CN107274397B - Automatic card frame identification method - Google Patents
Automatic card frame identification method Download PDFInfo
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- CN107274397B CN107274397B CN201710444592.XA CN201710444592A CN107274397B CN 107274397 B CN107274397 B CN 107274397B CN 201710444592 A CN201710444592 A CN 201710444592A CN 107274397 B CN107274397 B CN 107274397B
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- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000009877 rendering Methods 0.000 claims abstract description 18
- 238000010586 diagram Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 230000002159 abnormal effect Effects 0.000 claims abstract description 6
- 238000004458 analytical method Methods 0.000 claims abstract description 5
- 238000013178 mathematical model Methods 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 5
- 238000004088 simulation Methods 0.000 claims description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011946 reduction process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration by the use of local operators
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- G06T5/70—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
Abstract
The invention discloses an automatic identification method of a card frame, which controls the relation between an output sequence frame and frame time through a python script, reduces noise, calls different modes for analysis and calculation to obtain a consistent curve and a distribution diagram of points obtained by the curve and the relation between the frame and rendering time, and judges the frame with abnormal time on the diagram according to the curve, so that the identification of the bad frame is intelligent, the labor cost is reduced, and the rendering efficiency is integrally improved.
Description
Technical Field
The invention relates to the technical field of identification methods, in particular to an automatic identification method of a card frame.
Background
At present, in the field of cloud rendering and the field of CG movie and television, frames are rendered one by one and then viewed one by one. Particularly in the field of cloud rendering, after cloud rendering is completed, a frame is downloaded to a client locally to check whether a bad frame exists.
The technology for checking whether a bad frame exists in the market at present is very troublesome, and the overall working efficiency is reduced.
Disclosure of Invention
The present invention is directed to a method for automatically identifying a card frame to solve the above problems.
The invention realizes the purpose through the following technical scheme:
the invention comprises the following steps:
the method comprises the following steps: controlling the relation between an output sequence frame and frame time through a python script, and denoising and clearing interference data;
step two: calling various model simulation curves, carrying out fitting degree detection, analyzing and calculating, selecting the most suitable mathematical model, and obtaining a matched curve and a point distribution diagram obtained by the corresponding relation between the curve and the frame and the rendering time;
step three: and judging the deviation degree of the actual rendering calculation time and the mathematical model according to the curve, and finally outputting a frame with abnormal rendering time.
Preferably, according to the second step, the models are one of gaussian, polynomial or fourier.
Preferably, according to the first step, when the noise reduction process removes the interference data, the noise reduction process is performed through a Savitzky-Golay filter.
Preferably, the method performs the fitting degree detection and the analysis calculation through the regression standard deviation according to the step two.
The invention has the beneficial effects that:
the invention provides an automatic identification method of a card frame, which controls the relation between an output sequence frame and frame time through a python script, reduces noise, calls different modes for analysis and calculation to obtain a consistent curve and a distribution diagram of points obtained by the curve and the relation between the frame and rendering time, and judges the frame with abnormal time on the diagram according to the curve, so that the identification of the bad frame is intelligent, the labor cost is reduced, and the rendering efficiency is integrally improved.
Drawings
Fig. 1 is a schematic flow chart of an automatic card frame identification method according to the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
as shown in fig. 1: the invention comprises the following steps:
the method comprises the following steps: controlling the relation between an output sequence frame and frame time through a python script, denoising and clearing interference data, wherein denoising is carried out through a Savitzky-Golay filter when the interference data are cleared;
step two: calling various model simulation curves, carrying out fitting degree detection through regression standard deviation, analyzing and calculating, selecting the most suitable mathematical model, and obtaining a matched curve and a point distribution diagram obtained by the corresponding relation between the curve and the frame and the rendering time;
step three: and judging the deviation degree of the actual rendering calculation time and the mathematical model according to the curve, and finally outputting a frame with abnormal rendering time.
The multiple models are one of gaussian, polynomial or fourier, wherein gaussian and polynomial models have higher accuracy than fourier models.
