CN111445494A - Image processing method for extracting water-entering vacuole contour - Google Patents
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- 238000003672 processing method Methods 0.000 title claims abstract description 7
- 210000003934 vacuole Anatomy 0.000 title claims description 44
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 20
- 238000000605 extraction Methods 0.000 claims abstract description 14
- 238000012937 correction Methods 0.000 claims abstract description 9
- 238000006243 chemical reaction Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 15
- 239000007788 liquid Substances 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 7
- 230000008030 elimination Effects 0.000 claims description 4
- 238000003379 elimination reaction Methods 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 3
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 230000007797 corrosion Effects 0.000 abstract description 2
- 238000005260 corrosion Methods 0.000 abstract description 2
- 238000003706 image smoothing Methods 0.000 abstract description 2
- 238000011160 research Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 239000012530 fluid Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000003628 erosive effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000002679 ablation Methods 0.000 description 1
- 239000002775 capsule Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 235000001968 nicotinic acid Nutrition 0.000 description 1
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- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration by the use of local operators
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20032—Median filtering
Abstract
The invention relates to an image processing method for extracting an underwater cavitation bubble outline, which comprises the steps of introducing a background image before a moving body enters water and an underwater cavitation bubble image to be processed and identified by MAT L AB, converting the background image into a gray scale image, dividing an identification area, carrying out background rejection processing, converting the image into a binary image, carrying out expansion corrosion processing on the image by using a disc-shaped structural element to prevent the discontinuity phenomenon of the outline to be extracted, removing a small interference cavity in the image, filling a cavity existing in the water cavitation bubble, carrying out image smoothing processing, carrying out edge outline extraction on the processed image area, carrying out thin-layer processing on the extracted edge outline to enable the cavitation bubble edge to have only one layer of scattered points, carrying out refraction correction and pixel coordinate conversion on the obtained scattered points to obtain cavitation bubble outline scattered points under an experimental coordinate system, and finally writing scattered point coordinates into a file.
Description
Technical Field
The invention belongs to the field of image recognition, and particularly relates to a method for extracting and processing an underwater cavitation bubble profile generated after a moving body enters water by using an image processing technology so as to obtain a cavitation bubble profile curve.
Background
The study of the problem of entry of a moving body into water involves many disciplines, which is a very extensive phenomenon. For example, the underwater landing of an air-drop torpedo, the slamming of ships, the underwater entering of a cross-medium aircraft, the marine landing of a space re-entry capsule, the walking of aquatic organisms and the like, so that the research on the underwater problem has important significance in the fields of military affairs, engineering, bionics and the like.
In the last century, a lot of research has been conducted by many researchers into the field of the study of the entry of motiles into water, most of which have focused on the evolution and development of the entry vacuoles. The water entry vacuole is a very obvious phenomenon in the process of entering water of a moving body, when the moving body enters water, a flow field can be regarded as incompressible, the kinetic energy of the moving body is transferred to surrounding fluid, so that the liquid surface around a water entry point is raised and splashed, and the fluid below the liquid surface and close to the water entry point obtains the radial expansion speed and the moving body is separated in a flowing manner, so that the initial water entry vacuole is formed. Along with the moving body continues to enter water, air above the liquid level continuously flows into the vacuole under the action of pressure difference, and meanwhile, surrounding fluid still moves outwards in the radial direction, so that the vacuole is continuously elongated and expanded at the moment, and the vacuole closure phenomena such as surface closure, deep closure and the like can occur under different conditions after the vacuole is expanded to a certain degree, and the vacuole carried by the moving body is isolated from outside air and does not continue to be expanded at the moment, and the vacuole breaks out and falls off under the action of liquid pressure after the moving body moves to a certain depth, so that the moving body does not carry any vacuole and is in a completely wet state. The existence of the water-entering vacuole has important influence on the attitude stability, the structure and the flow characteristic of the moving body in the water-entering process of the moving body, so that the method has important significance on the research on the evolution characteristic of the water-entering vacuole of the moving body.
The evolution characteristic of the cavity bubbles entering water can be obtained by researching the evolution process of the cavity bubble form, and the development characteristic of the cavity bubbles can be shown more visually by extracting the cavity bubble profiles entering water at different water entering moments and performing superposition comparison. After the moving body enters water, five situations often exist: (a) the moving body almost has no water-entering vacuole because of extremely low speed water-entering or surface hydrophilicity; (b) only one end of the moving body far away from the liquid level is provided with partial vacuole after entering water; (c) the moving body is completely wrapped by the vacuole after entering water; (d) after the moving body enters water for a period of time, the tail end of the moving body slaps the wall surface of the cavity, so that the integrity of the cavity is damaged; (e) the vacuole deep closing occurs after the vacuole in water develops for a period of time, and the vacuole is divided into a vacuole close to the liquid surface and a vacuole carried by a moving body.
