CN110310274B - Plant flower number detection method - Google Patents
Plant flower number detection method Download PDFInfo
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Abstract
The invention discloses a method for detecting the number of plant flowers, which comprises the following steps: A. extracting a red component R and a blue component B from the RGB image, then carrying out enhancement processing on the image components, and subtracting the enhanced red component R and the enhanced blue component B to obtain a gray level image R-B; B. calculating the mean value and standard deviation of all non-0 elements in the grayscale image R-B and a Threshold value Threshold; C. the R-B image is transformed according to a transformation Threshold, the elements smaller than the Threshold are assigned to 0, and the values of the elements larger than the Threshold are unchanged. Then, performing opening and closing operation on the transformed image, and calculating the average area meanArea of a connected region of the transformed image; D. and carrying out filtering processing according to the meanArea, wherein the number of the residual connected areas after the filtering processing is the number of the flowers. The invention can improve the defects of the prior art, has higher identification precision and can realize online real-time detection.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method for detecting the number of plant flowers.
Background
The detection of the number of plant flowers in an actual environment is complex, and currently, two main methods are used: one is a detection method based on image processing, such as flower recognition by color features of RGB images, or flower detection by morphological features. The identification of the flower by using the color and the morphological characteristics usually needs to set an extraction threshold, and the final identification effect is determined to a great extent by whether the threshold value is proper or not; this method is fast in detection but relatively low in accuracy. The other method is to detect the plant flowers by machine learning, which generally has high requirements on the configuration of a computer, and needs a large number of samples to train for a long time, so that real-time detection is difficult to achieve.
Disclosure of Invention
The invention aims to provide a method for detecting the number of plant flowers, which can overcome the defects of the prior art, has high identification precision and can realize online real-time detection.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A method for detecting the number of plant flowers comprises the following steps:
A. extracting a red component R and a blue component B from the RGB image, then carrying out enhancement processing on the image components, and subtracting the enhanced red component R and the enhanced blue component B to obtain a gray level image R-B;
B. calculating the mean value and standard deviation of all non-0 elements in the grayscale image R-B and a Threshold value Threshold;
C. converting the R-B image according to a conversion Threshold value Threshold, assigning the value of an element smaller than the Threshold value to be 0, and keeping the value of an element larger than the Threshold value unchanged; then, performing opening and closing operation on the transformed image, and calculating the average area meanArea of a connected region of the transformed image;
D. and carrying out filtering processing according to the meanArea, wherein the number of the residual connected areas after the filtering processing is the number of the flowers.
Preferably, in step a, the enhancement processing of the image component comprises the steps of,
a1, solving a histogram of the image component, establishing a mapping function of the histogram and the original image component, and calculating the mean value and standard deviation of the brightness and the color saturation;
a2, respectively calculating two groups of image segmentation points according to brightness and color saturation, and respectively shearing the histogram by using the two groups of image segmentation points;
a3, carrying out weighted average on the distribution functions of the overlapped parts of the histogram regions obtained by the two cuts;
and A4, recombining the histogram area, and then obtaining the enhanced image component according to the mapping function of the histogram and the original image component.
Preferably, in step a2, the image segmentation points are calculated by,
the luminance image is divided into a plurality of points,
a point of color saturation division is determined,
wherein μ is a brightness average, μ ' is a color saturation average, σ is a brightness standard deviation, σ ' is a color saturation standard deviation, and k ' are weight coefficients, respectively.
Preferably, in step B, the switching Threshold is calculated according to the following formula,
wherein μ and σ represent the non-0 element mean and standard deviation, pixel, of R _ B, respectivelyi,jRepresenting non-0 elements, i and j respectively representing the ith row and the jth column of the matrix corresponding to the image R _ B; size (R _ B,1) and size (R _ B,2) represent the number of rows and columns of the matrix corresponding to the image R _ B, respectively.
Preferably, in the step D, the filtering process includes the steps of,
d1, converting the pixel points of the connected region into corresponding frequency values, and carrying out Fourier transform on the frequency values;
d2, establishing intensity distribution functions of different frequency bands, and changing the intensity distribution functions into normal distribution through linear transformation;
d3, deleting the region outside the (mu-3 sigma, mu +3 sigma) interval; mu is the mean value of normal distribution, and sigma is the standard deviation of normal distribution;
and D4, obtaining the processed connected region image through inverse Fourier transform.
Preferably, the connected component image obtained in step D4 is subjected to a grayscale smoothing process.
Adopt the beneficial effect that above-mentioned technical scheme brought to lie in: the method provided by the invention has the advantages that the conversion threshold value is dynamically variable, and the detection and identification precision is higher; compared with a machine learning method, the method provided by the invention has higher calculation speed and can realize online real-time detection.
Drawings
Fig. 1 is a schematic diagram of the present invention.
Fig. 2 is an original image taken in the first set of experiments.
Fig. 3 is a first set of experimentally captured images after original image processing.
Fig. 4 is an original image of the second set of experimental shots.
Fig. 5 is an image of the second set of experimental shots after the original image processing.
