CN101430775B - Automatic fry counting system based on computer vision - Google Patents
Automatic fry counting system based on computer vision Download PDFInfo
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- CN101430775B CN101430775B CN2008101621761A CN200810162176A CN101430775B CN 101430775 B CN101430775 B CN 101430775B CN 2008101621761 A CN2008101621761 A CN 2008101621761A CN 200810162176 A CN200810162176 A CN 200810162176A CN 101430775 B CN101430775 B CN 101430775B
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 47
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000004364 calculation method Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000006467 substitution reaction Methods 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 241000251468 Actinopterygii Species 0.000 abstract description 12
- 238000004458 analytical method Methods 0.000 abstract description 2
- 238000009434 installation Methods 0.000 description 6
- 238000009360 aquaculture Methods 0.000 description 4
- 244000144974 aquaculture Species 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004040 coloring Methods 0.000 description 3
- 230000002950 deficient Effects 0.000 description 2
- 239000004744 fabric Substances 0.000 description 2
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- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
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- Y02A40/81—Aquaculture, e.g. of fish
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Abstract
The present invention provides an automatic fish fry counting device and a counting method based on computer vision. The device consists of a water tank [4] for accommodating fish fry, two digital cameras [2, 7] which are arranged on the water tank at a mutual orthogonal position to form a fish fry image digital photography system, a computer [9] and a signal connecting device [10] which is connected with the computer and the digital photography system. The counting method comprises the following analysis procedures: obtaining a relation between grayscale data of fish fry photos and number of the fish fry by the grayscale data of a background photo without the fish fry and the grayscale data of a fry photo with known number of the fish fry, and then obtaining the number of the fish fry to be counted by the relation. Compared with the prior art, the image data acquired by two orthogonal cameras causes the automatic fish fry counting error to be below 5%, and only the image grayscale data are processed, which accelerates the counting speed.
Description
What technical field the present invention relates to is a kind of automatic fry counting apparatus and method based on computer vision, belongs to the agriculture Mesichthyes cultural technique field of living needs.
Background technology for solve human to the aquatic food demand continuous growth and the contradiction of the slump of disastrous proportions of fishery resources, culture fishery becomes the human main means that obtain aquatic products gradually.In aquaculture, the accurate counting of fry is the guarantee basis of breed standardized management such as science bait throwing in, cultivation density are controlled, the survival rate of fry is assessed and fry is purchased and sold, and is the basic assurance of implementing the aquaculture robotization.
Still do not have the counting mode of special fry in the past, but adopt traditional naked eyes counting, artificial account form to carry out.Normally earlier whole fries are collected, concentrate to be assigned in the gathering barrel, get wherein a gathering barrel again and do counting, calculate the number of fry in this gathering barrel after, multiply by the number of gathering barrel again, how many fries estimate all has.The seedling that this kind classic method is very little for volume, vitality is low has following several big shortcoming:
1, nearly 10,000 to 20,000 of fry number does not wait in each gathering barrel, and the counting accuracy rate is low, gate time is very of a specified duration;
2, naked eyes can further reduce the accuracy rate of counting because the time one produces tired phenomenon for a long time;
3, dried up operation, the fry survival rate is low;
Traditional artificial counting mode also has bowl counting, rule of three etc., but there is the shortcoming that human factor influences greatly, wastes time and energy and error is indefinite equally in these method of counting.Therefore, by traditional method of counting, fry accurately counting is very difficult.
