introduction
In image processing and computer vision, binarization is a common image processing technique used to convert an image into an image with only two possible values, usually black and white. OpenCV provides a powerful tool to implement image binarization, the cv2.threshold() function. This blog will introduce in depth the combined use of cv2.THRESH_BINARY and cv2.THRESH_OTSU and their optimization effect on image binarization.
1. Understand cv2.THRESH_BINARY
First, let's review what cv2.THRESH_BINARY is. This is a basic binarization method that uses a user-specified fixed threshold to classify pixels in an image into two categories: above the threshold and below the threshold. Simply put, pixels with values greater than the threshold are set to one value (usually 255), while pixels with values less than the threshold are set to another value (usually 0). For details, you can check out my blog:Detailed explanation of image thresholding operation in OpenCV (code implementation) - CSDN blog
ret, binary_image = cv2.threshold(image, threshold_value, max_value, cv2.THRESH_BINARY)image: Input grayscale image.threshold_value: Threshold used to segment pixels.max_value: The value set for pixels above the threshold.cv2.THRESH_BINARY: Specifies to use binary thresholding.
2. Understand cv2.THRESH_OTSU
cv2.THRESH_OTSUThe Otsu algorithm is used, which is a method of automatically determining thresholds. The algorithm analyzes the histogram of the image to find the threshold that best distinguishes the foreground from the background. This makes it particularly useful for images with large differences in contrast between foreground and background.
ret, binary_image = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)image: Input grayscale image.0: The threshold is set to 0 here, but it will actually be determined automatically bycv2.THRESH_OTSU.255: The value set for pixels above the threshold.cv2.THRESH_BINARY | cv2.THRESH_OTSU: Combines binary threshold and Otsu method.
3. Use cv2.THRESH_BINARY and cv2.THRESH_OTSU together
Using cv2.THRESH_BINARY and cv2.THRESH_OTSU together can bring out their strengths, especially for images with areas of varying contrast. This combination utilizes Otsu's algorithm to automatically select the optimal threshold and then binarize the image.
gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
ret, binary_image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)4. Advantages and applicable scenarios
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Adaptive threshold selection:
cv2.THRESH_OTSUAutomatically selects the best threshold based on the image histogram, suitable for images with unclear contrast and uncertain thresholds. -
Contrast enhancement: Through adaptive threshold selection, image contrast can be enhanced and important features in the image highlighted.
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Suitable for diverse images: This combination is suitable for images of different types and characteristics, reducing the tedious process of manually selecting thresholds.
5. Summary
By combining cv2.THRESH_BINARY and cv2.THRESH_OTSU we are able to achieve automatic optimization of image binarization. cv2.THRESH_OTSU adaptively selects the optimal threshold, while cv2.THRESH_BINARY binarizes the image, making image processing more efficient and precise. This combined method is especially useful when processing different types of images, providing a more convenient and flexible option for image processing.
In practical applications, an appropriate thresholding method can be selected based on image characteristics to achieve the best binarization effect and lay the foundation for subsequent image processing tasks. The above is the blog content about the combined use of cv2.THRESH_BINARY and cv2.THRESH_OTSU. If further modifications or additional content are needed, please feel free to ask.