CN110109602A - A kind of recommended method to draw a design and device - Google Patents
A kind of recommended method to draw a design and device Download PDFInfo
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- CN110109602A CN110109602A CN201910347586.1A CN201910347586A CN110109602A CN 110109602 A CN110109602 A CN 110109602A CN 201910347586 A CN201910347586 A CN 201910347586A CN 110109602 A CN110109602 A CN 110109602A
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- pattern
- user
- recommendation
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- sliding trace
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0487—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
- G06F3/0488—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/32—Digital ink
Abstract
The invention discloses a kind of recommended method to draw a design and devices, in one embodiment: detection user operates in the drafting of canvas area, obtains sliding trace；By neural network model, the sliding trace is identified and predicted, the pattern result for being identified and being predicted；Recommendation pattern corresponding with the pattern result is obtained, and shows the recommendation pattern.It by implementing above-described embodiment, can be predicted the figure that user is drawn during the drawing of user, to recommend corresponding pattern to user according to prediction result.
Description
Technical field
The present invention relates to Internet technical field more particularly to a kind of recommended method to draw a design and devices.
Background technique
Traditional drawing be completed by painter's every stroke, and need repeat picture identical patterns when, painter
Have to repeat identical movement, and improved to some extent using computer drawing to traditional drawing, utilizes current computer graphics
Software carries out the drafting case of identical figure, and the identical patterns herein referred to can be a pattern unit, is also possible to a width picture, special
It is not generally first to store pattern unit model manually by illustrator for pattern unit, and this model needs illustrator
Computer graphics technical ability with suitable experience more can be obtained easily, furthermore, the image-drawing unit needs largely stored
User oneself screens one by one could select current most suitable pattern.
So the solution of the drawing of great prospect will be become by how solving the above problems
Summary of the invention
Technical problem to be solved by the present invention lies in provide a kind of recommended method to draw a design and device, Neng Gou
The figure to be drawn during the drawing of user to user is predicted, to recommend corresponding figure to user according to prediction result
Case.
To solve the above problems, the embodiment of the present invention provides a kind of recommended method to draw a design, comprising:
The drafting that user is detected in canvas area operates, and obtains sliding trace；
By neural network model, the sliding trace is identified and predicted, the pattern knot for being identified and being predicted
Fruit；
Recommendation pattern corresponding with the pattern result is obtained, and shows the recommendation pattern.
Further, the neural network model includes to N number of convolutional layer, N number of down-sampling layer and full articulamentum, and N is not
Integer less than 1；
It is described that the sliding trace is identified and predicted by neural network model, the figure for being identified and being predicted
Case result, comprising:
Using the sliding trace as the object to be processed of convolutional layer, process of convolution is carried out, convolution processing result is obtained
The convolution processing result is inputted into down-sampling layer, down-sampling operation is carried out, obtains sampled result；
Using the sampled result as the object to be processed of next convolutional layer, aforesaid operations are repeated, until not having
Remaining convolutional layer and down-sampling layer；
Final sampled result is inputted into the full articulamentum, Classification and Identification and prediction is carried out, is identified and predicted
Pattern result.
Further, it is operated in the detection user in the drafting of canvas area, before obtaining sliding trace, further includes:
By neural network model, the history pattern drawn to user is identified and is predicted, obtains the pattern of the user
Draw style.
Further, described to obtain recommendation pattern corresponding with the pattern result, and show the recommendation pattern, specifically
,
Recommendation pattern corresponding with the pattern result is obtained, and style is drawn according to the pattern of the user, is pushed away to described
Recommend priority ranking and display that pattern carries out similarity.
Further, it is operated in the detection user in the drafting of canvas area, after obtaining sliding trace, further includes:
User's each dead time drawn after operation is completed is detected, and in described pause not less than default threshold,
Obtained sliding trace is inputted into neural network model.
The embodiment of the present invention also provides a kind of recommendation apparatus to draw a design, comprising:
Operation detection unit is drawn, is operated for detecting user in the drafting of canvas area, obtains sliding trace；
Identification and predicting unit, for the sliding trace being identified and being predicted, is obtained by neural network model
The pattern result of identification and prediction；
Recommendation unit for obtaining recommendation pattern corresponding with the pattern result, and shows the recommendation pattern.
