CN106529450A - Emoticon picture generating method and device - Google Patents
Emoticon picture generating method and device Download PDFInfo
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- CN106529450A CN106529450A CN201610958425.2A CN201610958425A CN106529450A CN 106529450 A CN106529450 A CN 106529450A CN 201610958425 A CN201610958425 A CN 201610958425A CN 106529450 A CN106529450 A CN 106529450A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/174—Facial expression recognition
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/001—Texturing; Colouring; Generation of texture or colour
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
Abstract
An embodiment of the invention discloses an emoticon picture generating method. The method comprises the following steps: obtaining a face image; carrying out face feature extraction on the obtained face image and forming a face feature image; and generating an emoticon picture according to the face feature image. Compared with the prior art, the emoticon picture generating method and device can quickly extract the face feature image and keep the face feature image as the emoticon picture, can quickly realize production of the emoticon pictures, can generate emoticon pictures according with the present trend, and are low in use threshold.
Description
Technical field
Embodiment of the present invention is related to areas of information technology, more particularly to a kind of expression picture generation method and device.
Background technology
Expression bag is a kind of pop culture of formation after social software is active.In mobile Internet period, people with
At present popular star, quotation, animation, video display sectional drawing are material, mix a series of words for matching, specific to express
Emotion.
However, during the embodiment of the present invention is realized, inventor has found that correlation technique at least has problems with：It is existing
There is technology when expression picture is made, general employing will obtain picture from network, using photo handling software and acquisition
Other pictures synthesized.Need to carry out picture more careful process by photo handling software to make expression to reach
The effect of table.This method needs operator to photo handling software is using maturation and process step is more, and process time is too
It is long.Use cost is high.
The content of the invention
Embodiment of the present invention provides a kind of expression picture generation method and device, can rapidly produce expression bag,
Use cost is low.
For solving above-mentioned technical problem, the technical scheme that embodiment of the present invention is adopted is：A kind of expression figure is provided
Piece generation method, including：
Obtain face's picture；
Face feature extraction is carried out to face's picture of the acquisition, and forms face feature picture；
The face feature picture is generated into expression picture.
Wherein, the face's picture to the acquisition carries out face feature extraction, and forms face feature picture and include：
Face's picture is converted to into binary data, is recognized in the binary data that conversion is formed and is extracted facial contour
And/or the corresponding binary data of face feature, determined according to the binary data of the facial contour and/or face feature that extract
The binary data of whole face, the binary data of whole face is changed to form face feature picture；The binary number
According to including：Mat data.
Wherein, it is described that face's picture is converted to into binary data, in the binary data that conversion is formed recognize and carry
The binary data of facial contour and/or face feature is taken, then changes to form face feature figure by the binary data of extraction
Piece includes：
Facial contour and/or face are carried out by calling Native methods to being changed the Mat data for being formed by face's picture
The detection identification of feature Mat data, then the Mat data of determination facial contour and/or face feature are in face's picture formation
The position being located in Mat data, determines the Mat of whole face according to the Mat data of the facial contour and/or face feature that extract
Data, obtain a Rect array according to the Mat data of the whole face.
Wherein, it is described that face feature picture generation expression picture is further included：
The face feature picture is carried out putting grey process；
The face feature picture put after ash process is synthesized with negative picture；
Picture after the synthesis is preserved.
Wherein, carrying out synthesis to the face feature picture put after ash process with negative picture includes：After synthesis
Picture carries out face's adjustment, and the part combined with negative picture by face feature picture is wiped.
For solving above-mentioned technical problem, another technical scheme that embodiment of the present invention is adopted is：A kind of expression is provided
Picture making device, including：
Picture acquisition module, for obtaining face's picture；
Characteristic extracting module, carries out face feature extraction for the picture to the acquisition, and forms face feature picture；
The face feature picture making is generated expression picture by expression picture generation module.
Wherein, the characteristic extracting module is further used for：
Face's picture is converted to into binary data, is recognized in the binary data that conversion is formed and is extracted facial contour
And/or the corresponding binary data of face feature, determined according to the binary data of the facial contour and/or face feature that extract
The binary data of whole face, the binary data of whole face is changed to form face feature picture；The binary number
According to including：Mat data.
