CN111881921A - Optimal grain direct selection method for rice color selector - Google Patents
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- 235000007164 Oryza sativa Nutrition 0.000 title claims abstract description 54
- 235000009566 rice Nutrition 0.000 title claims abstract description 54
- 235000013339 cereals Nutrition 0.000 title claims abstract description 43
- 238000010187 selection method Methods 0.000 title claims abstract description 7
- 240000007594 Oryza sativa Species 0.000 title 1
- 241000209094 Oryza Species 0.000 claims abstract description 53
- 239000000463 material Substances 0.000 claims abstract description 51
- 238000004383 yellowing Methods 0.000 claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000013507 mapping Methods 0.000 claims description 30
- 239000012535 impurity Substances 0.000 claims description 19
- 238000000034 method Methods 0.000 claims description 19
- 238000007664 blowing Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 230000009191 jumping Effects 0.000 claims description 3
- 241001268993 Heterochrosis Species 0.000 description 4
- 239000002985 plastic film Substances 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 239000003086 colorant Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 239000011149 active material Substances 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000007306 functionalization reaction Methods 0.000 description 1
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Abstract
The invention discloses a superior grain direct selection method for a rice color selector, which relates to the technical field of rice color selection, wherein an image to be identified is traversed pixel by pixel line by line, weighted gray processing is firstly carried out on effective material pixel points, if the gray value is smaller than a black threshold value, the pixel points are judged to be black pixel points, otherwise, color analysis is carried out on the pixel points, and if the gray value is within a preset RGB (red, green and blue) range of a yellow material, the pixel points are judged to be yellow pixel points; and if the sum of the number of the black pixels and the number of the yellow pixels on one rice grain is less than an allowable threshold value, judging the rice grain to be high-quality rice grain. According to the invention, the high-quality rice grains are directly screened out by accurately identifying the blackening and yellowing rice grains and/or the blackening and yellowing parts of the rice grains, so that the working efficiency and the accuracy of high-quality rice grain sorting are greatly improved.
Description
Technical Field
The invention relates to the technical field of rice color sorting, in particular to a high-quality grain direct sorting method for a rice color sorting machine.
Background
With the improvement of living standard, the consumption of rice by people gradually develops towards the direction of quality improvement, functionalization and greening. The rice screening is usually to remove the impurities with different colors mixed in the rice by utilizing color difference, and the main impurities with different colors comprise black impurities and yellow impurities. At present, the existing rice color sorting machine in the market has the problems that the sorting precision of the heterochromatic impurities is not high enough and the heterochromatic impurities need to be sorted for many times, and the problem is basically still that the recognition accuracy of the heterochromatic impurities is not high enough. Meanwhile, most of the existing rice color selection algorithms remove the heterochromatic grains in the materials instead of directly selecting high-quality rice grains, and heterochromatic impurities also have mixed color impurities, so that the accuracy of rice color selection is influenced.
Disclosure of Invention
Aiming at the problems, the invention provides a high-quality grain direct sorting method for a rice color sorting machine, which can greatly improve the working efficiency and the accuracy of high-quality grain sorting.
A grain-optimizing direct selection method for a rice color selector is characterized in that an image to be identified is traversed line by line pixel by pixel, weighted gray processing is carried out on effective material pixel points, if the gray value is smaller than a black threshold value, the pixel points are judged to be black pixel points, otherwise, color analysis is carried out on the pixel points, and if the gray value is within a preset yellow material RGB range, the pixel points are judged to be yellow pixel points; and if the sum of the number of the black pixels and the number of the yellow pixels on one rice grain is less than an allowable threshold value, judging the rice grain to be high-quality rice grain.
Further, the color analysis process of the material pixel point comprises the steps of establishing an RGB three-dimensional coordinate system, respectively mapping RGB values of the pixel point to three coordinate planes, and if mapping points of the pixel point on the three coordinate planes are all in mapping areas of a preset yellow material RGB range on the three coordinate planes, judging the pixel point to be a yellow pixel point.