Further, the neural network model includes to N number of convolutional layer, N number of down-sampling layer and full articulamentum, and N is not
Integer less than 1；
The identification and predicting unit, are specifically used for:
Using the sliding trace as the object to be processed of convolutional layer, process of convolution is carried out, convolution processing result is obtained
The convolution processing result is inputted into down-sampling layer, down-sampling operation is carried out, obtains sampled result；
Using the sampled result as the object to be processed of next convolutional layer, aforesaid operations are repeated, until not having
Remaining convolutional layer and down-sampling layer；
Final sampled result is inputted into the full articulamentum, Classification and Identification and prediction is carried out, is identified and predicted
Pattern result.
Further, the recommendation apparatus to draw a design, further includes:
The identification and predicting unit are also used to through neural network model, and the history pattern drawn to user is known
Not and prediction, the pattern for obtaining the user draw style.
Further, the recommendation unit, specifically for acquisition recommendation pattern corresponding with the pattern result, and according to
The pattern of the user draws style, and priority ranking and the display of similarity are carried out to the recommendation pattern.
Further, the recommendation apparatus to draw a design, further includes:
The drafting operates detection unit, is also used to detect user's each dead time drawn after operation is completed, and
In described pause not less than default threshold, obtained sliding trace is inputted into neural network model.
The implementation of the embodiments of the present invention has the following beneficial effects:
The present invention provides a kind of recommended method to draw a design and devices, in one embodiment: detection user is drawing
The drafting in cloth region operates, and obtains sliding trace；By neural network model, the sliding trace is identified and is predicted,
The pattern result for being identified and being predicted；Recommendation pattern corresponding with the pattern result is obtained, and shows the recommendation pattern.
By implementing above-described embodiment, can be predicted the figure that user is drawn during the drawing of user, thus according to
Prediction result recommends corresponding pattern to user.
Detailed description of the invention
Fig. 1 is the flow diagram of first embodiment provided by the invention；
Fig. 2 is the flow diagram of second embodiment provided by the invention；
Fig. 3 is the flow diagram of 3rd embodiment provided by the invention；
Fig. 4 is the structural schematic diagram of fourth embodiment provided by the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
It should be appreciated that ought use in this specification and in the appended claims, term " includes " and "comprising" instruction
Described feature, entirety, step, operation, the presence of element and/or component, but one or more of the other feature, whole is not precluded
Body, step, operation, the presence or addition of element, component and/or its set.
It is also understood that mesh of the term used in this description of the invention merely for the sake of description specific embodiment
And be not intended to limit the present invention.As description of the invention and it is used in the attached claims, unless on
Other situations are hereafter clearly indicated, otherwise " one " of singular, "one" and "the" are intended to include plural form.
It will be further appreciated that the term "and/or" used in description of the invention and the appended claims is
Refer to any combination and all possible combinations of one or more of associated item listed, and including these combinations.
It should be noted that number of steps description merely for convenience as used herein, does not execute to as to step
The restriction of sequencing.
In specific embodiment, intelligent terminal may include at least one of following item: smart phone, plate are personal
Computer (Pc), mobile phone, visual telephone, E-book reader, desktop type PC, PC on knee, netbook computer, individual
Digital assistants (PDA), portable media player (PMP), Motion Picture Experts Group (MPEG-1 or MPEG-2) audio layer
(MP3) player, portable medical device, camera or wearable device (for example, wear-type device (HMD) (such as electronic glasses),
Electronic Clothes, electronic bracelet, electronics necklace, electronic application accessory, electronics tatoo, smartwatch etc.).
Refering to fig. 1, Fig. 1 is the flow diagram of first embodiment of the invention.
S101, detection user operate in the drafting of canvas area, obtain sliding trace.
Wherein, the painting canvas is intelligent painting canvas, it is to be understood that the painting canvas can be Digitizing plate, electronics drawing pad,
Painting canvas in electronic blackboard also can be understood as operating in the painting canvas in the mapping software on intelligent terminal.
