CN115311505B - Silkworm cocoon classification method and purchase system based on cloud service big data platform - Google Patents
Silkworm cocoon classification method and purchase system based on cloud service big data platform Download PDFInfo
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Abstract
The invention relates to the field of image processing, in particular to a silkworm cocoon classification method and a purchasing system based on a cloud service big data platform, which comprise the following steps: the method comprises the steps of obtaining a rectangular surrounding frame of a silkworm cocoon image, reducing the rectangular surrounding frame of the silkworm cocoon image according to each reduction proportion to obtain a plurality of first surrounding frames, obtaining target pixel points, obtaining central pixel points of the silkworm cocoon image, obtaining a Gaussian fitting model according to the distance from each target pixel point to each central pixel point, obtaining a scatter diagram, fitting the scatter diagram by using least square to obtain a fitting curve, obtaining first quality of a silkworm cocoon sample corresponding to the silkworm cocoon image according to the scatter diagram, the fitting curve and the area of the silkworm cocoon image, obtaining second quality of the silkworm cocoon sample according to the same principle, obtaining the quality of the silkworm cocoon sample according to the first quality and the second quality of the silkworm cocoon sample, and purchasing the silkworm cocoon according to the quality of each silkworm cocoon sample. The invention ensures that the finally purchased silkworm cocoons with each quality more meet the actual requirements.
Description
Technical Field
The invention relates to the field of image processing, in particular to a silkworm cocoon classification method and a purchasing system based on a cloud service big data platform.
Background
The silkworm cocoons are also called silkworm clothes, and are the cocoons on the silkworm bodies as the name suggests, and the silkworm cocoons have wide functions in life, such as pharmacy, silk manufacturing, pupa oil extraction and livestock feed, so the silkworm cocoons have a wide market, a large amount of silkworm cocoon storage needs to be purchased when the factory produces and manufactures the silkworm cocoons, raw materials of the factory are guaranteed not to be interrupted, but the quality of the silkworm cocoons in the current market is uneven, so that the quality of the silkworm cocoons in each batch of the silkworm cocoons is uneven, if the silkworm cocoons are directly purchased according to the batch, the purchased silkworm cocoons with each quality do not correspond to the required silkworm cocoons with each quality of the silkworm cocoons, so the raw material cost and the storage cost of the factory are increased, and therefore, the classification and purchase of the silkworm cocoons with each quality is very important when the silkworm cocoons are purchased.
In the prior art, the evaluation of the quality of the silkworm cocoon is to divide a silkworm cocoon image by utilizing an Otsu threshold value division technology, divide spot areas in the silkworm cocoon image by an adaptive threshold value, and evaluate the quality of the silkworm cocoon according to the area of the spot areas, wherein the larger the spot area in the silkworm cocoon image is, the less the silk area of the silkworm cocoon is, the poorer the quality of the silkworm cocoon is; however, the cocoons themselves have certain colors, and the different colors of the cocoons can cause different quality of the cocoons, and the method only combines the areas of spot areas on the cocoons and does not combine the colors of the cocoons, so that the evaluation on the quality of the cocoons is inaccurate, and the classification of the cocoons is inaccurate.
Disclosure of Invention
The invention provides a silkworm cocoon classification method and a purchase system based on a cloud service big data platform, which aim to solve the problem of inaccurate classification of the existing silkworm cocoons.
The invention discloses a silkworm cocoon classification method based on a cloud service big data platform, which adopts the following technical scheme:
s1, acquiring a silkworm cocoon sample image, acquiring an HSV (hue, saturation and value) image of a silkworm cocoon according to the silkworm cocoon sample image, acquiring a rectangular enclosure frame of the HSV image, setting a plurality of reduction ratios, reducing the rectangular enclosure frame of the HSV image according to each reduction ratio to obtain a plurality of reduced rectangular enclosure frames, and taking the reduced rectangular enclosure frames as first enclosure frames;
s2, obtaining each symmetry axis of the rectangular surrounding frame, respectively obtaining target pixel points of the rectangular surrounding frame and each first surrounding frame on each symmetry axis, obtaining central pixel points of the silkworm cocoon image, and performing Gaussian fitting according to the distance from each target pixel point to each central pixel point to obtain Gaussian values of the corresponding target pixel points;
s3, obtaining a first difference value of each target pixel point according to the brightness value of each target pixel point and the Gaussian value of the corresponding target pixel point, drawing a scatter diagram by using the first difference value of each target pixel point and the distance from each target pixel point to the center pixel point, and fitting the scatter diagram by using least square to obtain a fitting curve;
and S4, obtaining the quality of the silkworm cocoon sample corresponding to the HSV image according to the scatter diagram, the fitting curve and the silkworm cocoon image area, and classifying the silkworm cocoons by using the quality of all the obtained silkworm cocoon samples.