Further, extracting effective material pixel points from the image to be recognized comprises background removing operation and edge removing operation, wherein the background removing operation is to obtain the RGB range of background pixels through a background pixel point sample set, and if the pixel points of the image to be recognized fall into the RGB range of the background pixels, the pixel points are determined as background pixel points; and the edge removing operation is to obtain the RGB range of the edge pixel through the edge pixel point sample set, and if the pixel point of the image to be identified falls into the RGB range of the edge pixel, the material edge pixel point is determined.
Further, 1024 frames of low-speed blanking images are adopted for the edge pixel point sample images, the material edge area is extracted, and the RGB range of the edge pixels is obtained.
The invention specifically comprises the following steps:
1. setting a weighted graying parameter (K)R,KG,KB) A black threshold M1, where KR、KG、KBTaking an integer of 0-1024; setting and initializing variables, wherein the variables comprise impurity pixel number M equal to 0, non-background pixel number N equal to 0, and background line identifier S equal to 0;
2. establishing an RGB three-dimensional coordinate system, determining the RGB range of the yellowing material to be screened, and mapping the RGB range onto three coordinate planes of the RGB three-dimensional coordinate system to form three mapping areas;
3. collecting a background pixel point sample set, and counting the RGB range of background pixels;
4. collecting a sample set of edge pixel points, and counting the RGB range of the edge pixels;
5. acquiring an image to be identified and preprocessing the image;
6. taking a row of pixel points;
7. traversing the pixel points of the selected row one by one;
8. judging whether the pixel point traversed currently is the last pixel point of the line,
if not, judging whether the pixel is a background pixel;
if not, setting S as 1, and adding 1 to the number N of non-background pixels, and then judging whether the pixel is a black pixel, if so, adding 1 to the number M of impurity pixels, and skipping to the step 7, otherwise, judging whether the pixel is a yellow pixel, if so, adding 1 to the number M of impurity pixels, and skipping to the step 7;
if the background pixel point is present, directly skipping to the step 7;
if yes, judging whether S is 1 or not;
if S is equal to 0, judging whether N is 0, if N is equal to 0, jumping to the step 6, if N is not equal to 0, judging whether M exceeds an allowable threshold value, if not, judging that the material is high-quality rice grains, and sending a valve blowing signal;
if S is 1, S is 0, and then step 6 is skipped.
Further, the preprocessing in the step 4 includes distortion correction and edge enhancement.
According to the invention, the high-quality rice grains are directly screened out by accurately identifying the blackening and yellowing rice grains and/or the blackening and yellowing parts of the rice grains, so that the working efficiency and the accuracy of high-quality rice grain sorting are greatly improved.
Drawings
FIG. 1 is a flow chart of a preferred grain direct sorting method;
fig. 2 is a black-and-white schematic diagram of a pixelized image to be recognized.
FIG. 3 is a schematic diagram of a RGB three-dimensional coordinate system;
fig. 4 is a schematic view of a polyline model.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Example 1
The rice color sorter is generally divided into a plurality of channels, such as 64 channels or 128 channels, each channel corresponds to a blow valve, and the color sorting algorithm only needs to process linear images in a single channel and make a blow valve judgment.
The invention discloses a high-quality grain direct sorting method for a rice color sorting machine, which can greatly improve the working efficiency and the accuracy of high-quality grain sorting, and specifically comprises the following steps as shown in figure 1:
1. setting a weighted graying parameter (K)R,KG,KB) A black threshold M1, where KR、KG、KBTaking an integer of 0-1024; setting and initializing variables, wherein the variables comprise impurity pixel number M equal to 0, non-background pixel number N equal to 0, and background line identifier S equal to 0.