Wherein, characteristic extracting module is further used for：
Facial contour and/or face are carried out by calling Native methods to being changed the Mat data for being formed by face's picture
The detection identification of feature Mat data, then the Mat data of determination facial contour and/or face feature are in face's picture formation
The position being located in Mat data, determines the Mat of whole face according to the Mat data of the facial contour and/or face feature that extract
Data, obtain a Rect array according to the Mat data of the whole face.
Wherein, expression picture generation module further includes to put grey module：For the face feature picture is put
Ash is processed；
Synthesis module：For synthesizing with negative picture to the face feature picture put after ash process；
Preserving module：For the picture after the synthesis is preserved.
Wherein, synthesis module is additionally operable to：
Synthesis is carried out to the face feature picture put after ash process with negative picture includes：Picture after synthesis is entered
Row face adjusts, and the part combined with negative picture by face feature picture is wiped.
The beneficial effect of embodiment of the present invention is：It is different from the situation of prior art, one kind of embodiment of the present invention
Expression picture generation method and device are capable of the expression picture generation method of present embodiment can be by the face for rapidly extracting
Face feature picture is saved as expression picture by portion's feature image, can rapidly be realized the making of expression picture, be made and meeting
Instantly the expression picture of fashion trend, low using threshold.
Description of the drawings
Fig. 1 is a kind of flow chart of expression picture generation method that first embodiment of the invention is provided；
Fig. 2 is the flow chart that the real present invention applies one methods described step 13 of example；
Fig. 3 is a kind of structure chart of expression picture producing device that second embodiment of the invention is provided；
Fig. 4 is the structure chart of electronic equipment provided in an embodiment of the present invention.
Specific embodiment
Embodiment one
Refering to Fig. 1, the first embodiment of the present invention, there is provided a kind of expression picture generation method, including：
Step 101：Obtain face's picture；
The picture of the acquisition is the picture for including face.
The photo for obtaining face includes：Acquisition human face photo of taking pictures is carried out by photographic head, or from locally stored
The existing picture comprising face is obtained in device or server.
Step 102：Face feature extraction is carried out to face's picture of the acquisition, and forms face feature picture；
The picture to the acquisition carries out face feature extraction, and forms face feature picture and include：
Facial contour in the picture of acquisition and/or face feature are identified, will be comprising the face wheel after identification
The extracting section of wide and/or face feature out, forms face feature picture.Specifically include：Face's picture is converted to two to enter
Data processed, recognize and extract the binary data of facial contour and/or face feature, root in the binary data that conversion is formed
The binary data of whole face is determined according to the binary data of the facial contour and/or face feature that extract, then will be whole
The binary data of face is changed to form face feature picture.The binary data includes：Mat data.
In the above-mentioned methods, the storehouse of opencv is used, image is processed in the storehouse of opencv.It is described
OpenCV is one and permits (increasing income) cross-platform computer vision library for issuing based on BSD, may operate in Linux,
On Windows, Mac OS or Android operation system.A series of its lightweight and efficiently by C functions and a small amount of C++ class structure
Into, while there is provided the interface of the language such as Python, Ruby, MATLAB, in terms of realizing image procossing and computer vision
Many general-purpose algorithms.
Further, processed using the opencv storehouses in android system aspect.First picture format is converted into
Mat data.Opencv carries out the detection of face data by calling Native methods to being changed the Mat data for being formed by picture,
Mainly it is identified by the data to face mask data and/or face feature, after identifying, according to the institute for identifying
The data for stating face mask data and/or face feature determine Mat data that the Mat data of whole face are formed in whole pictures
The Mat data of whole face are positioned by the position at middle place, determine the whole people in the Mat data that whole pictures are formed
Face Mat data, obtain one comprising whole face number Mat according to Rect arrays.
Step 103：The face feature picture is generated into expression picture.
It is described that face feature picture generation expression picture is included：By the Rect comprising human face data obtained above
The data conversion of array forms picture, the form of the picture include it is following any one:bitmap、jepg、jpg、tiff、
Gif, pcx, tga etc..Then the picture of formation is preserved according to corresponding picture format, is formed expression picture.
Prior art is different from, the expression picture generation method of present embodiment can be special by the face for rapidly extracting
Picture is levied, face feature picture is saved as into expression picture, can rapidly be realized the making of expression picture, make and meeting instantly
The expression picture of fashion trend is low using threshold.
