CN112487642A - Fatigue fracture morphology feature extraction method based on flooding filling algorithm - Google Patents
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Abstract
The invention discloses a fatigue fracture morphology feature extraction method based on a flooding filling algorithm, which comprises the following steps of: s1, preprocessing to obtain a preprocessed image; s2, performing flood filling segmentation numbering on the preprocessed image to obtain a sequence area; s3, drawing the image contour of the sequence area to obtain an image with a closed contour; acquiring an image characteristic value of a sequence region; s4, calculating the area of the region of the image with the closed contour; s5, calculating the intensity factor of each contour according to the area of the region; s6, performing linear fitting based on the image characteristic value and the intensity factor to obtain a fitting curve; and S7, acquiring the appearance characteristic of the fatigue fracture according to the image with the closed contour and the fitting curve. The problems of difficulty and inaccuracy in visual identification of gray level images obtained by an acquisition system and complex calculation of area and intensity factors in the prior art are solved, and the analysis conclusion is quicker and more accurate.
Description
Technical Field
The invention relates to the field of fatigue port morphology analysis, in particular to a fatigue fracture morphology feature extraction method based on a flooding filling algorithm.
Background
The fatigue fracture analysis of the material is to respectively carry out macroscopic analysis and microscopic analysis after the fracture surface of a sample is subjected to corrosion cleaning. The macroscopic analysis is to directly observe and analyze the broken parts by naked eyes, a magnifying glass and a solid microscope; the microscopic analysis is a method for imaging and analyzing the whole fracture, and mainly uses a transmission electron microscope and a scanning electron microscope. However, the magnification of macro analysis is usually 100 times or less, and it is difficult to determine and analyze the microstructure of the crack with the naked eye; the fracture images obtained by the electron microscope are amplified and subjected to gray scale processing, the lowest amplification factor is 1000 times, the highest resolution can only reach 0.01um, and most fracture images are observed and judged manually.
Fatigue cracks are divided into surface and internal initiation, differences between crack sources and plastic accumulation regions are difficult to distinguish due to accumulation of certain complex fracture morphologies such as plastic deformation, existing depressions are easy to be mistaken for crack sources, multiple expansion mechanisms often exist in a fatigue fracture morphology from initiation region nucleation of cracks, the cracks are enabled to expand from the sources along all directions, sometimes the expansion trend of the cracks is difficult to observe, and the like, and the defects are difficult to obtain and quickly determine through visual observation.
The number of the fatigue fracture cleavage surfaces is determined, manual measurement is needed, the area size calculation needs to extract the cleavage surface outline firstly, then the integral calculation of a computer is carried out, the statistical distribution of the occupation ratio needs to extract the whole crack propagation low-speed area and the area size of each cleavage surface outline calculation to obtain the occupation ratio of the cleavage surfaces, and the processes need frequent steps and calculation.
Disclosure of Invention
Aiming at the defects in the prior art, the fatigue fracture morphology feature extraction method based on the flooding filling algorithm provided by the invention provides a method for rapidly acquiring the fatigue fracture morphology feature.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the method for extracting the appearance characteristics of the fatigue fracture based on the flooding filling algorithm comprises the following steps:
s1, collecting fatigue fracture images and preprocessing the fatigue fracture images by using matlab to obtain preprocessed images;
s2, performing flood filling segmentation numbering on the preprocessed image to obtain a sequence area;
s3, drawing the image contour of the sequence area to obtain an image with a closed contour; acquiring image characteristic values of the sequence area, wherein the image characteristic values comprise entropy, gray average value and gray variance;
s4, calculating the area of the region of the image with the closed contour;
s5, calculating the intensity factor of each contour according to the area of the region;
s6, performing linear fitting based on the image characteristic value and the intensity factor to obtain a fitting curve;
and S7, acquiring the appearance characteristic of the fatigue fracture according to the image with the closed contour and the fitting curve.
Further, the specific method of step S1 is:
acquiring a fatigue fracture image through a digital camera, and performing graying, denoising and binarization processing by using matlab to obtain a preprocessed image;
or, carrying out image acquisition and gray level processing through a scanning electron microscope or a transmission electron microscope, and carrying out denoising and binarization processing by using matlab to obtain a preprocessed image.
