added_another_evaluation_script_LFW

test_model/evaluate_model.py

+import numpy as np
+import tensorflow as tf
+from sklearn.datasets import fetch_lfw_pairs
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import normalize
+import tensorflow as tf
+import os
+import random
+import numpy as np
+import cv2
+
+from facial_recognition.Siamese_model import image_embedder
+from facial_recognition.functions import split_dataset,create_triplets
+import seaborn as sns
+import matplotlib.pyplot as plt
+from keras.applications.inception_v3 import preprocess_input
+from pathlib import Path
+
+# Load the LFW pairs dataset
+lfw_pairs = fetch_lfw_pairs(subset='test', color=True, resize=0.5)
+
+# Prepare the images and labels
+pairs = lfw_pairs.pairs
+labels = lfw_pairs.target
+
+# Preprocess images
+def preprocess_image(image):
+    image = tf.image.resize(image, (224, 224))
+    #image = tf.cast(image, tf.float32) / 255.0  # Normalize to [0, 1]
+    return image
+
+# Load your pre-trained face recognition model
+def load_model():
+    # Replace this with the code to load your model
+    model = tf.keras.models.load_model(r'C:\Users\waelk\PycharmProjects\facial_recognition\facial_recognition\test_model\model\encoder.keras')
+    return model
+encoder = image_embedder((224,224,3))
+
+encoder.load_weights(os.path.join('model', 'encoder.keras'))
+model = encoder
+
+# Compute embeddings for pairs of images
+def compute_embeddings(model, pairs):
+    embeddings = []
+    for i in range(pairs.shape[0]):
+        img1 = preprocess_image(pairs[i, 0])
+        img2 = preprocess_image(pairs[i, 1])
+        embedding1 = model.predict(np.expand_dims(img1, axis=0))
+        embedding2 = model.predict(np.expand_dims(img2, axis=0))
+        embeddings.append((embedding1, embedding2))
+    return embeddings
+
+embeddings = compute_embeddings(model, pairs)
+
+# Compute distance between embeddings and predict labels
+def compute_distances(embeddings):
+    distances = []
+    for embedding1, embedding2 in embeddings:
+        distance = np.linalg.norm(embedding1 - embedding2)
+        distances.append(distance)
+    return np.array(distances)
+
+distances = compute_distances(embeddings)
+
+# Threshold to classify pairs as same or different
+threshold = 1.2  # You can tune this threshold based on a validation set
+
+predicted_labels = distances < threshold
+accuracy = accuracy_score(labels, predicted_labels)
+
+print(f'Accuracy: {accuracy:.4f}')
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