For overcoming the problems referred to above, there is Taiwan Zhongshan University Master's thesis " simple and easy fry is counted system design automatically " to introduce a kind of system that fry in the water tank is counted by Flame Image Process, comprise a sets of data harvester and computing method, wherein data collector comprises a square white light-permeable plastic cistern, one digital camera, be installed on the water tank water body with float, camera lens vertically points to water body, a computing machine and the data connection device that is connected video camera and computing machine; And computing method are programs of a cover image data and data processing.Wherein data processing method is a kind of method of utilizing the Computer Image Processing method to carry out fry numeration, fry counting, adopting colour imagery shot is the basis with the fry image in the water tank, colouring intensity (being the mean value of Red Green Blue color range on each pixel) is as analysis indexes in the software program extraction picture of utilization exploitation, calculate every mean intensity that fish is shared, and then obtain the quantity of fry in the entire image.This system has realized the automated enumeration of fry, the various defectives that traditional fry numeration, fry counting method is caused have been overcome, but, because this system adopts single camera vertically to take, fry on the different depth has distortion in various degree when imaging, this has just influenced the practicality of this method and the accuracy of actual count, and this article shows that its error is about 15%.In addition, this system adopts be the colouring intensity index as parameter, all to calculate the color range value of three kinds of primary colors on each pixel, when the picture pixel that photographs more for a long time, the data volume of processing can the utmost point increases severely and adds, arithmetic speed that can the appreciable impact system.
Summary of the invention is at above-mentioned defective, technical matters to be solved by this invention is exactly to propose a kind of more accurate, easier automatic fry counting apparatus and method based on computer vision, with reach automatic in the fry numeration, fry counting in the aquaculture, noiseless, high-precision, efficiently, effect cheaply, promote the development of aquaculture automatization level.
Automatic fry counting device based on computer vision provided by the invention, comprise a sets of data harvester and a method of counting, said data collector is to place the water tank of fry by one, one fry image digitization camera system, one computing machine constitutes with the signal connection device that is connected computing machine and digital shooting system, said fry image digitization camera system has two digital cameras to be installed on the water tank with the mutual alignment of quadrature, two cameras are connected with computing machine through signal connection device respectively, make the fry image of digital camera acquisition and input computing machine that fry plane and two distributed images of facade in water tank be arranged.
The counting method of counting of the automatic fry counting device based on computer vision provided by the invention has the following steps:
1. each background pictures K that obtains no fry opens from two cameras respectively, and every camera data are designated as I
0k, k=1,2,3 ..., K;
2. each background pictures that will obtain is converted to gray matrix, is designated as G
0k, and to the averaging of gray scale of each picture element of gained background pictures G
0m,
3. point out the fry that to pour dose known amounts to water tank into;
4. respectively obtain the fry photo that M organizes known fry quantity from two cameras respectively, take L for every group and open, every camera data are designated as I
Ij, i=1,2,3 ..., M, j=1,2,3 ..., L;
5. each photo is done to carry out the gray scale equalization as the said conversion of step 2 and to every group of each picture element of fry photo, obtain M group gray matrix G
Im, i=1,2,3 ..., M;
6. it is poor by two video cameras the gray scale of each group fry photo gray scale and background pictures to be done respectively, i.e. Δ G
i=G
Im-G
0m, i=1,2,3 ..., M;
Above step 1 to step 6 is carried out by passing through two resulting images of camera respectively;
7. find out the N that concerns of fry quantity and photo grey scale change with the method for data fitting
s=f
1(Δ G
i);
8. use relational expression N
s=f
1(Δ G
i) calculating the result of respectively organizing image of taking through two cameras: the fry on the plane is counted N
S1, the fry on the facade counts N
S2, and organize the actual fry number that drops into of these two fry numbers and each group with each and count N with the fry that data fitting method obtains in the measurement range, N=a * N
S1* N
S2 1/2+ b, wherein a and b are fitting coefficients;
9. store relational expression and point out standard test to finish, can pour fry to be counted into and carry out counting operation;
10. the fry photo J that respectively obtains unknown fry quantity from two cameras respectively opens, and every camera data are counted I
j', through getting gray matrix G ', through getting Δ G ' as gray scale difference, with step 7 gained relational expression N with step 6 and background pictures with step 2 conversion and after making the gray scale average treatment
s=f
1(Δ G
i) substitution, obtain fry and count calculating formula N '=f
1(Δ G '), and the fry that calculates is in the plane counted N
1' and the fry on facade count N
2';
11. the relational expression N=a * N that obtains with step 8
S1* N
S2 1/2+ b counts N with fry in the plane
1' and the fry on facade count N
2' substitution calculates the fry quantity Np in the photographic coverage, Np=a * N
1' * N
2'
1/2+ b;
12. calculate the area of taking pictures with focal length and camera lens size, and calculate volume (the horizontal photo area * facade photo diameter) V in actual photographed zone
p, handle the fry quantity N that obtains in the unit volume thus
Pp=N
p/ V
p
13. prompting input actual measurement calculates the actual volume V of fry water body in the water tank;
14. calculate the quantity N=N of fry reality in the water tank according to actual volume
Pp* V;
15. output result of calculation.