Wherein, the sliding trace is continuous or discrete sliding trace.
S102, pass through neural network model, the sliding trace is identified and predicted, the figure for being identified and being predicted
Case result.
Specifically, the neural network model includes to N number of convolutional layer, N number of down-sampling layer and full articulamentum, N is not small
In 1 integer；Step S102 carries out process of convolution comprising steps of using the sliding trace as the object to be processed of convolutional layer,
Obtain convolution processing result；The convolution processing result is inputted into down-sampling layer, down-sampling operation is carried out, obtains sampled result；
Using the sampled result as the object to be processed of next convolutional layer, aforesaid operations are repeated, until without remaining volume
Lamination and down-sampling layer；Final sampled result is inputted into the full articulamentum, carries out Classification and Identification and prediction, obtain identification and
The pattern result of prediction.
Wherein, to the identification of the sliding trace include to user's dynamics used in drawing course, shape color
Identification.
S103, recommendation pattern corresponding with the pattern result is obtained, and shows the recommendation pattern.
In specific embodiment, the pattern that user can select oneself desired according to the pattern of recommendation, selected figure
Case will replace current sliding trace.
By implementing above-mentioned technical proposal, sliding trace is inputted into neural network model, by multiple to sliding trace
Convolution sum sampling, finally can by identify and prediction provide the user with the higher pattern of ownership goal pattern similarity, thus
It helps user to reduce drawing process, saves non-drawing time.
Referring to Fig.2, Fig. 2 is the flow diagram of second embodiment of the invention.
On the basis of first embodiment, is operated in the detection user in the drafting of canvas area, obtain sliding trace
Before, further includes:
S100, pass through neural network model, the history pattern drawn to user is identified and predicted, obtains the user's
Pattern draws style.
In a preferred embodiment, described to obtain recommendation pattern corresponding with the pattern result, and pushed away described in display
Pattern is recommended, specific:
Recommendation pattern corresponding with the pattern result is obtained, and style is drawn according to the pattern of the user, is pushed away to described
Pattern is recommended to carry out the priority ranking of similarity and show
By according to the pattern of user draw style, to it is described recommendation pattern carry out similarity priority ranking can,
User's discovery can be quickly helped to be best suitable for the pattern of itself style, to save user to the screening time of pattern.
It is the flow diagram of third embodiment of the invention refering to Fig. 3, Fig. 3.
On the basis of above-mentioned first embodiment, operates, slided in the drafting of canvas area in the detection user
After track, further includes:
S104, detection user each dead time drawn after operation is completed, and pause described not less than default threshold
When value, obtained sliding trace is inputted into neural network model.
Specifically, the dead time, which refers to, fails to detect have the movement continued to execute on painting canvas, it is to be understood that
At this time next how user draws in thinking, if think time is not less than default threshold at this time, neural network model can root
It is identified and is predicted according to current sliding trace, recommend the pattern of identification and prediction to user.By implementing the program, Ke Yiti
For user's drawing thinking, non-drawing time is saved.
It is the structural schematic diagram of the 4th implementation column of the invention refering to Fig. 4, Fig. 4.
The embodiment of the present invention also provides a kind of recommendation apparatus to draw a design, comprising:
Operation detection unit 21 is drawn, is operated for detecting user in the drafting of canvas area, obtains sliding trace；
Wherein, the painting canvas is intelligent painting canvas, it is to be understood that the painting canvas can be Digitizing plate, electronics drawing pad,
Painting canvas in electronic blackboard also can be understood as operating in the painting canvas in the mapping software on intelligent terminal.
Wherein, the sliding trace is continuous or discrete sliding trace.
Identification and predicting unit 22, for the sliding trace being identified and being predicted, is obtained by neural network model
To the pattern result of identification and prediction；
Specifically, the neural network model includes to N number of convolutional layer, N number of down-sampling layer and full articulamentum, N is not small
In 1 integer；
The identification and predicting unit 22, are specifically used for:
Using the sliding trace as the object to be processed of convolutional layer, process of convolution is carried out, convolution processing result is obtained
The convolution processing result is inputted into down-sampling layer, down-sampling operation is carried out, obtains sampled result；
Using the sampled result as the object to be processed of next convolutional layer, aforesaid operations are repeated, until not having
Remaining convolutional layer and down-sampling layer；
Final sampled result is inputted into the full articulamentum, Classification and Identification and prediction is carried out, is identified and predicted
Pattern result.