Refering to Fig. 2, above-mentioned steps 103 are further included：
Step 1031：The face feature picture is carried out putting grey process；
After face characteristic is detected, the portion intercepts of face out, then need to be put ash.According to picture Mat data
And file is preserved into after data self size of the Rect arrays of face feature picture the face feature picture for detecting,
Formed correspondence picture format face feature picture, the picture format include it is following any one：bitmap、jepg、jpg、
Tiff, gif, pcx, tga etc..The face feature picture for preserving the corresponding form for being formed is obtained, is then passed through in the storehouse of opencv
ColorMatrix methods picture is carried out putting grey process, ColorMatrix is after calling setSaturation (0) function
Using paint.setColorFilter functions, finally picture can be carried out putting grey process with drawBitmap methods.
Further, gray scale can be adjusted.Put ash process after face feature picture it is undesirable when, to gray scale
Value is adjusted, to form the gray scale for meeting needs.The gray scale includes 0% to 100% brightness value, described 0% brightness
Value is that described 100% brightness value is the brightness value close to black close to the brightness value of white.
Step 1032：The face feature picture put after ash process is synthesized with negative picture；
Can it is opposed ash face feature picture move on negative picture.The negative picture is can be special with face
Levy the picture that picture is synthetically formed expression picture.Example, negative picture can be the figure of the body of the people comprising empty face
Piece.Then pass through the OnTouchListener methods inside succession android system and rewrite onTouch methods, to face
Feature image and negative picture are processed, by the numerical value of the coordinate and page layout of change face feature picture constantly to face
Portion's feature image moves adjustment on negative picture.Face feature picture is determined behind the position on negative picture, use
Blank expression bag as painting canvas, by face feature picture as picture, is used and is called in Android system by Canvas
DrawBitmap methods synthesize to two pictures.Picture user after synthesis can carry out face's adjustment, to face feature figure
The part combined with negative picture by piece is wiped, such as some redundances of erasing face.Specifically include：By what is formed after synthesis
Picture as painting canvas, with white as color, and by determine coordinate figure, to the correspondence position coated white on painting canvas, shape
Into erasing effect.Optionally, other colors in addition to white can also be coated.Erasing terminate after with calling drawBitmap side
Method synthesizes to picture.
Step 1033：Picture after the synthesis is preserved.
The picture for finally drawing is preserved after forming picture format.Preservation process is by the newly-built text inside system
Part, and call FileOutputStream methods, is compressed to picture and saves as picture format, the picture format include with
Descend any one：bitmap、jepg、jpg、tiff、gif、pcx、tga.
Prior art is different from, the expression picture generation method of present embodiment can be by the face feature picture for extracting
After carrying out putting ash process, the picture after ash is processed will be put and expression picture will be synthetically formed with negative picture, and can rapidly realize table
The making of feelings picture, makes the expression picture for meeting fashion trend instantly, low using threshold.
Embodiment two
Refering to Fig. 3, third embodiment of the invention, there is provided a kind of expression picture producing device, including：
Picture acquisition module 301：For obtaining the picture of face.
The picture of the acquisition is the picture for including face.
The photo for obtaining face includes：Acquisition human face photo of taking pictures is carried out by photographic head, or from locally stored
The existing picture comprising face is obtained in device or server.
Characteristic extracting module 302：Face feature extraction is carried out for the picture to the acquisition, and forms face feature figure
Piece；
Characteristic extracting module 302 is further used for, and the facial contour in the picture of acquisition and/or face feature are known
Not, after identification by the extracting section comprising the facial contour and/or face feature out, form face feature picture.Specifically
Including：Face's picture is converted to into binary data, conversion formed binary data in recognize and extract facial contour and/
Or the binary data of face feature, whole people is determined according to the binary data of the facial contour and/or face feature that extract
The binary data of whole face then is changed to form face feature picture by the binary data of face.The binary data
Including：Mat data.
The storehouse of opencv in said apparatus, is used, image is processed in the storehouse of opencv.It is described
OpenCV is one and permits (increasing income) cross-platform computer vision library for issuing based on BSD, may operate in Linux,
On Windows, Mac OS or Android operation system.A series of its lightweight and efficiently by C functions and a small amount of C++ class structure
Into, while there is provided the interface of the language such as Python, Ruby, MATLAB, in terms of realizing image procossing and computer vision
Many general-purpose algorithms.
