CN108960100A - A kind of recognition methods of the sugarcane sugarcane section based on image procossing - Google Patents
A kind of recognition methods of the sugarcane sugarcane section based on image procossing Download PDFInfo
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Abstract
本发明公开了一种基于图像处理的甘蔗蔗节的识别方法,其包括:一,将原始图像转为灰度图像并进行中值滤波和二值化处理;二,将步骤一得到的图像黑白两色置换,去除非线性结构元的部分;三,将步骤一的灰度图像背景换为黑色,甘蔗换为白色;四,将步骤三得到的图像进行Sobel边界检测,提取明显的边界特征;五,将步骤四得到的图像利用Hough变换检测直线,选取最长的直线,并计算与水平方向的夹角α;六,将步骤二得到的图像根据夹角α旋转,再进行列像素求和,求和函数的极值点和最大值点的横坐标即为茎节位置d;七,通过换算得到茎节在原始图像中的位置d1。本发明能够准确识别蔗节,为后续切割提供准确的位置信号,防止伤芽和浪费。
The invention discloses a method for identifying sugarcane knots based on image processing, which includes: first, converting the original image into a grayscale image and performing median filtering and binarization; second, black and white the image obtained in step one Two-color replacement to remove the part of the nonlinear structural element; third, the background of the grayscale image in step 1 is replaced with black, and the sugarcane is replaced with white; fourth, the image obtained in step 3 is subjected to Sobel boundary detection to extract obvious boundary features; 5. Use the Hough transform to detect the straight line on the image obtained in step 4, select the longest straight line, and calculate the angle α with the horizontal direction; 6. Rotate the image obtained in step 2 according to the angle α, and then perform column pixel summation , the abscissa of the extreme point and the maximum point of the summation function is the position d of the stem node; Seventh, the position d 1 of the stem node in the original image is obtained through conversion. The invention can accurately identify sugarcane nodes, provide accurate position signals for subsequent cutting, and prevent damage to buds and waste.
Description
技术领域technical field
本发明涉及甘蔗产业领域,特别涉及一种基于图像处理的甘蔗蔗节的识别方法。The invention relates to the field of sugarcane industry, in particular to a method for identifying sugarcane knots based on image processing.
背景技术Background technique
甘蔗是我国的重要制糖原料,其在我国糖业生产领域具有重要地位。在甘蔗产业发展的过程中,自动化的生产方式将极大地节省人力成本,并提高工作效率。含有蔗芽的蔗节可以用作蔗种,在蔗种的制备过程中要做到不伤芽,蔗节两端的蔗茎是蔗种初期成活的主要营养来源,必须留有足够的蔗茎长度,所以蔗种的制备过程中,切割的准确性非常重要。通过基于图像处理的识别技术,可提取甘蔗茎节的准确位置,从而为切割工作提供信号,使得蔗种的制备的过程能够实现自动化生产。Sugarcane is an important raw material for sugar production in my country, and it plays an important role in the field of sugar production in China. In the process of sugarcane industry development, automated production methods will greatly save labor costs and improve work efficiency. Cane nodes containing cane buds can be used as sugarcane seeds. During the preparation of sugarcane seeds, the buds should not be damaged. The cane stems at both ends of the cane nodes are the main source of nutrition for the initial survival of the sugarcane seeds, and sufficient length of the cane stems must be left , so in the preparation process of sugarcane seeds, the accuracy of cutting is very important. Through the recognition technology based on image processing, the exact position of sugarcane stem nodes can be extracted, thereby providing signals for cutting work, so that the process of sugarcane seed preparation can realize automatic production.
目前,国内对的基于图像处理的蔗节识别技术的研究并不充足,还有较多未考虑的方面,与工业化生产还有较大的差距。而且,现有的通过图像处理来进行茎节识别的方法中往往是仅涉及甘蔗的单一茎节的识别,提取的目标特征少。在工业化生产过程中,需要考虑甘蔗表面复杂特征的影响,提取多个目标特征,形成一组信号来源。而且工业生产使用的甘蔗原料并不顺直,往往有大量歪曲生长成的原料,从而容易导致对蔗节的位置识别不准确,在后续的切割以及蔗茎、蔗种的分离中容易造成伤芽,浪费蔗种。At present, domestic research on sugarcane knot recognition technology based on image processing is not sufficient, and there are still many aspects that have not been considered, and there is still a big gap with industrial production. Moreover, the existing methods for stem node recognition through image processing often only involve the recognition of a single stem node of sugarcane, and few target features are extracted. In the process of industrial production, it is necessary to consider the influence of complex features on the surface of sugarcane, extract multiple target features, and form a set of signal sources. Moreover, the sugarcane raw materials used in industrial production are not straight, and there are often a large number of distorted raw materials, which easily lead to inaccurate identification of the position of cane nodes, and are likely to cause damage to buds in subsequent cutting and separation of cane stems and cane seeds. , wasting sugarcane seeds.
公开于该背景技术部分的信息仅仅旨在增加对本发明的总体背景的理解,而不应当被视为承认或以任何形式暗示该信息构成已为本领域一般技术人员所公知的现有技术。The information disclosed in this Background section is only for enhancing the understanding of the general background of the present invention and should not be taken as an acknowledgment or any form of suggestion that the information constitutes the prior art that is already known to those skilled in the art.
