CN115546462A - Method for extracting shape features of product and counting based on image recognition - Google Patents
Method for extracting shape features of product and counting based on image recognition Download PDFInfo
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
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- G—PHYSICS
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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Abstract
The invention provides a method for extracting shape characteristics of a product and counting based on image recognition, which comprises the following steps: s1: acquiring a target picture; s2: reading a target picture and converting the target picture into a gray-scale image; s3: carrying out binarization processing on the gray level image to obtain a binarization image corresponding to the gray level image; s4: detecting the contour of the binary image, extracting the horizontal rectangular coordinate of the contour of the binary image, and drawing a rectangle through the coordinate; s5: writing a counting number in the upper left corner of each drawn rectangle, and obtaining the number of products in the graph according to the maximum value of the number; by the method for extracting the shape characteristics of the products and counting the products based on image recognition, the number of the products contained each time is only limited by a tiled field, the shape parameters of the products do not need to be maintained in advance, and when the number of the products is large, the products do not need to be placed into a measuring instrument for many times, so that the product counting efficiency is improved; the number of products is obtained without inquiring the corresponding table according to the shape rule, and the time is saved.
Description
Technical Field
The invention relates to the field of software application development, in particular to a method for extracting and counting shape features of products based on image recognition.
Background
When the products are packaged and sorted, the working procedures have the requirement of counting. For the magnetic core with the single weight of more than 10 grams, the counting of the number by weighing is a relatively accurate counting method, but for the products of a fine magnetic core type with the single weight of less than 0.5 gram, the error of the counting in the mode is large. In the prior art, products are closely arranged in a measuring tool on site, and the number of magnetic core products is obtained by inquiring a typesetting number and number comparison table.
The counting mode for products with smaller gram weight in the prior art has the following disadvantages:
1. products need to be arranged tightly in the measuring tool, and the corresponding table is inquired according to the shape rule to obtain the number of the products, so that time and labor are consumed;
2. when the number of products is large, the size of the measuring tool is limited, the process needs to be operated for many times, and the efficiency is low.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a method for extracting and counting shape features of a product based on image recognition.
In order to realize the purpose, the invention adopts the following technical scheme: a method for extracting and counting shape features of products based on image recognition comprises the following steps:
s1: acquiring a target picture;
an engineer acquires a target picture by taking a picture on line or selecting an existing picture;
the target picture comprises products to be counted, and the products to be counted are tiled without overlapping.
S2: reading a target picture and converting the target picture into a gray-scale image;
reading the pixel matrix information of the target picture through a cv2. Immead () function to read the target picture;
the pixel matrix information includes a R, G, B value for each pixel;
judging whether the target picture is a gray scale picture or not;
if the values of R, G, B corresponding to each pixel of the target picture are equal, the target picture is a gray scale picture, and the step S3 is entered;
if the values of R, G, B corresponding to each pixel of the target picture are not completely equal, the target picture is not a gray-scale image, and a cv2.CvtColor () function is called to convert the target picture into the gray-scale image.
S3: carrying out binarization processing on the gray level image to obtain a binarization image corresponding to the gray level image;
the binary image is an image with only two colors of black and white, each pixel point can be represented by 0 or 255, 0 represents black, and 255 represents white;
converting the gray-scale image into an image with pixels only having two values of 0 and 255 by calling a cv2.Adaptive threshold () function, and completely segmenting a target region and a background region to obtain a binary image corresponding to the gray-scale image;
the target area refers to an area occupied by products to be counted in the image, and the background area refers to other areas excluding the area occupied by the products to be counted in the image;
each product to be counted corresponds to one target area.