Further, the silkworm cocoon image is determined according to the following method:
acquiring a front image and a back image of a silkworm cocoon sample, acquiring binary images of the front image and the back image of the silkworm cocoon sample according to the front image and the back image of the silkworm cocoon sample, and performing dot multiplication on the binary images of the front image and the back image of the silkworm cocoon sample and the front image and the back image of the corresponding silkworm cocoon sample to obtain a corresponding silkworm cocoon image;
and converting the silkworm cocoon image into an HSV space to obtain the HSV image of the silkworm cocoon.
Further, the plurality of reduced rectangular bounding boxes are determined as follows:
and reducing the rectangular surrounding frames in an equal proportion according to each reduction proportion to obtain a plurality of reduced rectangular surrounding frames, wherein when the rectangular surrounding frames are reduced in the equal proportion, the rectangular surrounding frames are reduced in the equal proportion from large to small according to the reduction proportion.
Further, the method for obtaining the gaussian value of the target pixel point comprises the following steps:
performing Gaussian fitting according to the distance difference between the distance from the target pixel point corresponding to the rectangular enclosure frame to the central pixel point and the distance from the target pixel point corresponding to each first enclosure frame to the central pixel point to obtain a Gaussian fitting model;
and obtaining the Gaussian value of the target pixel point according to the Gaussian fitting model.
Further, the quality of the silkworm cocoon sample is determined according to the following method:
acquiring the shortest distance from each point in the scatter diagram to the fitted curve;
acquiring the hue difference of each point in the scatter diagram and the point which is on the connection line of the shortest distance of each point or is near the connection line and is closest to the fitting curve;
acquiring the area of a silkworm cocoon image;
multiplying the shortest distance from each point in the scatter diagram to the fitting curve by the corresponding hue difference to obtain a plurality of products, and accumulating each product to obtain an accumulated product;
and obtaining the quality of the cocoon sample corresponding to the HSV image according to the ratio of the accumulated product to the area of the HSV image.
Further, the method for classifying the silkworm cocoons comprises the following steps:
acquiring the number of the silkworm cocoons in the batch where the silkworm cocoon sample is located, and acquiring the silkworm cocoon sample in the batch of silkworm cocoons under each quality according to the quality of each silkworm cocoon sample and a preset silkworm cocoon quality grade;
obtaining the content of the silkworm cocoons of each quality in the batch of silkworm cocoons according to the ratio of the number of the silkworm cocoon samples in the batch of silkworm cocoons of each quality to the number of the silkworm cocoons of the batch of silkworm cocoon samples;
and acquiring the quality of the batch of silkworm cocoons, and obtaining the quality of each quality silkworm cocoon in the batch of silkworm cocoons according to the product of the content of each quality silkworm cocoon in the batch of silkworm cocoons and the quality of the batch of silkworm cocoons.
Silkworm cocoon acquisition system based on cloud service big data platform includes:
the image acquisition module is used for acquiring images of the silkworm cocoon samples;
the image input module is used for inputting the obtained silkworm cocoon sample image into a processor of the acquisition system;
an image processing module comprising: the device comprises a Gaussian fitting unit, a curve fitting unit and a silkworm cocoon classifying unit; wherein:
the Gaussian fitting unit is used for acquiring each symmetry axis of the rectangular enclosing frame, respectively acquiring target pixel points of the rectangular enclosing frame and each first enclosing frame on each symmetry axis, acquiring central pixel points of the silkworm cocoon image, and performing Gaussian fitting according to the distance from each target pixel point to the central pixel points to obtain Gaussian values of corresponding target pixel points;
the curve fitting unit is used for obtaining a first difference value of each target pixel point according to the brightness value of each target pixel point and the Gaussian value of the corresponding target pixel point, drawing a scatter diagram by using the first difference value of each target pixel point and the distance from each target pixel point to the central pixel point, and fitting the scatter diagram by using least square to obtain a fitting curve;
the silkworm cocoon classification unit is used for obtaining the quality of the silkworm cocoon sample corresponding to the HSV image according to the scatter diagram, the fitting curve and the silkworm cocoon image area and classifying the silkworm cocoons by using the quality of all the obtained silkworm cocoon samples;
and the output module is used for outputting the quality of each classified silkworm cocoon with each quality.