发明内容Contents of the invention
本发明的目的在于提供一种基于图像处理的甘蔗蔗节的识别方法,从而克服现有的通过图像处理来进行茎节识别的方法中仅涉及单一茎节的识别且识别出来的蔗节的位置容易不准确的缺点。The object of the present invention is to provide a kind of identification method of sugarcane node based on image processing, thereby overcome the existing method for identifying node by image processing only involves the identification of a single node and the position of the identified node Easily inaccurate shortcomings.
为实现上述目的,本发明提供了一种基于图像处理的甘蔗蔗节的识别方法,其中,包括如下步骤:To achieve the above object, the present invention provides a method for identifying sugarcane knots based on image processing, which includes the following steps:
步骤一,先采集原始图像,然后用matlab软件将原始图像转换为灰度图像,对灰度图像进行中值滤波,再进行二值化处理;Step 1, first collect the original image, then use matlab software to convert the original image into a grayscale image, carry out median filtering on the grayscale image, and then perform binarization processing;
步骤二,将经步骤一处理后的图像中的黑白两色置换,再利用形态学处理中的开操作去除非线性结构元的部分;Step 2, replace the black and white two colors in the image processed by step 1, and then use the open operation in the morphological processing to remove the part of the nonlinear structural element;
步骤三,将步骤一中得到的灰度图像的甘蔗背景置换为黑色,甘蔗位置置换为白色,并进行二值化处理;Step 3, replace the sugarcane background of the grayscale image obtained in step 1 with black, replace the position of sugarcane with white, and perform binarization;
步骤四,将经步骤三处理后的图像进行Sobel边界检测,提取明显的边界特征;Step 4, performing Sobel boundary detection on the image processed in step 3 to extract obvious boundary features;
步骤五,将经步骤四处理后的图像利用Hough变换检测直线,选取最长的直线,并计算最长直线与图像水平方向的夹角α;Step 5, using Hough transform to detect straight lines on the image processed in step 4, selecting the longest straight line, and calculating the angle α between the longest straight line and the horizontal direction of the image;
步骤六,利用步骤五得到的夹角α对经步骤二处理后的图像进行旋转,使图像的蔗节位于同一水平直线上,再对图像进行列像素求和,找到求和函数的极值点和最大值点,极值点和最大值点的横坐标即为甘蔗茎节的位置d;Step 6: Use the included angle α obtained in Step 5 to rotate the processed image in Step 2, so that the nodes of the image are on the same horizontal line, and then perform column pixel summation on the image to find the extreme point of the summation function and the maximum point, the abscissa of the extreme point and the maximum point is the position d of the sugarcane stem node;
步骤七,根据经步骤六处理后得到的甘蔗茎节的位置d,通过换算公式d1=d xcosα,便可得到甘蔗茎节在原始图像中的位置d1。In step seven, according to the position d of the sugarcane stem node obtained after the processing in step six, the position d 1 of the sugarcane stem node in the original image can be obtained through the conversion formula d 1 =d xcosα.
优选地,上述技术方案中,在步骤一中,图像二值化处理时,将图像中的最大像素值与最小像素值的平均值设定为阈值,把灰度大于或等于阈值的像素点设置为255,小于阈值的像素点设为0,为了节约计算机的存储,再进一步把0和255分别映射为0和1。Preferably, in the above technical solution, in step 1, during the image binarization process, the average value of the maximum pixel value and the minimum pixel value in the image is set as the threshold value, and the pixel points whose grayscale is greater than or equal to the threshold value are set is 255, and the pixels smaller than the threshold are set to 0. In order to save computer storage, 0 and 255 are further mapped to 0 and 1 respectively.
优选地,上述技术方案中,在步骤二中,由于甘蔗的茎节是直线的形状,为了识别并保留此特征,采用的是长度为15,从水平轴开始逆时针90°的扁平直线型结构元,公式为:其中:B为结构元,A为被处理的图像,开操作的结果是B在A中完全匹配并平移的并集。Preferably, in the above technical solution, in step 2, since the stem nodes of sugarcane are in the shape of a straight line, in order to recognize and retain this feature, a flat straight line structure with a length of 15° and 90° counterclockwise from the horizontal axis is adopted Yuan, the formula is: Among them: B is the structural element, A is the processed image, and the result of the opening operation is the union of B completely matched and translated in A.
优选地,上述技术方案中,在步骤四中,Sobel边界检测是使用两个(3x3)矩阵来对原图进行卷积运算,以计算出水平方向的灰度偏导的估计值Gx和竖直方向的灰度偏导的估计值Gy。Gx和Gy的数学表达式如下:Preferably, in the above technical solution, in step 4, the Sobel boundary detection is to use two (3x3) matrices to perform convolution operation on the original image to calculate the estimated value G x of the gray partial derivative in the horizontal direction and the vertical The estimated value G y of the gray partial derivative in the vertical direction. The mathematical expressions of G x and G y are as follows:
和 and
其中:A为被处理的图像;Among them: A is the processed image;
计算过程如下:The calculation process is as follows:
Gx=[f(x+1,y-1)+2*f(x+1,y)+f(x+1,y+1)]-[f(x-1,y-1)+2*f(x-1,y)+f(x-1,y+1)];G x =[f(x+1,y-1)+2*f(x+1,y)+f(x+1,y+1)]-[f(x-1,y-1)+ 2*f(x-1,y)+f(x-1,y+1)];
Gy=[f(x-1,y-1)+2f(x,y-1)+f(x+1,y-1)]-[f(x-1,y+1)+2*f(x,y+1)+f(x+1,y+1);G y =[f(x-1,y-1)+2f(x,y-1)+f(x+1,y-1)]-[f(x-1,y+1)+2* f(x,y+1)+f(x+1,y+1);
计算出每个点的Gx和Gy后,每个点的梯度G可由以下的公式得出:After calculating the G x and G y of each point, the gradient G of each point can be obtained by the following formula:
最后设定阈值Gmax,当梯度G大于Gmax,该点便是一个边界点。Finally, a threshold G max is set. When the gradient G is greater than G max , the point is a boundary point.