Because the light-tight moving body and the water-entering cavity wall surface exist simultaneously under water, the cavity contour extraction under water is often influenced by a plurality of interference factors, which brings certain difficulty to the precise extraction of the cavity contour. The interference factors existing in the extraction of the vacuole profile mainly include the following points:
(1) in the process of extracting the cavity contour, the edge contour of the moving body which is often wrapped in the cavity contour is also extracted, so that how to effectively remove the moving body contour is extremely important;
(2) when the distribution of ambient light of the underwater image is uneven, a part of the edge of the cavity outline becomes transparent, so that the cavity outline cannot be effectively identified;
(3) due to the existence of the retro-reflection flow at the position close to the liquid level, the surface of the cavity wall close to the liquid level generates a certain ablation phenomenon, so that the cavity wall becomes rough, the refractive indexes of all parts at the edge of the cavity are different, and the situation of discontinuity can be generated during the edge extraction;
(4) the influence of refraction exists underwater, so the obtained cavitation bubble edge points need to be subjected to refraction correction to obtain the correct coordinate positions under an experimental frame.
The method has important significance for researching the evolution characteristics of the moving body underwater vacuole, and Westingong and King smart in China preliminarily research underwater vacuole identification and position detection, but cannot accurately and effectively identify a large number of underwater vacuole images in batches. The same kind of patents or literatures are not discovered at present.
Disclosure of Invention
Technical problem to be solved
In order to avoid the defects of the prior art, the invention provides an image processing method capable of accurately identifying and extracting underwater cavitation bubble outlines in batches. The method can effectively extract the edge profile of the cavitation bubble entering water of the moving body and obtain the cavitation bubble profile scatter point file under the experimental coordinate system.
Technical scheme
An image processing method for extracting the water-entering vacuole contour is characterized by comprising the following steps:
step 1, importing a moving body underwater image and a background image which are experimentally shot into MAT L AB, wherein the background image refers to a shot image when the moving body does not enter the visual field of a high-speed camera, and the underwater image refers to an experimental image when the moving body passes through the liquid level;
step 2: the image is divided into regions according to the position of the moving body in the image, and the specific principle of the division is as follows:
wherein D ismL for the diameter of the moving bodymLength of moving body, xsTo divide the width of the image, ysIs the height of the divided image;
performing background elimination processing on the divided image, and subtracting the experimental image E (x, y) from the background image B (x, y) to obtain a target extraction image S (x, y):
S(x,y)＝B(x,y)-E(x,y)
and step 3: converting the image with the background removed into a binary image, creating a planar disc-shaped structural element with a specified radius, expanding the image by using the structural element, and then corroding the expanded image by using the structural element;
and 4, step 4: deleting cavities with the number of pixels smaller than a set value in the image by using a bwaeopen function, filling cavities in water cavities by using an imfill function, and finally performing small-radius smoothing on the binary image by using a medfill 2 median filter function; the medfilt2 median filter function can be expressed by the following equation:
wherein p isi,jThe value of the pixel element under the pixel coordinate (i, j) is shown, m and n are m × n neighborhoods around the corresponding pixel, and the function of the medfilt2 median filter function is to take the median of the m × n neighborhoods around the corresponding pixel;
and 5: performing edge extraction on the image processed in the step 4 based on a sobel operator, and applying morphological 'thin' operation to the extracted edge image by utilizing a bwmorph function to thin the edge into a line;
converting the pixel coordinate P (x, y) of the cavitation bubble edge scatter point into a real coordinate P under an experimental coordinate system1(x,y)：
P1(x,y)＝(P(x,y)-(x0,y0)E)
Wherein E is a matrix with the same dimension as P (x, y), and all elements of the matrix are 1; (x) distance between each pixel in the image in the experimental coordinate system0,y0) The pixel position corresponding to the coordinate origin in the image;
edge scatter point P after coordinate conversion is completed1(x, y) performing refraction correction, wherein the correction formula is as follows:
wherein p ismeasureIs a measure of the coordinates of the edge scatter points, from P1(x, y) gives, prealIs the true value of the edge scatter coordinates, d1Is the distance of the moving body from the front wall surface of the water tank, d2Is the distance between the lens of the high-speed camera and the front wall surface of the water tank, and n is the refractive index.
The specified radius described in step 3 is 10 pixels.