The invention has the beneficial effects that: according to the method, firstly, the silkworm cocoon samples in each batch of silkworm cocoons are extracted, the silkworm cocoon images corresponding to the silkworm cocoon samples are obtained according to the silkworm cocoon samples, the silkworm cocoon images are converted into HSV space, the silkworm cocoon quality can be evaluated in a mode of combining the color and the brightness of the silkworm cocoons, secondly, the distance between the central pixel point and the target pixel point of the silkworm cocoon images is used for replacing the brightness value in the silkworm cocoon images, not only is the calculated amount reduced, but also the final evaluation value of the silkworm cocoons is more accurate, and finally, gaussian fitting is performed on the silkworm cocoons of each quality in each batch of silkworm cocoons during purchase, the silkworm cocoon quality of each silkworm cocoon quality after Gaussian fitting is accumulated, the accumulated sum is matched with the real required quantity of the silkworm cocoons of each quality, the silkworm cocoon batch with the highest matching degree is purchased, the silkworm cocoons of each quality are purchased according to need, and the increase of factory storage cost and raw material cost is prevented.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an embodiment of a silkworm cocoon classification method based on a cloud service big data platform according to the present invention;
fig. 2 is a schematic diagram of a rectangular enclosing frame of a cocoon image in an embodiment of a cocoon classification method based on a cloud service big data platform according to the invention;
FIG. 3 is a schematic diagram of a plurality of rectangular bounding boxes in an embodiment of the cocoon classification method based on the cloud service big data platform of the present invention;
fig. 4 is a schematic diagram of a target pixel point in an embodiment of the method for classifying cocoons based on a cloud service big data platform according to the present invention;
FIG. 5 is a schematic diagram illustrating a distance difference in an embodiment of a method for classifying silkworm cocoons based on a cloud service big data platform according to the present invention;
fig. 6 is a flowchart of a silkworm cocoon purchasing system based on a cloud service big data platform according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
The embodiment of the silkworm cocoon classification method based on the cloud service big data platform, as shown in fig. 1, includes:
the method comprises the steps of S1, obtaining a silkworm cocoon sample image, obtaining an HSV (hue, saturation, value) image of the silkworm cocoon according to the silkworm cocoon sample image, obtaining a rectangular surrounding frame of the HSV image, setting a plurality of reduction ratios, reducing the rectangular surrounding frame of the HSV image according to each reduction ratio to obtain a plurality of reduced rectangular surrounding frames, and taking the reduced rectangular surrounding frames as first surrounding frames.
The method for acquiring the front image of the silkworm cocoon sample comprises the following specific steps: determining the cocoons to be detected in the current batch, randomly extracting Q cocoons samples from the cocoons in the current batch by related purchasing staff, wherein the Q value is as large as possible, so that the integral counting detection result of the content of the cocoons in different types in the current batch is credible, and the Q cocoons samples are flatly laid on a detection device by the related purchasing staff, wherein two fixed cameras are erected above and below the cocoons detection device and are used for collecting images of the front and back sides of the cocoons to further obtain images of the front and back sides of the cocoons in the current batch, wherein the two cameras are used for preventing spots at the lower parts of the cocoons from being shot when the cocoons samples are placed to cause the counting of errors when the cocoons in the current batch are classified.