Further, the specific method of step S2 includes the following sub-steps:
s2-1, calling a flodfil operator through VS software to carry out airspace, gradual change or fixed range, gradual change or floating range, four connected domain ranges with eight lower bits of operation identifier, eight connected domain ranges with eight lower bits of floating range and operation identifier, and obtaining a connected domain image of the preprocessed image through water filling;
and S2-2, sequentially carrying out region segmentation and sequence numbering according to the gradient change of the gray scale by taking the initial pixel point of the connected region image as a starting point to obtain a sequence region.
Further, the specific method of drawing the image contour of the sequence area in step S3 to obtain the image with the closed contour is as follows:
and searching the gray value at the sequence area boundary by adopting a mask function of VS software, and communicating boundary pixel points according to the gray value to form a closed contour to obtain an image with the closed contour.
Further, the specific method for acquiring the entropy of the sequence region in step S3 is as follows:
the method comprises the steps of loading an OpenCV library function in VS series software, searching all pixel points in each sequence region by applying a global threshold method to the sequence regions, and calling a doubtleentropy function to calculate the entropy of images in each sequence region.
Further, the specific method of step S5 is:
according to the formula:
calculating an intensity factor delta K; where n is a constant value of 0.6 when a crack is initiated from the surface and 0.5 when initiated from the interior; pi is the circumference ratio; are α is the area of the region.
Further, the specific method of step S6 is:
comparing the image characteristic values of the images with the closed contours to obtain the contrast of the images with the closed contours; and calling the image characteristic values by adopting Oringin software, respectively taking the entropy, the gray level average value, the gray level variance and the contrast of each image with a closed contour as independent variables, taking the intensity factor as a dependent variable, and performing linear function fitting to respectively obtain a fitting curve corresponding to the entropy, a fitting curve corresponding to the gray level average value, a fitting curve corresponding to the gray level variance and a fitting curve corresponding to the contrast of each image with a closed contour.
Further, the fatigue fracture morphology features in the step S7 include image features and numerical features; the image characteristics comprise a crack initiation source position, a plastic accumulation area, a crack trend and a stress deformation degree; the numerical characteristics include the magnitude relation and statistical distribution of the image characteristic values and the intensity factors obtained by linear fitting.
The invention has the beneficial effects that:
1. according to the method, the fracture morphology of the cracks under different fatigue lives can be quantitatively obtained by utilizing the sensitivity of the image pixel gray value and combining an image acquisition system and utilizing a flood filling operator and origin data fitting, and the crack source can be obtained by reversely deducing the crack trend in the contour extraction; calculating the area of the region to obtain the statistical occupation ratio of different crack appearances; the crack strength factors corresponding to different area areas can be obtained. The problems of difficulty and inaccuracy in visual identification of gray level images obtained by an acquisition system and complex calculation of area and intensity factors in the prior art are solved, and the analysis conclusion is quicker and more accurate.
2. The linear fitting part in the invention can represent the tissue stress strain response condition of each connected region of the fractured morphology, and has universal applicability.
3. The invention has simple and convenient calling and operation of the programming function and strong applicability.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is an original image in an embodiment;
FIG. 3 is a diagram illustrating the effect of extracting the low eight-bit eight-connected region of the operation identifier;
fig. 4 is a schematic diagram of the rendering of an image contour of a sequence region.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the fatigue fracture morphology feature extraction method based on the flooding filling algorithm includes the following steps:
s1, collecting fatigue fracture images and preprocessing the fatigue fracture images by using matlab to obtain preprocessed images;
s2, performing flood filling segmentation numbering on the preprocessed image to obtain a sequence area;
s3, drawing the image contour of the sequence area to obtain an image with a closed contour; acquiring image characteristic values of the sequence area, wherein the image characteristic values comprise entropy, gray average value and gray variance;
s4, calculating the area of the region of the image with the closed contour;
s5, calculating the intensity factor of each contour according to the area of the region;
s6, performing linear fitting based on the image characteristic value and the intensity factor to obtain a fitting curve;
and S7, acquiring the appearance characteristic of the fatigue fracture according to the image with the closed contour and the fitting curve.
The specific method of step S1 is:
acquiring a fatigue fracture image through a digital camera, and performing graying, denoising and binarization processing by using matlab to obtain a preprocessed image;
or, carrying out image acquisition and gray level processing through a scanning electron microscope or a transmission electron microscope, and carrying out denoising and binarization processing by using matlab to obtain a preprocessed image.