2. Establishing an RGB three-dimensional coordinate system, determining the RGB range of the yellowing material to be screened, and mapping the RGB range to three coordinate planes of the RGB three-dimensional coordinate system to form three mapping areas.
As is well known, the positive yellow RGB value is (255,255,0), and is located at a corner of the whole RGB space, and for convenience of calculation, this embodiment adopts a polygonal line model to fit nonlinear classification to distinguish yellow pixel points, i.e., a mapping region of the RGB range of the yellowing material on three coordinate planes of the RGB three-dimensional coordinate system is simplified into a polygonal line model.
The specific operation of judging the color space by the polyline model is described as follows: if the mapping region is located in the GR plane as shown in fig. 2, a broken line may be formed by two parallel straight lines and an oblique line intersecting the two parallel straight lines, so as to divide the region where the mapping region is located, and as long as the region falls into the same side (with respect to the broken line) as the target sample, the mapping region is considered to belong to the target sample. Through the broken line model simplification algorithm, although on the surface can probably make the material that is not in the material RGB within range of giving yellow colour also be rejected (the shadow part of figure 2), but can not influence the final purpose of rice look selection, because the final purpose of rice look selection is to select the rice of fine quality, free from the heterochrosis grain, as long as do not reject as the heterochrosis grain with the grain of normal colour rice, but the broken line method of this embodiment has only slightly widened the look selection range of yellow pixel, can not influence normal coloured grain of rice.
The broken line model is composed of two straight line parameters Z1 and Z1 and a diagonal line determined by intercept ZR and ZG, so that after the mapping areas on three coordinate planes of the RGB three-dimensional coordinate system are determined, the broken line model on each coordinate plane is determined according to the mapping areas for subsequent color analysis of effective material pixel points.
3. And collecting 1024 frames of background images as a background pixel point sample set, and counting the RGB range of the background pixels.
4. 1024 frames of low-speed blanking images are collected to serve as an edge pixel point sample set, an edge detection algorithm is used for extracting a material edge area (the width of the material edge is defined to be 2 pixels) based on an OpenCV algorithm library, the RGB value of a pixel point in a background area is set to be 0, and the RGB range of the edge pixel is obtained.
5. The method comprises the steps of obtaining an image to be recognized and preprocessing the image, wherein the image to be recognized can be a channel linear image only containing one material, or can be taken from a certain specific position of the channel linear image, and of course, the travel distance of the material in the time difference from the time when the crushed material is recognized through an algorithm to the time when a blowing valve acts is also considered. The preprocessing includes distortion correction and edge enhancement, which are common image processing techniques, and the specific algorithm adopted in the embodiment is briefly described here.
Distortion correction algorithm flow:
acquiring a background image: acquiring 1 frame of background image as a calibrated reference background image, wherein each line comprises 2700 RGB pixel points;
secondly, calculating a calibration coefficient: firstly, calculating the average value M of the line image on RGB three channelsR、MG、MBThen, the RGB values of 2700 pixels on the whole line of image are divided by M respectivelyR、MG、MBObtaining distortion coefficient, finally multiplying the distortion coefficient on RGB channel of 2700 pixel points by 27Integer taking processing is realized to obtain a final calibration coefficient;
calculating distortion correction: and (3) for each input frame of image, multiplying the RGB values of 2700 pixel points by corresponding calibration coefficients respectively, and then performing right shift by 7, so as to realize distortion correction on each pixel point.
The whole idea is to multiply the RGB value of each pixel point of a line of linear images by a corresponding coefficient, realize the leveling of the images and eliminate the problems of convex in the middle and concave on two sides.