Graying the front image of the silkworm cocoon samples in the current batch of silkworm cocoons to obtain a gray image, carrying out image denoising on the gray image by adopting Gaussian filtering, carrying out edge detection on the denoised image by a canny algorithm to obtain an edge image after the edge detection, carrying out morphological filling treatment on the edge image to obtain a binary image of the front image of the corresponding silkworm cocoon sample, carrying out dot multiplication operation on the binary image of the front image of each silkworm cocoon sample and the front image of the silkworm cocoon sample to obtain the corresponding silkworm cocoon image, wherein spots exist in the silkworm cocoon, so that each silkworm cocoon image is converted into an HSV color space to obtain an HSV image of the silkworm cocoon.
The outline of each cocoon image is surrounded by a rectangle to obtain a rectangular surrounding frame of each cocoon image, and a specific schematic diagram is shown in fig. 2.
The specific steps for obtaining the first enclosure frame are as follows: providing a plurality of reduction ratios, each reduction ratio having an equal variation, e.g.
The rectangular surrounding frame of each silkworm cocoon image is reduced according to the reduction ratio
Sequentially reducing in equal proportion to obtain a plurality of reduced rectangular surrounding frames, the specific schematic diagram is shown in fig. 3, it should be said thatIt is to be noted that, in FIG. 3, only the rectangular bounding box of each cocoon image is drawn in a reduced scale
And sequentially carrying out isometric reduction on the schematic diagram, and taking the reduced rectangular enclosure frame as a first enclosure frame.
S2, obtaining each symmetry axis of the rectangular surrounding frame, respectively obtaining target pixel points of the rectangular surrounding frame and each first surrounding frame on each symmetry axis, obtaining central pixel points of the silkworm cocoon image, and performing Gaussian fitting according to the distance from each target pixel point to each central pixel point to obtain Gaussian values of the corresponding target pixel points.
Each symmetry axis of the rectangular enclosure frame is obtained, pixel points of the rectangular enclosure frame and each first enclosure frame on each symmetry axis are obtained respectively, the specific schematic diagram is shown in fig. 4, and the pixel points of the rectangular enclosure frame and each first enclosure frame on each symmetry axis are used as target pixel points.
The brightness value of each target pixel point in each silkworm cocoon image in the HSV color space is obtained, and because the silkworm cocoons are in an oval shape, the gray value of the silkworm cocoons has the characteristics of high middle and low edge, the center pixel point of the silkworm cocoon image has the highest brightness value, and the brightness value is gradually decreased from the center pixel point to the edge. Therefore, the normal change of the brightness value on the silkworm cocoon image is approximately described by the two-dimensional single Gaussian model, wherein when the two-dimensional single Gaussian model is fitted, the central pixel point of the silkworm cocoon image is obtained, and the distance from the target pixel point to the central pixel point is used for fitting instead of the brightness value.
The specific steps of obtaining the Gaussian fitting model according to the distance from each target pixel point to the central pixel point are as follows: and performing Gaussian fitting according to the distance difference between the distance from the target pixel point corresponding to the rectangular enclosing frame to the central pixel point and the distance from the target pixel point corresponding to each first enclosing frame to the central pixel point to obtain a Gaussian fitting model. A schematic diagram of a specific distance difference is shown in figure 5,
representing the rectangular bounding boxThe distance between the target pixel point and the central pixel point, OB and OC represent the distance between the central pixel point and different first enclosing frames,
reducing
Is greater than
Reducing
The distance difference is decreased from the center part to the edge part of the cocoon image, and is consistent with the brightness variation trend of the pixel points in the cocoon image. And obtaining the Gaussian value of each target pixel point in the Gaussian fitting model according to the Gaussian fitting model.
S3, obtaining a first difference value of each target pixel point according to the brightness value of each target pixel point and the Gaussian value of the corresponding target pixel point, drawing a scatter diagram by using the first difference value of each target pixel point and the distance from each target pixel point to the center pixel point, and fitting the scatter diagram by using least square to obtain a fitting curve.
The Gaussian fitting model is obtained by the distance between the target pixel point and the central pixel point, so that each target pixel point necessarily has a corresponding point in the Gaussian fitting model, the difference value between the brightness value of each target pixel point and the Gaussian value of the corresponding point in the Gaussian fitting model is obtained, the difference value between the brightness value of each target pixel point and the Gaussian value of the corresponding point in the Gaussian fitting model is used as a first difference value of the target pixel point, and the distance from each target pixel point to the central pixel point is used
As an abscissa, a first difference corresponding to each target pixel point
Plotting a scatter diagram for the ordinate, proceeding with the scatter diagram using least squaresAnd performing line fitting to obtain a fitting curve.
