CN114657712B - Pattern pattern optimization method based on edge recognition - Google Patents
Pattern pattern optimization method based on edge recognition Download PDFInfo
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- CN114657712B CN114657712B CN202210237014.XA CN202210237014A CN114657712B CN 114657712 B CN114657712 B CN 114657712B CN 202210237014 A CN202210237014 A CN 202210237014A CN 114657712 B CN114657712 B CN 114657712B
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- D—TEXTILES; PAPER
- D05—SEWING; EMBROIDERING; TUFTING
- D05B—SEWING
- D05B19/00—Programme-controlled sewing machines
- D05B19/02—Sewing machines having electronic memory or microprocessor control unit
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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- Microelectronics & Electronic Packaging (AREA)
- Textile Engineering (AREA)
- Sewing Machines And Sewing (AREA)
Abstract
The invention discloses a pattern optimization method based on edge recognition. In order to solve the problems that the open loop control or the semi-closed loop control in the prior art is not suitable for large-batch pattern sewing and the deviation of the patterns of products is easy to exist; the invention comprises the following steps: s1: acquiring product sewing parameters in batches, and respectively shooting and acquiring image data streams after pattern sewing after the product sewing is finished; s2: by constructing an identification frame for the whole product and the pattern, calculating the relation between the proportion and the position, comparing the relation with parameters in a database, and judging whether adjustment parameters need to be generated or not; s3: matrixing a recognition frame of the pattern, acquiring edge areas of the pattern, pixelating the edge areas, and comparing the edge areas with a set resolution and pixel interval threshold value to judge whether adjustment parameters need to be generated; s4: parameters of the corresponding sewing equipment are adjusted. And the two-stage judgment and feedback are correspondingly adjusted, so that the consistency of the product and the template is ensured in the mass production process.
Description
Technical Field
The invention relates to the field of parameter optimization of sewing machines, in particular to a pattern optimization method based on edge recognition.
Background
In the conventional automatic sewing machine, a predetermined stitch pattern is generally formed by forming a stitch while moving a cloth in a X, Y direction based on sewing pattern data. Open loop control is often used to drive the respective pulse motors or semi-closed loop control.
However, the sewing of an automatic sewing machine using an open loop control or a semi-closed loop control is liable to cause inconsistency of sewing patterns in the mass production process. Even because of hardware reasons such as equipment wear, cause stitch position skew, the pattern position of same batch of products is not unified, has the deviation.
For example, an "automatic sewing machine" disclosed in chinese patent literature, which has a needle with a bulletin number CN 101100789B; a positioning unit having an operation amount detecting unit for detecting an operation amount of the pulse motor for moving the cloth holding member to an arbitrary position; a needle position detection unit that detects a needle position in the up-down direction; a pattern storage unit for storing sewing pattern data; an operation control unit that outputs command pulses to the pulse motor one by one in a single pulse while maintaining a predetermined deviation between the command pulses and an actual operation; and a required time storage unit which stores a required time for outputting the instruction pulse, and the motion control unit starts outputting the instruction pulse with reference to the required time at a timing when the instruction pulse output is completed when the needle reaches the object to be sewn on the needle board.
The scheme is not suitable for pattern sewing in a large batch, and pattern deviation is easy to exist among products in the same batch.
Disclosure of Invention
The invention mainly solves the problems that the prior art open-loop control or semi-closed-loop control is not suitable for large-batch pattern sewing and product pattern deviation is easy to exist; a pattern optimization method based on edge recognition is provided.
The technical problems of the invention are mainly solved by the following technical proposal:
the pattern optimization method based on edge recognition is characterized by comprising the following steps of:
s1: acquiring product sewing parameters in batches, and respectively shooting and acquiring image data streams after pattern sewing after the product sewing is finished;
s2: constructing a recognition frame for the whole product and the pattern patterns, and matching and recognizing the pattern patterns and the positions of the pattern patterns in the product; comparing the parameters with parameters in a database, and judging whether adjustment parameters need to be generated or not;
s3: matrixing a recognition frame of the pattern, acquiring edge areas of the pattern, pixelating the edge areas, and comparing the edge areas with a set resolution and pixel interval threshold value to judge whether adjustment parameters need to be generated;
s4: and (3) acquiring the adjustment parameters in the step S2 and the step S3, and adjusting the parameters of the corresponding sewing equipment.
Judging whether the sewn product has deviation from the template or not through two-stage judgment of pattern position judgment and edge stitch position judgment, and feeding back to perform corresponding adjustment, so that the consistency of the product and the template is ensured to be maintained in the mass production process. Only the corresponding parameters of the sewing equipment are regulated, and personalized regulation is carried out.