优选地,上述技术方案中,在步骤五中,Hough变换是将图像平面空间的直线变换至参数空间,其公式为:Preferably, in the above technical solution, in step five, the Hough transform is to transform the straight line in the image plane space to the parameter space, and its formula is:
直线上任意一点(x,y)都可以在ρ-θ空间中形成一条曲线,如果两个不同点进行上述操作后得到的曲线在平面ρ-θ相交,这就意味着它们位于同一条直线,通过设置阈值,交于一点的曲线数量超过了阈值,那么就代表这个交点的参数(θ,ρ)对应为原图中的一条直线;寻找前5个大于Hough矩阵中最大值0.3倍的点,变换为线段,合并距离小于20的线段,去除长度小于5的直线段,并通过for循环得到最长的线段;通过线段端点的坐标值求得直线的斜率k,再通过公式α=arctank,求得最长直线与图像水平方向的夹角α。Any point (x, y) on the straight line can form a curve in the ρ-θ space. If the curves obtained by performing the above operations on two different points intersect in the plane ρ-θ, it means that they are on the same straight line. By setting the threshold, the number of curves intersecting at one point exceeds the threshold, then the parameter (θ, ρ) representing the intersection point corresponds to a straight line in the original image; find the first 5 points that are greater than 0.3 times the maximum value in the Hough matrix, Convert to a line segment, merge the line segments whose distance is less than 20, remove the straight line segment whose length is less than 5, and obtain the longest line segment through the for loop; obtain the slope k of the line through the coordinate value of the end point of the line segment, and then use the formula α=arctank to find The angle α between the longest straight line and the horizontal direction of the image is obtained.
优选地,上述技术方案中,在步骤六中,利用matlab软件中的imrotate函数将图像进行旋转,利用sum求和函数,将图像中的每列像素求和,并绘制函数图像,利用findpeaks函数和max函数找到极值点和最大值点,极值点和最大值点的横坐标即为甘蔗茎节的位置d。Preferably, in the above-mentioned technical solution, in step six, utilize the imrotate function in the matlab software to rotate the image, utilize the sum summation function, sum each row of pixels in the image, and draw the function image, utilize the findpeaks function and The max function finds the extreme point and the maximum point, and the abscissa of the extreme point and the maximum point is the position d of the sugarcane stem node.
与现有技术相比,本发明具有如下有益效果:Compared with the prior art, the present invention has the following beneficial effects:
本发明为国内甘蔗蔗种工业化制备提供技术上的参考,其核心是利用基于matlab软件的图像处理技术通过对甘蔗表面多目标进行提取,以准确地识别出甘蔗的蔗节,有效避免漏检和错检的情况,从而为后续的切割以及蔗茎、蔗种的分离提供准确的位置信号,防止切割过程中伤芽和浪费蔗种,而且本发明还可以为进料倾斜提供修正参考。The present invention provides a technical reference for the industrial preparation of domestic sugarcane seeds. Its core is to use the image processing technology based on matlab software to extract multiple objects on the surface of sugarcane to accurately identify the nodes of sugarcane, effectively avoiding missed detection and Error detection, thereby providing accurate position signals for subsequent cutting and separation of cane stems and cane seeds, preventing damage to buds and waste of cane seeds during the cutting process, and the present invention can also provide correction references for feeding inclinations.
附图说明Description of drawings
图1是根据本发明基于图像处理的甘蔗蔗节的识别方法的流程图。Fig. 1 is a flowchart of a method for identifying sugarcane knots based on image processing according to the present invention.
图2是根据本发明的步骤一中把原始图像转换为灰度图像后的示意图。Fig. 2 is a schematic diagram of converting the original image into a grayscale image in step 1 according to the present invention.
图3是根据本发明的步骤一中进行中值滤波后的图像示意图。Fig. 3 is a schematic diagram of an image after median filtering in step 1 according to the present invention.
图4是根据本发明的步骤一中进行二值化处理后的图像示意图。FIG. 4 is a schematic diagram of an image after binarization processing in Step 1 according to the present invention.
图5是根据本发明的步骤二中黑白两色置换后的图像示意图。Fig. 5 is a schematic diagram of an image after black-and-white two-color replacement in step 2 according to the present invention.
图6是根据本发明的步骤二中进行开操作后的图像示意图。Fig. 6 is a schematic diagram of an image after performing an opening operation in step 2 according to the present invention.
图7是根据本发明的步骤三中将甘蔗位置设置为白,甘蔗背景设置为黑后的图像示意图。Fig. 7 is a schematic diagram of an image after setting the sugarcane position to white and the sugarcane background to black in step 3 according to the present invention.
图8是根据本发明的步骤四中Sobel边界检测后带有明显边界特征的图像示意图。FIG. 8 is a schematic diagram of an image with obvious boundary features after Sobel boundary detection in Step 4 of the present invention.
图9是根据本发明的步骤五中利用Hough变换检测出的最长直线的图像示意图。FIG. 9 is a schematic diagram of an image of the longest straight line detected by Hough transform in Step 5 according to the present invention.
