CN108519066B - Method for objectively evaluating fabric flatness based on four-side light source image - Google Patents
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Abstract
The invention discloses an objective evaluation method of fabric flatness based on four-side light source images; the method comprises the following steps: collecting three-dimensional plastic standard samples and fabric samples to be evaluated at various levels by using the same four-side light source image collecting device to respectively obtain a data set of each collected object; preprocessing each acquired data set; performing feature extraction on each preprocessed data group to obtain the features of the plastic standard sample or the fabric sample; and calculating the similarity between the characteristics of the fabric sample and the characteristics of the standard samples of all grades by using the characteristics extracted from each data group and a designed similarity evaluation model, and objectively evaluating the flatness grade of the fabric sample by taking the similarity as a basis. The invention has the characteristics of extremely high detection precision, objectivity, stability, reproducibility and automation.
Description
Technical Field
The invention relates to a textile performance testing method, in particular to a method for evaluating the flatness of a fabric based on image processing.
Background
The daily washing and protecting process of the family and the industrial washing process have various influences on the fabrics, so that the appearance characteristics of the fabrics are changed, and the aesthetic feeling and the use value of the fabrics are reduced. Therefore, the evaluation of the performance of maintaining the original apparent characteristics of the fabrics after washing and care plays an important role in textile trade and quality monitoring. The evaluation of the fabric flatness refers to the evaluation of the performance of maintaining the original flat and stiff appearance of the fabric after washing and drying treatment, and the performance has important influence on the appearance of the fabric after washing and protecting and is an important evaluation index of the fabric appearance and the use performance. Therefore, the American Association of textile chemists and dyeing assistants establishes relevant standards (such as AATCC 124) for standard classification of fabric flatness; GB/T13769 also sets out test methods for assessing the smoothness of the appearance of fabrics after washing. In the above standards, the evaluation of the fabric flatness mainly depends on subjective evaluation of the appearance of the fabric under the standard environment observed by an evaluator and compared with a standard template. The subjective method depends on the influence of physiological, psychological and environmental factors of appraisers, and has uncontrollable precision and irreproducibility. Although the method is still widely applied to the field of textile detection at present, the requirements of stability, reproducibility, automation and the like on the fabric flatness evaluation method in practical application are difficult to meet. Therefore, an objective assessment of the flatness of the fabric is required.
Disclosure of Invention
The invention aims to provide a stable, reproducible and automatic objective evaluation method for the fabric flatness based on four-side light source images.
The invention adopts the following technical scheme to solve the technical problems
An objective evaluation method for fabric flatness based on four-side light source images comprises the following specific steps:
and 4, calculating the similarity between the characteristics of the fabric sample and the characteristics of the standard samples of all grades by using the extracted characteristics of each data group and a designed similarity evaluation model, and objectively evaluating the flatness grade of the fabric sample by taking the similarity as a basis.
As a further preferable scheme of the objective evaluation method for fabric flatness grade based on the four-side light source image of the present invention, in step 1, the same four-side light source image acquisition device is used to acquire the three-dimensional plastic standard sample and the fabric sample to be evaluated at each grade, and the data set of each acquisition object is obtained respectively, specifically, the acquisition objects are sequentially placed on the image acquisition platform, the strip light sources with the length larger than the acquisition area and fixed on the four sides of the acquisition platform are respectively turned on, and the CCD camera fixed on the image acquisition platform support is used to respectively perform vertical gray level image acquisition on the acquisition objects, and the four acquired gray level images constitute the data set of the acquisition objects.
As a further preferable scheme of the objective evaluation method for fabric flatness grade based on four-side light source images of the present invention, in step 2, the preprocessing is performed on each acquired data set, that is, the four image data in each data set are respectively preprocessed to form a preprocessed image data set, and specifically, the preprocessing of each image data mainly includes the following steps:
step 2.1, denoising the gray image data by using a median filter for the image data;
step 2.2, solving the mean value of all pixel data of the image data after median filtering by using the image data after median filtering to obtain the global mean value of the image after median filtering;
step 2.3, performing two-dimensional quadratic polynomial fitting by using the image data subjected to median filtering, and subtracting the global mean value of the image subjected to median filtering from the fitting result to obtain the integral illumination deviation of the image;
and 2.4, subtracting the integral illumination deviation of the obtained image from the image data after median filtering to realize integral illumination uniformity correction of the image and obtain the image data after preprocessing.
