CN104849180A - Particle image real-time processing system and particle image real-time processing method - Google Patents
Particle image real-time processing system and particle image real-time processing method Download PDFInfo
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Abstract
The invention discloses a particle image real-time processing system and a particle image real-time processing method. In the system, a particle image is photographed by a high-speed camera; an original particle image is acquired by an image acquisition card from the high-speed camera, the particle image is primarily processed to obtain a first particle image, and the first particle image is transmitted by an FPGA to a first DSP; the first DSP is used for secondarily processing the first particle image to obtain a second particle image and first particle information, the secondary image processing includes particle identification, tracking of the first DSP and transmission of the second particle image and the first particle information to a second DSP; the second DSP is used for performing tertiary image processing on the second particle image according to the first particle information to obtain second particle information, the second particle information includes particle size, particle shape, particle position, particle concentration and particle speed, and the second particle information is transmitted by the second DSP to the FPGA.
Description
[technical field]
The present invention relates to a kind of particle image real time processing system and method.
[background technology]
Have ripe image processing techniques on the market at present.Current picture processing chip can be accomplished to encode in real time to high-definition image (typical resolution is 1920*1080, and frame per second is 60fps), decode.But existing software and hardware technology, for same rank resolution but the real-time process then Shortcomings of the image of higher frame per second.The transmission of instantaneous mass data, storage and information extraction are main problems faced.
Particle recognition and data transmission are the bases of the measurement of a lot of fluid and target identification (as biological detection).The collection of such as traditional PIV (Particle Image Velocimetry) Instruments Image and the data processing in later stage, visually to separate.Although these traditional particle recognition can carry out high-speed camera (being greater than 200fps), because data volume is huge, be difficult to online real-time processing data, thus there is potential limitation, as apparatus installation inconvenience, information processing not in time etc., especially remote monitoring is made troubles.
[summary of the invention]
In order to overcome the deficiencies in the prior art, the invention provides a kind of particle image real time processing system and method, thus easily realize long-range real time and on line monitoring particle.
A kind of particle image real time processing system, comprising: high speed camera, image pick-up card, FPGA, a DSP and the 2nd DSP;
Described high speed camera is for taking primary granule image;
Described image pick-up card is used for from described high speed camera, collect described primary granule image;
Described FPGA is used for from described image pick-up card, obtain described primary granule image, and the first image procossing is carried out to described primary granule image, described first image procossing comprises and carries out noise reduction and binaryzation to described primary granule image, obtain the first particle image, described first particle image is sent to a described DSP by described FPGA;
A described DSP is used for carrying out the second image procossing to described first particle image, obtain the first particle information and the second particle image, described second image procossing comprises particle recognition and tracking, described first particle information comprises the numbering of each particle, the coordinate of each granule boundary point, described second particle image and the first particle information are sent to described 2nd DSP by a described DSP;
Described 2nd DSP is used for carrying out the 3rd image procossing according to described first particle information to described second particle image, obtain the second particle information, described second particle information comprises: grain size, particle shape, particle position, granule density and particle speed, and described second particle information is sent to described FPGA by described 2nd DSP.
In one embodiment, described second particle information is stored in local storage by described FPGA.
In one embodiment, also comprise mixed-media network modules mixed-media and long-range control host machine, described second particle information is sent to described long-range control host machine by network by described FPGA.
In one embodiment, described FPGA is used for separated in time by some described primary granule images at local storage or send to described long-range control host machine.
In one embodiment, described long-range control host machine carries out visual presentation according to the information of described primary granule image and the second particle information.
A kind of particle image real-time processing method, comprises the steps:
High speed camera shooting primary granule image;
Image pick-up card gathers described primary granule image from described high speed camera;
FPGA obtains described particle image from described image pick-up card, and carries out the first image procossing to described particle image, obtains the first particle image, and described first particle image is sent to a DSP by described FPGA; Described first image procossing comprises and carries out noise reduction and binaryzation to primary granule image;
A described DSP carries out the second image procossing to described first particle image, obtain the first particle information and the second particle image, described second image procossing comprises particle recognition and tracking, described first particle information comprises the numbering of each particle, the coordinate of each granule boundary point, described second particle image and the first particle information are sent to the 2nd DSP by a described DSP;
Described 2nd DSP carries out the 3rd image procossing according to described first particle information to described second particle image, obtain the second particle information, described second particle information comprises: grain size, particle shape, particle position, granule density and particle speed, and described second particle information is sent to described FPGA by described 2nd DSP.