And S4, obtaining the quality of the silkworm cocoon sample corresponding to the HSV image according to the scatter diagram, the fitting curve and the silkworm cocoon image area, and classifying the silkworm cocoons by using the quality of all the obtained silkworm cocoon samples.
The specific steps for obtaining the first quality of the silkworm cocoon sample are as follows: acquiring a point on a shortest distance connecting line from each point in the scatter diagram to the fitting curve, acquiring a point on each connecting line or near the connecting line closest to the fitting curve, setting a specific range near the connecting line according to specific conditions, and acquiring the hue difference between each point and a point on the shortest distance connecting line or near the connecting line closest to the fitting curve
(wherein each point in the scatter diagram is bound to correspond to a target pixel point, and the hue of the target pixel point is the component of an H channel in the HSV color space), and obtaining the shortest distance from each point in the scatter diagram to the fitting curve
Obtaining area of cocoon image
According to
And
obtaining a first quality of each silkworm cocoon sample, wherein a specific expression is as follows:
in the formula:
indicating the first in the scatter diagram
Hue difference between a point and the closest point on or near the line connecting the points with the shortest distance,
indicating the first in the scatter diagram
The shortest distance of a point to the fitted curve,
represents the area of the image corresponding to the silkworm cocoon,
the first quality of the cocoon sample corresponding to the cocoon image is represented.
The fitting curve represents the normal quality of the target pixel points, and the quality of each point in the scatter diagram can be measured by calculating the color difference of each point and the point on the shortest distance connecting line or the point close to the connecting line and closest to the fitting curve, namely the quality of each target pixel point in the silkworm cocoon image can be measured; calculating the shortest distance from each point in the scatter diagram to the fitted curve, and measuring the quality of each point, namely measuring the quality of each target pixel point in the silkworm cocoon image; therefore, the quality of the silkworm cocoon image is measured from two dimensions of the shortest distance and the hue difference, and the larger the shortest distance and the hue difference is, the larger the deviation of the point from the fitting curve is, the worse the quality of the point is, namely
The larger the cocoon image, the worse the quality of the cocoon sample corresponding to the cocoon image.
Thus, the first quality of the silkworm cocoon sample is obtained, and the first quality of each silkworm cocoon sample can be obtained according to the first quality calculation formula.
Obtaining a back image of each silkworm cocoon sample according to the steps S1-S4, and obtaining a second quality of each silkworm cocoon sample
Obtaining the quality of each cocoon sample of the current batch according to the first quality and the second quality of each cocoon sample
And the quality of each silkworm cocoon sample is the sum of the corresponding first quality and the second quality, and each silkworm cocoon sample necessarily corresponds to one silkworm cocoon.
The method for classifying the silkworm cocoons comprises the following steps: acquiring the number of the silkworm cocoons in the batch where the silkworm cocoon sample is located, acquiring the silkworm cocoon sample in the batch of the silkworm cocoons under each quality according to the quality of each silkworm cocoon sample and a preset silkworm cocoon quality grade, acquiring the content of the silkworm cocoons with each quality in the batch according to the ratio of the silkworm cocoon sample in the batch of the silkworm cocoons under each quality to the number of the silkworm cocoons in the batch where the silkworm cocoon sample is located, acquiring the quality of the batch of the silkworm cocoons, and acquiring the quality of the silkworm cocoons with each quality in the batch of the silkworm cocoons according to the product of the content of the silkworm cocoons with each quality in the batch and the quality of the batch of the silkworm cocoons, wherein the silkworm cocoon quality grade is set to take the quality of each single silkworm cocoon at an interval of 10 as a quality grade, for example, the silkworm cocoon quality grade is set
Is a first-level one-step one,
in the second level, by analogy, the interval division of the quality of a single silkworm cocoon can be adjusted according to the specific implementation requirements, and the empirical value obtained by the method is 10. Thus, the quality classification of the batch of silkworm cocoons is completed.
Silkworm cocoon purchasing system based on cloud service big data platform includes: image acquisition module, image input module, image processing module, output module, wherein image processing module includes: a Gaussian fitting unit, a curve fitting unit and a cocoon classification unit, as shown in FIG. 6.
