Update README.md

README.md

@@ -16,4 +16,55 @@ The steps to create the embeddings can be described as:
 1. Resize the images to 512x512.
 2. Transform the images into their Fourier image.
 3. Input the images into the model using predict.
-4. The output will be a 128-length vector for use in classification models.
+4. The output will be a 128-length vector for use in classification models.
+
+The preprocessing code along with the predict can calculate the embeddings for classification.
+
+```
+
+# load an image and apply the fourier transform
+
+import numpy as np
+from PIL import Image
+from scipy.fftpack import fft2
+from tensorflow.keras.models import load_model, Model
+
+# Function to apply Fourier transform
+def apply_fourier_transform(image):
+    image = np.array(image)
+    fft_image = fft2(image)
+    return np.abs(fft_image)
+
+# Function to preprocess image
+def preprocess_image(image_path):
+    try:
+      image = Image.open(image_path).convert('L')
+      image = image.resize((512, 512))
+      image = apply_fourier_transform(image)
+      image = np.expand_dims(image, axis=-1)  # Expand dimensions to match model input shape
+      image = np.expand_dims(image, axis=0)   # Expand to add batch dimension
+      return image
+    except Exception as e:
+        print(f"Error processing image {image_path}: {e}")
+        return None
+
+# Function to load embedding model and calculate embeddings
+def calculate_embeddings(image_path, model_path='embedding_model.keras'):
+    # Load the trained model
+    model = load_model(model_path)
+
+    # Remove the final classification layer to get embeddings
+    embedding_model = Model(inputs=model.input, outputs=model.output)
+
+    # Preprocess the image
+    preprocessed_image = preprocess_image(image_path)
+
+    # Calculate embeddings
+    embeddings = embedding_model.predict(preprocessed_image)
+
+    return embeddings
+
+
+
+calculate_embeddings('filename.jpg')
+```