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I. Introduction
In the " OpenCV threshold processing function threshold processing 32-bit color image case " introduced the threshold function, but the threshold image threshold processing for some unevenly illuminated images, this global threshold segmentation method does not get good results.
In the image thresholding operation, we are more concerned about separating the target area and the background area from the binarized image. It is difficult to achieve the ideal segmentation effect only through a fixed threshold. The gray scale in the picture is not uniform, so usually the thresholds of different areas in the picture are different. In this way, a method is needed to perform threshold processing by calculating the local threshold according to the brightness or gray distribution of different areas of the image. This method is adaptive thresholding image processing. In fact, this can be called a local threshold method. The adaptiveThreshold in OpenCV is this method.
Two, adaptiveThreshold syntax introduction
Calling syntax:
adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C, dst=None)
Description:
- src: source image, must be an 8-bit grayscale image
- dst: The processed target image, the size and type are the same as the source image
- maxValue: used to specify the gray value of the pixel setting that meets the condition
- adaptiveMethod: The adaptive threshold algorithm used, there are two types of ADAPTIVE_THRESH_MEAN_C algorithm (local neighborhood block average) or ADAPTIVE_THRESH_GAUSSIAN_C (local neighborhood block Gaussian weighted sum). The calculation method of ADAPTIVE_THRESH_MEAN_C is to calculate the average value of the neighborhood and subtract the first The value of six parameters C, the calculation method of ADAPTIVE_THRESH_GAUSSIAN_C is to calculate the Gaussian uniform value of the neighborhood and subtract the value of the sixth parameter C. Use BORDER_REPLICATE | BORDER_ISOLATED mode when processing boundaries
- thresholdType: Threshold type, which can only be one of THRESH_BINARY or THRESH_BINARY_INV. For details, please refer to the table of "Image Threshold Processing" above
- blockSize: indicates the size of the neighborhood block, which is used to calculate the area threshold, generally 3, 5, 7...
- C: Represents a constant, which is a constant extracted from a uniform or weighted average, usually a positive number, but it can also be a negative number or zero
- Return value: the processed image
Three, supplementary explanation
- The binarization threshold of the image area with higher brightness will usually be higher, and the binarization threshold of the image area with lower brightness will be smaller.
- In a grayscale image, the area where the grayscale value changes obviously is often the contour of the object, so dividing the image into small blocks to calculate the threshold will often get the contour of the image. Therefore, the function adaptiveThreshold can not only binarize the grayscale image, but also perform edge extraction.
- The reason for edge extraction is that when the block is very small, such as block_size=3 or 5 or 7, the degree of "adaptation" is very high, that is, it is easy to appear that the pixel values in the block are almost the same, so that it cannot be binarized , But can only achieve binarization where the gradient is large at the edge, and it turns out that it is an edge extraction function
- When blockSize is set to a relatively large value, such as blockSize=21 or 31 or 41, adaptiveThreshold is a binary function
- blockSize must be an odd number greater than 1 (the principle is still unclear),
- If the average value method is used, the average value is 180, the difference delta is 10, and maxValue is 255. Then pixels with a grayscale less than 170 are 0, pixels greater than or equal to 170 are 255, if it is reverse binarization, pixels with grayscale less than 170 are 255, and pixels greater than or equal to 170 are 0
Four, case
import cv2
img = cv2.imread(r'F:\screenpic\1.jpg',cv2.IMREAD_GRAYSCALE)
newImg = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 3, 5)
cv2.imshow('img',img)
cv2.imshow('newImg',newImg)
cv2.waitKey(60000)
Operating effect:
Source image:
The following is the result image of setting different blockSizes. The block size of the left image is 31, and the blockSize of the right image is 3: It
can be seen that the blockSize is small, the contour recognition effect is obvious, and when the blockSize is large, it is a binary image.
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