Wherein, to the identification of the sliding trace include to user's dynamics used in drawing course, shape, color
Identification.
Recommendation unit 23 for obtaining recommendation pattern corresponding with the pattern result, and shows the recommendation pattern.
In specific embodiment, the pattern that user can select oneself desired according to the pattern of recommendation, selected figure
Case will replace current sliding trace.
By implementing above-mentioned technical proposal, sliding trace is inputted into neural network model, by multiple to sliding trace
Convolution sum sampling, finally can by identify and prediction provide the user with the higher pattern of ownership goal pattern similarity, thus
It helps user to reduce drawing process, saves non-drawing time.
In a preferred embodiment, the identification and predicting unit 22, are also used in the detection user in painting canvas area
The drafting in domain operates, before obtaining sliding trace, by neural network model, to the history pattern that user draws carry out identification and
Prediction, the pattern for obtaining the user draw style.
In a preferred embodiment, the recommendation unit 23 is specifically also used to obtain corresponding with the pattern result
Recommend pattern, and style is drawn according to the pattern of the user, the priority ranking of similarity is carried out to the recommendation pattern and shows
Show.
By according to the pattern of user draw style, to it is described recommendation pattern carry out similarity priority ranking can,
User's discovery can be quickly helped to be best suitable for the pattern of itself style, to save user to the screening time of pattern.
In a preferred embodiment, the drafting operates detection unit 21, is also used in the detection user in painting canvas
The drafting in region operates, after obtaining sliding trace, detection user each dead time drawn after operation is completed, and in institute
When stating pause not less than default threshold, obtained sliding trace is inputted into neural network model.
Specifically, the dead time, which refers to, fails to detect have the movement continued to execute on painting canvas, it is to be understood that
At this time next how user draws in thinking, if think time is not less than default threshold at this time, neural network model can root
It is identified and is predicted according to current sliding trace, recommend the pattern of identification and prediction to user.By implementing the program, Ke Yiti
For user's drawing thinking, non-drawing time is saved.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a computer-readable storage medium
In, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (10)
1. a kind of recommended method to draw a design characterized by comprising
The drafting that user is detected in canvas area operates, and obtains sliding trace；
By neural network model, the sliding trace is identified and predicted, the pattern result for being identified and being predicted；
Recommendation pattern corresponding with the pattern result is obtained, and shows the recommendation pattern.
2. the recommended method to draw a design as described in claim 1, which is characterized in that the neural network model includes to N
A convolutional layer, N number of down-sampling layer and full articulamentum, N are the integer not less than 1；
It is described that the sliding trace is identified and predicted by neural network model, the pattern knot for being identified and being predicted
Fruit, comprising:
Using the sliding trace as the object to be processed of convolutional layer, process of convolution is carried out, convolution processing result is obtained
The convolution processing result is inputted into down-sampling layer, down-sampling operation is carried out, obtains sampled result；
Using the sampled result as the object to be processed of next convolutional layer, aforesaid operations are repeated, until without residue
Convolutional layer and down-sampling layer；
Final sampled result is inputted into the full articulamentum, carries out Classification and Identification and prediction, the pattern for being identified and being predicted
As a result.
3. the recommended method to draw a design as described in claim 1, which is characterized in that in the detection user in canvas area
Drafting operation, before obtaining sliding trace, further includes:
By neural network model, the history pattern drawn to user is identified and is predicted, the pattern for obtaining the user is drawn
Style.
4. the recommended method to draw a design as claimed in claim 3, which is characterized in that the acquisition and the pattern result pair
The recommendation pattern answered, and show the recommendation pattern, specifically,
Recommendation pattern corresponding with the pattern result is obtained, and style is drawn according to the pattern of the user, the recommendation is schemed
Case carries out the priority ranking of similarity and display.