In one embodiment, also comprise the steps:
Described second particle information is stored in local storage by described FPGA.
In one embodiment, also comprise the steps:
Described second particle information is sent to long-range control host machine by mixed-media network modules mixed-media by described FPGA.
In one embodiment, described FPGA separated in time by some described primary granule images at local storage or send to described long-range control host machine.
In one embodiment, described long-range control host machine carries out visual presentation according to the information of described primary granule image and the second particle information.
The invention has the beneficial effects as follows: after the process of this programme, the image data amount that the final data amount obtained can collect than high speed camera reduces by two orders of magnitude, for about 1/100 of original image, data volume reduces greatly, this locality of the data that system is obtained stores and real-time network transmits and becomes possibility, and can be filed further by distance host, visual.
The performance that this system makes full use of high speed camera overcomes again the inconvenience that conventional particles identification (PIV etc.) apparatus installation is arranged, adds data transmission flexibly, storage mode, make PIV technology can light, apply to more wide spectrum neatly.
The key message (grain size, shape, position, concentration, speed etc.) that what whole image processing system exported is after extracting, can supply follow-up in-depth analysis and Visualization.Because this systemic-function is complete, can independent installation and deployment, in extreme environment monitoring (as seabed, high-altitude) and high definition are monitored etc. in real time, there is major application to be worth.
This programme can also be used for other multiple objects such as biological detection in conjunction with back-end software.
[accompanying drawing explanation]
Fig. 1 is the particle image real time processing system schematic diagram of an embodiment of the present invention.
[embodiment]
Below the preferred embodiment of invention is described in further detail.
As shown in Figure 1, a kind of particle image real time processing system of embodiment, comprising: high speed camera, image pick-up card, FPGA, a DSP, the 2nd DSP, mixed-media network modules mixed-media and long-range control host machine.
Described high speed camera is for taking primary granule image, and in one embodiment, the shooting speed of high speed camera is 500fps.
Described image pick-up card is used for from described high speed camera, collect described primary granule image.
Described FPGA is used for from described image pick-up card, obtain described primary granule image, and the first image procossing (pre-service) is carried out to described primary granule image, obtain the first particle image, described first image procossing comprises and carries out noise reduction and binaryzation to primary granule image, described first particle image is sent to a described DSP by described FPGA, meanwhile, FPGA can separated in time by some described primary granule images at local storage or send to described long-range control host machine;
A described DSP is used for carrying out the second image procossing to described first particle image, obtain the first particle information and the second particle image, described second image procossing comprises particle recognition and tracking, and described second particle image and the first particle information are sent to described 2nd DSP by a described DSP.Described first particle information comprises the numbering of each particle, the coordinate of each granule boundary point.Each DSP can have multiple core, can the every frame particle image of parallel processing.
Particle recognition and tracking are the technology of comparative maturity, and have multiple different algorithm at present, the present embodiment can adopt PIV algorithm to carry out particle recognition and tracking.
Described 2nd DSP is used for carrying out the 3rd image procossing according to described first particle information to described second particle image, obtain the second particle information, described second particle information comprises: grain size, particle shape, particle position, granule density and particle speed, and the second particle information is sent to described FPGA by described 2nd DSP.Through the process of the 2nd DSP, obtain the key message of particle image.
Described 3rd image procossing comprises: shape parameters of particles extracts, domain size distribution statistics, PIV (Particle ImageVelocimetry)/PTV (Particle Tracking Velocimetry, particles track tests the speed) algorithm,
Described second particle information can be stored in local storage by described FPGA, as in SD card.
Described FPGA also described second particle information sends to described long-range control host machine by mixed-media network modules mixed-media.
Described long-range control host machine carries out information filing, visual presentation according to the information of primary granule image and the second particle information, such as, draw the histogram of grain size and particle speed relation, can know and find out both sides relation from this histogram.
Image after treatment, data volume can reduce by two orders of magnitude, for about 1/100 of original image, like this, in real time key particles information transmission is become possibility to distance host, for real-time monitor particles provides the foundation, in extreme environment monitoring (as seabed, high-altitude) and high definition are monitored etc. in real time, major application is had to be worth.
In conjunction with back-end software (as the application software that distance host is installed), the present embodiment can also be applied to other biological detection etc.