图10是根据本发明的步骤六中将图6旋转后的图像示意图。Fig. 10 is a schematic diagram of an image after rotating Fig. 6 in step 6 according to the present invention.
图11是根据本发明的步骤六中对旋转后的图像进行列像素求和的统计图。Fig. 11 is a statistical diagram of the column pixel summation of the rotated image in step 6 according to the present invention.
图12是根据本发明的步骤七中在原始图像中标记出识别的蔗节位置的图像示意图。Fig. 12 is a schematic diagram of an image in which the positions of identified sugarcane nodes are marked in the original image in step 7 according to the present invention.
具体实施方式Detailed ways
下面结合附图,对本发明的具体实施方式进行详细描述,但应当理解本发明的保护范围并不受具体实施方式的限制。The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.
除非另有其它明确表示,否则在整个说明书和权利要求书中,术语“包括”或其变换如“包含”或“包括有”等等将被理解为包括所陈述的元件或组成部分,而并未排除其它元件或其它组成部分。Unless expressly stated otherwise, throughout the specification and claims, the term "comprise" or variations thereof such as "includes" or "includes" and the like will be understood to include the stated elements or constituents, and not Other elements or other components are not excluded.
图1至图12显示了根据本发明优选实施方式的一种基于图像处理的甘蔗蔗节的识别方法的结构示意图,参考图1,该基于图像处理的甘蔗蔗节的识别方法共包括如下七个步骤:Fig. 1 to Fig. 12 have shown the structure diagram of a kind of identification method of sugarcane section based on image processing according to the preferred embodiment of the present invention, with reference to Fig. 1, this identification method of sugarcane section based on image processing comprises following seven altogether step:
步骤一,先采集原始图像,可以是通过摄像头的拍摄来获得原始图像,原始图像为彩色图像。然后用matlab软件将原始图像转换为灰度图像,并对灰度图像进行中值滤波,再进行二值化处理。In step 1, an original image is first collected, which may be obtained by shooting with a camera, and the original image is a color image. Then use matlab software to convert the original image into a grayscale image, and carry out median filtering on the grayscale image, and then perform binarization processing.
彩色图像(即RGB图)的每个像素可以看作是以R、G、B为轴建立空间直角坐标系中的三维空间的一个点,而灰度图(即Gray图)的每个像素可以用直线R=G=B上的一个点来表示。于是,RGB图转Gray图的本质就是寻找一个三维空间到一维空间的映射,在matlab软件中,可以通过对RGB空间的一个点向R=G=B直线做垂线实现,公式为:Each pixel of a color image (i.e., an RGB image) can be regarded as a point in a three-dimensional space in a space Cartesian coordinate system with R, G, and B as axes, while each pixel of a grayscale image (i.e., a Gray image) can be It is represented by a point on the straight line R=G=B. Therefore, the essence of converting an RGB image to a Gray image is to find a mapping from a three-dimensional space to a one-dimensional space. In the matlab software, it can be realized by making a vertical line from a point in the RGB space to the R=G=B line. The formula is:
Gray=0.299*R+0.587*G+0.114*B; (1)Gray=0.299*R+0.587*G+0.114*B; (1)
把原始图像转换为灰度图像后的图像如图2所示。The image after converting the original image to a grayscale image is shown in Figure 2.
数字图像的采样或传输时,经常受到噪声的干扰,为了后续的图像操作,可以对受噪图像进行滤波,中值滤波是把图像像素点的值用该点的一个邻域中各点值的中值代替,中值滤波的处理中的邻域是mxn的模板M,公式为:When sampling or transmitting digital images, they are often disturbed by noise. For subsequent image operations, the noisy image can be filtered. Median filtering is to use the value of the image pixel point by the value of each point in a neighborhood of the point. The median is replaced, and the neighborhood in the processing of the median filter is the template M of mxn, and the formula is:
g(x,y)=med{f(x-m,y-n)},(m,n∈M); (2)g(x,y)=med{f(x-m,y-n)},(m,n∈M); (2)
其中:(x,y)为被处理点的坐标,m为处理范围距离点(x,y)的长,n为处理范围距离点(x,y)的宽,(m,n)便为距离点(x,y)的长为m,宽为n的领域。把图像进行中值滤波后的图像如图3所示。Among them: (x, y) is the coordinate of the point to be processed, m is the length of the processing range from the point (x, y), n is the width of the processing range from the point (x, y), and (m, n) is the distance Point (x, y) is a field of length m and width n. The image after median filtering is shown in Figure 3.
图像二值化处理就是将图像的像素点设置为0或者255,整个图像呈现明显的黑白效果,凸显图像中的特征。本发明优选地,在步骤一中,图像二值化处理时,将图像中的最大像素值与最小像素值的平均值设定为阈值,把灰度大于或等于阈值的像素点设置为255,小于阈值的像素点设为0,为了节约计算机的存储,再进一步把0和255分别映射为0和1。把图像进行二值化处理后的图像如图4所示。Image binarization processing is to set the pixel points of the image to 0 or 255, so that the entire image presents an obvious black and white effect, highlighting the features in the image. Preferably in the present invention, in step 1, during the image binarization process, the average value of the maximum pixel value and the minimum pixel value in the image is set as the threshold value, and the pixel points whose gray scale is greater than or equal to the threshold value are set to 255, Pixels smaller than the threshold are set to 0. In order to save computer storage, 0 and 255 are further mapped to 0 and 1, respectively. The image after binary processing is shown in Figure 4.