Automatic fry counting apparatus and method based on computer vision provided by the invention, utilize two digital cameras from the both direction of quadrature the water tank that is contained with fry to be carried out picture shooting respectively, can obtain facade and the three-dimensional information that distributes of plane image construction fry of fry in the water tank, remedy the deficiency of single plane distributed intelligence.In addition, when Flame Image Process, directly adopted the gray scale color range as evaluation index, on each pixel,, compare as the disposal route of index with colouring intensity as long as extract color range data, handled data volume significantly reduces, the also corresponding counting efficiency that improved.The present invention compared with prior art, the automatic fry counting accuracy rate is higher, error only is that counting rate is faster below 5%.
Appended drawings is an equipment therefor pie graph of the present invention, among the figure: 1-digital camera shell, the 2-digital camera, 3-digital camera stationary installation, 4-water tank, 5-digital camera stationary installation, 6-digital camera shell, 7-digital camera, 8-image display, the 9-computing machine, the 10-image pick-up card.
Embodiment
As shown in drawings, a kind of automatic fry counting device based on computer vision, two digital camera systems are arranged, comprise the digital camera system A that digital camera shell 1, digital camera 2, digital camera stationary installation 3 are formed, the digital camera system B that digital camera stationary installation 5, digital camera shell 6, digital camera 7 are formed.A computing machine 9 is arranged, and the data line of video camera is inserted in through image pick-up card 10 on the data-interface of computing machine, and computing machine also has image display 8.Water tank 4 is arranged, and water tank is the transparent plastic case of rectangular shape, in the water tank 4 water is arranged.Described image capturing apparatus A is fixed in the described digital camera shell 1 and by described digital camera stationary installation 3 with described digital camera 2 and is fixed in the water tank side, points to water body from horizontal direction.Described image capturing apparatus B is fixed in the described digital camera shell 5 and by described digital camera stationary installation 7 with digital picture camera 6 and bubbles through the water column, and points to water body from vertical direction.
The method of counting of said apparatus be take earlier the background picture of no fish and water body and as calculated the machine processing obtain background data, in water tank, put into the fry of known number again, two captured view data of digital picture camera are gathered by image pick-up card 10 and are entered computing machine 9, be shown in image display 8, computing machine carries out analytical calculation to image by described fry image analysis software, draw survey relation between fry number and view data, and this relation is stored.In described water tank 4, pack into the then fry of required counting, described image capturing apparatus A, B carry out view data to it and take, enter described computing machine 9 by described image pick-up card 10 collections, be shown in image display 8, by described fry image analysis software the data of obtaining are carried out analytical calculation again, calculate fry mantissa in the water tank with stored relational expression.
