CN112614090B - Method and system for identifying fish abdominal cavity structural features - Google Patents
Method and system for identifying fish abdominal cavity structural features Download PDFInfo
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- 241000251468 Actinopterygii Species 0.000 title claims abstract description 165
- 210000000683 abdominal cavity Anatomy 0.000 title claims abstract description 101
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000005520 cutting process Methods 0.000 claims abstract description 17
- 238000007781 pre-processing Methods 0.000 claims description 12
- 238000001914 filtration Methods 0.000 claims description 9
- 230000000877 morphologic effect Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 5
- 230000003187 abdominal effect Effects 0.000 claims 1
- 210000001835 viscera Anatomy 0.000 abstract description 7
- 238000004140 cleaning Methods 0.000 abstract description 5
- 210000000232 gallbladder Anatomy 0.000 description 5
- 238000000605 extraction Methods 0.000 description 3
- 238000002350 laparotomy Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 210000001015 abdomen Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 230000000694 effects Effects 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
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- 230000028327 secretion Effects 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
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Abstract
The application discloses a fish abdominal cavity structural feature identification method and system. The method comprises the following steps: extracting abdominal cavity structural features according to the collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural feature database; extracting abdominal cavity structural features of the visible light images of the fishes to be detected, and matching the abdominal cavity structural features of the visible light images of the fishes to be detected with data in a fish body abdominal cavity structural feature database to obtain an optimal matching result; and forming a working path of the cutting tool according to the optimal matching result, and guiding the operation of the sectioning device. The system comprises: the device comprises a characteristic database module, a characteristic matching module and a sectioning guidance module; this application has improved the accuracy of dissecting, has solved when fish body abdominal cavity dissects, dissects the device and easily lacerate gall and viscera to influence fish body quality, increase the problem of cleaning work volume.
Description
Technical Field
The application relates to the field of data processing, in particular to a method and a system for identifying fish abdominal cavity structural features.
Background
At present, in fish processing, the laparotomy is mainly completed manually, the laparotomy is generally cut by using a hand tool, the production mode has low efficiency, and the product quality standard is not easy to control. The prior device for completing the laparotomy of the fish body, which replaces manual operation, utilizes a cutter to cut the abdomen of the fish body, but when the cutter is used for cutting, the knife tip can cut the gall bladder and the viscera of the fish, so that the secretion in the gall bladder or the viscera flows out to influence the quality of the fish and increase the subsequent cleaning workload.
When fish abdominal cavity dissects among the prior art, dissect the device and easily rip gall and viscera to influence fish quality, increase the problem of cleaning work load, effective solution has not been proposed at present.
Disclosure of Invention
The main purpose of the application is to provide a fish abdominal cavity structure characteristic identification method and system, so that the problem that when a fish abdominal cavity is cut in the prior art, the gall bladder and internal organs are easily cut by a cutting device, the quality of the fish is affected, and the cleaning workload is increased is solved.
In order to achieve the above object, in a first aspect, the present application provides a method for identifying fish abdominal cavity structural features, comprising the following steps:
extracting abdominal cavity structural features according to the collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural feature database;
extracting abdominal cavity structural features of the visible light images of the fishes to be detected, and matching the abdominal cavity structural features of the visible light images of the fishes to be detected with data in a fish body abdominal cavity structural feature database to obtain an optimal matching result;
and forming a working path of the cutting tool according to the optimal matching result, and guiding the operation of the sectioning device.
Extracting abdominal cavity structural features according to collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural feature database, wherein the process is as follows:
collecting historical fish X-ray images and visible light images;
preprocessing the historical fish X-ray image and the visible light image;
extracting a fish body outer contour image and an abdominal cavity contour image from the preprocessed historical fish X-ray image and the preprocessed visible light image by adopting an edge contour fitting algorithm;
and (4) collecting all the fish body outline images and the abdominal cavity outline images to obtain a fish body abdominal cavity structure characteristic database.
The preprocessing of the historical fish X-ray image comprises the following steps: histogram equalization performs image enhancement and employs gaussian filtering to eliminate noise.
The preprocessing of the historical fish visible light image comprises the following steps: the RGB image is converted into a gray image, and Gaussian filtering is adopted to eliminate noise.
The method for extracting the fish body outer contour image and the abdominal cavity contour image from the preprocessed historical fish X-ray image and the preprocessed visible light image by adopting the edge contour fitting algorithm comprises the following steps:
acquiring a binary image of the preprocessed historical fish visible light image by adopting an OTSU algorithm (OTSU Otsu method);
performing morphological processing on the binary image of the visible light image, extracting an outer contour image of the fish body, and obtaining a fish body image circumscribed rectangle of the visible light image;
cutting the preprocessed historical fish X-ray image according to a fish body image external rectangle of the visible light image, and reserving a fish body part in the historical fish X-ray image;
obtaining a threshold value T by adopting a minimum error method, and carrying out binarization and morphological processing on the cut X-ray image to obtain a contour point set of an abdominal cavity communication domain;
and carrying out polygon approximation fitting on the contour points of the abdominal cavity communication region to obtain an abdominal cavity contour image which is a fitted polygon.
