CN108710850B - Wolfberry fruit identification method and system - Google Patents
Wolfberry fruit identification method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for identifying medlar fruits, wherein the method comprises the following steps: acquiring an initial medlar fruit RGB image; converting an initial wolfberry fruit RGB image into an HSV image, traversing pixel points in the HSV image, and obtaining a first coordinate set of pixel points of which H is more than 10 and less than 170 or S is less than 120 in the HSV image; removing pixel points with the same coordinate as the coordinate in the first coordinate set in the initial wolfberry fruit RGB image to obtain a first wolfberry fruit RGB image; traversing pixel points in the RGB image of the first wolfberry fruit, and removing the pixel points with R less than G + B or R less than 45 in the RGB image of the first wolfberry fruit to obtain a RGB image of a second wolfberry fruit; converting the RGB image of the second wolfberry fruit into a binary image, traversing pixel points in the binary image of the wolfberry fruit to obtain a second coordinate set of the pixel points with the pixel values larger than 150 in the binary image of the wolfberry fruit; and removing the pixel points with the same coordinate in the second wolfberry fruit RGB image and the second coordinate set to obtain the target wolfberry fruit RGB image.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a wolfberry fruit identification method and system.
Background
The Chinese wolfberry has a long history of homology of medicine and food, and the Chinese wolfberry planting area is more than 100 ten thousand mu at present. Besides no obvious change of planting areas of Ningxia, hebei and Gansu, the planting areas of Xinjiang, qinghai, inner Mongolia and other fields are continuously increased. Medlar picking is generally carried out in summer in hot weather, the working environment is poor, the manual picking efficiency is low, the cost is high, the problems of insufficient manual supply in the picking peak period and the like are gradually serious.
The visual processing technology and the double-arm control technology are combined to form a row of medlar picking robot, medlar can be automatically identified and picked, but the problem how to identify the medlar fruits under illumination and the medlar fruits under shadow in summer with sufficient sunlight is an urgent need to be solved in the field.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a method and a system for identifying medlar fruits;
the invention provides a medlar fruit identification method, which comprises the following steps:
s1, obtaining an initial wolfberry fruit RGB image;
s2, converting the initial RGB image of the wolfberry fruit into an HSV image of the wolfberry fruit, traversing pixel points in the HSV image of the wolfberry fruit to obtain a coordinate set of the pixel points with hue being more than 10 and less than 170 or saturation being less than 120 in the HSV image of the wolfberry fruit, and recording the coordinate set as a first coordinate set;
s3, removing pixel points of which the coordinates of the pixel points in the initial wolfberry fruit RGB image are the same as those in the first coordinate set to obtain a first wolfberry fruit RGB image;
s4, traversing pixel points in the RGB image of the first wolfberry fruit, and removing the pixel points with R less than G + B or R less than 45 in the RGB image of the first wolfberry fruit to obtain a RGB image of a second wolfberry fruit;
s5, converting the second RGB image of the wolfberry fruit into a grey-scale map of the wolfberry fruit, converting the grey-scale map of the wolfberry fruit into a binary map of the wolfberry fruit, traversing pixel points in the binary map of the wolfberry fruit to obtain a coordinate set of the pixel points with the pixel values larger than 150 in the binary map of the wolfberry fruit, and recording the coordinate set as a second coordinate set;
and S6, removing pixel points of the second wolfberry fruit RGB image with the same coordinate as the coordinate of the pixel points in the second coordinate set to obtain the target wolfberry fruit RGB image.
Preferably, in step S5, the converting the gray-scale map of the lycium barbarum fruit into a binary map of the lycium barbarum fruit specifically includes: and converting the grey-scale map of the medlar fruit into a binary map of the medlar fruit by using a Laplace operator.