As a further preferable scheme of the objective evaluation method for fabric flatness grade based on four-side light source images of the present invention, the feature extraction is performed on each preprocessed data group to obtain the features of the plastic standard sample or the fabric sample, and the feature extraction is performed on four image data in each preprocessed data group by using the imaging features of fabric folds in the side light source environment, and the fold sharpness distribution is respectively extracted from the four image data in each preprocessed data group to jointly form the features of each data group, specifically, the feature extraction on each preprocessed image data mainly comprises the following steps:
step 3.1, positioning the edge position in the preprocessed image data by using a Canny operator;
step 3.2, calculating the absolute value of the maximum gradient of the position of the image edge point in the illumination direction by using the obtained edge position, wherein the absolute value is called the wrinkle sharpness;
step 3.3, according to the image size, taking a certain number of maximum fold sharpness values in the front to calculate the frequency of each value, namely fold sharpness distribution;
as a further preferable scheme of the objective evaluation method for the fabric flatness grade based on the four-side light source image of the present invention, the objective evaluation of the flatness grade of the fabric sample based on the similarity between the features of the fabric sample and the features of the standard sample of each grade calculated by using the features extracted from each data set and the designed similarity evaluation model, and the objective evaluation of the flatness grade of the fabric sample based on the similarity mainly comprises the following steps:
step 4.1, calculating a correlation coefficient by utilizing each sharpness distribution of four fold sharpness distributions contained in the characteristics of the fabric sample and four fold sharpness distributions in the characteristics of six grades of plastic standards to respectively form a similarity matrix of 6x4, wherein elements Ai and j in the matrix represent the correlation coefficient of the sharpness distribution of the fabric sample and the jth sharpness distribution of the ith grade of plastic standard, and four similarity matrices can be obtained for each fabric sample;
step 4.2, utilizing four similarity matrixes obtained by the characteristics of each fabric sample to calculate the maximum value of each row of each similarity matrix to obtain four 6-dimensional single-side similarity vectors, wherein an element Bi in each vector represents the maximum similarity between the wrinkle sharpness distribution of the fabric corresponding to the vector and the four wrinkle sharpness distributions of the ith grade plastic standard sample, and for each fabric sample, four single-side similarity vectors can be obtained;
step 4.3, summing the four obtained single-side similarity vectors to obtain a 6-dimensional total similarity vector, wherein a vector element Ci represents the total similarity of the fabric sample and the plastic standard sample of the ith grade;
and 4.4, taking the dimension of the maximum value of the total similarity vector by using the obtained total similarity vector, wherein the grade of the plastic standard sample corresponding to the dimension is the objective evaluation grade of the fabric sample.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
aiming at the defects of strong subjectivity, poor stability, poor repeatability and the like of the conventional fabric flatness testing method, the invention provides the objective evaluation method of the fabric flatness based on the four-side light source image, so that the influence of factors such as physiology, psychology and objective environment of evaluation personnel on an evaluation result is reduced, the evaluation result is objective and stable, and the flatness of different fabric samples can be truly and reliably evaluated.
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FIG. 1 is a schematic flow chart of the objective evaluation method for fabric flatness based on four-side light source images according to the present invention;
FIG. 2 is a gray scale image data acquired by a side light source of the DP 1 plastic standard, the light source being located at the upper side of the image acquisition area;
FIG. 3 is a schematic diagram of Canny edge detection for the image of FIG. 2;
FIG. 4 is a histogram of the wrinkle sharpness distribution of FIG. 1;
fig. 5 is a schematic diagram of the total similarity vector of the test fabric sample.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
in order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
The invention provides an objective evaluation method of fabric flatness based on four-side light source images, which is characterized by comprising the following specific steps:
and 4, calculating the similarity between the characteristics of the fabric sample and the characteristics of the standard samples of all grades by using the extracted characteristics of each data group and a designed similarity evaluation model, and objectively evaluating the flatness grade of the fabric sample by taking the similarity as a basis.