The image acquisition module is used for acquiring images of the silkworm cocoon samples.
And the image input module is used for inputting the acquired silkworm cocoon sample image into a processor of the acquisition system.
An image processing module comprising: the device comprises a Gaussian fitting unit, a curve fitting unit and a silkworm cocoon classification unit; wherein:
and the Gaussian fitting unit is used for acquiring each symmetry axis of the rectangular surrounding frame, respectively acquiring target pixel points of the rectangular surrounding frame and each first surrounding frame on each symmetry axis, acquiring central pixel points of the silkworm cocoon image, and performing Gaussian fitting according to the distance from each target pixel point to the central pixel points to obtain Gaussian values of the corresponding target pixel points.
And the curve fitting unit is used for obtaining a first difference value of each target pixel point according to the brightness value of each target pixel point and the Gaussian value of the corresponding target pixel point, drawing a scatter diagram by using the first difference value of each target pixel point and the distance from each target pixel point to the central pixel point, and fitting the scatter diagram by using least square to obtain a fitting curve.
And the silkworm cocoon classification unit is used for obtaining the quality of the silkworm cocoon sample corresponding to the HSV image according to the scatter diagram, the fitting curve and the silkworm cocoon image area, and classifying the silkworm cocoons by using the quality of all the obtained silkworm cocoon samples.
And the output module is used for outputting the quality of each classified silkworm cocoon with each quality.
The method comprises the steps of obtaining the number of the cocoons of the current batch where the cocoons are located, dividing the cocoons samples according to quality grades to obtain the cocoons samples of the batch of cocoons under each quality, obtaining the content of the cocoons of each quality in the batch of cocoons according to the ratio of the cocoons samples of the batch of cocoons under each quality to the number of the cocoons of the batch, obtaining the quality of the batch of cocoons, and obtaining the quality of the cocoons of each quality in the batch of cocoons according to the product of the content of the cocoons of each quality in the batch of cocoons and the quality of the batch of cocoons.
After the mass of each quality of silkworm cocoons in the current batch is obtained, due to the fact that the demanded quantities of different types of silkworm cocoons in a factory are different and the purchase quantities are different, the stock quantity and the demanded quantity of each quality of silkworm cocoons are obtained, and the real demanded quantity of each quality of silkworm cocoons is obtained by subtracting the corresponding stock quantity according to the demanded quantity of each quality of silkworm cocoons.
The method comprises the steps of obtaining the quality of each quality silkworm cocoon in each batch of silkworm cocoons during purchase, carrying out Gaussian fitting on the quality of each quality silkworm cocoon in each batch of silkworm cocoons to obtain the quality of each quality silkworm cocoon in each batch of silkworm cocoons after Gaussian fitting, accumulating the quality of each quality silkworm cocoon in each batch of silkworm cocoons after Gaussian fitting to obtain the quality of each quality silkworm cocoon after accumulation, matching the quality of each quality silkworm cocoon after accumulation with the real demand of each quality silkworm cocoon, and purchasing the silkworm cocoon batch with the highest matching degree.
The specific steps of accumulating the quality of each quality silkworm cocoon in each batch of silkworm cocoons after Gaussian fitting are as follows: obtaining the total number of silkworm cocoon batches during purchase
To, for
Arranging and combining the silkworm cocoons in batches, wherein the number of the arranged and combined elements is
And is and
for example, two batches are first extracted from the total number of silkworm cocoon batches as a permutation and combination element, i.e.
When in use, will
And (3) arranging and combining every two silkworm cocoons in batches to obtain a plurality of combinations, repeating the steps to obtain all combinations, and accumulating the mass of each quality silkworm cocoon in each batch of silkworm cocoons after Gaussian fitting in each combination to obtain the accumulated mass of each quality silkworm cocoon.
And matching the quality of all the accumulated silkworm cocoons with the real demand of the quality of each silkworm cocoon, and purchasing the silkworm cocoons in the combination with the highest matching degree in batches. The highest matching degree is the combination of the quality of each type of silkworm cocoon and the real demand of each type of silkworm cocoon quality, which is the closest.