A system for identifying fruit of lycium barbarum comprising:
the initial image acquisition module is used for acquiring an initial medlar fruit RGB image;
the first set calculation module is used for converting the initial RGB image of the wolfberry fruit into an HSV image of the wolfberry fruit, traversing pixel points in the HSV image of the wolfberry fruit to obtain a coordinate set of the pixel points with hue being more than 10 and less than 170 or saturation being less than 120 in the HSV image of the wolfberry fruit, and recording the coordinate set as a first coordinate set;
the first image processing module is used for removing pixel points with the same coordinates in the initial wolfberry fruit RGB image and the first coordinate set to obtain a first wolfberry fruit RGB image;
the second image processing module is used for traversing pixel points in the RGB image of the first wolfberry fruit, and removing pixel points with R being less than G + B or R being less than 45 in the RGB image of the first wolfberry fruit to obtain a RGB image of the second wolfberry fruit;
the second set calculation module is used for converting the second RGB image of the wolfberry fruit into a grey-scale map of the wolfberry fruit, converting the grey-scale map of the wolfberry fruit into a binary map of the wolfberry fruit, traversing pixel points in the binary map of the wolfberry fruit to obtain a coordinate set of the pixel points with the pixel values larger than 150 in the binary map of the wolfberry fruit, and recording the coordinate set as a second coordinate set;
and the third image processing module is used for removing pixel points with the same coordinate in the second wolfberry fruit RGB image and the second coordinate set to obtain the target wolfberry fruit RGB image.
Preferably, the second set calculating module is specifically configured to: and converting the grey-scale map of the medlar fruit into a binary map of the medlar fruit by using a Laplace operator.
The method comprises the steps of obtaining an initial RGB image of a medlar fruit; converting the initial wolfberry fruit RGB image into a wolfberry fruit HSV image, traversing pixel points in the wolfberry fruit HSV image to obtain a coordinate set of pixel points with hue less than 10 and saturation less than 170 or saturation less than 120 in the wolfberry fruit HSV image, and recording the coordinate set as a first coordinate set; removing pixel points with the same coordinate as the coordinate in the first coordinate set in the initial wolfberry fruit RGB image to obtain a first wolfberry fruit RGB image; traversing pixel points in the RGB image of the first wolfberry fruit, and removing the pixel points with R being less than G + B or R being less than 45 in the RGB image of the first wolfberry fruit to obtain the RGB image of the second wolfberry fruit; converting the RGB image of the second wolfberry fruit into a grey-scale map of the wolfberry fruit, converting the grey-scale map of the wolfberry fruit into a binary map of the wolfberry fruit, traversing pixel points in the binary map of the wolfberry fruit to obtain a coordinate set of the pixel points with pixel values larger than 150 in the binary map of the wolfberry fruit, and recording the coordinate set as a second coordinate set; and removing pixel points with the same coordinate in the second wolfberry fruit RGB image and the second coordinate set to obtain a target wolfberry fruit RGB image, and thus, combining the RGB color space with HSV color space and Laplace operator, quickly and efficiently identifying the wolfberry fruits under illumination and the wolfberry fruits under shadow, so that the identification rate of the wolfberry fruits is improved, and the benefit of an enterprise automatically picking the wolfberry fruits by identifying the wolfberry fruits is improved.
Drawings
Fig. 1 is a schematic flow chart of a method for identifying fruits of lycium barbarum provided by the invention;
fig. 2 is a schematic block diagram of a system for identifying a fruit of chinese wolfberry according to the present invention.
Detailed Description
Referring to fig. 1, the method for identifying a medlar fruit provided by the invention comprises the following steps:
s1, acquiring an initial RGB image of the wolfberry fruit.
And S2, converting the initial RGB image of the wolfberry fruit into an HSV image of the wolfberry fruit, traversing pixel points in the HSV image of the wolfberry fruit to obtain a coordinate set of the pixel points with hue less than 10 and saturation less than 170 or saturation less than 120 in the HSV image of the wolfberry fruit, and recording the coordinate set as a first coordinate set.
And S3, removing pixel points of which the coordinates of the pixel points in the initial wolfberry fruit RGB image are the same as those in the first coordinate set to obtain a first wolfberry fruit RGB image.
In the specific scheme, an initial wolfberry fruit RGB image is converted into a wolfberry fruit HSV image, pixel point traversal is carried out on the wolfberry fruit HSV image, pixel points with the same coordinate in the initial wolfberry fruit RGB image are removed through H being more than 10 and less than 170 or S being less than 120, and a first wolfberry fruit RGB image is obtained, wherein H is hue, and S is saturation.
And S4, traversing pixel points in the RGB image of the first wolfberry fruit, and removing pixel points of which R is less than G + B or R is less than 45 in the RGB image of the first wolfberry fruit to obtain the RGB image of the second wolfberry fruit.
In the specific scheme, pixel points which meet the requirement that R is more than or equal to G + B and more than or equal to 45 in the RGB image of the first wolfberry fruit are reserved, and pixel points which do not meet the requirement are removed to obtain the RGB image of the second wolfberry fruit.