步骤二,将经步骤一处理后的图像中的黑白两色置换,再利用形态学处理中的开操作去除非线性结构元的部分。具体操作是先找到像素值为1的像素点,将其像素值改为0,找到像素值为0的像素点,将其像素值改为1,从而实现将二值化图像中的黑白两色置换,置换后的图像如图5所示。Step two, replace the black and white colors in the image processed by step one, and then use the open operation in the morphological processing to remove the part of the nonlinear structural element. The specific operation is to first find the pixel point with a pixel value of 1, change its pixel value to 0, find the pixel point with a pixel value of 0, and change its pixel value to 1, so as to realize the black and white two colors in the binarized image Replacement, the image after replacement is shown in Figure 5.
本发明优选地,在步骤二中,由于甘蔗的茎节是直线的形状,为了识别并保留此特征,采用的是长度为15,从水平轴开始逆时针90°的扁平直线型结构元,公式为:Preferably, in the present invention, in step 2, since the stem nodes of sugarcane are in the shape of a straight line, in order to identify and retain this feature, a flat linear structural element with a length of 15 and 90° counterclockwise from the horizontal axis is used, the formula for:
其中:B为结构元,A为被处理的图像,开操作的结果是B在A中完全匹配并平移的并集。即开操作除去了图像中所有不能包含结构元的部分,平滑目标的轮廓,断开了图像间细小的连接部分,同时去除尖锐的突出部分。开操作后的图像如图6所示。Among them: B is the structural element, A is the processed image, and the result of the opening operation is the union of B completely matched and translated in A. The opening operation removes all the parts that cannot contain structural elements in the image, smoothes the contour of the target, breaks the small connection parts between the images, and removes the sharp protruding parts at the same time. The image after the opening operation is shown in Figure 6.
步骤三,将步骤一中得到的未进行中值滤波和二值化处理的的灰度图像的甘蔗背景置换为黑色,甘蔗位置置换为白色,并进行二值化处理。具体的操作是:由于原始图像中的甘蔗采用的是白色背景,在数据类型为uint8的数字图像中,背景部分的像素值为255或者接近255,利用matlab软件中的find函数找出背景部分的像素点,并将像素值设置为0,此时背景部分呈现为黑色。同理,将甘蔗部分(即非背景部分)的像素值设置为255,甘蔗部分呈现为白色。置换后的图像如图7所示。Step 3: Replace the sugarcane background of the grayscale image obtained in step 1 without median filtering and binarization processing with black, and replace the position of sugarcane with white, and perform binarization processing. The specific operation is: since the sugarcane in the original image uses a white background, in the digital image whose data type is uint8, the pixel value of the background part is 255 or close to 255, use the find function in the matlab software to find out the pixel value of the background part Pixel, and set the pixel value to 0, and the background part will appear black at this time. Similarly, if the pixel value of the sugarcane part (that is, the non-background part) is set to 255, the sugarcane part will appear white. The image after replacement is shown in Figure 7.
步骤四,将经步骤三处理后的图像进行Sobel边界检测,提取明显的边界特征;本发明优选地,在步骤四中,Sobel算子检测边界是使用两个(3x3)矩阵来对原图进行卷积运算,以计算出水平方向的灰度偏导的估计值Gx和竖直方向的灰度偏导的估计值Gy。Gx和Gy的数学表达式如下:Step 4, carry out Sobel boundary detection to the image after step 3 processing, extract obvious boundary feature; The present invention preferably, in step 4, Sobel operator detection boundary is to use two (3x3) matrices to carry out original picture Convolution operation to calculate the estimated value G x of the gray partial derivative in the horizontal direction and the estimated value G y of the gray partial derivative in the vertical direction. The mathematical expressions of G x and G y are as follows:
其中:A为被处理的图像;Among them: A is the processed image;
计算过程如下:The calculation process is as follows:
Gx=[f(x+1,y-1)+2*f(x+1,y)+f(x+1,y+1)]-[f(x-1,y-1)+2*f(x-1,y)+f(x-1,y+1)];(6)G x =[f(x+1,y-1)+2*f(x+1,y)+f(x+1,y+1)]-[f(x-1,y-1)+ 2*f(x-1,y)+f(x-1,y+1)]; (6)
Gy=[f(x-1,y-1)+2*f(x,y-1)+f(x+1,y-1)]-[f(x-1,y+1)+2*f(x,y+1)+f(x+1,y+1);(7)G y =[f(x-1,y-1)+2*f(x,y-1)+f(x+1,y-1)]-[f(x-1,y+1)+ 2*f(x,y+1)+f(x+1,y+1);(7)
计算出每个点的Gx和Gy后,每个点的梯度G可由以下的公式得出:After calculating the G x and G y of each point, the gradient G of each point can be obtained by the following formula:
最后设定阈值Gmax,当梯度G大于Gmax,该点便是一个边界点。提取明显的边界特征后得到的图像如图8所示。Finally, a threshold G max is set. When the gradient G is greater than G max , the point is a boundary point. The image obtained after extracting obvious boundary features is shown in Figure 8.