Edge enhancement algorithm flow:
firstly, acquiring background images: acquiring 1024 frames of background images as a background pixel point sample set;
secondly, calculating background pixel range and mean value: counting the upper limit and the lower limit of RGB based on a background pixel point sample set to obtain a background range (Rmin, Rmax) & (Gmin, Gmax) & (Bmin, Bmax) and a background mean value (Rmean, Gmean, Bmean);
slide scan linear image: for each acquired frame of linear image, traversing by using a sliding window with 4 (empirical value) pixel points, when the RGB value of the pixel point is 1 in the background range and is marked as background, otherwise, the RGB value of the pixel point is 0 and is marked as material, and the state of the sliding window can be described by using 4-bit binary 4' bXXXXXX (the high-order is the first-come pixel);
fourth, identifying the edge of the material: dividing the edge into two types of 'sliding in edge' and 'sliding out edge' according to the traversal direction of the sliding window, identifying the sliding in edge when the state of the sliding window is 4'b1100, identifying 2 pixel points at the lower level of the state as the edge, identifying the sliding out edge when the state of the sliding window is 4' b0011, and identifying 2 pixel points at the upper level of the state as the edge;
fifthly, treating the edge of the material: the RGB values of 2 pixels for which the above step was identified as an edge are corrected to a background mean value (Rmean, Gmean, Bmean).
The whole idea is to correct 2 pixel areas at the edge of the material image into a background value so as to reduce the interference of the edge pixel value on the algorithm effect. Because the image preprocessing is carried out in real time, the edge enhancement algorithm can only process a single-frame linear image and extract an edge rule based on the distribution condition of linear pixel points.
6. Taking a row of pixel points;
7. traversing the pixel points of the selected row one by one;
8. judging whether the pixel point traversed currently is the last pixel point of the line,
if not, judging whether the pixel is a background pixel;
if not, setting S as 1, and adding 1 to the number N of non-background pixels, and then judging whether the pixel is a black pixel, if so, adding 1 to the number M of impurity pixels, and skipping to the step 7, otherwise, judging whether the pixel is a yellow pixel, if so, adding 1 to the number M of impurity pixels, and skipping to the step 7;
if the background pixel point is present, directly skipping to the step 7;
if yes, judging whether S is 1 or not;
if S is equal to 0, judging whether N is 0, if N is equal to 0, jumping to the step 6, if N is not equal to 0, judging whether M exceeds an allowable threshold value, if not, judging that the material is high-quality rice grains, and sending a valve blowing signal;
if S is 1, S is 0, and then step 6 is skipped.
The above process is described with reference to fig. 2. The obtained plastic sheet image comprises background pixel points and plastic sheet pixel points by adjusting the orientation of the camera, the number of the pixel points of each image is determined, and the number of the pixel points of each line is also determined. In the algorithm, pixel point traversal is performed in a row unit, and whether the traversal of a plastic sheet image is finished or not is judged. When a row is all background, S is not set to 1, and as can be seen from fig. 2, all backgrounds of a row appear at both the top and the bottom of the image, it cannot be determined whether the image has been traversed based on S alone. When the full background line is located at the top of the image, N is inevitably 0, so that whether the image is completely traversed can be determined by combining S and N, that is, when S ≠ 0 and N ≠ 0 are mentioned in the flow, it is determined that the image is completely traversed.
On the other hand, if one plastic sheet pixel exists in one row of pixels, S is set to 1, and then the next row of pixels can automatically go on to continue traversal after the traversal of the current row is finished until the image traversal is finished.
For convenience of description, the above process involves the discrimination of the background, but does not mention the discrimination of the material edge, and for the discrimination of the material edge, it is only required to add an edge pixel point discrimination logic directly after the background pixel point discrimination logic, if the edge pixel point is the background pixel point, the step 7 is skipped, if the edge pixel point is not the background pixel point, the step S is set to 1, the number N of the non-background pixel points is self-added by 1, and then the discrimination logic of the black/yellow pixel points is entered.
