CN112784894A - Automatic labeling method for rock slice microscopic image - Google Patents
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Abstract
The invention discloses an automatic marking method of a rock slice microscopic image, which is improved aiming at the link of manually dotting and drawing a picture frame of the image, realizes the automatic marking of a complex rock slice image, classifies and marks different particle types in the same image, performs dotting and drawing on each particle, and gives a label name corresponding to the particle; the invention realizes the self-animation point-drawing frame of the rock slice image, greatly saves the time of expert marking, can effectively assist the expert in carrying out batch complex rock slice image marking work, has higher accuracy and greatly improves the working efficiency.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to an automatic labeling method for a rock slice microscopic image.
Background
The rock slice microscopic image is an image obtained by grinding a rock sample into a slice having a thickness of 0.03 mm, placing the slice on a polarizing microscope stage, and taking a photograph with a high-definition camera by a slice appraiser. In the field of geology, rock slice identification is performed by observing and analyzing the composition and content of minerals in rock slice images, and classifying and naming the same. However, in general, a rock slice image has numerous mineral particles, low contrast between different particles, and a complex internal microstructure of the same particle, which causes a difficult sandstone image identification work, and has a high requirement on an identification worker, and meanwhile, the identification conclusion has strong subjectivity, often different identification experts have different conclusions, and the identification efficiency is also low, and generally, an identification worker with abundant experience has only a numerical value for the sandstone slice that can be identified every day.
In the identification process, related labeled data is more important, original data is changed into algorithm available data in data labeling, and the traditional data labeling mainly depends on manual labeling of data such as texts and pictures, so that a large amount of manpower and material resources are consumed, and the requirement cannot be met gradually. Once complicated outline drawing is involved, the difficulty of manual labeling is greatly increased, so that the system provides an automatic and intelligent labeling method to assist manual labeling so as to improve the labeling efficiency.
At present, marking of the rock slices depends on manual marking of relevant experts, namely, the types of all particles are accurately judged on an image full of a plurality of rock particles and impurities, then a dot picture frame is described manually to draw the particle outlines, and finally corresponding labels are marked on different particles. The manual labeling method consumes time and energy of experts, and occasionally errors occur; when an expert points the particle outline through the marking software, if the particle in a picture exceeds 100, the long-time point memory is problematic, and the whole process is limited in energy.
Disclosure of Invention
Aiming at the defects in the prior art, the automatic marking method for the rock slice microscopic image provided by the invention solves the problems in the prior art.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: an automatic labeling method for a rock slice microscopic image comprises the following steps:
s1, acquiring an orthogonal polarization image and a single polarization image of the rock slice;
s2, carrying out image fusion processing on the orthogonal polarization image and the single polarization image to obtain a new fused image;
s3, performing superpixel segmentation on the fused image to obtain a binary image with particles and gap filler;
s4, performing edge detection on each area block in the binary image, and determining a corresponding edge coordinate point;
s5, training a U-net image semantic segmentation network based on different types of binary images with edge coordinate points;
and S6, taking the binary image to be labeled with the grains and the gap filler as the input of the U-net image semantic segmentation network, obtaining the output result with the grain type label of the rock slice image, and realizing automatic labeling of the rock slice microscopic image.
Further, the step S2 is specifically:
s21, performing illumination equalization processing on the single-polarized image with uneven illumination;
s22, performing smooth denoising processing on the single-polarized-light image subjected to illumination equalization processing;
s23, taking the single polarization image subjected to the smoothing and denoising processing as a reference image, and carrying out image alignment processing on the reference image and the orthogonal polarization image;
and S24, carrying out image fusion on the images subjected to the image alignment processing to obtain new fused images.
Further, the step S3 is specifically:
s31, extracting a gray level co-occurrence matrix of the fused new image by adopting a GLCM algorithm, and calculating a self-adaptive K value;
s32, performing edge detection segmentation on a new image fused by a self-adaptive K-value superpixel segmentation SLCM algorithm;
s33, performing region fusion on each region of the segmented image based on color and distance, combining the segmentation rules of polycrystalline bodies and rock debris, and combining quartz and feldspar particles of the polycrystalline bodies and rock debris particles to obtain a final image segmentation result;
and S34, forming a binary image with grains and fillers based on the image segmentation result.
Further, the gray level co-occurrence matrix in step S31 is P (i, j, d, θ), where d is a spatial distance, θ is a direction angle, i is a row where the start point is located, and j is a column where the start point is located;
the adaptive K value in step S31 is:
in the formula, X and Y are the length and width of an image, Energy is entropy, Contrast is Contrast, Asm is Energy, and Correlation is a Correlation parameter.
Further, the step S32 is specifically:
a1, uniformly distributing seed points in the fused new image according to the set number K of the super pixels;
a2, reselecting the seed points in the n x n area of each currently determined seed point;
a3, searching pixel points in the neighborhood of the reselected seed point, and distributing class labels to the pixel points;
and A4, repeating the steps A2-A3 until the searched clustering center of the pixel points does not change any more, and realizing the edge detection segmentation of the image based on the distribution class labels.
