CN107146193A - A kind of GPU parallel calculating methods based on double video cards applied to image procossing - Google Patents
A kind of GPU parallel calculating methods based on double video cards applied to image procossing Download PDFInfo
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- CN107146193A CN107146193A CN201710296867.XA CN201710296867A CN107146193A CN 107146193 A CN107146193 A CN 107146193A CN 201710296867 A CN201710296867 A CN 201710296867A CN 107146193 A CN107146193 A CN 107146193A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/20—Processor architectures; Processor configuration, e.g. pipelining
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Abstract
A kind of GPU parallel calculating methods based on double video cards applied to image procossing, including：The GPU resource of the double video cards of initialization；Image memory is divided into video data block one and video data block two, video data block one and video data block two are continuous in physical space, the overlapping region size of setting video data block one and video data block two；Create and start two threads, the corresponding GPU resource of two video cards is called respectively, perform image processing program, obtain video data block one and the respective result of video data block two；The data of overlapping region are abandoned, merge video data block.The present invention improves the computational efficiency of image procossing, for current main flow 4k image frames, the GPU concurrent mechanisms of double video cards can greatly improve the real-time of image procossing, it is less than common class effect in cost simultaneously, real-time processing meaning for high-definition picture is very big, it is ensured that the pixel of new adjacent edges calculates consistent with artwork.
Description
Technical field
The present invention relates to a kind of image processing method, especially a kind of GPU based on double video cards applied to image procossing
Parallel calculating method.
Background technology
Modern GPU provides two programmable parallel processing components of vertex processor and fragment processor.Held using GPU
During the general computational tasks such as row image procossing, the groundwork to be done is the figure that duty mapping to be solved to GPU is supported
On rendering pipeline.Usual way be attributes such as position, color, the normal vectors on input data summit of calculating task or
The graphic plotting such as person's texture key element is expressed, and corresponding Processing Algorithm is then broken down into a series of execution step, and is rewritten
For GPU summit processing routine or fragment processing routine, and fragment programs are called to be handled；Finally, it is stored in frame buffer
Drawing result be exactly algorithm output data.
Applications of the GPU to image procossing is very universal, but it is synchronous to single image not coordinate multiple video cards
The application handled, is handled in real time especially for high-resolution medical image, and the treatment effeciency of single video card can not expire sometimes
The real-time of sufficient image.
The content of the invention
It is an object of the invention to provide a kind of method for the real-time and treatment effeciency for improving image procossing.
To achieve these goals, the technical scheme is that：
A kind of GPU parallel calculating methods based on double video cards applied to image procossing, it comprises the following steps：
Step one：The GPU resource of the double video cards of initialization；
Step 2：Image memory is divided into video data block one and video data block two, video data block one and image
Data block two is continuous in physical space, the overlapping region size of setting video data block one and video data block two；
Step 3：Create and start two threads, the corresponding GPU resource of two video cards is called respectively, perform image procossing
Program, obtains video data block one and the respective result of video data block two；
Step 4：The data of overlapping region are abandoned, merge the result of video data block one and video data block two, it is complete
Concurrent processing of the GPU of video card in pairs to an image.
Further, step one is specially：
Step 101：Library file needed for GPU operations is installed；
Step 102：Video card equipment is initialized, the video card that GPU resource can be called is found in platform；
Step 103：Video card facility information is initialized, the required program object of GPU operations is set up；
Step 104：Compile GPU concurrent program modules.
Further, in step 2, image memory dividing mode is：Horizontal segmentation, longitudinally split or oblique segmentation；Level
During segmentation：
Step 201：By image memory using horizontal division line even partition as two pieces；
Step 202：Equal-sized overlapping region one and overlapping region are built respectively along horizontal division line to both sides up and down
Two, the data of overlapping region one and overlapping region two are consistent with the data of bottom layer image internal memory, build video data block one, including
Part and overlapping region two above horizontal division line, build video data block two, including part below horizontal division line and
Overlapping region one；
Step 203：Neighborhood calculating is carried out to video data block one and video data block two, neighbour is carried out to video data block one
When domain is calculated, the offer data of overlapping region two, when carrying out neighborhood calculating to video data block two, the offer number of overlapping region one
According to.
Further, when longitudinally split in described step two：First by 90 ° of image transposition, according still further to horizontal segmentation processing.
Further, the overlapping region size described in step 202 is the size that is calculated according to neighborhood to determine, overlapping
The size in region is twice of neighborhood computed altitude.
Further, step 3 is specially：
Step 301：Create and start two threads, the corresponding GPU resource of two video cards is called respectively.