How to determine whether the currently traversed pixel is a black pixel? The weighted gray scale processing is carried out, and the calculation formula of the gray scale processing is K ═ R multiplied by KR+G×KG+B×KB) Then shifted to the right by 10 bits (equivalent to divide by 1024), and K is the frontR、KG、KBTaking integers 0-1024 corresponding to KR、KG、KBThe actual value is 0-1. If the gray value is smaller than the black threshold value M1, the pixel point is judged to be a black pixel point; and if the number of the black pixel points of the n continuous lines of the image to be recognized exceeds the threshold value M2 of the number of the bad points, judging that the material is a blackened material, sending a valve blowing signal, and blowing out the material. Here, the number of lines n, the weighted graying parameter KR、KG、KBThe black threshold M1 and the bad point threshold M2 can be set according to different rice, different impurities and different screening requirements, for example, the individual size range of the blackening material and/or the blackening range allowed to exist in normal rice grains determine the selection of the line number n and the bad point threshold M2, and the blackening degree determines the weighted graying parameter KR、KG、KBAnd selection of the black threshold M1.
How to determine whether the currently traversed pixel is a yellow pixel? And (3) carrying out color analysis on the yellow material, and if the yellow material is in a preset RGB range, judging that the pixel point is a yellow pixel point, specifically comprising the following steps of:
firstly, establishing an RGB three-dimensional coordinate system, as shown in FIG. 3, determining an RGB range of the yellowing material to be screened, and mapping the RGB range to three coordinate planes of the RGB three-dimensional coordinate system to form three mapping areas.
As is well known, the positive yellow RGB value is (255,255,0), and is located at a corner of the whole RGB space, and for convenience of calculation, this embodiment adopts a polygonal line model to fit nonlinear classification to distinguish yellow pixel points, i.e., a mapping region of the RGB range of the yellowing material on three coordinate planes of the RGB three-dimensional coordinate system is simplified into a polygonal line model.
The specific operation of judging the color space by the polyline model is described as follows: if the mapping region is located in the GR plane as shown in fig. 4, a broken line may be formed by two parallel straight lines and an oblique line intersecting the two parallel straight lines, so as to divide the region where the mapping region is located, and as long as the region falls into the same side (with respect to the broken line) as the target sample, the mapping region is considered to belong to the target sample. Through the broken line model simplification algorithm, although on the surface can probably make the material that is not in the material RGB within range of giving yellow colour also be rejected (the shadow part of figure 2), but can not influence the final purpose of rice look selection, because the final purpose of rice look selection is to select the rice of fine quality, free from the heterochrosis grain, as long as do not reject as the heterochrosis grain with the grain of normal colour rice, but the broken line method of this embodiment has only slightly widened the look selection range of yellow pixel, can not influence normal coloured grain of rice.
The broken line model is composed of two straight line parameters Z1 and Z1 and a diagonal line determined by intercept ZR and ZG, so that after the mapping areas on three coordinate planes of the RGB three-dimensional coordinate system are determined, the broken line model on each coordinate plane is determined according to the mapping areas for subsequent color analysis of effective material pixel points.
Secondly, respectively mapping the RGB values of the pixel points traversed currently to three coordinate planes of an RGB three-dimensional coordinate system. Referring to fig. 3, the coordinates of the active material pixel point (Rw, Gw, Bw) in the RB plane are (Rw,0, Bw), the coordinates in the GB plane are (0, Gw, Bw), and the coordinates in the RG plane are (Rw, Gw, 0). And judging which side of the broken line model the mapping point of the pixel point is located in three coordinate planes, and if the mapping points of the pixel point on the three coordinate planes are all located on the same side of the mapping area, judging the pixel point to be a yellow pixel point.
The two identification methods not only improve the accuracy of black/yellow pixel point identification, but also can not easily generate misjudgment, thereby greatly improving the working efficiency and the accuracy of black/yellow material elimination; meanwhile, partial blackening/yellowing materials can be removed, the blackening/yellowing range can be set according to actual needs, and the method has high applicability.
It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by one of ordinary skill in the art and related arts based on the embodiments of the present invention without any creative effort, shall fall within the protection scope of the present invention.