Detailed Description
Referring to fig. 1, one embodiment of the present invention includes the steps of:
A. extracting a red component R and a blue component B from the RGB image, then carrying out enhancement processing on the image components, and subtracting the enhanced red component R and the enhanced blue component B to obtain a gray level image R-B;
B. calculating the mean value and standard deviation of all non-0 elements in the grayscale image R-B and a Threshold value Threshold;
C. converting the R-B image according to a conversion Threshold value Threshold, assigning the value of an element smaller than the Threshold value to be 0, and keeping the value of an element larger than the Threshold value unchanged; then, performing opening and closing operation on the transformed image, and calculating the average area meanArea of a connected region of the transformed image;
D. and carrying out filtering processing according to the meanArea, wherein the number of the residual connected areas after the filtering processing is the number of the flowers.
In step a, the enhancement processing of the image component includes the steps of,
a1, solving a histogram of the image component, establishing a mapping function of the histogram and the original image component, and calculating the mean value and standard deviation of the brightness and the color saturation;
a2, respectively calculating two groups of image segmentation points according to brightness and color saturation, and respectively shearing the histogram by using the two groups of image segmentation points;
a3, carrying out weighted average on the distribution functions of the overlapped parts of the histogram regions obtained by the two cuts;
and A4, recombining the histogram area, and then obtaining the enhanced image component according to the mapping function of the histogram and the original image component.
In step a2, the image segmentation points are calculated by,
the luminance image is divided into a plurality of points,
a point of color saturation division is determined,
wherein μ is a brightness average, μ ' is a color saturation average, σ is a brightness standard deviation, σ ' is a color saturation standard deviation, and k ' are weight coefficients, respectively.
In step B, a switching Threshold is calculated according to the following formula,
wherein μ and σ represent the non-0 element mean and standard deviation, pixel, of R _ B, respectivelyi,jRepresenting non-0 elements, i and j respectively representing the ith row and the jth column of the matrix corresponding to the image R _ B; size (R _ B,1) and size (R _ B,2) represent the number of rows and columns of the matrix corresponding to the image R _ B, respectively.
In step D, the filtering process includes the following steps,
d1, converting the pixel points of the connected region into corresponding frequency values, and carrying out Fourier transform on the frequency values;
d2, establishing intensity distribution functions of different frequency bands, and changing the intensity distribution functions into normal distribution through linear transformation;
d3, deleting the region outside the (mu-3 sigma, mu +3 sigma) interval; mu is the mean value of normal distribution, and sigma is the standard deviation of normal distribution;
and D4, obtaining the processed connected region image through inverse Fourier transform, and then performing gray level smoothing processing.
Referring to fig. 2-5, the present invention can rapidly and accurately identify the number of flowers of a plant.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (2)
1. A method for detecting the number of plant flowers is characterized by comprising the following steps:
A. extracting a red component R and a blue component B from the RGB image, then carrying out enhancement processing on the image components, and subtracting the enhanced red component R and the enhanced blue component B to obtain a gray level image R-B;
the enhancement processing of the image component comprises the steps of,
a1, solving a histogram of the image component, establishing a mapping function of the histogram and the original image component, and calculating the mean value and standard deviation of the brightness and the color saturation;
a2, respectively calculating two groups of image segmentation points according to brightness and color saturation, and respectively shearing the histogram by using the two groups of image segmentation points;
the method for calculating the image segmentation points is that,
the luminance image is divided into a plurality of points,
a point of color saturation division is determined,
wherein mu is a brightness average value, mu ' is a color saturation average value, sigma is a brightness standard deviation, sigma ' is a color saturation standard deviation, and k ' are weight coefficients respectively;
a3, carrying out weighted average on the distribution functions of the overlapped parts of the histogram regions obtained by the two cuts;
a4, recombining the histogram area, and then obtaining an enhanced image component according to the mapping function of the histogram and the original image component;
B. calculating the mean value and standard deviation of all non-0 elements in the grayscale image R-B and a Threshold value Threshold;
C. converting the R-B image according to a conversion Threshold value Threshold, assigning the value of an element smaller than the Threshold value to be 0, and keeping the value of an element larger than the Threshold value unchanged; then, performing opening and closing operation on the transformed image, and calculating the average area meanArea of a connected region of the transformed image;
D. filtering according to the meanArea, wherein the number of the residual connected areas after filtering is the number of the flowers;
the filtering process includes the steps of performing a filtering process,
d1, converting the pixel points of the connected region into corresponding frequency values, and carrying out Fourier transform on the frequency values;
d2, establishing intensity distribution functions of different frequency bands, and changing the intensity distribution functions into normal distribution through linear transformation;
d3, deleting the region outside the (mu-3 sigma, mu +3 sigma) interval; mu is the mean value of normal distribution, and sigma is the standard deviation of normal distribution;
and D4, obtaining the processed connected region image through inverse Fourier transform, and performing gray level smoothing processing on the connected region image.
2. The method for detecting the number of plant flowers according to claim 1, wherein: in step B, a switching Threshold is calculated according to the following formula,
wherein μ and σ represent the non-0 element mean and standard deviation, pixel, of R _ B, respectivelyi,jRepresenting non-0 elements, i and j respectively representing the ith row and the jth column of the matrix corresponding to the image R _ B; size (R _ B,1) and size (R _ B,2) represent the number of rows and columns of the matrix corresponding to the image R _ B, respectively.
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