The set value in step 4 is 40.
Advantageous effects
The image processing method capable of accurately identifying and extracting the water-entering vacuole outlines in batches can quickly reduce an identification area and eliminate interference factors of images, and the outline scattered points extracted finally only have one layer of scattered points, so that subsequent water-entering vacuole data processing is facilitated. The method has the advantages of high identification efficiency, accurate contour extraction result, high processing speed and capability of batch processing.
Drawings
FIG. 1 is a flow chart of a method for extracting an entry vacuole profile in an embodiment of the present invention;
FIG. 2 is a schematic diagram of the cavitation of the moving body after entering water at a certain speed;
FIG. 3 is a diagram of an established two-dimensional Cartesian coordinate system;
fig. 4 is an image after the area division processing is performed;
FIG. 5 is an image after background subtraction processing;
FIG. 6 is an image after conversion to a binary image and expansion erosion processing;
FIG. 7 is an image after internal cavity filling;
fig. 8 is a schematic diagram showing comparison between the extracted water-entering vacuole profile and the original drawing.
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
the technical scheme adopted by the invention for solving the technical problems is an image edge extraction method based on a sobel operator, which comprises the following parts:
(1) introducing a background image before the moving body enters water and a water entering vacuole image to be identified by processing into MAT L AB, and converting into a gray scale image;
(2) dividing the identification area and carrying out background elimination processing;
(3) converting the image into a binary image, and performing expansion corrosion treatment on the image by using a disc-shaped structural element to prevent the discontinuous phenomenon of the contour to be extracted;
(4) removing a small interference cavity in the image, filling the cavity existing in the water vacuole, and performing image smoothing;
(5) and extracting the edge contour of the processed image area, carrying out thin-layer processing on the extracted edge contour to ensure that the vacuole edge has only one layer of scattered points, carrying out refraction correction and pixel coordinate transformation on the obtained scattered points to obtain the vacuole contour scattered points under an experimental coordinate system, and finally writing the scattered point coordinates into a file.
The method comprises the following specific steps:
firstly, a moving body underwater image and a background image which are experimentally shot are led into MAT L AB, wherein the background image refers to a shot image when the moving body does not enter the visual field of the high-speed camera, and the underwater image refers to an experimental image when the moving body passes through the liquid level, after the image is led into the image, the image is subjected to gray level conversion, and a two-dimensional Cartesian coordinate system is established as shown in FIG. 3, wherein the origin of the coordinate system is the underwater point of the moving body, the y axis is the vertical downward direction, and the x axis is the horizontal component direction of the speed vector of the moving body.
Step two: the image is divided into regions according to the position of the moving body in the image, so that the calculation amount in the processing process is greatly reduced, the unnecessary background interference can be avoided, and the edge detection precision and efficiency are improved.
The specific principle of division is as follows:
wherein DmL for the diameter of the moving bodymLength of moving body, xsTo divide the width of the image, ysIn order to divide the height of the image, fig. 4 shows the water entering vacuole image after the image division is finished, so that the recognition range of the image after the division is finished is reduced, and the water entering vacuole is completely reserved.
In order to further accurately extract the water vacuoles, the divided images need to be subjected to background elimination, wherein the background image B (x, y) with integer pixel coordinates and the experimental image E (x, y) have a common background, so that the background of the experimental image can be subtracted through matrix operation to obtain a target extraction image S (x, y), and a target navigation body can be reserved independently. The operation can be performed by the following formula:
S(x,y)＝B(x,y)-E(x,y)
figure 5 shows the image after the water-in vacuoles are background-removed. It can be seen that the irrelevant background is completely removed, and the complete shape of the water-entering vacuole is reserved, so that the edge recognition and extraction are convenient.
Step three: the image with the background removed is converted into a binary image, a structural element with a plane disc shape and a designated radius of 10 pixels is created, the image is subjected to expansion processing by using the structural element, and then the expanded image is subjected to erosion processing by using the structural element, so that unimportant tiny edges are removed and discontinuity at the fracture edges is eliminated, as shown in fig. 6.
Step four: and deleting cavities with the number of pixels smaller than 40 in the image by using a bwaeopen function, filling cavities in water cavities by using an imfill function, and smoothing the binary image by using a medfill 2 median filter function, so that the noise of the image is eliminated to the maximum extent. The median filter function of medfilt2 may be represented by:
wherein p isi,jFor the value of the pixel element at pixel coordinate (i, j), m, n is m × n neighborhood around the corresponding pixel, the median filter function of medfilt2 is to take the median of m × n neighborhood around the corresponding pixel, the processed image is shown in fig. 7.