Step S5, converting the second RGB image of the wolfberry fruit into a grey-scale map of the wolfberry fruit, converting the grey-scale map of the wolfberry fruit into a binary map of the wolfberry fruit, traversing pixel points in the binary map of the wolfberry fruit to obtain a coordinate set of the pixel points with pixel values larger than 150 in the binary map of the wolfberry fruit, recording the coordinate set as a second coordinate set, and converting the grey-scale map of the wolfberry fruit into the binary map of the wolfberry fruit specifically comprises the following steps: and converting the grey-scale map of the medlar fruit into a binary map of the medlar fruit by using a Laplace operator.
And S6, removing pixel points with the same coordinate as the coordinate in the second coordinate set in the second wolfberry fruit RGB image to obtain a target wolfberry fruit RGB image.
In the specific scheme, the second wolfberry fruit RGB image is converted into a wolfberry fruit gray map, the wolfberry fruit gray map is converted into a wolfberry fruit binary map through a Laplacian operator, pixel points in the wolfberry fruit binary map are traversed, whether the pixel value of each pixel point is larger than 150 is judged, and the pixel points which accord with the judged same coordinate point in the second wolfberry fruit RGB image are removed to obtain the target wolfberry fruit RGB image.
Referring to fig. 2, the present invention provides a system for identifying a fruit of chinese wolfberry, comprising:
and the initial image acquisition module is used for acquiring an initial medlar fruit RGB image.
And the first set calculation module is used for converting the initial RGB image of the wolfberry fruit into an HSV image of the wolfberry fruit, traversing pixel points in the HSV image of the wolfberry fruit to obtain a coordinate set of the pixel points with hue being more than 10 and less than 170 or saturation being less than 120 in the HSV image of the wolfberry fruit, and recording the coordinate set as a first coordinate set.
And the first image processing module is used for removing pixel points with the same coordinates in the initial wolfberry fruit RGB image and the first coordinate set to obtain a first wolfberry fruit RGB image.
In the specific scheme, an initial wolfberry fruit RGB image is converted into a wolfberry fruit HSV image, pixel traversal is carried out on the wolfberry fruit HSV image, pixels with the same coordinate in the initial wolfberry fruit RGB image are removed through H being more than 10 and less than 170 or S being less than 120, and a first wolfberry fruit RGB image is obtained, wherein H is hue, and S is saturation.
And the second image processing module is used for traversing pixel points in the RGB image of the first wolfberry fruit, and removing pixel points of which R is less than G + B or R is less than 45 in the RGB image of the first wolfberry fruit to obtain the RGB image of the second wolfberry fruit.
In the specific scheme, pixel points which meet the requirements that R is larger than or equal to G + B and R is larger than or equal to 45 in the RGB image of the first wolfberry fruit are reserved, and the pixel points which do not meet the requirements are removed to obtain the RGB image of the second wolfberry fruit.
The second set calculation module is used for converting the second RGB image of the wolfberry fruit into a grey-scale map of the wolfberry fruit, converting the grey-scale map of the wolfberry fruit into a binary map of the wolfberry fruit, traversing pixel points in the binary map of the wolfberry fruit, obtaining a coordinate set of the pixel points with pixel values larger than 150 in the binary map of the wolfberry fruit, and recording the coordinate set as a second coordinate set, wherein the second set calculation module is specifically used for: and converting the grey-scale map of the medlar fruit into a binary map of the medlar fruit by using a Laplace operator.
And the third image processing module is used for removing pixel points with the same coordinate in the second wolfberry fruit RGB image and the second coordinate set to obtain a target wolfberry fruit RGB image.
In the specific scheme, the second wolfberry fruit RGB image is converted into a wolfberry fruit gray map, the wolfberry fruit gray map is converted into a wolfberry fruit binary map through a Laplacian operator, pixel points in the wolfberry fruit binary map are traversed, whether the pixel value of each pixel point is larger than 150 is judged, and the pixel points which accord with the judged same coordinate point in the second wolfberry fruit RGB image are removed to obtain the target wolfberry fruit RGB image.