S4: detecting the contour of the binary image, extracting the horizontal rectangular coordinate of the contour of the binary image, and drawing a rectangle through the coordinate;
the method comprises the following substeps:
s41: detecting the contour of the binary image and finding out the contour value in the image;
acquiring a contour and a contour value of each target region in the binary image by calling a cv2.FindContours () function, wherein the contour is a set of a series of points, and the contour value refers to a coordinate value of each point;
s42: acquiring a vertical boundary minimum rectangle of the outline according to the outline value of the image;
acquiring the minimum rectangular frame information of the vertical boundary of each target area by calling a cv2.BoundingRec () function, wherein the minimum rectangular frame information of the vertical boundary comprises the coordinate value of the upper left point of the rectangle, the width of the rectangle and the height of the rectangle;
calculating coordinates of four vertexes of the rectangle according to the coordinate value of the upper left point of the rectangle, the width of the rectangle and the height of the rectangle;
the specific calculation method comprises the following steps:
setting the coordinates of the upper left point of the rectangle as (x, y), the height of the rectangle as h and the width of the rectangle as w;
the coordinates of the upper right point of the rectangle are (x + w, y);
the coordinates of the lower left point of the rectangle are (x, y-h);
then the coordinates of the lower right point of the rectangle are (x + w, y-h);
the rectangle is parallel to the upper and lower boundaries of the image;
s43: drawing a rectangle with the minimum vertical boundary;
drawing the vertical boundary minimum rectangle through a cv2.Rectangle () function according to the four vertex coordinates of the vertical boundary minimum rectangle obtained in the step S42;
and finishing drawing to obtain the minimum vertical boundary rectangle of all target areas in the binary image.
S5: writing a counting number in the upper left corner of each drawn rectangle, and obtaining the number of products in the target picture according to the maximum value of the number;
calling the coordinates of the upper left corner of each rectangle by a cv2.Puttext () function according to the coordinates of the upper left corner of each rectangle obtained in the step S42, and writing a counting number into the upper left corner of each rectangle, wherein the numbering is an Arabic numeral starting from 1; the maximum value of the number is the number of the target area, namely the number of the products to be counted in the target image.
Compared with the prior art, the invention has the beneficial effects that:
according to the method for extracting the shape characteristics of the product and counting based on the image recognition, the field product does not need to be fixedly arranged according to the requirement of a measuring tool, only the product needs to be tiled and not overlapped, the picture of the product to be recognized is obtained, the product characteristics are summarized from the picture directly, and the correct number of the product is obtained based on the characteristic counting;
the number of products contained in each time is only limited by a tiled field, the shape parameters of the products do not need to be maintained in advance, and when the number of the products is large, the products do not need to be placed into a measuring instrument for many times, so that the product counting efficiency is improved;
the number of products is obtained without inquiring the corresponding table according to the shape rule, and the time is saved.
Drawings
FIG. 1 is a flow chart illustrating the steps of a method for extracting and counting shape features of a product based on image recognition according to the present invention.
Detailed Description
In order to further understand the objects, structures, features and functions of the present invention, the following embodiments are described in detail.
A method for extracting and counting shape features of products based on image recognition comprises the following steps:
s1: acquiring a target picture;
an engineer acquires a target picture by taking a picture on line or selecting an existing picture;
the target picture comprises products to be counted, and the products to be counted are tiled without overlapping.
S2: reading a target picture and converting the target picture into a gray-scale image;
reading the pixel matrix information of the target picture through a cv2. Immead () function to read the target picture;
the pixel matrix information includes a R, G, B value for each pixel;
r, G, B refers to R (red), G (green) and B (blue), and RGB values refer to brightness and are expressed by integers; r, G, B each have 256 levels of brightness, numerically represented as from 0-255;
judging whether the target picture is a gray scale picture or not;
if the values of R, G, B corresponding to each pixel of the target picture are equal, the target picture is a gray scale picture, and the step S3 is entered;
if the values of R, G, B corresponding to each pixel of the target picture are not completely equal, the target picture is not a gray-scale image, and a cv2.CvtColor () function is called to convert the target picture into the gray-scale image.