Further, processed using the opencv storehouses in android system aspect.First picture format is converted into
Mat data.Opencv carries out the detection of face data by calling Native methods to being changed the Mat data for being formed by picture,
Mainly it is identified by the data to face mask data and/or face feature, after identifying, according to the institute for identifying
The data for stating face mask data and/or face feature determine Mat data that the Mat data of whole face are formed in whole pictures
The Mat data of whole face are positioned by the position at middle place, determine the whole people in the Mat data that whole pictures are formed
Face Mat data, obtain one comprising whole face number Mat according to Rect arrays.
Expression picture generation module 303：The face feature picture making is generated into expression picture.
The data conversion of the Rect arrays comprising human face data obtained above is formed into picture format, the picture format
Including it is following any one:Bitmap, jepg, jpg, tiff, gif, pcx, tga etc..Then by the picture format correspondence for being formed
Picture preserved, formed expression picture.
Wherein, expression picture generation module 303 further includes to put grey module 3031：For by the face feature picture
Carry out putting grey process；
After face characteristic is detected, the portion intercepts of face out, then need to be put ash.Put 3031 basis of grey module
After data self size of the Rect arrays of picture Mat data and face feature picture the face feature picture for detecting
Preserve into file, form the face feature picture of correspondence picture format, the picture format include it is following any one：
Bitmap, jepg, jpg, tiff, gif, pcx, tga etc..The face feature picture for preserving the corresponding form for being formed is obtained, then
Picture is carried out putting grey process by the ColorMatrix methods in the storehouse of opencv, ColorMatrix is by calling
Paint.setColorFilter functions are used after setSaturation (0) function, finally will can be schemed with drawBitmap methods
Piece carries out putting grey process.
Further, put grey module 3031 to be additionally operable to be adjusted gray scale.Put the face feature picture after ash is processed not
When meeting the requirements, gray value is adjusted, to form the gray scale for meeting needs.The gray scale includes 0% to 100% brightness
Value, described 0% brightness value is that described 100% brightness value is the brightness value close to black close to the brightness value of white.
Synthesis module 3032：For synthesizing with negative picture to the face feature picture put after ash process.
Can it is opposed ash face feature picture move on negative picture.The negative picture is can be special with face
Levy the picture that picture is synthetically formed expression picture.Example, negative picture can be the figure of the body of the people comprising empty face
Piece.Synthesis module 3031 is by by inheriting OnTouchListener methods and rewriting inside android system
OnTouch methods, are processed to face feature picture and negative picture, by changing the coordinate and the page of face feature picture
The numerical value of layout constantly moves adjustment to face feature picture on negative picture.Determine face feature picture in negative figure
Behind position on piece, with Canvas using blank expression bag as painting canvas, by face feature picture as picture, with calling Andorid
DrawBitmap methods in system synthesize to two pictures.Picture user after synthesis can carry out face's adjustment, to face
The part combined with negative picture by portion's feature image is wiped, such as some redundances of erasing face.Specifically include：Will synthesis
The picture for being formed afterwards, by the coordinate figure of determination, is coated with white as color and to the correspondence position on painting canvas as painting canvas
White, forms erasing effect.Erasing terminate after with calling drawBitmap methods to synthesize picture.
Preserving module 3033：For the picture after the synthesis is preserved.
Preserving module 3033 is preserved after the picture for finally drawing will be formed picture format.Preserving module 3033 passes through
The new files inside the system, and FileOutputStream methods are called, picture is compressed and saves as picture format, institute
State picture format include it is following any one：bitmap、jepg、jpg、tiff、gif、pcx、tga.
Prior art is different from, the expression picture generation method of present embodiment can be by the face feature picture for extracting
After carrying out putting ash process, the picture after ash is processed will be put and expression picture will be synthetically formed with negative picture, and can rapidly realize table
The making of feelings picture, makes the expression picture for meeting fashion trend instantly, low using threshold.
Embodiment three
Fig. 4 is the hardware architecture diagram of the electronic equipment 400 of the expression picture generation method that the embodiment of the present application is provided,
As shown in figure 4, the electronic equipment 400 includes：
One or more processors 410 and memorizer 420, in Fig. 4 by taking a processor 410 as an example.
Processor 410 and memorizer 420 can pass through bus or other modes connect, with by bus connection in Fig. 4
As a example by.
