@CopyRight Nitesh Rawal # 👇
Image Compression with K-Means Clustering Project is Build Using Python , K-mean and scikit-learn:.:
- Explain the steps involved in k-means clustering.
- Apply k-means clustering with scikit-learn and Python to compress images.
- Create interactive, GUI components in Jupyter notebooks using Jupyter widgets
Project Structure # 🛠️ and used technologies project on Image Compression with K-Means Clustering is divided into the following tasks:
Task 1: Introduction and Overview • Introduction to the image compression problem with machine learning. • See a demo of the final product you will build by the end of this project. • Introduction to the Rhyme interface. • Import essential modules and helper functions from NumPy, Matplotlib, scikit-learn, and Jupyter Widgets.
Task 2: Data Pre-processing • Import images from a local directory and store them as numpy arrays. • Explore the image attributes. • Normalize the pixel values and unroll the arrays into vectors.
Task 3: Visualizing the Color Space using Point Clouds • Visualize the set of pixels from the original image as a two 2-D point clouds in color space.
Task 4: Visualizing the K-Means Reduced Color Space • Understand the math and steps involved in the k-means clustering algorithm. • Perform k-means clustering with scikit-learn's MiniBatchKMeans to reduce the number of possible colors in the image from over 16 million to 16. • Compare and contrast the color space of the original image with that of the k-means compressed image.
Task 5: Creating Interactive Controls with Jupyter Widgets • Use the interact function to automatically create UI controls for function arguments. • Define an argument to control the value of k using a slider. • Define an argument to pick any image from a specified directory.
Task 6: K-means Image Compression with Interactive Controls • Ensures that k-means image compression is performed only on the slider widget's mouse release events. • Repurpose the data pre-processing and k-means clustering logic from previous tasks to operate on images of your choice. • Visualize how the image changes as the number of clusters fed to the k-means algorithm is varied. .
Image Compression with K-Mean Clustering #MLproject Unsupervised Technique based on 16 million colors represented as 16 colors to compress the image That involved k-means clustering with scikit-learn and Python to compress images by Creating interactive, GUI components in Jupyter notebooks using Jupyter widgets where i have used essential modules and helper functions from NumPy, Matplotlib, scikit-learn, and Jupyter Widgets. staring from #Datapreprocessing to Import images from a local directory and store them as numpy arrays. then Visualize the set of pixels from the original image as a two 2-D point clouds in color space. and Perform k-means clustering with scikit-learn's to reduce the number of possible colors in the image from over 16 million to 16 also Created an Interactive Controls with Jupyter Widgets.