In the embodiment, an initial RGB image of the medlar fruit is obtained; converting the initial wolfberry fruit RGB image into a wolfberry fruit HSV image, traversing pixel points in the wolfberry fruit HSV image to obtain a coordinate set of pixel points with hue less than 10 and saturation less than 170 or saturation less than 120 in the wolfberry fruit HSV image, and recording the coordinate set as a first coordinate set; removing pixel points with the same coordinate as the coordinate in the first coordinate set in the initial wolfberry fruit RGB image to obtain a first wolfberry fruit RGB image; traversing pixel points in the RGB image of the first wolfberry fruit, and removing the pixel points with R being less than G + B or R being less than 45 in the RGB image of the first wolfberry fruit to obtain the RGB image of the second wolfberry fruit; converting the RGB image of the second wolfberry fruit into a grey-scale map of the wolfberry fruit, converting the grey-scale map of the wolfberry fruit into a binary map of the wolfberry fruit, traversing pixel points in the binary map of the wolfberry fruit to obtain a coordinate set of the pixel points with pixel values larger than 150 in the binary map of the wolfberry fruit, and recording the coordinate set as a second coordinate set; and removing pixel points with the same coordinate in the second wolfberry fruit RGB image and the second coordinate set to obtain a target wolfberry fruit RGB image, and thus, combining the RGB color space with the HSV color space and the Laplace operator, quickly and efficiently identifying the wolfberry fruits under illumination and the wolfberry fruits under shadows, so that the identification rate of the wolfberry fruits is improved, and the benefit of an enterprise automatically picking the wolfberry fruits by identifying the wolfberry fruits is further improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (2)
1. A wolfberry fruit identification method is characterized by comprising the following steps:
s1, obtaining an initial wolfberry fruit RGB image;
s2, converting the initial RGB image of the wolfberry fruit into an HSV image of the wolfberry fruit, traversing pixel points in the HSV image of the wolfberry fruit to obtain a coordinate set of the pixel points with hue being more than 10 and less than 170 or saturation being less than 120 in the HSV image of the wolfberry fruit, and recording the coordinate set as a first coordinate set;
s3, removing pixel points of which the coordinates of the pixel points in the initial wolfberry fruit RGB image are the same as those in the first coordinate set to obtain a first wolfberry fruit RGB image;
s4, traversing pixel points in the RGB image of the first wolfberry fruit, and removing the pixel points with R less than G + B or R less than 45 in the RGB image of the first wolfberry fruit to obtain a RGB image of a second wolfberry fruit;
s5, converting the second RGB image of the wolfberry fruit into a grey-scale map of the wolfberry fruit, converting the grey-scale map of the wolfberry fruit into a binary map of the wolfberry fruit, traversing pixel points in the binary map of the wolfberry fruit to obtain a coordinate set of the pixel points with the pixel values larger than 150 in the binary map of the wolfberry fruit, and recording the coordinate set as a second coordinate set;
s6, removing pixel points of the second wolfberry fruit RGB image, wherein the pixel point coordinates are the same as those of the second coordinate set, and obtaining a target wolfberry fruit RGB image;
in step S5, the converting the gray level map of the lycium barbarum fruit into a binary map of the lycium barbarum fruit specifically includes: and converting the grey-scale map of the medlar fruit into a binary map of the medlar fruit by using a Laplace operator.
2. A system for identifying a fruit of lycium barbarum, comprising:
the initial image acquisition module is used for acquiring an initial medlar fruit RGB image;
the first set calculation module is used for converting the initial RGB image of the wolfberry fruit into an HSV image of the wolfberry fruit, traversing pixel points in the HSV image of the wolfberry fruit to obtain a coordinate set of the pixel points with hue being more than 10 and less than 170 or saturation being less than 120 in the HSV image of the wolfberry fruit, and recording the coordinate set as a first coordinate set;
the first image processing module is used for removing pixel points with the same coordinate in the initial wolfberry fruit RGB image and the coordinate in the first coordinate set to obtain a first wolfberry fruit RGB image;
the second image processing module is used for traversing pixel points in the RGB image of the first wolfberry fruit, and removing pixel points with R being less than G + B or R being less than 45 in the RGB image of the first wolfberry fruit to obtain a RGB image of the second wolfberry fruit;
the second set calculation module is used for converting the second RGB image of the wolfberry fruit into a grey-scale map of the wolfberry fruit, converting the grey-scale map of the wolfberry fruit into a binary map of the wolfberry fruit, traversing pixel points in the binary map of the wolfberry fruit to obtain a coordinate set of the pixel points with the pixel values larger than 150 in the binary map of the wolfberry fruit, and recording the coordinate set as a second coordinate set;
the third image processing module is used for removing pixel points with the same coordinate in the second wolfberry fruit RGB image and the coordinate in the second coordinate set to obtain a target wolfberry fruit RGB image; the second set calculating module is specifically configured to: and converting the grey-scale map of the medlar fruit into a binary map of the medlar fruit by using a Laplace operator.
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