pip install skimage metrics

Опубликовано: 04 Ноябрь 2024
на канале: CodeHive

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Certainly! Let's dive into a tutorial on installing and using the skimage.metrics module, which is part of the scikit-image library for image processing in Python.
Before we start using skimage.metrics, we need to install the scikit-image library. Open your terminal or command prompt and run the following command:
This command will install the scikit-image library, which includes the skimage.metrics module.
Now that we have scikit-image installed, let's import the relevant modules for using skimage.metrics in your Python script or Jupyter notebook.
In this example, we are importing the structural_similarity function, which is commonly used for comparing the structural similarity between two images.
Let's load two sample images for comparison. You can replace these with your own images.
Make sure to convert the images to floating-point format using img_as_float for accurate comparisons.
Now, let's use the ssim function to calculate the Structural Similarity Index between the two images.
The full=True parameter returns additional information about the SSIM calculation, but for simplicity, we are only printing the index in this example.
The Structural Similarity Index ranges from -1 to 1, where 1 indicates identical images. A higher index value implies a higher similarity between the images.
Here's the full code example:
Feel free to explore other metrics and functions provided by skimage.metrics for various image processing tasks!
I hope this tutorial helps you get started with using skimage.metrics in your Python projects!
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