Add Tasnif package documentation

readme.md

@@ -1 +1,55 @@
 # Tasnif
+
+Tasnif is a Python package designed for clustering images into user-defined classes based on their visual content. It utilizes deep learning to generate image embeddings, Principal Component Analysis (PCA) for dimensionality reduction, and K-means for clustering. Tasnif supports processing on both GPU and CPU, making it versatile for different computational environments.
+
+## Features
+
+- Generate embeddings for images using a pre-trained model.
+- Dimensionality reduction using PCA to enhance clustering performance.
+- Clustering of images into user-specified classes with K-means.
+- Visualization support by creating image grids for each cluster.
+- Efficient image reading and preprocessing utilities.
+
+## Installation
+
+To install Tasnif, you need Python 3.6 or later. Clone this repository to your local machine and install the required dependencies:
+
+```bash
+pip install tasnif
+```
+
+## Usage
+
+Import `Tasnif` and initialize it with the desired number of classes, PCA dimensions, and whether to use GPU:
+
+```python
+from tasnif import Tasnif
+
+# Initialize Tasnif with 5 classes, PCA dimensions set to 16, and GPU usage
+classifier = Tasnif(num_classes=5, pca_dim=16, use_gpu=False)
+```
+
+Read the images from a directory, calculate the embeddings, PCA, and perform K-means clustering:
+
+```python
+# Read images from a specified directory
+classifier.read('path/to/your/images')
+
+# Calculate embeddings, PCA, and perform clustering
+classifier.calculate()
+```
+
+Finally, export the clustered images and visualization grids to a specified directory:
+
+```python
+# Export clustered images and grids
+classifier.export('path/to/output')
+```
+
+## Contributing
+
+Contributions to `Tasnif` are welcome! Please fork the repository and submit a pull request with your proposed changes.
+
+## License
+
+Tasnif is released under the MIT License. See the [LICENSE](LICENSE) file for more details.