In summary, the invention provides an automatic identification method for a card frame, which controls the relationship between an output sequence frame and a frame time through a python script, performs noise reduction processing, calls different mode analysis calculations to obtain a consistent curve and a distribution diagram of points obtained by the curve and the relationship between the frame and rendering time, and judges a frame with abnormal time on the diagram according to the curve, so that the identification of the bad frame is intelligent, the labor cost is reduced, and the rendering efficiency is integrally improved.
As will be apparent to those skilled in the art, many modifications can be made to the invention without departing from the spirit and scope thereof, and it is intended that the present invention cover all modifications and equivalents of the embodiments of the invention covered by the appended claims.
Claims (3)
1. An automatic identification method of a card frame is characterized by comprising the following steps:
the method comprises the following steps: controlling the relation between an output sequence frame and frame time through a python script, and denoising and clearing interference data;
step two: calling various model simulation curves, carrying out fitting degree detection, analyzing and calculating, selecting the most suitable mathematical model, and obtaining a matched curve and a point distribution diagram obtained by the corresponding relation between the curve and the frame and the rendering time; the mathematical model is one of gauss, polynomial or fourier;
step three: and judging the deviation degree of the actual rendering calculation time and the mathematical model according to the curve, and finally outputting a frame with abnormal rendering time.
2. The method of claim 1, wherein the method further comprises: according to the first step, when the noise reduction processing is used for eliminating interference data, the noise reduction processing is carried out through a Savitzky-Golay filter.
3. The method of claim 1, wherein the method further comprises: and according to the second step, performing fitting degree detection and analysis calculation through the regression standard deviation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710444592.XA CN107274397B (en) | 2017-06-13 | 2017-06-13 | Automatic card frame identification method |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710444592.XA CN107274397B (en) | 2017-06-13 | 2017-06-13 | Automatic card frame identification method |
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| CN107274397A CN107274397A (en) | 2017-10-20 |
| CN107274397B true CN107274397B (en) | 2020-08-11 |
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| CN201710444592.XA Active CN107274397B (en) | 2017-06-13 | 2017-06-13 | Automatic card frame identification method |
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102201122A (en) * | 2011-05-16 | 2011-09-28 | 大连大学 | Motion capture system, data noise reduction method and system of motion capture |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102982159B (en) * | 2012-12-05 | 2016-07-06 | 上海创图网络科技发展有限公司 | A kind of three-dimensional web page many scenes fast switch over method |
| US9483685B2 (en) * | 2014-04-28 | 2016-11-01 | University Of Pittsburgh - Of The Commonwealth System Of Higher Education | System and method for automated identification of abnormal ciliary motion |
| CN104376595B (en) * | 2014-11-28 | 2017-03-29 | 史文中 | A kind of three-dimensional road generation method cooperateed with based on airborne LiDAR and GIS |
| CN106528398B (en) * | 2015-09-15 | 2019-09-06 | 网易（杭州）网络有限公司 | The visual analysis method of Games Software performance |
| CN105676470B (en) * | 2016-03-24 | 2018-04-10 | 清华大学 | A kind of visual spatial resolution enhancement method and system of three-dimensional scenic |
| CN106504185B (en) * | 2016-10-26 | 2020-04-07 | 腾讯科技（深圳）有限公司 | Rendering optimization method and device |
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102201122A (en) * | 2011-05-16 | 2011-09-28 | 大连大学 | Motion capture system, data noise reduction method and system of motion capture |
Non-Patent Citations (1)
| Title |
|---|
| 非线性编辑网络卡帧现象原因分析;周林栋等;《维护与维修》;20081231;全文 * |
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Address after: 518000 901-B, Bike Science and Technology Building, No. 9, Kexue Road, Central District, High tech Zone, Nanshan District, Shenzhen, Guangdong Patentee after: Shenzhen Ruiyun Technology Co.,Ltd. Address before: 518000 901-B, Bike Science and Technology Building, No. 9, Kexue Road, Central District, High tech Zone, Nanshan District, Shenzhen, Guangdong Patentee before: SHENZHEN RAYVISION TECHNOLOGY CO.,LTD. |
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