The particle image real-time processing method of an embodiment, comprises the steps:
High speed camera shooting primary granule image;
Image pick-up card gathers described primary granule image from described high speed camera;
FPGA obtains described particle image from described image pick-up card, and carries out the first image procossing to described particle image, obtains the first particle image, and described first particle image is sent to a DSP by described FPGA; Described first image procossing comprises and carries out noise reduction and binaryzation to primary granule image;
A described DSP carries out the second image procossing to described first particle image, obtain the first particle information and the second particle image, described second image procossing comprises particle recognition and tracking, and described second particle image and the first particle information are sent to the 2nd DSP by a described DSP; Described first particle information comprises the numbering of each particle, the coordinate of each granule boundary point; Described 2nd DSP carries out the 3rd image procossing according to described first particle information to described second particle image, obtain the second particle information, described second particle information comprises: grain size, particle shape, particle position, granule density and particle speed, and described second particle information is sent to described FPGA by described 2nd DSP.
Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, some simple deduction or replace can also be made, all should be considered as belonging to the scope of patent protection that the present invention is determined by submitted to claims.
Claims (10)
1. a particle image real time processing system, is characterized in that, comprising: high speed camera, image pick-up card, FPGA, a DSP and the 2nd DSP;
Described high speed camera is for taking primary granule image;
Described image pick-up card is used for from described high speed camera, collect described primary granule image;
Described FPGA is used for from described image pick-up card, obtain described primary granule image, and carries out the first image procossing to described primary granule image, and obtain the first particle image, described first particle image is sent to a described DSP by described FPGA; Described first image procossing comprises and carries out noise reduction and binaryzation to described primary granule image;
A described DSP is used for carrying out the second image procossing to described first particle image, obtain the first particle information and the second particle image, described second image procossing comprises particle recognition and tracking, described first particle information comprises the numbering of each particle, the coordinate of each granule boundary point, described second particle image and the first particle information are sent to described 2nd DSP by a described DSP;
Described 2nd DSP is used for carrying out the 3rd image procossing according to described first particle information to described second particle image, obtain the second particle information, described second particle information comprises: grain size, particle shape, particle position, granule density and particle speed, and described second particle information is sent to described FPGA by described 2nd DSP.
2. particle image real time processing system as claimed in claim 1, it is characterized in that, described second particle information is stored in local storage by described FPGA.
3. particle image real time processing system as claimed in claim 1, it is characterized in that, also comprise mixed-media network modules mixed-media and long-range control host machine, described second particle information is sent to described long-range control host machine by network by described FPGA.
4. particle image real time processing system as claimed in claim 3, is characterized in that, described FPGA is used for separated in time by some described primary granule images at local storage or send to described long-range control host machine.
5. particle image real time processing system as claimed in claim 4, it is characterized in that, described long-range control host machine carries out visual presentation according to the information of described primary granule image and the second particle information.
6. a particle image real-time processing method, is characterized in that, comprises the steps:
High speed camera shooting primary granule image;
Image pick-up card gathers described primary granule image from described high speed camera;
FPGA obtains described particle image from described image pick-up card, and carries out the first image procossing to described particle image, obtains the first particle image, and described first particle image is sent to a DSP by described FPGA; Described first image procossing comprises and carries out noise reduction and binaryzation to primary granule image;
A described DSP carries out the second image procossing to described first particle image, obtain the first particle information and the second particle image, described second image procossing comprises particle recognition and tracking, described first particle information comprises the numbering of each particle, the coordinate of each granule boundary point, described second particle image and the first particle information are sent to the 2nd DSP by a described DSP;
Described 2nd DSP carries out the 3rd image procossing according to described first particle information to described second particle image, obtain the second particle information, described second particle information comprises: grain size, particle shape, particle position, granule density and particle speed, and described second particle information is sent to described FPGA by described 2nd DSP.
7. particle image real-time processing method as claimed in claim 6, is characterized in that, also comprise the steps:
Described second particle information is stored in local storage by described FPGA.
8. particle image real-time processing method as claimed in claim 6, is characterized in that, also comprise the steps:
Described second particle information is sent to long-range control host machine by mixed-media network modules mixed-media by described FPGA.
9. particle image real time processing system as claimed in claim 8, is characterized in that, described FPGA separated in time by some described primary granule images at local storage or send to described long-range control host machine.