Further, the step S4 is specifically:
s41, carrying out edge detection on each area block in the binary image to obtain edge coordinates of each particle;
and S42, screening the edge coordinates of each particle based on a gradient screening method, and deleting redundant coordinates to obtain the final edge coordinate point of each particle.
Further, the step S5 is specifically:
s51, connecting the coordinate points corresponding to the area blocks to form a closed independent area, and adding labels and attributes to the area blocks;
s52, filling different colors in different types of independent areas based on the labels and the attributes of the independent areas;
and S53, training the U-net image semantic segmentation network based on the binary images with the color filling completed.
The invention has the beneficial effects that:
(1) the invention can automatically label the complex rock slice image, realize the classification labeling of different particle types in the same graph, draw points and trace frames for each particle, and endow the corresponding label name for the particle;
(2) the invention realizes the self-animation point-drawing frame of the rock slice image, greatly saves the time for marking by experts, can effectively assist the experts in carrying out batch complex rock slice image marking work, has higher accuracy and greatly improves the working efficiency.
Drawings
FIG. 1 is a flow chart of an automatic labeling method for a rock slice microscopic image provided by the invention.
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, an automatic labeling method for a rock slice microscopic image includes the following steps:
s1, acquiring an orthogonal polarization image and a single polarization image of the rock slice;
s2, carrying out image fusion processing on the orthogonal polarization image and the single polarization image to obtain a new fused image;
s3, performing superpixel segmentation on the fused image to obtain a binary image with particles and gap filler;
s4, performing edge detection on each area block in the binary image, and determining a corresponding edge coordinate point;
s5, training a U-net image semantic segmentation network based on different types of binary images with edge coordinate points;
and S6, taking the binary image to be labeled with the grains and the gap filler as the input of the U-net image semantic segmentation network, obtaining the output result with the grain type label of the rock slice image, and realizing automatic labeling of the rock slice microscopic image.
The step S2 is specifically:
s21, performing illumination equalization processing on the single-polarized image with uneven illumination;
wherein, the illumination equalization processing is carried out for enhancing the contrast set of the image and keeping the detail part of the image;
s22, performing smooth denoising processing on the single-polarized-light image subjected to illumination equalization processing to achieve the purpose of edge-preserving denoising;
s23, taking the single polarization image subjected to the smoothing and denoising processing as a reference image, and carrying out image alignment processing on the reference image and the orthogonal polarization image;
specifically, the processed single-polarization image is used as a reference image, and points at the same position in space are in one-to-one correspondence with a plurality of orthogonal polarization images of the same rock slice shot at different angles after three steps of feature extraction (ORB), feature matching (bidirectional cross matching) and image transformation (RANSAC), so that the aim of automatically aligning the plurality of misaligned orthogonal polarization images with reference to the single-polarization image is fulfilled;
and S24, carrying out image fusion on the images subjected to the image alignment processing to obtain new fused images.
The step S3 is specifically:
s31, extracting a gray level co-occurrence matrix of the fused new image by adopting a GLCM algorithm, and calculating a self-adaptive K value;
the gray level co-occurrence matrix is P (i, j, d, theta), wherein d is a spatial distance, theta is a direction angle, i is a row where the starting point is located, and j is a column where the starting point is located;
the adaptive K value in step S31 is:
in the formula, X and Y are the length and width of an image, Energy is entropy, Contrast is Contrast, Asm is Energy, and Correlation is a Correlation parameter.
Wherein, Contrast ═ Σi∑j(i-j)2P(i,j)
Asm＝∑i∑jP(i,j)2
S32, performing edge detection segmentation on a new image fused by a self-adaptive K-value superpixel segmentation SLCM algorithm;
s33, performing region fusion on each region of the segmented image based on color and distance, combining the segmentation rules of polycrystalline bodies and rock debris, and combining quartz and feldspar particles of the polycrystalline bodies and rock debris particles to obtain a final image segmentation result;
specifically, during regional fusion, the distance from each searched pixel point to the seed point (clustering center) is respectively calculated based on the color and the distance, and then quartz and feldspar particles of the polycrystalline body and rock debris particles are combined by combining the partition rules of the polycrystalline body and the rock debris;
and S34, forming a binary image with grains and fillers based on the image segmentation result.
In the generated binary image, white is a particle and black is a gap filler.
The step S32 is specifically:
a1, uniformly distributing seed points in the fused new image according to the set number K of the super pixels;
assuming that N pixel points are totally arranged in an image and are pre-divided into K super pixels with the same size, the size of each super pixel is N/K;
a2, reselecting the seed points in the n x n area of each currently determined seed point;
the specific method comprises the following steps: calculating gradient values of all pixel points in the neighborhood, and moving the seed point to the place with the minimum gradient in the neighborhood;
a3, searching pixel points in the neighborhood of the reselected seed point, and distributing class labels to the pixel points;
specifically, the search range of the SLIC algorithm is limited to 2S × 2S, which can accelerate algorithm convergence;
and A4, repeating the steps A2-A3 until the searched clustering center of the pixel points does not change any more, and realizing the edge detection segmentation of the image based on the distribution class labels.