The specific method of step S2 includes the following substeps:
s2-1, calling a flodfil operator through VS software to carry out airspace, gradual change or fixed range, gradual change or floating range, four connected domain ranges with eight lower bits of operation identifier, eight connected domain ranges with eight lower bits of floating range and operation identifier, and obtaining a connected domain image of the preprocessed image through water filling;
and S2-2, sequentially carrying out region segmentation and sequence numbering according to the gradient change of the gray scale by taking the initial pixel point of the connected region image as a starting point to obtain a sequence region.
The specific method for drawing the image contour of the sequence area in step S3 to obtain an image with a closed contour is as follows: and searching the gray value at the sequence area boundary by adopting a mask function of VS software, and communicating boundary pixel points according to the gray value to form a closed contour to obtain an image with the closed contour.
The specific method for acquiring the entropy of the sequence region in step S3 is as follows: the method comprises the steps of loading an OpenCV library function in VS series software, searching all pixel points in each sequence region by applying a global threshold method to the sequence regions, and calling a doubtleentropy function to calculate the entropy of images in each sequence region.
The specific method of step S5 is: according to the formula:
calculating an intensity factor delta K; where n is a constant value of 0.6 when a crack is initiated from the surface and 0.5 when initiated from the interior; pi is the circumference ratio; are α is the area of the region.
The specific method of step S6 is: comparing the image characteristic values of the images with the closed contours to obtain the contrast of the images with the closed contours; and calling the image characteristic values by adopting Oringin software, respectively taking the entropy, the gray level average value, the gray level variance and the contrast of each image with a closed contour as independent variables, taking the intensity factor as a dependent variable, and performing linear function fitting to respectively obtain a fitting curve corresponding to the entropy, a fitting curve corresponding to the gray level average value, a fitting curve corresponding to the gray level variance and a fitting curve corresponding to the contrast of each image with a closed contour.
The fatigue fracture morphology features in the step S7 comprise image features and numerical features; the image characteristics comprise a crack initiation source position, a plastic accumulation area, a crack trend and a stress deformation degree; the numerical characteristics include the magnitude relation and statistical distribution of the image characteristic values and the intensity factors obtained by linear fitting.
In an embodiment of the present invention, an original image is shown in fig. 2, a connected region image extracted by using a region eight-connected with eight lower bits of an operation identifier after the original image is preprocessed is shown in fig. 3, a sequence region obtained by segmenting and sequence numbering the connected region image is subjected to contour rendering, and an image with a closed contour is shown in fig. 4. And subsequent operation is carried out on the image with the closed contour, so that the fatigue disconnection morphological characteristics can be quickly extracted.
In the specific implementation process, fracture morphology analysis: the method mainly comprises two parts of image characteristics and numerical characteristics. The image features are: the method has the functions of determining the position of a crack initiation source, determining a plastic accumulation region, determining the trend of cracks and helping naked eyes to identify various special shapes such as a small plane, a fisheye, a dark region, a light area, an initiation region, an expansion region, a transient interruption region and the like. The extracted characteristic regions can be subjected to sequence numbering according to different gray values through analysis, the reaction components of the regions under the same number and characteristic value data are similar, and the stress deformation degree is equivalent; the extraction of the profile can analyze the crack trend, the crack accumulation part and the crack initiation source; the main functions of the numerical characteristics are to explain the magnitude relation and the statistical subsection of the image characteristic value and the intensity factor by linear fitting: 1. the statistical distribution of the fitting curve can determine the relationship between the image gray level chaos degree, the gray level average value, the variance, the image area contrast and the intensity factor of the entropy reaction. 2. The position and size distribution of the intensity factor on the whole section can be further realized according to the distribution of the sequence region where the intensity factor value is located on the whole image according to the size relation between the contrast of the image region and the intensity factor.
In conclusion, the fracture morphology of the cracks under different fatigue lives can be quantitatively obtained by utilizing the sensitivity of the image pixel gray value and combining an image acquisition system and utilizing the flooding filling operator and the origin data fitting, and the crack source can be obtained by reversely deducing the crack trend in the contour extraction; calculating the area of the region to obtain the statistical occupation ratio of different crack appearances; the crack strength factors corresponding to different area areas can be obtained. The problems of difficulty and inaccuracy in visual identification of gray level images obtained by an acquisition system and complex calculation of area and intensity factors in the prior art are solved, and the analysis conclusion is quicker and more accurate.
