face image
Including at least one face.
Further, before the face feature of facial image is obtained, including：
Human face sketch pictures are obtained, and obtain the face feature of the human face sketch pictures；
Obtain the gray value of each pixel in the human face sketch pictures；
The human face sketch pictures are carried out according to the face feature of the human face sketch pictures and the gray value
Mark, obtains human face sketch picture sample collection；
According to the human face sketch picture sample collection, preset model is trained based on setting machine learning algorithm, is obtained
Obtain sketch generation model.
Further, the acquisition human face sketch pictures include：
Classified according to default style information to the human face sketch pictures, obtain human face sketch picture subset；
Wherein, the default style information includes cartoon style, common style and distorting mirror style；
Correspondingly, according to the face feature of the human face sketch pictures and the gray value to the human face sketch picture
Collection is labeled, and obtains human face sketch picture sample collection, including：
According to the face feature of the human face sketch pictures and the gray value to the human face sketch picture subset into
Rower is noted, and obtains human face sketch picture sample subset.
Further, the facial image by after mark inputs to sketch and generates model, obtains the facial image pair
The sketch picture answered, including：
Selection target default style information；
Facial image after mark is inputted to sketch and generates model, the corresponding target of the facial image is obtained and presets wind
Format sketch picture.
The embodiment of the present application also provides a kind of storage medium for including terminal device executable instruction, and the terminal device can
Execute instruction by terminal device processor when being performed for performing a kind of generation method of sketch.
The computer-readable storage medium of the embodiment of the present application, can use any of one or more computer-readable media
Combination.Computer-readable medium can be computer-readable signal media or computer-readable recording medium.It is computer-readable
Storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device or
Device, or any combination above.The more specifically example (non exhaustive list) of computer-readable recording medium includes：Tool
There are the electrical connections of one or more conducting wires, portable computer diskette, hard disk, random access memory (RAM), read-only storage
(ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only storage (CD-
ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer-readable storage
Medium can be any includes or the tangible medium of storage program, the program can be commanded execution system, device or device
Using or it is in connection.
Computer-readable signal media can include in a base band or as carrier wave a part propagation data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium beyond storage medium is read, which, which can send, propagates or transmit, is used for
By instruction execution system, device either device use or program in connection.
The program code included on computer-readable medium can be transmitted with any appropriate medium, including --- but it is unlimited
In wireless, electric wire, optical cable, RF etc., or above-mentioned any appropriate combination.
Can with one or more programming languages or its combination come write for perform the application operation computer
Program code, programming language include object oriented program language-such as Java, Smalltalk, C++, also wrap
Include conventional procedural programming language-such as " C " language or similar programming language.Program code can be complete
Ground is performed, partly performed on the user computer on the user computer, the software kit independent as one performs, partly exists
Part performs or is performed completely on remote computer or server on the remote computer on subscriber computer.It is being related to
In the situation of remote computer, remote computer can pass through the network of any kind --- including LAN (LAN) or wide area
Net (WAN)-be connected to subscriber computer, or, it may be connected to outer computer (such as utilize ISP
To pass through Internet connection).
Certainly, a kind of storage medium for including computer executable instructions that the embodiment of the present application is provided, its computer
The generation operation for the sketch that executable instruction is not limited to the described above, can also carry out the element that the application any embodiment is provided
Relevant operation in the generation method retouched.
Note that it above are only preferred embodiment and the institute's application technology principle of the application.It will be appreciated by those skilled in the art that
The application is not limited to specific embodiment described here, can carry out for a person skilled in the art various obvious changes,
The protection domain readjusted and substituted without departing from the application.Therefore, although being carried out by above example to the application
It is described in further detail, but the application is not limited only to above example, in the case where not departing from the application design, also
It can include other more equivalent embodiments, and scope of the present application is determined by scope of the appended claims.