Preferably, the step S2 specifically includes:
s201: respectively constructing a product identification frame and a pattern identification frame according to the points of the product and the extreme edge of the pattern;
s202: matching in a database according to the pattern shape in the pattern recognition frame to obtain product sewing information corresponding to the pattern;
s203: obtaining the proportion and position data of the actual pattern and the product through the proportion relation and the position relation of the pattern recognition frame and the product recognition frame;
s204: comparing the ratio and position data of the actual pattern and the product with corresponding product sewing information in a database to obtain a ratio error and a position error; judging whether the proportional error and the position error are in the corresponding error domain, if so, entering the next step, otherwise, generating the adjustment parameters.
The product sewing information comprises the relative positions of the pattern patterns and the products and the proportion of the pattern patterns to the products; and (3) calculating the relation between the pattern and the product through the relation of the identification frame, and judging whether parameter adjustment is needed through the error between the pattern and the template. The adjusted parameters are sewing parameters which influence the positions and the proportions of the pattern.
Preferably, the points of the two lateral edges of the product are used as the standard to form a vertical frame of the product; forming a transverse frame of the product by taking points at the edges of the two vertical sides of the product as standards; the closed area formed by the vertical product frame and the horizontal product frame is a product identification frame;
forming a pattern vertical frame by taking points at the edges of the two lateral sides of the pattern as standards; forming a pattern transverse frame by taking points at the extreme edges of the two vertical sides of the pattern as standards; the enclosed area formed by the vertical pattern frame and the horizontal pattern frame is a pattern recognition frame.
And the product identification frame and the pattern identification frame are constructed, the relation between the pattern and the product is calculated through the relation of the identification frames, and the calculation is simpler and more convenient.
Preferably, the step S3 specifically includes the following steps:
s301: uniformly dividing the pattern recognition frame into a plurality of matrix areas;
s302: traversing the matrix area, and respectively judging whether the edges of the pattern patterns exist in the matrix area; if yes, entering the next step; otherwise, eliminating the corresponding matrix area;
s303: respectively pixelating the matrix area to obtain the minimum resolution when a single pixel is only a pattern or a background;
s304: calculating the distance between the tip pixels of the edge of the pattern;
s305: comparing the resolution and the pixel spacing of the tip with preset thresholds respectively, and entering the next step if the resolution and the pixel spacing of the tip are both within the threshold range; otherwise, generating corresponding adjustment parameters.
The adjustment parameters generated in the step correspond to the sewing precision of the stitch, so that the stitch is denser, and burrs are reduced.
Preferably, if the area ratio of the pattern area to the matrix area is 95% or less or 5% or less, the matrix area is removed.
And matrix areas with small influence on the stitch edges are eliminated, so that the influence is reduced, the calculation is more accurate, and the calculation efficiency is improved.
Preferably, the determination of the minimum resolution is:
a. continuously reducing the size of the single pixel in a rated proportion;
b1. filling all pixels into the pattern when the area ratio of the pattern in each pixel is more than 75%;
b2. filling all pixels into the background when the area ratio of the pattern in each pixel is less than 30%;
b3. and (c) returning to the step (a) to continuously reduce the pixel size when the area ratio of the pattern in the single pixel is more than or equal to 30% and less than or equal to 75%.
The individual pixel size is continually reduced until a minimum resolution is obtained where the individual pixel is merely a design pattern or background.
Preferably, if the generated adjustment parameters include locked controller parameters, the corresponding parameters are differenced, whether the difference exceeds a modification threshold value of the corresponding controller parameters is judged, and if yes, an alarm is provided for the corresponding user; if not, the locked controller parameters are maintained. The parameters are prevented from being tampered randomly, and the data security is guaranteed.
The beneficial effects of the invention are as follows:
1. judging whether the sewn product has deviation from the template or not through two-stage judgment of pattern position judgment and edge stitch position judgment, and feeding back to perform corresponding adjustment, so that the consistency of the product and the template is ensured to be maintained in the mass production process.
2. And the sewing parameters are batched, the feedback data is adjusted in a personalized way, and only the corresponding sewing equipment parameters are adjusted, so that the local optimization is realized, and the efficiency is improved.
3. And the product identification frame and the pattern identification frame are constructed, the relation between the pattern and the product is calculated through the relation of the identification frames, and the calculation is simpler and more convenient.
Drawings
FIG. 1 is a flow chart of a pattern optimization method based on edge recognition.
Detailed Description
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings.
Examples:
the pattern optimization method based on edge recognition in this embodiment, as shown in fig. 1, includes the following steps:
s1: and acquiring product sewing parameters in batches, and respectively shooting and acquiring image data streams after pattern sewing after the product sewing is finished.
And the operator inputs the sewing parameters of the products in batches, and carries out controller configuration operation, locking operation of the controller parameters and/or unlocking operation of the controller parameters according to the user permission.
And calling a controller parameter configuration interface according to the controller parameter configuration instruction, and executing configuration operation of the controller parameters through the controller parameter configuration interface.