Step 302：Image processing program is performed in two threads, each thread corresponds to a video data block respectively,
Two threads are synchronously waited to complete corresponding processing using thread control interface, two video card run times of record are figure compared with elder
The time handled as internal memory.
Further, step 4 is specially：
Step 401：Video data block one abandons the data of overlapping region two, and video data block two abandons overlapping region one
Data；
Step 402：Merge the result of video data block one and video data block two；
Step 403：GPU resource is discharged, result is exported.
Beneficial effects of the present invention：
The present invention improves the computational efficiency of image procossing, and whole efficiency can lift 70%~80%, for current
Main flow 4k image frames, the GPU concurrent mechanisms of double video cards can greatly improve the real-time of image procossing, while being less than in cost
DSP or the FPGA hardware design of common class effect, the real-time processing and realistic meaning for high-definition picture are very big, lead to simultaneously
Cross setting overlapping region, it is ensured that the pixel of new adjacent edges calculates consistent with artwork.
Brief description of the drawings
By the way that exemplary embodiment of the invention is described in more detail with reference to accompanying drawing, it is of the invention above-mentioned and its
Its purpose, feature and advantage will be apparent, wherein, in exemplary embodiment of the invention, identical reference number
Typically represent same parts.
Fig. 1 is image memory horizontal segmentation figure.
Fig. 2 is the longitudinally split figure of image memory.
Fig. 3 is overlapping region schematic diagram.
Embodiment
The preferred embodiment of the present invention is more fully described below with reference to accompanying drawings.Although showing the present invention in accompanying drawing
Preferred embodiment, however, it is to be appreciated that may be realized in various forms the present invention without the embodiment party that should be illustrated here
Formula is limited.
As Figure 1-3, a kind of GPU parallel calculating methods based on double video cards applied to image procossing, it is characterized in that
It comprises the following steps：
Step one：The GPU resource of the double video cards of initialization, it is ensured that the validity of equipment；Specially：GPU operations are installed required
Library file；Video card equipment is initialized, the video card that GPU resource can be called is found in platform；Video card facility information is initialized,
Set up the required program object of GPU operations；Compile GPU concurrent program modules.
Step 2：The overlapping region that image memory is divided between physically continuous two pieces, two pieces of internal memories of setting is big
Small, overlapping region size is the size that is calculated according to neighborhood to determine, the size of overlapping region is the two of neighborhood computed altitude
Times；It is specific as shown in figure 3, two image memory blocks are 1, shadow region, shadow region above section respectively, below shadow region
And with shadow region area identical part；2nd, part below shadow region and shadow region, up and down two pieces of calculating consider be
It is different：The calculating of top half need not enter shadow region below and with shadow region area identical part, shade
The neighborhood for being only to provide data below region and with shadow region area identical part to be accomplished to medium line is calculated；Lower half
The calculating divided does not enter shadow region, and the neighborhood that shadow region is only to provide data to complete medium line is calculated.
Step 3：Create and start two threads, the corresponding GPU resource of two video cards is called respectively, perform image procossing
Program, synchronously wait two threads to complete corresponding processing using thread control interface, two video card run times of record are longer
Person is the time that image memory is handled, and obtains video data block one and the respective result of video data block two；
Step 4：The data of overlapping region are abandoned, merge the result of video data block one and video data block two, it is complete
Concurrent processing of the GPU of video card in pairs to an image.Specially：Video data block one abandons the data of overlapping region two, figure
As data block two abandons the data of overlapping region one；Merge the result of video data block one and video data block two；Release
GPU resource, exports result.
Consider the concurrent mechanism of more video cards, the treatment mechanism with double video cards is basically identical, general PC frameworks are difficult to adopt
The deployment of more than two video card.So we are used as the primary structure of many GPU concurrent processing using double video cards.
It is described above various embodiments of the present invention, described above is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.In the case of without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes will be apparent from for the those of ordinary skill in art field.
Claims (8)
1. a kind of GPU parallel calculating methods based on double video cards applied to image procossing, it is characterized in that it comprises the following steps：
Step one：The GPU resource of the double video cards of initialization；
Step 2：Image memory is divided into video data block one and video data block two, video data block one and view data
Block two is continuous in physical space, the overlapping region size of setting video data block one and video data block two；
Step 3：Create and start two threads, the corresponding GPU resource of two video cards is called respectively, perform image processing program,
Obtain video data block one and the respective result of video data block two；
Step 4：The data of overlapping region are abandoned, merge the result of video data block one and video data block two, complete double
Concurrent processing of the GPU of video card to an image.