According to the optimal matching result, a working path of the cutting tool is formed to guide the operation of the sectioning device, and the method comprises the following steps:
according to the optimal matching result, obtaining the center line of the short side of the external rectangle of the visible light image fish body image as a reference line;
finding a point with the longest vertical distance from the fitted polygon to the reference line as a tool entering point;
the working path is a fitted polygon.
The optimal matching result is obtained by the following process:
collecting visible light images of fishes to be detected;
the method for preprocessing the visible light image of the fish to be detected comprises the following steps: converting an RGB image into a gray image and Gaussian filtering.
Extracting a fish body outline image of the preprocessed visible light image of the fish to be detected by using an edge outline fitting algorithm; the edge contour fitting algorithm comprises: OTSU algorithm, binarization, and morphological processing.
And matching the fish body outline image of the visible light image of the fish to be detected as the abdominal cavity structural feature with the data in the fish body abdominal cavity structural feature database to obtain an optimal matching result.
In a second aspect, the present application further provides a fish abdominal cavity structural feature identification system, which is implemented by the fish abdominal cavity structural feature identification method, and includes: the device comprises a characteristic database module, a characteristic matching module and a sectioning guidance module;
the feature database module, the feature matching module and the sectioning guidance module are sequentially connected;
the characteristic database module is used for extracting abdominal cavity structural characteristics according to collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural characteristic database;
the characteristic matching module is used for extracting the abdominal cavity structural characteristics of the visible light image of the fish to be detected, and matching the abdominal cavity structural characteristics of the visible light image of the fish to be detected with the data in the fish body abdominal cavity structural characteristic database to obtain an optimal matching result;
the sectioning guidance module is used for forming a working path of the cutting tool according to the optimal matching result and guiding the operation of the sectioning device.
The beneficial technical effects are as follows:
the application provides a fish abdominal cavity structural feature recognition method and system, extract abdominal cavity structural feature, establish fish body abdominal cavity structural feature database, will wait to detect the abdominal cavity structural feature of fish visible light image and match with data in the fish body abdominal cavity structural feature database, obtain the optimum matching result, thereby guide the work of dissecting the device, the accuracy of dissecting has been improved, when having solved dissecting in the fish body abdominal cavity, dissect the device and easily rip gall bladder and viscera, thereby influence fish body quality, increase the problem of cleaning work volume.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a flow chart of a method for identifying structural features of fish abdominal cavities according to an embodiment of the present application;
FIG. 2 is a flow chart for building a feature database provided according to an embodiment of the present application;
FIG. 3 is a flow chart of an optimal matching result provided according to an embodiment of the present application;
fig. 4 is a flowchart for extracting an external contour image and an internal contour image of a fish body according to an embodiment of the present disclosure;
fig. 5 is a schematic block diagram of a fish abdominal cavity structural feature identification system according to an embodiment of the application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
In addition, the term "plurality" shall mean two as well as more than two.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The feature extraction technology is a concept in computer vision and image processing, and utilizes a computer to extract image information of a sample and extract features of the sample from the image, and the result of feature extraction is to divide points on the image into different subsets, wherein the subsets often belong to isolated points, continuous curves or continuous areas. Therefore, a fish abdominal cavity structural feature extraction and identification method is provided, a fish body geometric form and abdominal cavity structural feature database is established according to the fish body abdominal cavity structural feature, the penetration amount of a sectioning device is guided, and the gall bladder and the internal organs of the fish are prevented from being scratched.
In a first aspect, the present application provides a method for identifying fish abdominal cavity structural features, as shown in fig. 1, including the following steps:
step S1: extracting abdominal cavity structural features according to the collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural feature database;
step S2: extracting abdominal cavity structural features of the visible light images of the fishes to be detected, and matching the abdominal cavity structural features of the visible light images of the fishes to be detected with data in a fish body abdominal cavity structural feature database to obtain an optimal matching result;
step S3: and forming a working path of the cutting tool according to the optimal matching result, and guiding the operation of the sectioning device.
Extracting the abdominal cavity structural features according to the collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural feature database, as shown in FIG. 2, the process is as follows:
step S11: collecting historical fish X-ray images and visible light images;
step S12: preprocessing the historical fish X-ray image and the visible light image;
step S13: extracting a fish body outer contour image and an abdominal cavity contour image from the preprocessed historical fish X-ray image and the preprocessed visible light image by adopting an edge contour fitting algorithm;
step S14: and (4) collecting all the fish body outline images and the abdominal cavity outline images to obtain a fish body abdominal cavity structure characteristic database.