And calling a configuration interface of the locking identification bit according to a locking instruction of the locking identification bit associated with each controller parameter, and triggering the locking of the locking identification bit associated with each controller parameter through the configuration interface of the locking identification bit so as to execute the locking operation of the controller parameter.
And according to the unlocking instruction of the user, the unlocking operation of the locked controller parameter is executed by modifying the locking identification bit of the locked controller parameter.
Shooting products produced by each sewing device through a camera to obtain corresponding product image data streams.
S2: constructing a recognition frame for the whole product and the pattern patterns, and matching and recognizing the pattern patterns and the positions of the pattern patterns in the product; and comparing the parameters with parameters in a database, and judging whether the adjustment parameters need to be generated.
S201: and respectively constructing a product identification frame and a pattern identification frame according to the points of the product and the extreme edge of the pattern.
Forming a vertical frame of the product by taking points at the edges of the two lateral sides of the product as standards; forming a transverse frame of the product by taking points at the edges of the two vertical sides of the product as standards; the closed area formed by the vertical product frame and the horizontal product frame is a product identification frame.
Forming a pattern vertical frame by taking points at the edges of the two lateral sides of the pattern as standards; forming a pattern transverse frame by taking points at the extreme edges of the two vertical sides of the pattern as standards; the enclosed area formed by the vertical pattern frame and the horizontal pattern frame is a pattern recognition frame.
And the product identification frame and the pattern identification frame are constructed, the relation between the pattern and the product is calculated through the relation of the identification frames, and the calculation is simpler and more convenient.
S202: and matching in a database according to the pattern shape in the pattern recognition frame to obtain the product sewing information corresponding to the pattern.
The product sewing information comprises the relative positions of the pattern patterns and the products and the proportion of the pattern patterns and the products.
S203: and obtaining the proportion and position data of the actual pattern and the product through the proportion relation and the position relation of the pattern recognition frame and the product recognition frame.
In this embodiment, a coordinate system is established with the lower left corner of the product identification frame as the origin. The ratio of the pattern to the product can be obtained by the length and the width of the product identification frame and the pattern identification frame.
The position relation between the pattern and the product can be obtained through the coordinates of the center point of the pattern recognition frame and the center point of the product recognition frame.
S204: and comparing the ratio and position data of the actual pattern and the product with corresponding product sewing information in a database to obtain a ratio error and a position error.
Judging whether the proportional error and the position error are in the corresponding error domain, if so, entering the next step, otherwise, generating the adjustment parameters. The adjusted parameters are sewing parameters which influence the positions and the proportions of the pattern.
And (3) calculating the relation between the pattern and the product through the relation of the identification frame, and judging whether parameter adjustment is needed through the error between the pattern and the template.
S3: and matrixing the identification frame of the pattern, acquiring edge regions of the pattern, pixelating the edge regions, and comparing the pixelated edge regions with a set resolution and pixel interval threshold value to judge whether adjustment parameters are required to be generated.
S301: the pattern recognition frame is uniformly divided into a plurality of matrix areas.
S302: traversing the matrix area, and respectively judging whether the edges of the pattern patterns exist in the matrix area; if yes, entering the next step; otherwise, eliminating the corresponding matrix area.
And if the area ratio of the pattern to the matrix area is more than or equal to 95% or less than or equal to 5%, eliminating the matrix area.
And matrix areas with small influence on the stitch edges are eliminated, so that the influence is reduced, the calculation is more accurate, and the calculation efficiency is improved.
S303: the matrix areas are pixelated separately, resulting in a minimum resolution when the individual pixels are only in the pattern or background.
The determination process of the minimum resolution is:
a. the size of individual pixels is continually reduced at nominal scale. In this embodiment, the least bisection method is used.
b1. When the area ratio of the pattern in a single pixel is more than 75%, the pixels are all filled with the pattern.
b2. When the area ratio of the pattern in a single pixel is less than 30%, the pixel is fully filled as a background.
b3. And (c) returning to the step (a) to continuously reduce the pixel size when the area ratio of the pattern in the single pixel is more than or equal to 30% and less than or equal to 75%.
The individual pixel size is continually reduced until a minimum resolution is obtained where the individual pixel is merely a design pattern or background.
S304: the spacing between pattern edge tip pixels is calculated. And identifying the edge of the pattern, and acquiring the stitch tip of the edge to obtain a tip pixel. The spacing between adjacent tip pixels is calculated.
S305: comparing the resolution and the pixel spacing of the tip with preset thresholds respectively, and entering the next step if the resolution and the pixel spacing of the tip are both within the threshold range; otherwise, generating corresponding adjustment parameters.
The adjustment parameters generated in the step correspond to the sewing precision of the stitch, so that the stitch is denser, and burrs are reduced.
