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Home Backend Development Python Tutorial How to use OpenCV-Python to accurately identify the keyboard in the picture and locate the coordinates of each key?

How to use OpenCV-Python to accurately identify the keyboard in the picture and locate the coordinates of each key?

Apr 01, 2025 pm 09:30 PM
python

Use OpenCV-Python to accurately identify the keyboard in the picture and locate the key coordinates

How to use OpenCV-Python to accurately identify the keyboard in the picture and locate the coordinates of each key?

This article explores how to use the OpenCV-Python library to identify the keyboard from the pictures taken by the camera and to obtain the coordinates of each key exactly. This is a challenging task that requires the combination of multiple image processing techniques.

The simple key-by-key screenshot positioning method is inefficient, especially when there are too many keys or irregular shapes. A more efficient approach requires more complex steps:

1. Image preprocessing: First, preprocess the image, such as grayscale, binarization and noise reduction, to reduce noise and interference and improve the accuracy of subsequent processing.

2. Contour detection: Use OpenCV's contour detection function (such as cv2.findContours ) to identify keyboard key contours. This requires appropriate threshold segmentation to ensure accurate identification of key profiles.

3. Shape matching (optional): Because the key shape is relatively regular, you can consider using a shape matching algorithm (such as Hu moment) to assist in identifying the key. This can improve the recognition accuracy in complex contexts.

4. Coordinate extraction: Finally, extract the bounding box coordinates of each key outline to obtain the position information of each key.

It should be noted that simple contour detection may not cope with complex background and lighting conditions. In order to obtain higher accuracy and efficiency, it may be necessary to adjust the image preprocessing method and feature extraction method according to actual conditions. For more complex scenarios, considering using more advanced techniques such as deep learning object detection models may work better. A robust solution requires careful algorithm design and parameter adjustment.

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