The preprocessing of the historical fish X-ray image comprises the following steps: histogram equalization performs image enhancement and employs gaussian filtering to eliminate noise.
The preprocessing of the historical fish visible light image comprises the following steps: the RGB image is converted into a gray image, and Gaussian filtering is adopted to eliminate noise.
The method comprises the following steps of respectively extracting a fish body outer contour image and an abdominal cavity contour image from the preprocessed historical fish X-ray image and the preprocessed visible light image by adopting an edge contour fitting algorithm, as shown in fig. 4:
step S131: acquiring a binary image of the preprocessed historical fish visible light image by adopting an OTSU algorithm (OTSU Otsu method), which comprises the following specific steps:
assuming that the threshold t separates the foreground and the background of the image (here, the foreground refers to a fish body, and the background refers to a non-fish body part), the ratio of the number of pixels of the foreground to the number of pixels of the whole image is set as w0Mean gray value is set to u0The ratio of the number of pixels of the background to the number of pixels of the whole image is w1Mean gray value of u1. The mean gray values of the image are then: u-w0*u0+w1*u1
Traversing t from the minimum gray value to the maximum gray value, and when g is enabled to be w0*w1*(uo-u1)2At maximum, t is the selected threshold. ,
the formula of binarization is as follows:
step S132: performing morphological processing on the binary image of the visible light image, extracting an outer contour image of the fish body, and obtaining a fish body image circumscribed rectangle of the visible light image, wherein the specific steps are as follows:
and (3) carrying out morphological treatment after binarization:
let F (x) and G (x) be two discrete functions defined on discrete spaces F and G, where F (x) is the input image and G (x) is the structural element. Then (x) the erosion and swelling for g (x) are defined as:
and (4) obtaining the external rectangle of the fish body image through the fish body image obtained in the step. And cutting the X-ray image to only leave the circumscribed rectangle of the fish body image. (thus, the abdominal cavity is regarded as the foreground and the rest of the fish body as the background, instead of processing the background of the conveyor belt in the X-ray image.)
Step S133: cutting the preprocessed historical fish X-ray image according to a fish body image external rectangle of the visible light image, and reserving a fish body part in the historical fish X-ray image;
step S134: obtaining a threshold value T by adopting a minimum error method, carrying out binarization and morphological processing on the cut X-ray image to obtain a contour point set of an abdominal cavity connected domain, which specifically comprises the following steps:
principle of minimum error method: assuming that the gray scale of the foreground is normally distributed and the density is f1(x) Mean and variance are μ1，σ1The number of foreground pixels in the whole image is m, the gray level of the background is normally distributed, and the density is f2(x) Mean and variance are μ2，σ2The number of background pixels occupying the entire image is (1-m).
The mixed probability density is: f (x) m f1(x)+(1-m)*f2(x)
When the selected threshold is T, the total error probability that the foreground is misclassified as background and the background is misclassified as foreground is:
a threshold T is found that minimizes the total error probability.
Step S135: and performing polygon approximation fitting on the contour points of the abdominal cavity communication domain to obtain an abdominal cavity contour image, namely a fitted polygon, and implementing by using findcontours and cvApproxPoly commands in opencv.
According to the optimal matching result, a working path of the cutting tool is formed to guide the operation of the sectioning device, and the method comprises the following steps:
according to the optimal matching result, the center line of the short side of the external rectangle of the visible light image fish body image is obtained as a reference line, namely a line passing through the midpoint of the short side and being parallel to the long side;
finding a point with the longest vertical distance from the fitted polygon to the reference line as a tool entering point;
the working path is a fitted polygon.
As shown in fig. 3, the process of obtaining the optimal matching result is as follows:
step S21: collecting visible light images of fishes to be detected;
step S22: the method for preprocessing the visible light image of the fish to be detected comprises the following steps: converting the RGB image into a gray image and eliminating noise by adopting Gaussian filtering;
step S23: extracting a fish body outline image of the preprocessed visible light image of the fish to be detected by using an edge outline fitting algorithm; the edge contour fitting algorithm comprises: OTSU algorithm, binarization, and morphological processing.
Step S24: and matching the fish body outline image of the visible light image of the fish to be detected as the abdominal cavity structural feature with the data in the fish body abdominal cavity structural feature database to obtain an optimal matching result.