S3: carrying out binarization processing on the gray level image to obtain a binarization image corresponding to the gray level image;
the binary image is an image with only two colors of black and white, each pixel point can be represented by 0 or 255, 0 represents black, and 255 represents white;
carrying out adaptive threshold processing on the image by calling a cv2.Adaptive threshold () function, converting the gray image into an image with pixels only having two values of 0 and 255, and completely segmenting a target region and a background region to obtain a binary image corresponding to the gray image;
the self-adaptive threshold value is that a local optimal threshold value is calculated according to each small region on the image, pixel points with local all pixel values smaller than the threshold value in the gray image are set to be 0, and pixel points larger than the threshold value are set to be 255, and a binary image is obtained;
the target area refers to an area occupied by products to be counted in the image, and the background area refers to other areas except the area occupied by the products to be counted in the image;
each product to be counted corresponds to one target area;
furthermore, the image is sensitive to noise, and when the image is subjected to binarization processing, the image needs to be subjected to noise reduction processing.
S4: detecting the contour of the binary image, extracting the horizontal rectangular coordinate of the contour of the binary image, and drawing a rectangle through the coordinate;
the method comprises the following substeps:
s41: detecting the contour of the binary image and finding out the contour value in the image;
acquiring a contour and a contour value of each target region in the binary image by calling a cv2.FindContours () function, wherein the contour is a set of a series of points, and the contour value refers to a coordinate value of each point;
s42: acquiring a vertical boundary minimum rectangle of the outline according to the outline value of the image;
acquiring the minimum rectangular frame information of the vertical boundary of each target area by calling a cv2.BoundingRec () function, wherein the minimum rectangular frame information of the vertical boundary comprises the coordinate value of the upper left point of a rectangle, the width of the rectangle and the height of the rectangle;
calculating coordinates of four vertexes of the rectangle according to the coordinate value of the upper left point of the rectangle, the width of the rectangle and the height of the rectangle;
the rectangle is parallel to the upper and lower boundaries of the image;
the specific calculation method comprises the following steps:
setting the coordinates of the upper left point of the rectangle as (x, y), the height of the rectangle as h and the width of the rectangle as w;
the coordinates of the upper right point of the rectangle are (x + w, y);
the coordinates of the lower left point of the rectangle are (x, y-h);
then the coordinates of the lower right point of the rectangle are (x + w, y-h);
for example: the coordinate of the upper left point of the rectangle is (4,5), the height of the rectangle is 3, and the width of the rectangle is 2;
the coordinate of the upper right point of the rectangle is (4 +2, 5), i.e. (6,5);
the coordinate of the lower left point of the rectangle is (4,5-3), i.e., (4,2);
the coordinate of the lower right point of the rectangle is (4 +2, 5-3), i.e. (6,2);
acquiring the minimum rectangular frame information of the vertical boundary of each target area through a function, wherein the minimum rectangular frame information comprises coordinate values of upper left points of rectangles, the width of the rectangles and the height of the rectangles, each rectangle is not overlapped because each product to be counted is not overlapped, and the number of the rectangles is the number of the products to be counted;
s43: drawing a rectangle with the minimum vertical boundary;
drawing the vertical boundary minimum rectangle through a cv2.Rectangle () function according to the four vertex coordinates of the vertical boundary minimum rectangle obtained in the step S42;
and finishing drawing to obtain the minimum vertical boundary rectangle of all target areas in the binary image.
S5: writing a counting number in the upper left corner of each drawn rectangle, and obtaining the number of products in the target picture according to the maximum value of the number;
calling coordinates of the upper left corner of the rectangle by the cv2.Puttext () function according to the drawn minimum rectangle of the vertical boundary and the frame information of the minimum rectangle of the vertical boundary, which are obtained in the step S42, and writing a count number into the upper left corner of each drawn rectangle, wherein the number is an Arabic number from 1; the maximum value of the number is the number of the target area, namely the number of the products to be counted in the target image.
