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Updated on 2025-03-04

Python OpenCV uses threshold method for image processing

introduction

Image thresholding is a very basic and important technology in computer vision and image processing. Through thresholding operations, the pixel values ​​of images can be divided into two categories according to certain standards, which are often used in tasks such as image segmentation, object detection, and text extraction. Threshold processing is crucial to improving image analysis efficiency by setting a threshold to distinguish the foreground and background of an image.

In Python, OpenCV provides convenient functions to implement various threshold processing techniques. This article will introduce in-depth how to use thresholding methods for image processing in OpenCV, and provide some commonly used thresholding techniques and application examples.

1. Basic concepts of threshold processing

The threshold processing of an image refers to the comparison of the pixel value of the image with the set threshold value, and the other is the pixels smaller than (or larger than) the threshold value. Common image thresholding results are usually binary images, that is, limiting the image pixel values ​​to two possible values ​​(such as 0 and 255).

The goal of thresholding processing is to separate the foreground of the image from the background by selecting a suitable threshold, thereby facilitating subsequent image analysis tasks.

2. Threshold processing in OpenCV

In OpenCV, thresholding passes()Functions are implemented. The basic syntax of a function is as follows:

retval, dst = (src, thresh, maxval, type)
  • src: Enter the image (grayscale).
  • thresh: Threshold.
  • maxval: Maximum pixel value.
  • type: Threshold type, which determines how to perform threshold processing.

according totypeDifferent parameters,()Different types of threshold processing methods can be implemented.

3. Common Threshold Types

3.1 Binary threshold

The most common threshold processing method is binarization, which means that pixels larger than a certain threshold are set to the maximum value (usually 255) and pixels smaller than the threshold are set to the minimum value (usually 0). This method can well separate the targets in the image from the background.

import cv2

# Read the image and convert it to grayscaleimg = ('', cv2.IMREAD_GRAYSCALE)

# Set thresholdthresh_value = 127

# Binary processingret, binary = (img, thresh_value, 255, cv2.THRESH_BINARY)

# Display processed images('Binary Threshold', binary)
(0)
()

In this code,cv2.THRESH_BINARYRepresents the binary threshold method, the threshold is greater than127The pixels of255(White), less than127The pixels of0(black).

3.2 Reverse binarization threshold

In contrast to the binarization threshold, the reverse binarization threshold sets the portion smaller than the threshold to the maximum value and the portion larger than the threshold to the minimum value.

ret, binary_inv = (img, thresh_value, 255, cv2.THRESH_BINARY_INV)

('Inverted Binary Threshold', binary_inv)
(0)
()

In this code,cv2.THRESH_BINARY_INVIndicates reverse binarization, setting pixels smaller than the threshold to255, pixels larger than the threshold are set to0。

3.3 Truncated Threshold

The truncation threshold sets the portion greater than the threshold as the threshold itself, and the rest remains unchanged. The truncation threshold is helpful for image detail preservation, but is not suitable for binarized scenarios.

ret, truncated = (img, thresh_value, 255, cv2.THRESH_TRUNC)

('Truncated Threshold', truncated)
(0)
()

In this code,cv2.THRESH_TRUNCThe pixel values ​​greater than the threshold will be truncated to the threshold itself, and the other pixel values ​​will remain unchanged.

3.4 Smoothing Threshold

The smoothing threshold will set the pixel value smaller than the threshold to0, the pixel value greater than the threshold is set to the threshold itself. It is often used to perform slight blurring effects on images.

ret, tozero = (img, thresh_value, 255, cv2.THRESH_TOZERO)

('ToZero Threshold', tozero)
(0)
()

In this code,cv2.THRESH_TOZEROIndicates that the pixel value greater than the threshold is retained, and the pixel value less than the threshold is set to0。

4. Adaptive Threshold

In some uneven light images, using a fixed threshold may not result in good results. At this point, we can use adaptive thresholding technology, which automatically adjusts the threshold according to the local area of ​​the image. OpenCV provides()Functions to implement adaptive threshold processing.

adaptive_thresh = (img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)

('Adaptive Threshold', adaptive_thresh)
(0)
()

Here,cv2.ADAPTIVE_THRESH_MEAN_CIndicates that the average value of pixels in neighborhood area is calculated as the threshold value.11is the size of the neighborhood area.2It is a constant.

5. Otsu’s threshold method

Otsu's thresholding method is a method of automatically selecting the best threshold. It performs threshold selection by maximizing inter-class variance, suitable for images with bimodal features presented by image grayscale histograms. In OpenCV, you can usecv2.THRESH_OTSUTo enable the Otsu threshold method.

ret, otsu_thresh = (img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

('Otsu Thresholding', otsu_thresh)
(0)
()

Otsu's threshold method automatically calculates the best segmentation threshold based on the pixel distribution of the image, and is usually used for images with obvious background and foreground comparisons.

6. Application scenarios of threshold processing

Threshold processing is widely used in the following fields:

  • Image segmentation: Separate the foreground from the background, often used in tasks such as target recognition and image analysis.
  • Document analysis: Extract the text area in the document and perform OCR (optical character recognition) processing.
  • Object detection: Detect certain specific objects or shapes in the image.
  • Medical imaging analysis: Assist doctors in diagnosis by dividing different types of tissues or organs.

7. Summary

Image thresholding is a very basic and important step in image processing. It can divide images into two categories (foreground and background), thereby providing a good foundation for subsequent image analysis and processing. OpenCV provides a variety of thresholding methods, including conventional binarization, reverse binarization, truncation, smoothing thresholding, and adaptive thresholding and Otsu’s thresholding methods. Choosing the appropriate threshold processing method will greatly improve the accuracy and efficiency of image analysis. In practical applications, selecting the appropriate threshold method according to different image characteristics is an important step in image processing.

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