Claims (8)
1. A fatigue fracture morphology feature extraction method based on a flooding filling algorithm is characterized by comprising the following steps:
s1, collecting fatigue fracture images and preprocessing the fatigue fracture images by using matlab to obtain preprocessed images;
s2, performing flood filling segmentation numbering on the preprocessed image to obtain a sequence area;
s3, drawing the image contour of the sequence area to obtain an image with a closed contour; acquiring image characteristic values of the sequence area, wherein the image characteristic values comprise entropy, gray average value and gray variance;
s4, calculating the area of the region of the image with the closed contour;
s5, calculating the intensity factor of each contour according to the area of the region;
s6, performing linear fitting based on the image characteristic value and the intensity factor to obtain a fitting curve;
and S7, acquiring the appearance characteristic of the fatigue fracture according to the image with the closed contour and the fitting curve.
2. The method for extracting the morphology feature of the fatigue fracture based on the flooding filling algorithm is characterized in that the specific method in the step S1 is as follows:
acquiring a fatigue fracture image through a digital camera, and performing graying, denoising and binarization processing by using matlab to obtain a preprocessed image;
or, carrying out image acquisition and gray level processing through a scanning electron microscope or a transmission electron microscope, and carrying out denoising and binarization processing by using matlab to obtain a preprocessed image.
3. The method for extracting the morphology feature of the fatigue fracture based on the flooding filling algorithm is characterized in that the specific method of the step S2 comprises the following sub-steps:
s2-1, calling a flodfil operator through VS software to carry out airspace, gradual change or fixed range, gradual change or floating range, four connected domain ranges with eight lower bits of operation identifier, eight connected domain ranges with eight lower bits of floating range and operation identifier, and obtaining a connected domain image of the preprocessed image through water filling;
and S2-2, sequentially carrying out region segmentation and sequence numbering according to the gradient change of the gray scale by taking the initial pixel point of the connected region image as a starting point to obtain a sequence region.
4. The method for extracting the morphology feature of the fatigue fracture based on the flooding filling algorithm according to claim 1, wherein the step S3 is to draw the image contour of the sequence area, and the specific method for obtaining the image with the closed contour is as follows:
and searching the gray value at the sequence area boundary by adopting a mask function of VS software, and communicating boundary pixel points according to the gray value to form a closed contour to obtain an image with the closed contour.
5. The method for extracting the morphology feature of the fatigue fracture based on the flooding filling algorithm is characterized in that the specific method for acquiring the entropy of the sequence area in the step S3 is as follows:
the method comprises the steps of loading an OpenCV library function in VS series software, searching all pixel points in each sequence region by applying a global threshold method to the sequence regions, and calling a doubtleentropy function to calculate the entropy of images in each sequence region.
6. The method for extracting the morphology feature of the fatigue fracture based on the flooding filling algorithm is characterized in that the specific method in the step S5 is as follows:
according to the formula:
calculating an intensity factor delta K; where n is a constant value of 0.6 when a crack is initiated from the surface and 0.5 when initiated from the interior; pi is the circumference ratio; are α is the area of the region.
7. The method for extracting the morphology feature of the fatigue fracture based on the flooding filling algorithm is characterized in that the specific method in the step S6 is as follows:
comparing the image characteristic values of the images with the closed contours to obtain the contrast of the images with the closed contours; and calling the image characteristic values by adopting Oringin software, respectively taking the entropy, the gray level average value, the gray level variance and the contrast of each image with a closed contour as independent variables, taking the intensity factor as a dependent variable, and performing linear function fitting to respectively obtain a fitting curve corresponding to the entropy, a fitting curve corresponding to the gray level average value, a fitting curve corresponding to the gray level variance and a fitting curve corresponding to the contrast of each image with a closed contour.
8. The method for extracting the fatigue fracture morphology feature based on the flooding filling algorithm is characterized in that the fatigue fracture morphology feature in the step S7 comprises an image feature and a numerical feature; the image characteristics comprise a crack initiation source position, a plastic accumulation area, a crack trend and a stress deformation degree; the numerical characteristics include the magnitude relation and statistical distribution of the image characteristic values and the intensity factors obtained by linear fitting.
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