2. a kind of GPU parallel calculating methods based on double video cards applied to image procossing according to claim 1, it is special
Levy is that step one is specially：
Step 101：Library file needed for GPU operations is installed；
Step 102：Video card equipment is initialized, the video card that GPU resource can be called is found in platform；
Step 103：Video card facility information is initialized, the required program object of GPU operations is set up；
Step 104：Compile GPU concurrent program modules.
3. a kind of GPU parallel calculating methods based on double video cards applied to image procossing according to claim 1, it is special
Levy in being described step two, image memory dividing mode is：Horizontal segmentation, longitudinally split or oblique segmentation.
4. a kind of GPU parallel calculating methods based on double video cards applied to image procossing according to claim 3, it is special
Levy when being horizontal segmentation in described step two：
Step 201：By image memory using horizontal division line even partition as two pieces；
Step 202：Equal-sized overlapping region one and overlapping region two are built respectively along horizontal division line to both sides up and down, weight
The data in folded region one and overlapping region two are consistent with the data of bottom layer image internal memory, build video data block one, including level
Part and overlapping region two above cut-off rule, build video data block two, including part below horizontal division line and overlapping
Region one；
Step 203：Neighborhood calculating is carried out to video data block one and video data block two, neighborhood meter is carried out to video data block one
During calculation, the offer data of overlapping region two, when carrying out neighborhood calculating to video data block two, the offer data of overlapping region one.
5. a kind of GPU parallel calculating methods based on double video cards applied to image procossing according to claim 4, it is special
Levy when being longitudinally split in described step two：Level point is carried out first by 90 ° of image transposition, the step of according still further to claim 4
Cut processing.
6. a kind of GPU parallel calculating methods based on double video cards applied to image procossing according to claim 4, it is special
It is that overlapping region size described in step 202 is the size that is calculated according to neighborhood to determine to levy, and the size of overlapping region is
Twice of neighborhood computed altitude.
7. a kind of GPU parallel calculating methods based on double video cards applied to image procossing according to claim 1, it is special
Levy is that step 3 is specially：
Step 301：Create and start two threads, the corresponding GPU resource of two video cards is called respectively.
Step 302：Image processing program is performed in two threads, each thread corresponds to a video data block, used respectively
Thread control interface synchronously waits two threads to complete corresponding processing, and two video card run times of record are in image compared with elder
Deposit the time of processing.
8. a kind of GPU parallel calculating methods based on double video cards applied to image procossing according to claim 1, it is special
Levy is that step 4 is specially：
Step 401：Video data block one abandons the data of overlapping region two, and video data block two abandons the number of overlapping region one
According to；
Step 402：Merge the result of video data block one and video data block two；
Step 403：GPU resource is discharged, result is exported.
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Cited By (3)
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| CN111429413A (en) * | 2020-03-18 | 2020-07-17 | 中国建设银行股份有限公司 | Image segmentation method and device and computer readable storage medium |
| CN111984417A (en) * | 2020-08-26 | 2020-11-24 | 展讯通信(天津)有限公司 | Image processing method and device for mobile terminal, storage medium and terminal |
| CN118071736A (en) * | 2024-04-17 | 2024-05-24 | 华伦医疗用品(深圳)有限公司 | GPU-based medical endoscope image real-time processing effect evaluation method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111429413A (en) * | 2020-03-18 | 2020-07-17 | 中国建设银行股份有限公司 | Image segmentation method and device and computer readable storage medium |
| CN111984417A (en) * | 2020-08-26 | 2020-11-24 | 展讯通信(天津)有限公司 | Image processing method and device for mobile terminal, storage medium and terminal |
| WO2022042587A1 (en) * | 2020-08-26 | 2022-03-03 | 展讯通信(天津)有限公司 | Image processing method and apparatus for mobile terminal, and storage medium and terminal |
| CN118071736A (en) * | 2024-04-17 | 2024-05-24 | 华伦医疗用品(深圳)有限公司 | GPU-based medical endoscope image real-time processing effect evaluation method |
| CN118071736B (en) * | 2024-04-17 | 2024-06-28 | 华伦医疗用品(深圳)有限公司 | GPU-based medical endoscope image real-time processing effect evaluation method |
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Inventor after: Zhang Shiping Inventor after: Wang Xiaofang Inventor before: Wang Xiaofang |
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Effective date of registration: 20181029 Address after: 518000 B1 701-72, Kexing Science Park, 15 Keyuan Road, Nanshan District, Shenzhen, Guangdong. Applicant after: Cabin (Shenzhen) Medical Technology Co., Ltd. Address before: 210000 Room 403, 3 tower 141, Ma Tai Street, Nanjing, Jiangsu. Applicant before: Nanjing Mizong Electronic Technology Co., Ltd. |
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Application publication date: 20170908 |