Mean filtering method and system

Abstract

The invention discloses a mean filtering method and system. The method comprises the following steps: S1, obtaining a detection area image, a filtering kernel width and a filtering kernel height; s2, traversing pixel points of a to-be-detected area in the detection area image; s3, taking the current pixel point as an anchor point, establishing a rectangular detection frame with the width of a filtering kernel and the height of the filtering kernel, traversing pixel coordinates in the rectangular detection frame, judging whether the pixel coordinates are in an area needing to be detected or not, and if yes, executing S4; if not, traversing the next pixel coordinate; s5 is executed until the rectangular detection frame of one pixel point is traversed; s4, accumulating the pixel gray value and the point number of the pixel coordinate to obtain a total pixel gray value and a total point number; and S5, dividing the total pixel gray value by the total point number to obtain a mean value until the pixel points of the area needing to be detected are traversed to obtain a mean value map. According to the method, the area needing to be detected at any position and in any shape can be flexibly set, the influence of other images outside the area and the boundary is avoided, and a good pretreatment effect is achieved.

Description

technical field [0001] The invention relates to the technical field of industrial automation machine vision, in particular to a mean filtering method and system. Background technique [0002] In the field of industrial automation machine vision, various types of defect detection projects are often encountered, and the mean filtering method is widely used in the preprocessing steps of such projects. At present, many methods on the market process the entire image or intercept the image of a rectangular area, and process it by filling pixels when processing the image boundary. For example, the mean filter in the image processing open source library opencv commonly used in the field of machine vision is like this. The disadvantages of this processing have the following two points. First, the processing area can only be a rectangle or the entire image, and the effect is not good for processing complex images of arbitrary shapes. The second is that because the image boundary is p...

AI Technical Summary

Problems solved by technology

The disadvantages of this kind of processing are as follows. First, the processing area can only be a rectangle or the whole image, and the effect is not good for processing complex images with arbitrary shapes.

The second is that because the image boundary is processed by filling pixels, when processing the image boundary pixels, it will be affected by pixels outside the image or outside the area, and the boundary pixels will be processed as defects, which will make it indistinguishable from the actual defects on the boundary, resulting in missed inspection of products and false positives

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