Feat/model evaluation

.gitignore

@@ -140,3 +140,4 @@ venv/
 *.pyc
 .vscode/
 __pyc
+Images/

Model-Training/eval-mark-I.py

@@ -0,0 +1,78 @@
+import os
+import numpy as np
+from keras.models import load_model
+from keras.preprocessing import image
+from sklearn.metrics.pairwise import euclidean_distances
+
+# Function to load and preprocess images
+def load_and_preprocess_image(img_path, target_size=(160, 160)):
+    img = image.load_img(img_path, target_size=target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0
+    return img_array
+
+# Function to generate embeddings
+def generate_embeddings(model, dataset_path):
+    embeddings = []
+    labels = []
+
+    for class_name in os.listdir(dataset_path):
+        class_path = os.path.join(dataset_path, class_name)
+        if not os.path.isdir(class_path):
+            continue
+
+        for img_name in os.listdir(class_path):
+            img_path = os.path.join(class_path, img_name)
+            img_array = load_and_preprocess_image(img_path)
+            embedding = model.predict(img_array)
+            embeddings.append(embedding[0])
+            labels.append(class_name)
+
+    embeddings = np.array(embeddings)
+    labels = np.array(labels)
+    return embeddings, labels
+
+# Function to calculate intra-cluster distances
+def calculate_intra_cluster_distances(embeddings, labels):
+    unique_labels = np.unique(labels)
+    distances = []
+
+    for label in unique_labels:
+        cluster_embeddings = embeddings[labels == label]
+        avg_embedding = np.mean(cluster_embeddings, axis=0)
+        max_distance = np.max(euclidean_distances(cluster_embeddings, [avg_embedding]))
+        distances.append(max_distance)
+
+    return np.array(distances)
+
+# Load the pre-trained FaceNet model (replace 'facenet_model.h5' with your model file)
+model_path = 'facenet_model.h5'
+model = load_model(model_path)
+
+# Path to the dataset
+dataset_path = 'path_to_your_dataset'
+
+# Generate embeddings for the original model
+embeddings_original, labels = generate_embeddings(model, dataset_path)
+
+# Load the fine-tuned model (replace 'facenet_model_finetuned.h5' with your fine-tuned model file)
+finetuned_model_path = 'facenet_model_finetuned.h5'
+finetuned_model = load_model(finetuned_model_path)
+
+# Generate embeddings for the fine-tuned model
+embeddings_finetuned, _ = generate_embeddings(finetuned_model, dataset_path)
+
+# Calculate intra-cluster distances for both models
+intra_distances_original = calculate_intra_cluster_distances(embeddings_original, labels)
+intra_distances_finetuned = calculate_intra_cluster_distances(embeddings_finetuned, labels)
+
+# Compare intra-cluster distances
+intra_distance_change = intra_distances_finetuned - intra_distances_original
+
+# Output the results
+print(f"Intra-Cluster Distance Change: {intra_distance_change}")
+print(f"Mean Distance Change: {np.mean(intra_distance_change)}")
+print(f"Positive Impact: {np.sum(intra_distance_change < 0)}")
+print(f"Negative Impact: {np.sum(intra_distance_change > 0)}")
+print(f"Average Impact: {np.sum(intra_distance_change == 0)}")