Referring to fig. 1, a flow chart of a method for objectively evaluating the flatness of a fabric based on a four-side light source image according to a preferred embodiment of the present invention is shown.
The method of the embodiment comprises the following steps:
step 1: and (3) acquiring the three-dimensional plastic standard samples and the fabric samples to be evaluated at all levels by using the same four-side light source image acquisition device, and respectively obtaining a data set of each acquired object in the step, wherein the acquired objects are all the plastic standard samples and the fabric samples. There were 6 plastic standards, DP 1, DP 2, DP 3, DP 3.5, DP 4, DP 5, provided respectively for GB/T13769, defined in order as the 1 st to 6 th grade plastic standards. The fabric samples were 1 piece in total. The size of the acquisition platform is 40cm multiplied by 40cm, the light sources on four sides are all 60cm multiplied by 5cm, and the light sources are respectively fixed at the 15cm positions of the outer sides of the four edges of the acquisition platform and are parallel to the edges of the acquisition platform. The CCD camera model is Canon EOS 200D, is arranged at a position 50cm right above the center of the acquisition platform, and acquires gray level image data vertically to the acquisition platform, wherein the actual size of an acquisition area is 20cm multiplied by 30cm, the acquired image data is 648 pixels multiplied by 432 pixels, and the gray level value interval is [0,255 ]. The whole collecting device is arranged in a darkroom.
Through the device, all the collected objects including 6 plastic standard samples and 1 sample cloth are shot for four times respectively. In the four times of shooting, the light sources on the four sides are respectively turned on for each time of shooting, four gray level images corresponding to different side light sources are obtained, and a data group of the collected object is formed. Referring to fig. 2, the grayscale image data acquired by the side light source of the DP 1 plastic standard sample is shown, and the light source is located at the upper side of the image acquisition area.
Step 2: preprocessing each acquired data set
In this step, the four image data in each data set are respectively preprocessed to form a preprocessed image data set, and specifically, the preprocessing of each image data mainly includes the following steps:
and 2.1, setting the image data as a matrix I, and denoising the grayscale image data by using a median filter for the image data, wherein preferably, the size of the filter is selected to be 3 x 3, and the filtered image is a matrix Im.
Step 2.2, solving the mean value of all pixel data of the image data after median filtering by using the image data after median filtering to obtain the global mean value of the image after median filtering, and enabling the global mean value to be m;
and 2.3, performing two-dimensional quadratic polynomial fitting by using the image data subjected to median filtering to obtain a result of a matrix F with the same size as the original image I, and subtracting the global mean m of the image subjected to median filtering from the fitting result to obtain the overall illumination deviation D of the image, wherein the overall illumination deviation D is shown as the following formula:
D＝F-m
step 2.4, subtracting the overall illumination deviation D of the obtained image from the image data Im after median filtering to realize the overall illumination uniformity correction of the image, and obtaining the image data Ip after preprocessing, as shown in the following formula:
Ip＝Im-D
and step 3: performing feature extraction on each preprocessed data group to obtain the features of the plastic standard sample or the fabric sample
In this step, using the imaging characteristics of the fabric fold in the side light environment, the fold sharpness distribution is respectively extracted for the four image data in each preprocessed data set, and the features of each data set are jointly formed, specifically, the feature extraction for each preprocessed image data mainly includes the following steps:
step 3.1, utilizing a Canny operator to position the edge position in the preprocessed image data, preferably, respectively taking the upper threshold and the lower threshold of the Canny operator as 0.1 and 0.05, respectively, taking the variance of a filter as 1, and referring to fig. 3, wherein the diagram is a Canny edge detection schematic diagram of the image in fig. 2;
and 3.2, calculating the absolute value of the maximum gradient of the position of the image edge point in the illumination direction by using the obtained edge position, wherein the absolute value is called the wrinkle sharpness. The size of the calculation region of the absolute value of the maximum gradient is preferably 5 × 1 when the illumination direction is the image vertical direction, and 1 × 5 when the illumination direction is the image horizontal direction;
and 3.3, according to the image size of 648 pixels × 432 pixels, taking the first n maximum fold acutances and counting the frequency H of each value, wherein the frequency H is a vector with one dimension of 256 and is called fold acutance distribution. Preferably, the selected number n is 3240, and is obtained by the following formula:
n＝α×(h×w)
h and w are the pixel height and width of the image respectively, and a is a coefficient parameter, preferably, the value is 3. Referring to fig. 4, a histogram of the wrinkle sharpness distribution of fig. 1 is shown.
