Image preprocessing is a crucial step in image analysis and computer vision tasks. It involves various operations to prepare images for further analysis or feature extraction. OpenCV is a powerful library for image preprocessing in Python.
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๐๐ง๐ญ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง In computer science, digital image processing uses algorithms to perform image processing on digital images to extract some useful information. Digital image processing has many advantages as compared to analogue image processing. A wide range of algorithms can be applied to input data which can avoid problems such as noise and signal distortion during processing. As we know, images are defined in two dimensions, so DIP can be modelled in multidimensional systems.
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๐๐ฎ๐ซ๐ฉ๐จ๐ฌ๐ ๐จ๐ ๐๐ฆ๐๐ ๐ ๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐ง๐ The main purpose of the DIP is divided into the following 5 groups:
Visualization: The objects which are not visible, are observed. Image sharpening and restoration: It is used for better image resolution. Image retrieval: An image of interest can be seen Measurement of pattern: In an image, all the objects are measured. Image Recognition: Each object in an image can be distinguished.
- ๐๐๐ซ๐ ๐ข๐ฌ ๐ ๐ฅ๐ข๐ฌ๐ญ ๐จ๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ฅ๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ ๐๐จ๐ฆ๐ฆ๐จ๐ง๐ฅ๐ฒ ๐ฎ๐ฌ๐๐ ๐๐จ๐ซ ๐ข๐ฆ๐๐ ๐ ๐ญ๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐ง๐ : Pillow (PIL Fork) OpenCV (Open Source Computer Vision Library) Scikit-Image (skimage) NumPy Matplotlib