S4: and (3) acquiring the adjustment parameters in the step S2 and the step S3, and adjusting the parameters of the corresponding sewing equipment.
If the generated adjustment parameters comprise locked controller parameters, making a difference between the corresponding parameters, judging whether the difference exceeds a modification threshold value of the corresponding controller parameters, and if so, providing an alarm for a corresponding user; if not, the locked controller parameters are maintained. The parameters are prevented from being tampered randomly, and the data security is guaranteed.
According to the scheme of the embodiment, whether the sewn product has deviation from the template or not is judged through two-stage judgment of pattern position judgment and edge stitch position judgment, and corresponding adjustment is fed back, so that the consistency of the product and the template is ensured in a mass production process. Only the corresponding parameters of the sewing equipment are regulated, and personalized regulation is carried out.
It should be understood that the examples are only for illustrating the present invention and are not intended to limit the scope of the present invention. Further, it is understood that various changes and modifications may be made by those skilled in the art after reading the teachings of the present invention, and such equivalents are intended to fall within the scope of the claims appended hereto.
Claims (5)
1. The pattern optimization method based on edge recognition is characterized by comprising the following steps of:
s1: acquiring product sewing parameters in batches, and respectively shooting and acquiring image data streams after pattern sewing after the product sewing is finished;
s2: constructing a recognition frame for the whole product and the pattern patterns, and matching and recognizing the pattern patterns and the positions of the pattern patterns in the product; comparing the parameters with parameters in a database, and judging whether adjustment parameters need to be generated or not;
s3: matrixing a recognition frame of the pattern, acquiring edge areas of the pattern, pixelating the edge areas, and comparing the edge areas with a set resolution and pixel interval threshold value to judge whether adjustment parameters need to be generated;
s4: acquiring the adjustment parameters in the step S2 and the step S3, and adjusting the parameters of corresponding sewing equipment;
the step S2 specifically includes:
s201: respectively constructing a product identification frame and a pattern identification frame according to the points of the product and the extreme edge of the pattern;
s202: matching in a database according to the pattern shape in the pattern recognition frame to obtain product sewing information corresponding to the pattern;
s203: obtaining the proportion and position data of the actual pattern and the product through the proportion relation and the position relation of the pattern recognition frame and the product recognition frame;
s204: comparing the ratio and position data of the actual pattern and the product with corresponding product sewing information in a database to obtain a ratio error and a position error; judging whether the proportional error and the position error are in the corresponding error domain, if so, entering the next step, otherwise, generating an adjustment parameter;
forming a vertical frame of the product by taking points at the edges of the two lateral sides of the product as standards; forming a transverse frame of the product by taking points at the edges of the two vertical sides of the product as standards; the closed area formed by the vertical product frame and the horizontal product frame is a product identification frame;
forming a pattern vertical frame by taking points at the edges of the two lateral sides of the pattern as standards; forming a pattern transverse frame by taking points at the extreme edges of the two vertical sides of the pattern as standards; the enclosed area formed by the vertical pattern frame and the horizontal pattern frame is a pattern recognition frame.
2. The pattern optimization method based on edge recognition according to claim 1, wherein the step S3 specifically comprises the following steps:
s301: uniformly dividing the pattern recognition frame into a plurality of matrix areas;
s302: traversing the matrix area, and respectively judging whether the edges of the pattern patterns exist in the matrix area; if yes, entering the next step; otherwise, eliminating the corresponding matrix area;
s303: respectively pixelating the matrix area to obtain the minimum resolution when a single pixel is only a pattern or a background;
s304: calculating the distance between the tip pixels of the edge of the pattern;
s305: comparing the resolution and the pixel spacing of the tip with preset thresholds respectively, and entering the next step if the resolution and the pixel spacing of the tip are both within the threshold range; otherwise, generating corresponding adjustment parameters.
3. The pattern optimization method based on edge recognition according to claim 2, wherein the matrix area is rejected if the area ratio of the pattern to the matrix area is 95% or more or 5% or less.
4. The pattern optimization method based on edge recognition according to claim 2, wherein the minimum resolution determination process is:
a. continuously reducing the size of the single pixel in a rated proportion;
b1. filling all pixels into the pattern when the area ratio of the pattern in each pixel is more than 75%;
b2. filling all pixels into the background when the area ratio of the pattern in each pixel is less than 30%;
b3. and (c) returning to the step (a) to continuously reduce the pixel size when the area ratio of the pattern in the single pixel is more than or equal to 30% and less than or equal to 75%.
5. The pattern optimization method based on edge recognition according to claim 1, wherein if the generated adjustment parameters include locked controller parameters, the corresponding parameters are differenced, whether the difference exceeds a modification threshold value of the corresponding controller parameters is judged, and if yes, an alarm is provided for the corresponding user; if not, the locked controller parameters are maintained.
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