Memorizer 420 can be used to store non-volatile software journey as a kind of non-volatile computer readable storage medium storing program for executing
Sequence, non-volatile computer executable program and module, the expression picture generation method such as in the embodiment of the present application are corresponding
(picture acquisition module 301 for example, shown in accompanying drawing 3, characteristic extracting module 302, expression picture generate mould to programmed instruction/module
Block is 303).Processor 410 is stored in the non-volatile software program in memorizer 420, instruction and module by operation, so as to
Various function application and data processing is performed, that is, realizes said method embodiment expression picture generation method.
Memorizer 420 can include storing program area and storage data field, and wherein, storing program area can store operation system
Application program required for system, at least one function；Storage data field can store the use institute according to expression picture producing device
Data of establishment etc..Additionally, memorizer 420 can include high-speed random access memory, non-volatile memories can also be included
Device, for example, at least one disk memory, flush memory device or other non-volatile solid state memory parts.In some embodiments
In, memorizer 420 is optional including relative to the remotely located memorizer of processor 410, and these remote memories can pass through net
Network is connected to expression picture producing device.The example of above-mentioned network include but is not limited to the Internet, intranet, LAN,
Mobile radio communication and combinations thereof.
One or more of module stores in the memorizer 420, when by one or more of processors
During 410 execution, the expression picture generation method in above-mentioned any means embodiment is performed, for example, performed in Fig. 1 described above
Method and step 101 to step 103, the method and step 1031 in Fig. 2 to step 1032, module 301-303, module in Fig. 3
The function of 3031-3033.
The method provided by the executable the embodiment of the present application of the said goods, possesses the corresponding functional module of execution method and has
Beneficial effect.Ins and outs of detailed description in the present embodiment, not can be found in the method provided by the embodiment of the present application.
The electronic equipment of the embodiment of the present application is present in a variety of forms, including but not limited to:
(1) mobile communication equipment:The characteristics of this kind equipment is that possess mobile communication function, and to provide speech, data
Communicate as main target.This Terminal Type includes:Smart mobile phone (such as iPhone), multimedia handset, feature mobile phone, and it is low
End mobile phone etc..
(2) super mobile personal computer equipment:This kind equipment belongs to the category of personal computer, has calculating and processes work(
Can, typically also possess mobile Internet access characteristic.This Terminal Type includes:PDA, MID and UMPC equipment etc., such as iPad.
(3) portable entertainment device:This kind equipment can show and play content of multimedia.The kind equipment includes:Audio frequency,
Video player (such as iPod), handheld device, e-book, and intelligent toy and portable car-mounted navigator.
(4) server:The equipment of the service of calculating is provided, the composition of server includes that processor, hard disk, internal memory, system are total
Line etc., server are similar with general computer architecture, but due to needing to provide highly reliable service, therefore processing energy
The aspects such as power, stability, reliability, safety, extensibility, manageability require higher.
(5) other have the electronic installation of data interaction function.
The embodiment of the present application provides a kind of non-volatile computer readable storage medium storing program for executing, the computer-readable storage medium
Matter is stored with computer executable instructions, and the computer executable instructions are executed by one or more processors, such as in Fig. 4
One processor 410, can cause said one or multiple processors to can perform the expression picture in above-mentioned any means embodiment
Generation method, for example, performs method and step 101 in Fig. 1 described above to step 103, the method and step 1031 in Fig. 2 to
Step 1032, the function of module 301-303, module 3031-3033 in Fig. 3.
Device embodiment described above is only schematic, and the wherein described unit as separating component explanation can
To be or may not be physically separate, as the part that unit shows can be or may not be physics list
Unit, you can local to be located at one, or can also be distributed on multiple NEs.Which is selected according to the actual needs can
In some or all of module realizing the purpose of this embodiment scheme.
Through the above description of the embodiments, those of ordinary skill in the art can be understood that each embodiment
The mode of general hardware platform can be added to realize by software, naturally it is also possible to by hardware.Those of ordinary skill in the art can
Realize that with understanding all or part of flow process in above-described embodiment method can be by computer program to instruct the hard of correlation
Completing, described program can be stored in a computer read/write memory medium part, and the program is upon execution, it may include as above
State the flow process of the embodiment of each method.Wherein, described storage medium can be magnetic disc, CD, read-only memory (Read-
OnlyMemory, ROM) or random access memory (Random Access Memory, RAM) etc..