And 4, step 4: the similarity between the characteristics of the fabric sample and the characteristics of the standard samples of all grades is calculated by using the characteristics extracted from each data group and a designed similarity evaluation model, and the flatness grade of the fabric sample is objectively evaluated based on the similarity
In this step, the objective assessment of the flatness rating of the features extracted from the data set for each fabric sample mainly comprises the following steps:
step 4.1, calculating correlation coefficients by using each of the four fold sharpness distributions contained in the characteristics of the fabric sample and the four fold sharpness distributions in the characteristics of the six grade plastic standards respectively to form a similarity matrix A of 6x4, wherein the element A in the matrix isi,jA correlation coefficient representing the sharpness distribution of the fabric sample with the jth wrinkle sharpness distribution of the ith grade of plastic standard, for each fabric sample, four similarity matrices are available; specifically, the matrix a can be obtained by the following formula:
Ai，j＝cov(Hmi，j，H)i＝1，2，3...6j＝1，2，3，4
wherein, Hmi,jJth fold sharpness distribution for the ith grade of plastic standards, H is any of the four fold sharpness distributions for the fabric samples, cov() As a function of the correlation coefficient.
Step 4.2, utilizing four similarity matrixes obtained from the characteristics of each fabric sample to calculate the maximum value of each row of each similarity matrix to obtain four 6-dimensional single-side similarity vectors B, wherein elements B in each vectoriRepresenting the maximum similarity between the wrinkle sharpness distribution of the fabric corresponding to the vector and four wrinkle sharpness distributions of the ith grade plastic standard sample, and obtaining four single-side similarity vectors for each fabric sample; for any one of the unilateral similarity vectors B, the calculation can be expressed by the following formula:
step 4.3, (3) using the obtained four unilateral similarity vectors to sum up to obtain a 6-dimensional total similarity vector, vector element CiRepresents the total similarity of the fabric sample to the ith grade of plastic standard sample; the calculation formula is as follows:
wherein,
is the single-sided similarity vector of the jth and plastic standard of the fabric sample, and the plastic standard is the ith grade plastic standard.
And 4.4, taking the dimension of the maximum value of the total similarity vector by using the obtained total similarity vector, wherein the grade of the plastic standard sample corresponding to the dimension is the objective evaluation grade of the fabric sample. Referring to fig. 5, a diagram of the total similarity vector of the tested fabric sample shows that the fabric sample belongs to the class DP 2.