步骤五,将经步骤四处理后的图像利用Hough变换检测直线,选取最长的直线,并计算最长直线与图像水平方向的夹角α。本发明优选地,Hough变换是将图像平面空间的直线变换至参数空间,其公式为:Step 5: Use the Hough transform to detect straight lines on the image processed in step 4, select the longest straight line, and calculate the angle α between the longest straight line and the horizontal direction of the image. Preferably in the present invention, the Hough transform is to transform the straight line in the image plane space to the parameter space, and its formula is:
直线上任意一点(x,y)都可以在ρ-θ空间中形成一条曲线,如果两个不同点进行上述操作后得到的曲线在平面ρ-θ相交,这就意味着它们位于同一条直线,通过设置阈值,交于一点的曲线数量超过了阈值,那么就代表这个交点的参数(θ,ρ)对应为原图中的一条直线;寻找前5个大于Hough矩阵中最大值0.3倍的点,变换为线段,合并距离小于20的线段,去除长度小于5的直线段,并通过for循环得到最长的线段,得到的最长直线在灰度图像的位置如图9所示;通过线段端点的坐标值求得直线的斜率k,再通过公式α=arctank,便能求得最长直线与图像水平方向的夹角α。Any point (x, y) on the straight line can form a curve in the ρ-θ space. If the curves obtained by performing the above operations on two different points intersect in the plane ρ-θ, it means that they are on the same straight line. By setting the threshold, the number of curves intersecting at one point exceeds the threshold, then the parameter (θ, ρ) representing the intersection point corresponds to a straight line in the original image; find the first 5 points that are greater than 0.3 times the maximum value in the Hough matrix, Convert to a line segment, merge the line segments with a distance of less than 20, remove the line segment with a length of less than 5, and obtain the longest line segment through a for loop. The position of the longest line in the grayscale image is shown in Figure 9; The slope k of the straight line is obtained from the coordinate value, and then the angle α between the longest straight line and the horizontal direction of the image can be obtained through the formula α=arctank.
步骤六,步骤五中得到的夹角α是图像中甘蔗最长直线与图像水平方向的夹角,这就是甘蔗相对于工作台的倾斜度。利用步骤五得到的夹角α对经步骤二开操作处理后如图6所示的图像进行旋转,使图像的蔗节位于同一水平的直线上,旋转后的图像如图10所示。再对甘蔗图像进行列像素求和,找到求和函数的极值点和一个最大值点,极值点和最大值点的横坐标即为甘蔗茎节的位置d。本发明优选地,在步骤六中,利用matlab软件中的imrotate函数将经步骤二开操作处理后图像进旋转,利用sum求和函数,将图像中的每列像素求和,并绘制函数图像,进行列像素求和的统计图如图11所示。利用findpeaks函数和max函数找到极值点和最大值点,极值点和最大值点的横坐标即为甘蔗茎节的位置d。如图10所示,甘蔗蔗节部分为白色,此部分所有像素点的值为1,其余部分是黑色,像素点的值为0,因此在列像素相加时,白色部分的像素值累加得到极大值或者最大值,其余部分像素值累加值还是0,因此极值点和最大值点的横坐标即为甘蔗茎节的位置d,一个图像中具有多个极值点和最大值点,便表示存在多个甘蔗茎节。Step six, the included angle α obtained in step five is the angle between the longest straight line of the sugarcane in the image and the horizontal direction of the image, which is the inclination of the sugarcane relative to the workbench. Use the included angle α obtained in step 5 to rotate the image shown in Figure 6 after the opening operation in step 2, so that the sugarcane nodes of the image are located on the same horizontal straight line, and the rotated image is shown in Figure 10. Then, sum the column pixels of the sugarcane image to find the extreme point and a maximum point of the summation function. The abscissa of the extreme point and the maximum point is the position d of the sugarcane stem node. Preferably in the present invention, in step 6, utilize the imrotate function in the matlab software to rotate the image after step 2 is opened and processed, utilize the sum summation function to sum each row of pixels in the image, and draw the function image, Figure 11 shows the statistical diagram for the column pixel summation. Use the findpeaks function and the max function to find the extreme point and the maximum point, and the abscissa of the extreme point and the maximum point is the position d of the sugarcane stem node. As shown in Figure 10, the part of the sugarcane node is white, and the value of all pixels in this part is 1, and the rest is black, and the value of the pixel is 0. Therefore, when the column pixels are added, the pixel values of the white part are accumulated to obtain The maximum value or the maximum value, the cumulative value of the rest of the pixel values is still 0, so the abscissa of the extreme point and the maximum point is the position d of the sugarcane stem node, and there are multiple extreme points and maximum points in one image. It means that there are multiple sugarcane stem nodes.
步骤七,根据经步骤六处理后得到的甘蔗茎节的位置d,通过下面的换算公式进行计算:Step 7, according to the position d of the sugarcane stem node obtained after the processing of step 6, calculate by the following conversion formula:
d1=d x cosα; (10)d 1 = dx cos α; (10)
便可得到甘蔗茎节在原始图像中的位置d1。根据得到的蔗节位置d1在原始图像中标记出来后如图12所示。Then the position d 1 of the sugarcane stem node in the original image can be obtained. According to the obtained cane node position d 1 , it is marked in the original image as shown in Fig. 12 .
下面结合matlab软件的具体操作对本发明作进一步的描述:Below in conjunction with the concrete operation of matlab software, the present invention will be further described:
第一,将采集的图像进行灰度变换,再对灰度图像进行中值滤波,消除图像中的噪点,然后将图像进行二值化处理,转化为黑白两色图像。Firstly, grayscale transformation is performed on the collected image, and then median filtering is performed on the grayscale image to eliminate the noise in the image, and then the image is binarized to convert it into a black and white two-color image.