Embodiments of the present invention are the foregoing is only, the scope of the claims of the present invention is not thereby limited, it is every using this
Equivalent structure or equivalent flow conversion that description of the invention and accompanying drawing content are made, or directly or indirectly it is used in other correlations
Technical field, is included within the scope of the present invention.
Claims (10)
1. a kind of expression picture generation method, it is characterised in that include：
Obtain face's picture；
Face feature extraction is carried out to face's picture of the acquisition, and forms face feature picture；
The face feature picture is generated into expression picture.
2. method according to claim 1, it is characterised in that the face's picture to the acquisition carries out face feature
Extracting, and form face feature picture includes：
Face's picture is converted to into binary data, conversion formed binary data in recognize and extract facial contour and/
Or the corresponding binary data of face feature, determined according to the binary data of the facial contour and/or face feature that extract whole
The binary data of individual face, the binary data of whole face is changed to form face feature picture；The binary data
Including：Mat data.
3. method according to claim 2, it is characterised in that described that face's picture is converted to binary data, is turning
The binary data for recognizing and extracting facial contour and/or face feature in the binary data to be formed is changed, then by extraction
Binary data is changed and to form face feature picture and include：
Facial contour and/or face feature is carried out by calling Native methods to being changed the Mat data for being formed by face's picture
The detection identification of Mat data, the Mat numbers that then the Mat data of determination facial contour and/or face feature are formed in face's picture
According to the position at middle place, the Mat data of whole face are determined according to the Mat data of the facial contour and/or face feature that extract,
A Rect array is obtained according to the Mat data of the whole face.
4. the method according to claim 1-3 any claim, it is characterised in that described by the face feature picture
Generate expression picture to further include：
The face feature picture is carried out putting grey process；
The face feature picture put after ash process is synthesized with negative picture；
Picture after the synthesis is preserved.
5. method according to claim 4, it is characterised in that put the face feature picture after ash is processed and negative to described
Picture carries out synthesis to be included：Face's adjustment is carried out to the picture after synthesis, the portion combined with negative picture to face feature picture
Divide and wiped.
6. a kind of expression picture producing device, it is characterised in that include：
Picture acquisition module, for obtaining face's picture；
Characteristic extracting module, carries out face feature extraction for the picture to the acquisition, and forms face feature picture；
The face feature picture making is generated expression picture by expression picture generation module.
7. device according to claim 6, it is characterised in that the characteristic extracting module is further used for：
Face's picture is converted to into binary data, conversion formed binary data in recognize and extract facial contour and/
Or the corresponding binary data of face feature, determined according to the binary data of the facial contour and/or face feature that extract whole
The binary data of individual face, the binary data of whole face is changed to form face feature picture；The binary data
Including：Mat data.
8. device according to claim 7, it is characterised in that characteristic extracting module is further used for：
Facial contour and/or face feature is carried out by calling Native methods to being changed the Mat data for being formed by face's picture
The detection identification of Mat data, the Mat numbers that then the Mat data of determination facial contour and/or face feature are formed in face's picture
According to the position at middle place, the Mat data of whole face are determined according to the Mat data of the facial contour and/or face feature that extract,
A Rect array is obtained according to the Mat data of the whole face.
9. the device according to claim 6-8 any claim, it is characterised in that expression picture generation module is further
Including putting grey module：For the face feature picture is carried out putting grey process；
Synthesis module：For synthesizing with negative picture to the face feature picture put after ash process；
Preserving module：For the picture after the synthesis is preserved.
10. device according to claim 9, it is characterised in that synthesis module is additionally operable to：
Synthesis is carried out to the face feature picture put after ash process with negative picture includes：Face is carried out to the picture after synthesis
Portion adjusts, and the part combined with negative picture by face feature picture is wiped.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107240143A (en) * | 2017-05-09 | 2017-10-10 | 北京小米移动软件有限公司 | Bag generation method of expressing one's feelings and device |
| WO2019015522A1 (en) * | 2017-07-18 | 2019-01-24 | 腾讯科技（深圳）有限公司 | Emoticon image generation method and device, electronic device, and storage medium |
| CN111009006A (en) * | 2019-12-10 | 2020-04-14 | 广州久邦世纪科技有限公司 | Image processing method based on human face characteristic points |
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