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**Feature extraction** is another critical aspect. This is where we extract the essential characteristics of the image. For our grass image, these features might include the length and width of grass blades, the density of the grass, or the curvature of the blades. We use advanced algorithms to automatically identify and measure these features, creating a numeric representation of the image.
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So, why did we need RTF? Before RTF, compatibility between word processors was a nightmare. If you created a document in, say, WordStar, and someone else opened it in WordPerfect, the formatting would likely be all messed up, and often, some data could even be lost. RTF solved this problem by providing a common language that all compliant word processors could understand. This universal language meant that documents could be easily shared and edited, regardless of the software used. It was a game-changer back in the day, paving the way for easier collaboration and information exchange. It also helped that it was supported across many platforms, making it easier to transport documents across different operating systems. This capability was very important when technology was not as advanced as it is today and different operating systems were quite different from one another.
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* **Docker Compose:** Docker Compose is usually installed along with Docker Desktop. Verify your installation by running `docker-compose --version` in your terminal.