A=imread('ganzhe.jpg');%读入原始图像A=imread('ganzhe.jpg');% read in the original image
figure(1),imshow(A);figure(1),imshow(A);
B=rgb2gray(A);%转为灰度图像B=rgb2gray(A);% converted to grayscale image
figure(2),imshow(B);%图像如图2所示figure(2),imshow(B);% image as shown in Figure 2
C=medfilt2(B,[3,3]);%对灰度图像使用3X3的矩阵进行中值滤波,figure(3),imshow(C);%图像如图3所示C=medfilt2(B,[3,3]);% uses 3X3 matrix to carry out median filtering to the grayscale image, figure(3),imshow(C);% image as shown in Figure 3
Imax=max(max(C));Imax=max(max(C));
Imin=min(min(C));Imin=min(min(C));
T=round(Imax-(Imax-Imin)/2);%将像素中间值选为阈值T=round(Imax-(Imax-Imin)/2); % select the median value of the pixel as the threshold
D=(C)>=T;%C中大于T的像素取1,小于T的取0D=(C)>=T; in %C, the pixel greater than T takes 1, and the pixel smaller than T takes 0
figure(4),imshow(D);%图像如图4所示figure(4), imshow(D);% image as shown in Figure 4
第二,将二值化图像中黑白两色置换,再采用长度为15,与水平方向呈90度的直线型结构元进行开操作,去除图像中非线性结构元的部分。Second, the black and white colors in the binary image are replaced, and then the linear structural element with a length of 15 and 90 degrees to the horizontal direction is used for opening operation to remove the part of the non-linear structural element in the image.
index1=find(D>0);%找出白色的像素点index1=find(D>0);% Find the white pixels
index2=find(D<1);%找出黑色的像素点index2=find(D<1);% find black pixels
counter1=sum(index1);%白色像素点的数量counter1=sum(index1);% the number of white pixels
counter2=sum(index2);%黑色像素点的数量counter2=sum(index2);% the number of black pixels
figure(5),imshow(E);%图像如图5所示figure(5), imshow(E);% image as shown in Figure 5
se=strel('line',15,90);%选用直线型结构元,长度15,与水平角度为90度se=strel('line',15,90); % choose a straight-line structural element, the length is 15, and the horizontal angle is 90 degrees
O=imopen(E,se);%进行形态学开操作O=imopen(E,se); % perform morphological open operation
figure(6),imshow(O);%图像如图6所示figure(6), imshow(O);% image as shown in Figure 6
第三,将灰度图像中甘蔗背景置换为黑色,甘蔗位置置换为白色,再进行二值化处理。Third, replace the background of sugarcane in the grayscale image with black, replace the position of sugarcane with white, and then perform binarization.
index=find(B>250);index = find(B>250);
F(index)=0;F(index)=0;
G=F;G=F;
index1=find(F>0);index1 = find(F>0);
G(index1)=255;G(index1)=255;
Imax1=max(max(G));Imax1=max(max(G));
Imin1=min(min(G));Imin1=min(min(G));
T1=round((Imax1-Imin1)/2);T1=round((Imax1-Imin1)/2);
H1=(G)>=T1;H1=(G)>=T1;
figure(7),imshow(H1);%图像如图7所示figure(7),imshow(H1);% image as shown in Figure 7
第四,将上述图像进行Sobel边界检测,提取带有明显边界特征的图像。Fourth, the above images are subjected to Sobel boundary detection to extract images with obvious boundary features.
R1=edge(H1,'Sobel');R1=edge(H1,'Sobel');
figure(8),imshow(R1);%图像如图8所示figure(8), imshow(R1);% image as shown in Figure 8
第五,利用Hough变换检测并选取最长的直线,计算最长直线与图像水平方向的夹角。Fifth, use the Hough transform to detect and select the longest straight line, and calculate the angle between the longest straight line and the horizontal direction of the image.
[H,Theta,Rho]=hough(R1);[H, Theta, Rho] = hough(R1);
P=houghpeaks(H,5,'threshold',ceil(0.3*max(H(:))));%在Hough矩阵中寻找前5个大于Hough矩阵中最大值0.3倍的峰值P=houghpeaks(H,5,'threshold',ceil(0.3*max(H(:))));% Find the first 5 peaks in the Hough matrix that are greater than 0.3 times the maximum value in the Hough matrix
lines=houghlines(R1,Theta,Rho,P,'FillGap',20,'MinLength',5);%合并距离小于20的线段,丢弃所有长度小于5的直线段lines=houghlines(R1,Theta,Rho,P,'FillGap',20,'MinLength',5);% Merge line segments whose distance is less than 20, and discard all straight line segments whose length is less than 5
end%确定最长的线段end% determines the longest line segment
figure(9),imshow(A);%图像如图9所示figure(9), imshow(A);% image as shown in Figure 9
plot(xy_long(:,1),xy_long(:,2),'LineWidth',2,'Color','r');plot(xy_long(:,1),xy_long(:,2),'LineWidth',2,'Color','r');
gradient=(xy_long(2,2)-xy_long(1,2))/(xy_long(2,1)-xy_long(1,1));gradient=(xy_long(2,2)-xy_long(1,2))/(xy_long(2,1)-xy_long(1,1));
x=atan(gradient);x = atan(gradient);
jiaodu=x*180/pi;jiaodu=x*180/pi;
第六,利用夹角对图像进行旋转,对处理后的甘蔗进行列像素求和,找到求和函数的极值点以及极值点的横坐标。Sixth, use the included angle to rotate the image, perform column pixel summation on the processed sugarcane, and find the extreme point of the summation function and the abscissa of the extreme point.