Concrete fry numeration, fry counting process is:
1. in water tank, inject water and adorn appropriate digital camera system and computer system, start computing machine and video camera;
2. start-up routine, computing machine respectively intercepts background pictures 30 width of cloth from two cameras, and totally 60 width of cloth are designated as I
D0k, d=1,2, k=1,2,3 ..., 30;
3. computing machine each background pictures that will obtain is converted to gray matrix, is designated as G
D0k, and use formula
To the averaging of gray scale of each picture element of gained background pictures, event memory;
4. computer prompted can be poured the fry of dose known amounts to water tank into;
5. put into 10,20,30,40,50 fries in water tank one by one, fry is that size is 2 ± 0.5 centimetres a parapelecus argenteus fry;
6. computing machine continues order to computing machine input one by one, and computing machine intercepts 5 groups in the photo of varying number fry successively from two cameras, and every group two direction taken 30, is designated as I
Dij, d=1,2 represents top and two video cameras in side respectively, i=1,2,3 ..., 5, j=1,2,3 ..., 30;
7. computing machine is done to carry out the gray scale equalization as the said conversion of step 2 and to every group of each picture element of fry photo to each photo, obtains 5 groups of gray matrix G
Dim, d=1,2, i=1,2,3 ..., 5;
Computing machine the gray scale of the gray scale of every group of fry photo and background pictures is done poor, i.e. Δ G
Di=G
Im-G
D0m, d=1,2, i=1,2,3 ..., 5, find out the N that concerns of fry quantity and photo grey scale change again with the method for data fitting
s≈ (1/400) * Δ G
Di(i.e. the gray scale difference value that causes of 1 fish be 400, two be that 800,10 gray scale difference values that cause are 4000), and deposit the result;
9. use N
s≈ (1/400) * Δ G
DiThe gray scale difference of two camera image of each group of substitution, the fry that obtains on the plane is counted N
Is1, the fry on the facade counts N
Is2, i=1,2,3 ..., 5, with the method for data fitting obtain the relation between the fry number that obtains of the fry number of being surveyed (N) and both direction Flame Image Process: N=a * N
S1* N
S2 1/2+ b=0.82 * N
S1* N
S2 1/2+ 2.4, and store results;
10. the computer prompted standard test is finished, and can carry out counting operation;
11. put into fry to be counted to water tank, and continue order to the computing machine input;
12. computing machine respectively intercepts 30 in the fry photo of unknown fry quantity from two cameras, totally 60, counts I
Dj', d=1,2, j=1,2,3 ..., 30, through with step 2 conversion and after making the gray scale average treatment gray matrix G ', through getting Δ G ' as gray scale difference, use N with step 6 and background pictures
s=(1/400) * Δ G
DiConcern substitution, calculate fry and count N '=(1/400) Δ G ' that wherein, the photo average gray difference that photographs up is 12000, result of calculation plane quantity is N
1Article '=30,, the photo average gray difference that photographs in the side is 12800, and result of calculation facade quantity is N
2Article '=24;
13. computing machine is used in the fry on plane and counts N
1' and count N the fry of facade
2' substitution relational expression N=0.82 * N
S1* N
S2 1/2+ 2.4, calculate this and measure the interior fry quantity N of two camera photographic coverages
p=0.82 * 30 * 24
1/2+ 2.4=122.91 bar, and deposit the result;
13. computing machine calculates the area of taking pictures with focal length and camera lens size, and calculates volume (the being about horizontal photo area * facade photo diameter) V in actual photographed zone
p=5425cm
3, use relational expression N
Pp=N
p/ V
pProcessing obtains the fry quantity N in the unit volume
PpArticle=0.022656 ,/cm
3And deposit the result;
11. computer prompted input actual measurement calculates the actual volume of fry water body in the water tank;
12. water body volume V is 64000cm in the water tank that the computing machine input calculates according to actual measurement
3
13. computing machine calculates the actual quantity N=N of fry in the water tank according to actual volume
Pp* V=0.022656 * 64000=1450 bar;
14. computing machine output result of calculation.
15. repeated for 10 to 14 steps, carry out the of the same race onesize fry quantity of other group unknown numbers.
The actual fry of throwing in water tank is about 1500 in this example, and above-mentioned count results is 1450, and systematic error then of the present invention only is 3.3%.
Claims (2)
1. automatic fry counting device based on computer vision, be to constitute with the signal connection device that is connected computing machine and digital shooting system by water tank, a fry image digitization camera system, a computing machine of placing fry, it is characterized in that: fry image digitization camera system has two digital cameras to be installed on the water tank with the mutual alignment of quadrature, two cameras are connected with computing machine through signal connection device respectively, make the fry picture signal of digital camera acquisition and input computing machine that fry plane and two distributed images of facade in water tank be arranged.