The beneficial effects of the invention are: according to the method, firstly, the silkworm cocoon samples in each batch of silkworm cocoons are extracted, the silkworm cocoon images corresponding to the silkworm cocoon samples are obtained according to the silkworm cocoon samples, the silkworm cocoon images are converted into HSV space, the silkworm cocoon quality can be evaluated in a mode of combining the color and the brightness of the silkworm cocoons, secondly, the distance between the central pixel point and the target pixel point of the silkworm cocoon images is used for replacing the brightness value in the silkworm cocoon images, not only is the calculated amount reduced, but also the final evaluation value of the silkworm cocoons is more accurate, and finally, gaussian fitting is performed on the silkworm cocoons of each quality in each batch of silkworm cocoons during purchase, the silkworm cocoon quality of each silkworm cocoon quality after Gaussian fitting is accumulated, the accumulated sum is matched with the real required quantity of the silkworm cocoons of each quality, the silkworm cocoon batch with the highest matching degree is purchased, the silkworm cocoons of each quality are purchased according to need, and the increase of factory storage cost and raw material cost is prevented.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A silkworm cocoon classification method based on a cloud service big data platform is characterized by comprising the following steps:
s1, acquiring a silkworm cocoon sample image, acquiring an HSV (hue, saturation and value) image of a silkworm cocoon according to the silkworm cocoon sample image, acquiring a rectangular enclosure frame of the HSV image, setting a plurality of reduction ratios, reducing the rectangular enclosure frame of the HSV image according to each reduction ratio to obtain a plurality of reduced rectangular enclosure frames, and taking the reduced rectangular enclosure frames as first enclosure frames;
s2, obtaining each symmetry axis of the rectangular enclosing frame, respectively obtaining target pixel points of the rectangular enclosing frame and each first enclosing frame on each symmetry axis, obtaining central pixel points of the silkworm cocoon image, and performing Gaussian fitting according to the distance from each target pixel point to the central pixel points to obtain Gaussian values of corresponding target pixel points;
s3, obtaining a first difference value of each target pixel point according to the brightness value of each target pixel point and the Gaussian value of the corresponding target pixel point, drawing a scatter diagram by using the first difference value of each target pixel point and the distance from each target pixel point to the center pixel point, and fitting the scatter diagram by using least square to obtain a fitting curve;
s4, obtaining the quality of the silkworm cocoon sample corresponding to the HSV image according to the scatter diagram, the fitting curve and the silkworm cocoon image area, and classifying the silkworm cocoons by using the quality of all the obtained silkworm cocoon samples;
the quality of the silkworm cocoon sample is determined according to the following method:
acquiring the shortest distance from each point in the scatter diagram to the fitted curve;
acquiring the hue difference of each point in the scatter diagram and the point which is on the connection line of the shortest distance of each point or is near the connection line and is closest to the fitting curve;
acquiring the area of a silkworm cocoon image;
multiplying the shortest distance from each point in the scatter diagram to the fitting curve by the corresponding hue difference to obtain a plurality of products, and accumulating each product to obtain an accumulated product;
obtaining the quality of the cocoon sample corresponding to the HSV image according to the ratio of the accumulated product to the area of the HSV image;
the method for classifying the silkworm cocoons comprises the following steps:
acquiring the number of the silkworm cocoons in the batch where the silkworm cocoon samples are located, and acquiring the silkworm cocoon samples in the batch of silkworm cocoons under each quality according to the quality of each silkworm cocoon sample and a preset silkworm cocoon quality grade;
obtaining the content of the silkworm cocoons of each quality in the batch of silkworm cocoons according to the ratio of the number of the silkworm cocoon samples in the batch of silkworm cocoons of each quality to the number of the silkworm cocoons of the batch of silkworm cocoon samples;
and acquiring the quality of the batch of silkworm cocoons, and acquiring the quality of each quality of silkworm cocoons in the batch of silkworm cocoons according to the product of the content of each quality of silkworm cocoons in the batch of silkworm cocoons and the quality of the batch of silkworm cocoons.