E=imrotate(O,jiaodu,'nearest','crop');%将开操作图像进行旋转E=imrotate(O, jiaodu, 'nearest', 'crop'); % will rotate the operation image
figure(10),imshow(E);%图像如图10所示figure(10), imshow(E); % image as shown in Figure 10
y=sum(E);%对旋转后开操作图像列像素求和的统计图y=sum(E);% Statistical diagram of the sum of pixels in the image row after rotation
figure('NumberTitle','off','Name','像素统计极大值点'),plot(y);figure('NumberTitle','off','Name','Pixel statistical maximum point'),plot(y);
title('列像素求和统计图');title('column pixel summation chart');
xlabel('x');xlabel('x');
ylabel('y');ylabel('y');
[a,b]=findpeaks(y,'minpeakdistance',50,'minpeakheight',9);%寻找极大值[a,b]=findpeaks(y,'minpeakdistance',50,'minpeakheight',9);% find the maximum value
hold on;hold on;
[c,d]=max(y);%%寻找最值点,d是最值所在坐标[c,d]=max(y); %% looking for the maximum value point, d is the coordinate of the maximum value
hold on;hold on;
plot(b,a,'ro');%把极值点画出来plot(b,a,'ro');% draw the extreme points
plot(d,c,'ro');%把最值点画出来plot(d,c,'ro');% draw the most value points
第七,经过公式计算得到并标记甘蔗茎节的位置。Seventh, the position of the sugarcane stem node is obtained and marked through formula calculation.
figure('NumberTitle','off','Name','甘蔗茎节标记'),imshow(A);figure('NumberTitle','off','Name','mark of sugarcane stem node'),imshow(A);
b1=b*cosd(abs(jiaodu));b1=b*cosd(abs(jiaodu));
for i=1:length(b1)for i=1:length(b1)
rectangle('Position',[b1(i)-8,70,20,150]);rectangle('Position',[b1(i)-8,70,20,150]);
endend
d=d*cosd(abs(jiaodu));d=d*cosd(abs(jiaodu));
rectangle('Position',[d-8,70,20,150]);rectangle('Position', [d-8,70,20,150]);
随机选取30组黄蔗样品进行实验,对其编号为1到30,每根甘蔗上的蔗节数不同,共275个茎节。将编号1~30的甘蔗图像分别进行识别,统计识别出来的蔗节的数目,将其与实际数目对比,统计误差率,实验数据如下表所示:30 groups of yellow cane samples were randomly selected for the experiment, numbered from 1 to 30, and the number of nodes on each sugarcane was different, with a total of 275 stem nodes. Recognize the sugarcane images numbered 1-30 respectively, count the number of identified sugarcane nodes, compare it with the actual number, and calculate the error rate. The experimental data are shown in the following table:
从表中可以看出,有17根甘蔗的蔗节测出率为100%,所有甘蔗蔗节的漏检数不超过1个,总共275个蔗节,共测出263个蔗节,准确率达95.6%。通过分析试验现象,漏检的部位主要分布于甘蔗的首个或者末个蔗节,这两个部分的蔗节颜色比其他部分要淡,在图像处理中,特征不明显,出现漏检。因此,可以进一步优化程序,对图像的对比度进行增强,从而减少漏检现象。It can be seen from the table that the detection rate of 17 sugarcane nodes is 100%, and the number of missed detection of all sugarcane nodes is no more than 1. There are a total of 275 sugarcane nodes, and a total of 263 sugarcane nodes have been detected. Up to 95.6%. Through the analysis of the test phenomenon, the missed detection parts are mainly distributed in the first or last cane node of sugarcane. The color of the sugarcane nodes in these two parts is lighter than that of other parts. In the image processing, the features are not obvious, and the detection is missed. Therefore, the program can be further optimized to enhance the contrast of the image, thereby reducing the phenomenon of missed detection.
本发明为国内甘蔗蔗种工业化制备提供技术上的参考,其核心是利用基于matlab软件的图像处理技术通过对甘蔗表面多目标进行提取,以准确地识别出甘蔗的蔗节,有效避免漏检和错检的情况,从而为后续的切割以及蔗茎、蔗种的分离提供准确的位置信号,防止切割过程中伤芽和浪费蔗种,而且本发明通过图像旋转还可以为进料时倾斜的原料进行修正,从而提高了识别的准确性。The present invention provides a technical reference for the industrial preparation of domestic sugarcane seeds. Its core is to use the image processing technology based on matlab software to extract multiple objects on the surface of sugarcane to accurately identify the nodes of sugarcane, effectively avoiding missed detection and Error detection, thereby providing accurate position signals for subsequent cutting and separation of cane stems and cane seeds, preventing damage to buds and waste of cane seeds during the cutting process, and the present invention can also be used for raw materials that are tilted during feeding through image rotation. Correction, thereby improving the accuracy of recognition.
前述对本发明的具体示例性实施方案的描述是为了说明和例证的目的。这些描述并非想将本发明限定为所公开的精确形式,并且很显然,根据上述教导,可以进行很多改变和变化。对示例性实施例进行选择和描述的目的在于解释本发明的特定原理及其实际应用,从而使得本领域的技术人员能够实现并利用本发明的各种不同的示例性实施方案以及各种不同的选择和改变。本发明的范围意在由权利要求书及其等同形式所限定。The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling others skilled in the art to make and use various exemplary embodiments of the invention, as well as various Choose and change. It is intended that the scope of the invention be defined by the claims and their equivalents.
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