Claims (6)
1. A grain-optimizing direct selection method for a rice color selector is characterized in that an image to be identified is traversed pixel by pixel line, weighted gray processing is carried out on effective material pixel points, if the gray value is smaller than a black threshold value, the pixel points are judged to be black pixel points, otherwise, color analysis is carried out on the pixel points, and if the gray value is within a preset yellow material RGB range, the pixel points are judged to be yellow pixel points; and if the sum of the number of the black pixels and the number of the yellow pixels on one rice grain is less than an allowable threshold value, judging the rice grain to be high-quality rice grain.
2. The method as claimed in claim 1, wherein the color analysis process of the material pixel point comprises establishing a three-dimensional coordinate system of RGB, mapping the RGB values of the pixel point to three coordinate planes, and determining the pixel point to be a yellow pixel point if the mapping points of the pixel point on the three coordinate planes are all within the mapping area of the preset RGB range of the yellow material on the three coordinate planes.
3. The method for selecting rice color directly according to claim 2, wherein the extracting of effective material pixels from the image to be recognized comprises a background removing operation and an edge removing operation,
the background removing operation is to obtain the RGB range of the background pixel through the background pixel point sample set, and if the pixel point of the image to be identified falls into the RGB range of the background pixel, the pixel point is determined as the background pixel point;
and the edge removing operation is to obtain the RGB range of the edge pixel through the edge pixel point sample set, and if the pixel point of the image to be identified falls into the RGB range of the edge pixel, the material edge pixel point is determined.
4. The optimal grain direct selection method for the rice color selector as claimed in claim 3, wherein 1024 frames of low-speed blanking images are adopted for the edge pixel point sample images, the edge regions of the materials are extracted, and the RGB ranges of the edge pixels are obtained.
5. The excellent grain direct selection method for the rice color selector as claimed in claim 4, which is characterized by comprising the following steps:
step 1, setting weighted graying parameter (K)R,KG,KB) A black threshold M1, where KR、KG、KBTaking an integer of 0-1024; setting and initializing variables, wherein the variables comprise impurity pixel number M equal to 0, non-background pixel number N equal to 0, and background line identifier S equal to 0;
step 2, establishing an RGB three-dimensional coordinate system, determining an RGB range of the yellowing material to be screened, and mapping the RGB range to three coordinate planes of the RGB three-dimensional coordinate system to form three mapping areas;
step 3, collecting a background pixel point sample set, and counting the RGB range of the background pixels;
step 4, collecting a sample set of edge pixel points, and counting the RGB range of the edge pixels;
step 5, acquiring an image to be identified and preprocessing the image;
step 6, taking a row of pixel points;
step 7, traversing the pixel points of the selected row one by one;
step 8, judging whether the pixel point traversed currently is the last pixel point of the row,
if not, judging whether the pixel is a background pixel;
if not, setting S as 1, and adding 1 to the number N of non-background pixels, and then judging whether the pixel is a black pixel, if so, adding 1 to the number M of impurity pixels, and skipping to the step 7, otherwise, judging whether the pixel is a yellow pixel, if so, adding 1 to the number M of impurity pixels, and skipping to the step 7;
if the background pixel point is present, directly skipping to the step 7;
if yes, judging whether S is 1 or not;
if S is equal to 0, judging whether N is 0, if N is equal to 0, jumping to the step 6, if N is not equal to 0, judging whether M exceeds an allowable threshold value, if not, judging that the material is high-quality rice grains, and sending a valve blowing signal;
if S is 1, S is 0, and then step 6 is skipped.
6. A method for selecting rice grains directly according to claim 5, wherein the pretreatment in the step 4 comprises distortion correction and edge enhancement.
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| CN113109240A (en) * | 2021-04-08 | 2021-07-13 | 国家粮食和物资储备局标准质量中心 | Method and system for determining imperfect grains of grains implemented by computer |
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