The step S4 is specifically:
s41, carrying out edge detection on each area block in the binary image to obtain edge coordinates of each particle;
and S42, screening the edge coordinates of each particle based on a gradient screening method, and deleting redundant coordinates to obtain the final edge coordinate point of each particle.
For example, in the process of screening an irregular polygon boundary point, if the boundary line is a straight line, only coordinate points at two ends of the straight line need to be retained, and if the boundary line is irregular, coordinates of points with extremely large gradient differences are retained.
The step S5 is specifically:
s51, connecting the coordinate points corresponding to the area blocks to form a closed independent area, and adding labels and attributes to the area blocks;
s52, filling different colors in different types of independent areas based on the labels and the attributes of the independent areas;
and S53, training the U-net image semantic segmentation network based on the binary images with the color filling completed.
Specifically, in the training process of the U-net image semantic segmentation network, an expert can synchronously carry out human-computer interactive modification on the automatically labeled image so as to improve the accuracy of network labeling.
Claims (7)
1. An automatic labeling method for a rock slice microscopic image is characterized by comprising the following steps:
s1, acquiring an orthogonal polarization image and a single polarization image of the rock slice;
s2, carrying out image fusion processing on the orthogonal polarization image and the single polarization image to obtain a new fused image;
s3, performing superpixel segmentation on the fused image to obtain a binary image with particles and gap filler;
s4, performing edge detection on each area block in the binary image, and determining a corresponding edge coordinate point;
s5, training a U-net image semantic segmentation network based on different types of binary images with edge coordinate points;
and S6, taking the binary image to be labeled with the grains and the gap filler as the input of the U-net image semantic segmentation network, obtaining the output result with the grain type label of the rock slice image, and realizing automatic labeling of the rock slice microscopic image.
2. The automatic labeling method for the rock slice microscopic image according to claim 1, wherein the step S2 specifically comprises:
s21, performing illumination equalization processing on the single-polarized image with uneven illumination;
s22, performing smooth denoising processing on the single-polarized-light image subjected to illumination equalization processing;
s23, taking the single polarization image subjected to the smoothing and denoising processing as a reference image, and carrying out image alignment processing on the reference image and the orthogonal polarization image;
and S24, carrying out image fusion on the images subjected to the image alignment processing to obtain new fused images.
3. The automatic labeling method for the rock slice microscopic image according to claim 1, wherein the step S3 specifically comprises:
s31, extracting a gray level co-occurrence matrix of the fused new image by adopting a GLCM algorithm, and calculating a self-adaptive K value;
s32, performing edge detection segmentation on a new image fused by a self-adaptive K-value superpixel segmentation SLCM algorithm;
s33, performing region fusion on each region of the segmented image based on color and distance, combining the segmentation rules of polycrystalline bodies and rock debris, and combining quartz and feldspar particles of the polycrystalline bodies and rock debris particles to obtain a final image segmentation result;
and S34, forming a binary image with grains and fillers based on the image segmentation result.
4. The automatic labeling method for a rock slice microscopic image according to claim 3, wherein the gray level co-occurrence matrix in step S31 is P (i, j, d, θ), where d is a spatial distance, θ is a direction angle, i is a row where a starting point is located, and j is a column where the starting point is located;
the adaptive K value in step S31 is:
in the formula, X and Y are the length and width of an image, Energy is entropy, Contrast is Contrast, Asm is Energy, and Correlation is a Correlation parameter.
5. The automatic labeling method for the rock slice microscopic image according to claim 3, wherein the step S32 is specifically as follows:
a1, uniformly distributing seed points in the fused new image according to the set number K of the super pixels;
a2, reselecting the seed points in the n x n area of each currently determined seed point;
a3, searching pixel points in the neighborhood of the reselected seed point, and distributing class labels to the pixel points;
and A4, repeating the steps A2-A3 until the searched clustering center of the pixel points does not change any more, and realizing the edge detection segmentation of the image based on the distribution class labels.
6. The automatic labeling method for the rock slice microscopic image according to claim 1, wherein the step S4 specifically comprises:
s41, carrying out edge detection on each area block in the binary image to obtain edge coordinates of each particle;
and S42, screening the edge coordinates of each particle based on a gradient screening method, and deleting redundant coordinates to obtain the final edge coordinate point of each particle.
7. The automatic labeling method for the rock slice microscopic image according to claim 1, wherein the step S5 specifically comprises:
s51, connecting the coordinate points corresponding to the area blocks to form a closed independent area, and adding labels and attributes to the area blocks;
s52, filling different colors in different types of independent areas based on the labels and the attributes of the independent areas;
and S53, training the U-net image semantic segmentation network based on the binary images with the color filling completed.
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| CN114897917A (en) * | 2022-07-13 | 2022-08-12 | 西南石油大学 | Multi-level rock casting body slice image segmentation method |
| CN114897917B (en) * | 2022-07-13 | 2022-10-28 | 西南石油大学 | Multi-level rock casting body slice image segmentation method |
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