The present invention has been described in relation to the above embodiments, which are only examples of the implementation of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the invention. Rather, it is intended that all such modifications and variations be included within the spirit and scope of this invention.
Claims (6)
1. A method for extracting and counting shape features of a product based on image recognition is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring a target picture;
the target picture comprises products to be counted, and the products to be counted are tiled and not overlapped;
s2: reading a target picture and converting the target picture into a gray-scale image;
reading the target picture by reading pixel matrix information of the target picture;
the pixel matrix information includes a R, G, B value for each pixel;
judging whether the target picture is a gray scale picture or not;
if the value of R, G, B corresponding to each pixel of the target picture is equal, the target picture is a gray-scale picture, and the step S3 is entered;
if the values of R, G, B corresponding to each pixel of the target picture are not completely equal, the target picture is not a gray scale image, and the target picture is converted into the gray scale image;
s3: carrying out binarization processing on the gray level image to obtain a binarization image corresponding to the gray level image;
the binary image is an image with only two colors of black and white, each pixel point can be represented by 0 or 255, 0 represents black, and 255 represents white;
converting the gray-scale image into an image with pixels only having two values of 0 and 255, and completely segmenting a target area and a background area to obtain a binary image corresponding to the gray-scale image;
the target area refers to an area occupied by products to be counted in the image, and the background area refers to other areas except the area occupied by the products to be counted in the image;
each product to be counted corresponds to one target area;
s4: detecting the contour of the binary image, extracting the horizontal rectangular coordinate of the contour of the binary image, and drawing a rectangle through the coordinate;
acquiring contour information of a binary image, acquiring a vertical boundary minimum rectangular coordinate of the contour according to the contour information of the binary image, and drawing the vertical boundary minimum rectangle of the contour according to the coordinate;
s5: and writing a counting number in the upper left corner of each drawn rectangle, and obtaining the number of products in the target picture according to the maximum value of the number.
2. The method for extracting and counting the shape features of the product based on the image recognition as claimed in claim 1, wherein:
in step S2, the specific determination method is:
if the values of R, G, B corresponding to each pixel of the target picture are equal, the target picture is a gray scale picture;
if the values of R, G, B corresponding to each pixel of the target picture are not completely equal, the target picture is not a grayscale picture.
3. The method for extracting and counting shape features of products based on image recognition as claimed in claim 1, wherein:
step S4 includes the following substeps:
s41: detecting the contour of the binary image and finding out the contour value in the image;
acquiring a contour and a contour value of each target area in a binary image, wherein the contour is a set of a series of points, and the contour value refers to a coordinate value of each point;
s42: acquiring a vertical boundary minimum rectangle of the outline according to the outline value of the image;
acquiring the information of a minimum rectangular frame of a vertical boundary of each target area, wherein the information of the minimum rectangular frame of the vertical boundary comprises coordinate values of a left upper point of a rectangle, the width of the rectangle and the height of the rectangle;
calculating coordinates of four vertexes of the rectangle according to the coordinate value of the upper left point of the rectangle, the width of the rectangle and the height of the rectangle;
s43: drawing a rectangle with the minimum vertical boundary;
drawing a vertical boundary minimum rectangle according to the vertical boundary minimum rectangle coordinate acquired in the step S42;
and finishing drawing to obtain the minimum vertical boundary rectangle of all target areas in the binary image.
4. The method for extracting and counting the shape features of the product based on the image recognition as claimed in claim 1, wherein:
in step S5, count numbers are sequentially written on the minimum rectangles of the vertical boundaries of all the target regions in the binarized image, the number being an arabic numeral starting from 1.
5. A method for extracting and counting shape features of products based on image recognition as claimed in claim 3, wherein:
in step S42, the vertical boundary minimum rectangle is parallel to the upper and lower boundaries of the image.
6. The method for extracting and counting shape features of products based on image recognition as claimed in claim 4, wherein:
in step S5, the maximum number value is the number of target areas, and the number of target areas is the number of products to be counted in the target image.
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