💥 Computer Vision Project 💥

This project implements a comprehensive workflow for flower recognition using a combination of traditional and deep learning techniques.

The flower recognition dataset (tf_flowers) is loaded from TensorFlow Datasets. It contains images of five types of flowers: 🌼 daisies, 🌷 tulips, 🌻 sunflowers, 🌾 dandelions, and 🌹 roses.

We load and split the dataset into training and testing sets.

We count the number of classes and examples in the training and test sets, and plot the class distribution.

Images are augmented (rotated, flipped, resized) and preprocessed (normalized, converted to grayscale, histogram equalization, and denoised).

We extract three types of features from each image:

• HOG Features: Describes the structure and appearance of the images.
• LBP Features: Captures texture information.
• CNN Features: Extracted using a pre-trained VGG16 model.

Features are reduced using:

• PCA (Principal Component Analysis)
• LDA (Linear Discriminant Analysis)
• ICA (Independent Component Analysis)

We train RandomForest and SVM classifiers on the reduced feature sets (PCA, LDA, ICA) and evaluate their performance using accuracy, precision, recall, and F1-score.

Results are visualized using bar plots to compare the performance of different dimensionality reduction techniques and SVM kernels.

• 🐍 Python 3.x
• 🤖 TensorFlow
• 📚 TensorFlow Datasets
• 📷 OpenCV
• 🖼️ scikit-image
• 📈 scikit-learn
• 📊 Matplotlib
• 🔢 NumPy

1. 📥 Clone this repository.
2. 📦 Install the required libraries.
3. ▶️ Run the main script to execute the workflow.

This project demonstrates an effective pipeline for flower recognition using a combination of traditional image processing techniques and deep learning models. The results indicate that the choice of dimensionality reduction technique and classifier significantly impacts the performance of the model.

This project is licensed under the MIT License.

Made with 💖 by Hamza Sajjad.