Those of ordinary skill in the art will understand that: the invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (1)
1. An objective evaluation method for fabric flatness based on four-side light source images is characterized by comprising the following specific steps:
step 1, collecting three-dimensional plastic standard samples and fabric samples to be evaluated at various levels by using the same four-side light source image collecting device to respectively obtain a data set of each collected object;
step 2, preprocessing each data set acquired in the step 1;
step 3, extracting the characteristics of each preprocessed data group to obtain the characteristics of the plastic standard sample or the fabric sample;
step 4, calculating the similarity between the characteristics of the fabric sample and the characteristics of the standard samples of all grades by using the extracted characteristics of each data group and a designed similarity evaluation model, and objectively evaluating the flatness grade of the fabric sample by taking the similarity as a basis;
in step 1, the same four-side light source image acquisition device is used for acquiring three-dimensional plastic standards of various grades and fabric samples to be evaluated to respectively obtain data sets of each acquisition object, specifically, the acquisition objects are sequentially placed on an image acquisition platform, strip-shaped light sources which are fixed on the four sides of the acquisition platform and have the length larger than an acquisition area are respectively started, a CCD camera fixed on a bracket of the image acquisition platform is used for respectively acquiring gray level images in the vertical direction of the acquisition objects, and four acquired gray level images form the data sets of the acquisition objects;
in step 2, the preprocessing of each acquired data set is to preprocess the four image data in each data set respectively to form a preprocessed image data set, and specifically, the preprocessing of each image data mainly includes the following steps:
step 2.1, denoising the gray image data by using a median filter for the image data;
step 2.2, solving the mean value of all pixel data of the image data after median filtering by using the image data after median filtering to obtain the global mean value of the image after median filtering;
step 2.3, performing two-dimensional quadratic polynomial fitting by using the image data subjected to median filtering, and subtracting the global mean value of the image subjected to median filtering from the fitting result to obtain the integral illumination deviation of the image;
step 2.4, the integral illumination deviation of the obtained image is subtracted from the image data after median filtering to realize the integral illumination uniformity correction of the image and obtain the image data after pretreatment,
the feature extraction of each preprocessed data group is performed to obtain the features of the plastic standard sample or the fabric sample, and the feature extraction of each preprocessed image data mainly comprises the following steps of, by using the imaging features of fabric wrinkles in a side light source environment, respectively extracting wrinkle sharpness distribution for four image data in each preprocessed data group to jointly form the features of each data group:
step 3.1, positioning the edge position in the preprocessed image data by using a Canny operator;
step 3.2, calculating the absolute value of the maximum gradient of the position of the image edge point in the illumination direction by using the obtained edge position, wherein the absolute value is called the wrinkle sharpness;
step 3.3, according to the image size, taking a certain number of maximum wrinkle sharpness values to count the frequency of each value, namely wrinkle sharpness distribution;
the method comprises the following steps of utilizing the extracted features of each data group and utilizing a designed similarity evaluation model to calculate the similarity between the features of the fabric sample and the features of the standard samples of all grades, and objectively evaluating the flatness grade of the fabric sample by taking the similarity as a basis, specifically, objectively evaluating the flatness grade of the features extracted from the data group of each fabric sample, wherein the objectively evaluating the flatness grade mainly comprises the following steps:
step 4.1, calculating a correlation coefficient by utilizing each sharpness distribution of four fold sharpness distributions contained in the characteristics of the fabric sample and four fold sharpness distributions in the characteristics of six grades of plastic standards to respectively form a similarity matrix of 6x4, wherein elements Ai and j in the matrix represent the correlation coefficient of the sharpness distribution of the fabric sample and the jth sharpness distribution of the ith grade of plastic standard, and four similarity matrices can be obtained for each fabric sample;
step 4.2, utilizing four similarity matrixes obtained by the characteristics of each fabric sample to calculate the maximum value of each row of each similarity matrix to obtain four 6-dimensional single-side similarity vectors, wherein an element Bi in each vector represents the maximum similarity between the wrinkle sharpness distribution of the fabric corresponding to the vector and the four wrinkle sharpness distributions of the ith grade plastic standard sample, and for each fabric sample, four single-side similarity vectors can be obtained;
step 4.3, summing the four obtained single-side similarity vectors to obtain a 6-dimensional total similarity vector, wherein a vector element Ci represents the total similarity of the fabric sample and the plastic standard sample of the ith grade;
and 4.4, taking the dimension of the maximum value of the total similarity vector by using the obtained total similarity vector, wherein the grade of the plastic standard sample corresponding to the dimension is the objective evaluation grade of the fabric sample.
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