Claims (10)
- A kind of 1. generation method of sketch, it is characterised in that including：Obtain the face feature of facial image；The facial image is labeled according to the face feature；Facial image after mark is inputted to sketch and generates model, obtains the corresponding sketch picture of the facial image, wherein, The sketch generation model includes the model based on machine learning algorithm generation.
- 2. generation method according to claim 1, it is characterised in that the face feature includes face position and face category Property, it is described that the facial image is labeled according to the face feature, including：The facial image is split according to the face position, obtains multiple face subgraphs；At least one target person face image is chosen from the multiple face subgraph；The target person face image of selection is labeled according to the face attribute；Model is generated correspondingly, inputting the facial image after mark to sketch, obtains the corresponding sketch map of the facial image Piece, including：Target person face image after mark is inputted to sketch and generates model, obtains the target person face image corresponding the One sketch picture.
- 3. generation method according to claim 2, it is characterised in that obtaining the target person face image corresponding the After one sketch picture, further include：The first sketch picture and remaining face subgraph are merged, obtain the second sketch picture, remaining described face Subgraph is the face subgraph in addition to the target person face image in the multiple face subgraph.
- 4. generation method according to claim 2, it is characterised in that it is described according to the face position to the face figure As being split, multiple face subgraphs are obtained, including：Split according to facial image described in the connectivity pair of the face position region, obtain multiple face subgraphs Picture；It is described to choose at least one target person face image from the multiple face subgraph, including：At least one target person face image is chosen from the multiple face subgraph, the target person face image includes At least one face.
- 5. generation method according to claim 1, it is characterised in that before the face feature of facial image is obtained, bag Include：Human face sketch pictures are obtained, and obtain the face feature of the human face sketch pictures；Obtain the gray value of each pixel in the human face sketch pictures；The human face sketch pictures are labeled according to the face feature of the human face sketch pictures and the gray value, Obtain human face sketch picture sample collection；According to the human face sketch picture sample collection, preset model is trained based on setting machine learning algorithm, obtains element Retouch generation model.
- 6. generation method according to claim 5, it is characterised in that the acquisition human face sketch pictures include：Classified according to default style information to the human face sketch pictures, obtain human face sketch picture subset；Wherein, The default style information includes cartoon style, common style and distorting mirror style；Correspondingly, according to the face feature of the human face sketch pictures and the gray value to the human face sketch pictures into Rower is noted, and obtains human face sketch picture sample collection, including：According to the face feature of the human face sketch pictures and the gray value to the human face sketch picture subset into rower Note, obtains human face sketch picture sample subset.
- 7. generation method according to claim 6, it is characterised in that the facial image by after mark is inputted to sketch Model is generated, obtains the corresponding sketch picture of the facial image, including：Selection target default style information；Facial image after mark is inputted to sketch and generates model, obtains the corresponding target default style of the facial image Sketch picture.
- A kind of 8. generating means of sketch, it is characterised in that including：Face feature acquisition module, for obtaining the face feature of facial image；Facial image labeling module, for being labeled according to the face feature to the facial image；Sketch picture acquisition module, generating model for inputting the facial image after mark to sketch, obtaining the face figure As corresponding sketch picture, wherein, the sketch generation model includes the model based on machine learning algorithm generation.
- A kind of 9. terminal device, it is characterised in that including：Processor, memory and storage on a memory and can handled The computer program run on device, the processor are realized such as any one of claim 1-7 when performing the computer program The generation method.