Step five: and (3) carrying out edge extraction on the image processed in the step four based on a sobel operator, wherein the extracted edge layer often contains a plurality of layers of pixel points, so that a bwmorph function is required to be utilized to apply morphological 'thin' operation to the extracted edge image, and the edge is thinned into lines.
Obtaining the pixel coordinates of the cavitation bubble edge scattering points through the fifth step, in order to obtain the coordinates of the water-entering cavitation bubble edge scattering points under an experimental coordinate system, in an experimental image, through calibrating a moving body, the distance between each pixel point in the image can be measured to be mm under the experimental coordinate system, and the pixel position corresponding to the origin of coordinates in the image is (x)0,y0) Therefore, the true coordinate transformation relationship in the experimental coordinate system corresponding to the scatter matrix P (x, y) can be calculated by the following formula:
P1(x,y)＝(P(x,y)-(x0,y0)E)
where E is a matrix of the same dimension as P (x, y), and all elements are 1.
In the experimental process, because the experimental image shot by the high-speed camera has refraction error caused by light passing through the water body and the air, the edge scatter point P after the coordinate conversion is completed needs to be subjected to1(x, y) performing refraction correction, wherein the correction formula is as follows:
wherein p ismeasureIs a measure of the coordinates of the edge scatter points, from P1(x, y) gives, prealIs the true value of the edge scatter coordinates, d1Is the distance of the moving body from the front wall surface of the water tank, d2Is the distance between the lens of the high-speed camera and the front wall surface of the water tank, and n is the refractive index.
And at this moment, the edge of the vacuole with the water is identified and extracted. The extracted edge and the original vacuole are placed under the same image for comparison, as shown in fig. 8, the edge recognition is good, and scattered points of the vacuole edge entering the water can be accurately extracted.
Claims (3)
1. An image processing method for extracting the water-entering vacuole contour is characterized by comprising the following steps:
step 1, importing a moving body underwater image and a background image which are experimentally shot into MAT L AB, wherein the background image refers to a shot image when the moving body does not enter the visual field of a high-speed camera, and the underwater image refers to an experimental image when the moving body passes through the liquid level;
step 2: the image is divided into regions according to the position of the moving body in the image, and the specific principle of the division is as follows:
wherein D ismL for the diameter of the moving bodymLength of moving body, xsTo divide the width of the image, ysIs the height of the divided image;
performing background elimination processing on the divided image, and subtracting the experimental image E (x, y) from the background image B (x, y) to obtain a target extraction image S (x, y):
S(x,y)＝B(x,y)-E(x,y)
and step 3: converting the image with the background removed into a binary image, creating a planar disc-shaped structural element with a specified radius, expanding the image by using the structural element, and then corroding the expanded image by using the structural element;
and 4, step 4: deleting cavities with the number of pixels smaller than a set value in the image by using a bwaeopen function, filling cavities in water cavities by using an imfill function, and finally performing small-radius smoothing on the binary image by using a medfill 2 median filter function; the medfilt2 median filter function can be expressed by the following equation:
wherein p isi,jThe value of the pixel element under the pixel coordinate (i, j) is shown, m and n are m × n neighborhoods around the corresponding pixel, and the function of the medfilt2 median filter function is to take the median of the m × n neighborhoods around the corresponding pixel;
and 5: performing edge extraction on the image processed in the step 4 based on a sobel operator, and applying morphological 'thin' operation to the extracted edge image by utilizing a bwmorph function to thin the edge into a line;
converting the pixel coordinate P (x, y) of the cavitation bubble edge scatter point into a real coordinate P under an experimental coordinate system1(x,y)：
P1(x,y)＝(P(x,y)-(x0,y0)E)
Wherein E is a matrix with the same dimension as P (x, y), and all elements of the matrix are 1; (x) distance between each pixel in the image in the experimental coordinate system0,y0) The pixel position corresponding to the coordinate origin in the image;
edge scatter point P after coordinate conversion is completed1(x, y) performing refraction correction, wherein the correction formula is as follows:
wherein p ismeasureIs a measure of the coordinates of the edge scatter points, from P1(x, y) gives, prealIs the true value of the edge scatter coordinates, d1Is the distance of the moving body from the front wall surface of the water tank, d2Is the distance between the lens of the high-speed camera and the front wall surface of the water tank, and n is the refractive index.
2. The method as claimed in claim 1, wherein the specified radius in step 3 is 10 pixels.
3. The method as claimed in claim 1, wherein the setting value in step 4 is 40.
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Application publication date: 20200724 |