5. the recommended method to draw a design as described in claim 1, which is characterized in that in the detection user in canvas area
Drafting operation, after obtaining sliding trace, further includes:
User's each dead time drawn after operation is completed is detected, and in described pause not less than default threshold, will
The sliding trace input neural network model arrived.
6. a kind of recommendation apparatus to draw a design characterized by comprising
Operation detection unit is drawn, is operated for detecting user in the drafting of canvas area, obtains sliding trace；
Identification and predicting unit, for the sliding trace being identified and being predicted, is identified by neural network model
With the pattern result of prediction；
Recommendation unit for obtaining recommendation pattern corresponding with the pattern result, and shows the recommendation pattern.
7. the recommendation apparatus to draw a design as claimed in claim 6, which is characterized in that the neural network model includes to N
A convolutional layer, N number of down-sampling layer and full articulamentum, N are the integer not less than 1；
The identification and predicting unit, are specifically used for:
Using the sliding trace as the object to be processed of convolutional layer, process of convolution is carried out, convolution processing result is obtained
The convolution processing result is inputted into down-sampling layer, down-sampling operation is carried out, obtains sampled result；
Using the sampled result as the object to be processed of next convolutional layer, aforesaid operations are repeated, until without residue
Convolutional layer and down-sampling layer；
Final sampled result is inputted into the full articulamentum, carries out Classification and Identification and prediction, the pattern for being identified and being predicted
As a result.
8. the recommendation apparatus to draw a design as claimed in claim 6, which is characterized in that further include:
The identification and predicting unit, are also used to through neural network model, to the history pattern that user draws carry out identification and
Prediction, the pattern for obtaining the user draw style.
9. the recommendation apparatus to draw a design as claimed in claim 8, which is characterized in that the recommendation unit, specifically for obtaining
Recommendation pattern corresponding with the pattern result is taken, and style is drawn according to the pattern of the user, the recommendation pattern is carried out
The priority ranking of similarity and display.
10. the recommendation apparatus to draw a design as claimed in claim 6, which is characterized in that further include:
The drafting operates detection unit, is also used to detect user's each dead time drawn after operation is completed, and in institute
When stating pause not less than default threshold, obtained sliding trace is inputted into neural network model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201910347586.1A CN110109602A (en) | 2019-04-26 | 2019-04-26 | A kind of recommended method to draw a design and device |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910347586.1A CN110109602A (en) | 2019-04-26 | 2019-04-26 | A kind of recommended method to draw a design and device |
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| CN201910347586.1A Pending CN110109602A (en) | 2019-04-26 | 2019-04-26 | A kind of recommended method to draw a design and device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111596810A (en) * | 2020-06-24 | 2020-08-28 | 腾讯科技（深圳）有限公司 | Scribble identification method, device, equipment and storage medium |
| CN112965637A (en) * | 2021-04-08 | 2021-06-15 | 湖北工业大学 | Drawing board |
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| CN108664651A (en) * | 2018-05-17 | 2018-10-16 | 腾讯科技（深圳）有限公司 | A kind of pattern recommends method, apparatus and storage medium |
| CN109308324A (en) * | 2018-09-08 | 2019-02-05 | 中山大学 | A kind of image search method and system based on hand drawing style recommendation |
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2019
- 2019-04-26 CN CN201910347586.1A patent/CN110109602A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108664651A (en) * | 2018-05-17 | 2018-10-16 | 腾讯科技（深圳）有限公司 | A kind of pattern recommends method, apparatus and storage medium |
| CN109308324A (en) * | 2018-09-08 | 2019-02-05 | 中山大学 | A kind of image search method and system based on hand drawing style recommendation |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111596810A (en) * | 2020-06-24 | 2020-08-28 | 腾讯科技（深圳）有限公司 | Scribble identification method, device, equipment and storage medium |
| CN111596810B (en) * | 2020-06-24 | 2021-10-12 | 腾讯科技（深圳）有限公司 | Scribble identification method, device, equipment and storage medium |
| CN112965637A (en) * | 2021-04-08 | 2021-06-15 | 湖北工业大学 | Drawing board |
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