2. the method for counting of the automatic fry counting device based on computer vision as claimed in claim 1 is characterized in that having the following steps:
1) each background pictures K that obtains no fry opens from two cameras respectively, and every camera data are designated as I
0k, k=1,2,3 ..., K;
Each background pictures that 2) will obtain is converted to gray matrix, is designated as G
0k, and to the averaging of gray scale of each picture element of gained background pictures G
0m,
3) point out the fry that to pour dose known amounts to water tank into;
4) respectively obtain the fry photo that M organizes known fry quantity from two cameras respectively, take L for every group and open, every camera data are designated as I
Ij, i=1,2,3 ..., M, i=1,2,3 ..., L;
5) each photo is done to carry out the gray scale equalization as the said conversion of step 2 and to every group of each picture element of fry photo, obtain M group gray matrix G
Im, i=1,2,3 ..., M;
6) by two video cameras the gray scale of each group fry photo gray scale and background pictures is done respectively poor, i.e. Δ G
i=G
Im-G
0m, i=1,2,3 ..., M;
Above step 1 to step 6 is carried out by passing through two resulting images of camera respectively;
7) find out the N that concerns of fry quantity and photo grey scale change with the method for data fitting
s=f
1(Δ G
i);
8) use relational expression N
s=f
1(Δ G
i) calculating the result of respectively organizing image of taking through two cameras: the fry on the plane is counted N
S1, the fry on the facade counts N
S2, and organize the actual fry number that drops into of these two fry numbers and each group with each and count N with the fry that data fitting method obtains in the measurement range, N=a * N
S1* N
S2 1/2+ b, wherein a and b are fitting coefficients;
9) storage relational expression and point out standard test to finish can be poured fry to be counted into and carry out counting operation;
10) the fry photo J that respectively obtains unknown fry quantity from two cameras respectively opens, and every camera data are counted I
j', through getting gray matrix G ', through getting Δ G ' as gray scale difference, with step 7 gained relational expression N with step 6 and background pictures with step 2 conversion and after making the gray scale average treatment
s=f
1(Δ G
i) substitution, obtain fry and count calculating formula N '=f
1(Δ G '), and the fry that calculates is in the plane counted N
1' and the fry on facade count N
2';
11) the relational expression N=a that obtains with step 8 * N
S1* N
S2 1/2+ b counts N with fry in the plane
1' and the fry on facade count N
2' substitution calculates the fry quantity Np in the photographic coverage, Np=a * N
1' * N
2'
1/2+ b;
12) calculate the area of taking pictures with focal length and camera lens size, and calculate volume (the horizontal photo area * facade photo diameter) V in actual photographed zone
p, handle the fry quantity N that obtains in the unit volume thus
Pp=N
p/ V
p
13) prompting input actual measurement calculates the actual volume V of fry water body in the water tank;
14) calculate the quantity N=N of fry reality in the water tank according to actual volume
Pp* V;
15) output result of calculation.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008101621761A CN101430775B (en) | 2008-11-17 | 2008-11-17 | Automatic fry counting system based on computer vision |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008101621761A CN101430775B (en) | 2008-11-17 | 2008-11-17 | Automatic fry counting system based on computer vision |
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| Publication Number | Publication Date |
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| CN101430775A CN101430775A (en) | 2009-05-13 |
| CN101430775B true CN101430775B (en) | 2011-09-07 |
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ID=40646158
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| CN103766259A (en) * | 2014-01-26 | 2014-05-07 | 四川大学 | Fish microscopic observation device |
| RU2827415C1 (en) * | 2023-06-14 | 2024-09-25 | федеральное государственное бюджетное образовательное учреждение высшего образования "Пензенский государственный аграрный университет" | Method for assessing growth of fish in freshwater reservoirs by inverse calculations on fish scales on computer |
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- 2008-11-17 CN CN2008101621761A patent/CN101430775B/en not_active Expired - Fee Related
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103766259A (en) * | 2014-01-26 | 2014-05-07 | 四川大学 | Fish microscopic observation device |
| CN103766259B (en) * | 2014-01-26 | 2015-11-18 | 四川大学 | Fish Microexamination device |
| RU2827415C1 (en) * | 2023-06-14 | 2024-09-25 | федеральное государственное бюджетное образовательное учреждение высшего образования "Пензенский государственный аграрный университет" | Method for assessing growth of fish in freshwater reservoirs by inverse calculations on fish scales on computer |
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| Publication number | Publication date |
|---|---|
| CN101430775A (en) | 2009-05-13 |
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