- 10. a kind of storage medium, is stored thereon with computer program, it is characterised in that the program is realized when being executed by processor Generation method as described in any in claim 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711392583.7A CN107967667A (en) | 2017-12-21 | 2017-12-21 | Generation method, device, terminal device and the storage medium of sketch |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711392583.7A CN107967667A (en) | 2017-12-21 | 2017-12-21 | Generation method, device, terminal device and the storage medium of sketch |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107967667A true CN107967667A (en) | 2018-04-27 |
Family
ID=61994335
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711392583.7A Pending CN107967667A (en) | 2017-12-21 | 2017-12-21 | Generation method, device, terminal device and the storage medium of sketch |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107967667A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109785439A (en) * | 2018-12-27 | 2019-05-21 | 深圳云天励飞技术有限公司 | Human face sketch image generating method and Related product |
| CN109816746A (en) * | 2018-12-27 | 2019-05-28 | 深圳云天励飞技术有限公司 | Sketch image generation method and Related product |
| CN112995534A (en) * | 2021-02-05 | 2021-06-18 | 北京字跳网络技术有限公司 | Video generation method, device, equipment and readable storage medium |
| CN113129208A (en) * | 2019-12-31 | 2021-07-16 | 深圳云天励飞技术有限公司 | Human face image generation method based on sketch and related product |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070036429A1 (en) * | 2005-08-09 | 2007-02-15 | Fuji Photo Film Co., Ltd. | Method, apparatus, and program for object detection in digital image |
| CN1971615A (en) * | 2006-11-10 | 2007-05-30 | 中国科学院计算技术研究所 | Method for generating cartoon portrait based on photo of human face |
| CN102194131A (en) * | 2011-06-01 | 2011-09-21 | 华南理工大学 | Fast human face recognition method based on geometric proportion characteristic of five sense organs |
| CN102609964A (en) * | 2012-01-17 | 2012-07-25 | 湖北莲花山计算机视觉和信息科学研究院 | Portrait paper-cut generation method |
| CN102682420A (en) * | 2012-03-31 | 2012-09-19 | 北京百舜华年文化传播有限公司 | Method and device for converting real character image to cartoon-style image |
| CN104091174A (en) * | 2014-07-13 | 2014-10-08 | 西安电子科技大学 | Portrait style classification method based on support vector machine |
| CN104123741A (en) * | 2014-06-24 | 2014-10-29 | 小米科技有限责任公司 | Method and device for generating human face sketch |
| CN105279186A (en) * | 2014-07-17 | 2016-01-27 | 腾讯科技（深圳）有限公司 | Image processing method and system |
| CN105335990A (en) * | 2014-06-05 | 2016-02-17 | 腾讯科技（深圳）有限公司 | Human portrait material image generation method and apparatus |
| CN105654420A (en) * | 2015-12-21 | 2016-06-08 | 小米科技有限责任公司 | Face image processing method and device |
-
2017
- 2017-12-21 CN CN201711392583.7A patent/CN107967667A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070036429A1 (en) * | 2005-08-09 | 2007-02-15 | Fuji Photo Film Co., Ltd. | Method, apparatus, and program for object detection in digital image |
| CN1971615A (en) * | 2006-11-10 | 2007-05-30 | 中国科学院计算技术研究所 | Method for generating cartoon portrait based on photo of human face |
| CN102194131A (en) * | 2011-06-01 | 2011-09-21 | 华南理工大学 | Fast human face recognition method based on geometric proportion characteristic of five sense organs |
| CN102609964A (en) * | 2012-01-17 | 2012-07-25 | 湖北莲花山计算机视觉和信息科学研究院 | Portrait paper-cut generation method |
| CN102682420A (en) * | 2012-03-31 | 2012-09-19 | 北京百舜华年文化传播有限公司 | Method and device for converting real character image to cartoon-style image |
| CN105335990A (en) * | 2014-06-05 | 2016-02-17 | 腾讯科技（深圳）有限公司 | Human portrait material image generation method and apparatus |
| CN104123741A (en) * | 2014-06-24 | 2014-10-29 | 小米科技有限责任公司 | Method and device for generating human face sketch |
| CN104091174A (en) * | 2014-07-13 | 2014-10-08 | 西安电子科技大学 | Portrait style classification method based on support vector machine |