10. particle image real-time processing method as claimed in claim 9, it is characterized in that, described long-range control host machine carries out visual presentation according to the information of described primary granule image and the second particle information.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105160284A (en) * | 2015-09-21 | 2015-12-16 | 广东暨通信息发展有限公司 | Glass bottle bottom mould number recognition device |
| CN107808369A (en) * | 2017-12-01 | 2018-03-16 | 清华大学深圳研究生院 | The micro- dynamic particle observation of deep sea in-situ and identifying system and method |
| CN108020679A (en) * | 2017-12-20 | 2018-05-11 | 清华大学深圳研究生院 | Deep sea in-situ microparticle flow velocity measuring system and method |
| CN108106981A (en) * | 2017-12-18 | 2018-06-01 | 大连理工大学 | A kind of method of liquid flow measurement in saturated porous media |
| CN108645761A (en) * | 2018-05-30 | 2018-10-12 | 西安科技大学 | The visualization system and method for test dust particle motion feature and parameter |
| CN112699753A (en) * | 2020-12-22 | 2021-04-23 | 柏美迪康环境科技（上海）股份有限公司 | Dust monitoring and intelligent tracing system and method |
| CN116330516A (en) * | 2023-05-29 | 2023-06-27 | 乳山市东方硅胶有限公司 | Particle size control system of silica gel particle production equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202216890U (en) * | 2011-06-30 | 2012-05-09 | 河海大学 | Sand grain analysis device based on digital image technology |
| CN103647937A (en) * | 2013-11-21 | 2014-03-19 | 北京环境特性研究所 | An image tracking system and an image data processing method thereof |
| CN103994756A (en) * | 2014-04-23 | 2014-08-20 | 清华大学深圳研究生院 | Marine observation micro-particle measurer |
| US20140285233A1 (en) * | 2013-03-19 | 2014-09-25 | Texas Instrument Incorporated | Efficient reconfigurable logic tile |
| US20140322654A1 (en) * | 2013-04-30 | 2014-10-30 | Canon Kabushiki Kaisha | Lithography apparatus, and method for manufacturing article |
-
2015
- 2015-06-02 CN CN201510295989.8A patent/CN104849180A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202216890U (en) * | 2011-06-30 | 2012-05-09 | 河海大学 | Sand grain analysis device based on digital image technology |
| US20140285233A1 (en) * | 2013-03-19 | 2014-09-25 | Texas Instrument Incorporated | Efficient reconfigurable logic tile |
| US20140322654A1 (en) * | 2013-04-30 | 2014-10-30 | Canon Kabushiki Kaisha | Lithography apparatus, and method for manufacturing article |
| CN103647937A (en) * | 2013-11-21 | 2014-03-19 | 北京环境特性研究所 | An image tracking system and an image data processing method thereof |
| CN103994756A (en) * | 2014-04-23 | 2014-08-20 | 清华大学深圳研究生院 | Marine observation micro-particle measurer |
Non-Patent Citations (2)
| Title |
|---|
| 张怀柱等: "一种嵌入式颗粒显微图像统计系统的设计与实现", 《电子器件》 * |
| 朱芳等: "基于PCI总线的FPGA+双DSP结构信号处理系统", 《计算机测量与控制》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105160284A (en) * | 2015-09-21 | 2015-12-16 | 广东暨通信息发展有限公司 | Glass bottle bottom mould number recognition device |
| CN107808369A (en) * | 2017-12-01 | 2018-03-16 | 清华大学深圳研究生院 | The micro- dynamic particle observation of deep sea in-situ and identifying system and method |
| CN108106981A (en) * | 2017-12-18 | 2018-06-01 | 大连理工大学 | A kind of method of liquid flow measurement in saturated porous media |
| CN108020679A (en) * | 2017-12-20 | 2018-05-11 | 清华大学深圳研究生院 | Deep sea in-situ microparticle flow velocity measuring system and method |
| CN108645761A (en) * | 2018-05-30 | 2018-10-12 | 西安科技大学 | The visualization system and method for test dust particle motion feature and parameter |
| CN112699753A (en) * | 2020-12-22 | 2021-04-23 | 柏美迪康环境科技（上海）股份有限公司 | Dust monitoring and intelligent tracing system and method |
| CN116330516A (en) * | 2023-05-29 | 2023-06-27 | 乳山市东方硅胶有限公司 | Particle size control system of silica gel particle production equipment |
| CN116330516B (en) * | 2023-05-29 | 2023-08-29 | 乳山市东方硅胶有限公司 | Particle size control system of silica gel particle production equipment |
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Application publication date: 20150819 |