In a second aspect, the present application further provides a fish abdominal cavity structural feature identification system, which is implemented by using the fish abdominal cavity structural feature identification method, as shown in fig. 5, and includes: the device comprises a characteristic database module, a characteristic matching module and a sectioning guidance module;
the feature database module, the feature matching module and the sectioning guidance module are sequentially connected;
the characteristic database module is used for extracting abdominal cavity structural characteristics according to collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural characteristic database;
the characteristic matching module is used for extracting the abdominal cavity structural characteristics of the visible light image of the fish to be detected, and matching the abdominal cavity structural characteristics of the visible light image of the fish to be detected with the data in the fish body abdominal cavity structural characteristic database to obtain an optimal matching result;
the sectioning guidance module is used for forming a working path of the cutting tool according to the optimal matching result and guiding the operation of the sectioning device.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (7)
1. A method for identifying fish abdominal cavity structural features is characterized by comprising the following steps:
extracting abdominal cavity structural features according to the collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural feature database;
extracting abdominal cavity structural features of the visible light images of the fishes to be detected, and matching the abdominal cavity structural features of the visible light images of the fishes to be detected with data in a fish body abdominal cavity structural feature database to obtain an optimal matching result;
forming a working path of the cutting tool according to the optimal matching result, and guiding the operation of the sectioning device; extracting abdominal cavity structural features according to collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural feature database, wherein the process is as follows:
collecting historical fish X-ray images and visible light images;
preprocessing the historical fish X-ray image and the visible light image;
extracting a fish body outer contour image and an abdominal cavity contour image from the preprocessed historical fish X-ray image and the preprocessed visible light image respectively by adopting an edge contour fitting algorithm;
and collecting all the fish body outer contour images and the abdominal cavity contour images to obtain a fish body abdominal cavity structure characteristic database.
2. The method for identifying fish abdominal structural features of claim 1, wherein the preprocessing the historical fish X-ray images comprises: histogram equalization performs image enhancement and employs gaussian filtering to eliminate noise.
3. The method for identifying fish abdominal cavity structural features of claim 1, wherein the preprocessing the historical fish visible light image comprises: the RGB image is converted into a gray image, and Gaussian filtering is adopted to eliminate noise.
4. The method for identifying fish abdominal cavity structural features of claim 1, wherein the step of extracting the fish body outline image and the abdominal cavity outline image respectively from the preprocessed historical fish X-ray image and the visible light image by using an edge outline fitting algorithm comprises the following steps:
acquiring a binary image of the preprocessed historical fish visible light image by adopting an OTSU algorithm;
performing morphological processing on the binary image of the visible light image, extracting an outer contour image of the fish body, and obtaining a fish body image circumscribed rectangle of the visible light image;
cutting the preprocessed historical fish X-ray image according to a fish body image external rectangle of the visible light image, and reserving a fish body part in the historical fish X-ray image;
obtaining a threshold value T by adopting a minimum error method, and carrying out binarization and morphological processing on the cut X-ray image to obtain a contour point set of an abdominal cavity communication domain;
and carrying out polygon approximation fitting on the contour points of the abdominal cavity communication region to obtain an abdominal cavity contour image which is a fitted polygon.
5. The method for identifying fish abdominal cavity structural features of claim 1, wherein the forming of the working path of the cutting tool according to the optimal matching result and the guiding of the operation of the cutting device comprises the following steps:
according to the optimal matching result, obtaining the center line of the short side of the external rectangle of the visible light image fish body image as a reference line;
finding a point with the longest vertical distance from the fitted polygon to the reference line as a tool entering point;
the working path is a fitted polygon.
6. The method for identifying fish abdominal cavity structural features of claim 1, wherein the optimal matching result is obtained by the following process:
collecting visible light images of fishes to be detected;
the method for preprocessing the visible light image of the fish to be detected comprises the following steps: converting the RGB image into a gray image and carrying out Gaussian filtering;
extracting a fish body outline image of the preprocessed visible light image of the fish to be detected by using an edge outline fitting algorithm;
and matching the fish body outline image of the visible light image of the fish to be detected as the abdominal cavity structural feature with the data in the fish body abdominal cavity structural feature database to obtain an optimal matching result.
7. A fish abdominal cavity structural feature identification system, which is realized by the fish abdominal cavity structural feature identification method according to any one of claims 1-6, and comprises the following steps: the device comprises a characteristic database module, a characteristic matching module and a sectioning guidance module;
the feature database module, the feature matching module and the sectioning guidance module are sequentially connected;
the characteristic database module is used for extracting abdominal cavity structural characteristics according to collected historical fish X-ray images and visible light images to obtain a fish body abdominal cavity structural characteristic database;
the characteristic matching module is used for extracting the abdominal cavity structural characteristics of the visible light image of the fish to be detected, and matching the abdominal cavity structural characteristics of the visible light image of the fish to be detected with the data in the fish body abdominal cavity structural characteristic database to obtain an optimal matching result;
the sectioning guidance module is used for forming a working path of the cutting tool according to the optimal matching result and guiding the operation of the sectioning device.
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