| CN105279186A (en) * | 2014-07-17 | 2016-01-27 | 腾讯科技（深圳）有限公司 | Image processing method and system |
| CN105654420A (en) * | 2015-12-21 | 2016-06-08 | 小米科技有限责任公司 | Face image processing method and device |
Non-Patent Citations (2)
| Title |
|---|
| PAUL C. CONILIONE ；DIANHUI WANG: "Automatic localization and annotation of facial features using machine learning techniques", 《SOFT COMPUTING》 * |
| 华博，李帅，湛永松: "基于相关分析的肖像素描漫画生成系统", 《计算机应用与软件》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109785439A (en) * | 2018-12-27 | 2019-05-21 | 深圳云天励飞技术有限公司 | Human face sketch image generating method and Related product |
| CN109816746A (en) * | 2018-12-27 | 2019-05-28 | 深圳云天励飞技术有限公司 | Sketch image generation method and Related product |
| CN109785439B (en) * | 2018-12-27 | 2023-08-01 | 深圳云天励飞技术有限公司 | Face sketch image generation method and related products |
| CN113129208A (en) * | 2019-12-31 | 2021-07-16 | 深圳云天励飞技术有限公司 | Human face image generation method based on sketch and related product |
| CN113129208B (en) * | 2019-12-31 | 2024-03-26 | 深圳云天励飞技术有限公司 | Sketch-based face image generation method and related products |
| CN112995534A (en) * | 2021-02-05 | 2021-06-18 | 北京字跳网络技术有限公司 | Video generation method, device, equipment and readable storage medium |
| CN112995534B (en) * | 2021-02-05 | 2023-01-24 | 北京字跳网络技术有限公司 | Video generation method, device, equipment and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11798246B2 (en) | Electronic device for generating image including 3D avatar reflecting face motion through 3D avatar corresponding to face and method of operating same | |
| CN108833818B (en) | Video recording method, device, terminal and storage medium | |
| CN105830422B (en) | Foldable electronic and its interface alternation method | |
| CN105229673B (en) | Apparatus and associated method | |
| EP2720126B1 (en) | Method and apparatus for generating task recommendation icon in a mobile device | |
| CN107967667A (en) | Generation method, device, terminal device and the storage medium of sketch | |
| CN108021905A (en) | image processing method, device, terminal device and storage medium | |
| KR20160015727A (en) | Method and apparatus for visualizing music information | |
| KR101944112B1 (en) | Method and apparatus for creating user-created sticker, system for sharing user-created sticker | |
| US20230419582A1 (en) | Virtual object display method and apparatus, electronic device, and medium | |
| US11461949B2 (en) | Electronic device for providing avatar and operating method thereof | |
| KR20140079012A (en) | Mobile apparatus having function of face recognition with additional component | |
| CN105518579A (en) | Information processing device and information processing method | |
| KR102090948B1 (en) | Apparatus saving conversation and method thereof | |
| CN110827195B (en) | Virtual article adding method and device, electronic equipment and storage medium | |
| CN109331455A (en) | Movement error correction method, device, storage medium and the terminal of human body attitude | |
| JP2020507159A (en) | Picture push method, mobile terminal and storage medium | |
| CN111339938A (en) | Information interaction method, device, equipment and storage medium | |
| CN108665510A (en) | Rendering intent, device, storage medium and the terminal of continuous shooting image | |
| CN108055461B (en) | Self-photographing angle recommendation method and device, terminal equipment and storage medium | |
| CN109302563A (en) | Stabilization processing method, device, storage medium and mobile terminal | |
| CN108960213A (en) | Method for tracking target, device, storage medium and terminal | |
| CN107622473A (en) | Image rendering method, device, terminal and computer-readable recording medium | |
| CN112149599A (en) | Expression tracking method and device, storage medium and electronic equipment | |
| CN112766389B (en) | Image classification method, training method, device and equipment of image classification model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180427 |
|
| RJ01 | Rejection of invention patent application after publication |