2. The silkworm cocoon classification method based on the cloud service big data platform according to claim 1, wherein the silkworm cocoon image is determined according to the following method:
acquiring a front image and a back image of a silkworm cocoon sample, acquiring binary images of the front image and the back image of the silkworm cocoon sample according to the front image and the back image of the silkworm cocoon sample, and performing dot multiplication on the binary images of the front image and the back image of the silkworm cocoon sample and the front image and the back image of the corresponding silkworm cocoon sample to obtain a corresponding silkworm cocoon image;
and converting the silkworm cocoon image into an HSV space to obtain the HSV image of the silkworm cocoon.
3. The silkworm cocoon classification method based on the cloud service big data platform according to claim 1, wherein the plurality of reduced rectangular bounding boxes are determined according to the following method:
and reducing the rectangular surrounding frames in an equal proportion according to each reduction proportion to obtain a plurality of reduced rectangular surrounding frames, wherein when the rectangular surrounding frames are reduced in the equal proportion, the rectangular surrounding frames are reduced in the equal proportion from large to small according to the reduction proportion.
4. The silkworm cocoon classification method based on the cloud service big data platform according to claim 1, wherein the gaussian value of the target pixel point is determined according to the following method:
performing Gaussian fitting according to the distance between the target pixel point corresponding to the rectangular enclosing frame and the central pixel point and the distance between the target pixel point corresponding to each first enclosing frame and the central pixel point to obtain a Gaussian fitting model;
and obtaining the Gaussian value of the target pixel point according to the Gaussian fitting model.
5. Silkworm cocoon purchasing system based on cloud service big data platform, its characterized in that includes:
the image acquisition module is used for acquiring a silkworm cocoon sample image;
the image input module is used for inputting the acquired silkworm cocoon sample image into a processor of the acquisition system;
an image processing module comprising: the device comprises a Gaussian fitting unit, a curve fitting unit and a silkworm cocoon classification unit; wherein:
the Gaussian fitting unit is used for acquiring each symmetry axis of the rectangular surrounding frame, respectively acquiring target pixel points of the rectangular surrounding frame and each first surrounding frame on each symmetry axis, acquiring central pixel points of the silkworm cocoon image, and performing Gaussian fitting according to the distance from each target pixel point to the central pixel points to obtain Gaussian values of the corresponding target pixel points;
the curve fitting unit is used for obtaining a first difference value of each target pixel point according to the brightness value of each target pixel point and the Gaussian value of the corresponding target pixel point, drawing a scatter diagram by using the first difference value of each target pixel point and the distance from each target pixel point to the central pixel point, and fitting the scatter diagram by using least square to obtain a fitting curve;
the silkworm cocoon classification unit is used for obtaining the quality of the silkworm cocoon sample corresponding to the HSV image according to the scatter diagram, the fitting curve and the silkworm cocoon image area and classifying the silkworm cocoons by using the quality of all the obtained silkworm cocoon samples;
the quality of the silkworm cocoon sample is determined according to the following method:
obtaining the shortest distance from each point in the scatter diagram to the fitted curve;
acquiring the hue difference of each point in the scatter diagram and the point which is on the connection line of the shortest distance of each point or is near the connection line and is closest to the fitting curve;
acquiring the area of a silkworm cocoon image;
multiplying the shortest distance from each point in the scatter diagram to the fitting curve by the corresponding hue difference to obtain a plurality of products, and accumulating each product to obtain an accumulated product;
obtaining the quality of the cocoon sample corresponding to the HSV image according to the ratio of the accumulated product to the area of the HSV image;
the method for classifying the silkworm cocoons comprises the following steps:
acquiring the number of the silkworm cocoons in the batch where the silkworm cocoon sample is located, and acquiring the silkworm cocoon sample in the batch of silkworm cocoons under each quality according to the quality of each silkworm cocoon sample and a preset silkworm cocoon quality grade;
obtaining the content of the silkworm cocoons of each quality in the batch of silkworm cocoons according to the ratio of the number of the silkworm cocoon samples in the batch of silkworm cocoons of each quality to the number of the silkworm cocoons of the batch of silkworm cocoon samples;
acquiring the quality of the batch of silkworm cocoons, and acquiring the quality of each quality of silkworm cocoons in the batch of silkworm cocoons according to the product of the content of each quality of silkworm cocoons in the batch of silkworm cocoons and the quality of the batch of silkworm cocoons;
and the output module is used for outputting the quality of each classified silkworm cocoon with each quality.
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