added notes on DEEPFACE

DEEPFACE

@@ -0,0 +1,74 @@
+#
+# https://pypi.org/project/deepface/
+#
+
+from deepface import DeepFace
+import os
+import cv2
+import numpy as np
+
+# Function to compute embeddings for a given image path
+def compute_embeddings(image_path, model_name='VGG-Face'):
+    try:
+        # This returns a list of embeddings for each face found in the image
+        embeddings = DeepFace.represent(img_path=image_path, model_name=model_name)
+        return [res['embedding'] for res in embeddings]
+    except Exception as e:
+        print(f"Error processing image {image_path}: {e}")
+        return []
+
+# Function to find the best matches between reference and target images
+def find_best_matches(reference_images, target_images, model_name='VGG-Face', metric='cosine'):
+    # Store embeddings for reference images
+    reference_embeddings = {}
+
+    for ref_img in reference_images:
+        ref_embeddings = compute_embeddings(ref_img, model_name=model_name)
+        if ref_embeddings:
+            reference_embeddings[ref_img] = ref_embeddings
+    
+    # Store best matches
+    best_matches = []
+
+    # Loop through each target image
+    for target_img in target_images:
+        target_embeddings = compute_embeddings(target_img, model_name=model_name)
+
+        for target_emb in target_embeddings:
+            for ref_img, ref_emb_list in reference_embeddings.items():
+                # Compare each reference embedding with the target embedding
+                for ref_emb in ref_emb_list:
+                    # Compute the distance between embeddings using DeepFace's distance functions
+                    distance = DeepFace.find_distance(ref_emb, target_emb, distance_metric=metric)
+                    
+                    # Store match details (target image, reference image, and distance)
+                    best_matches.append({
+                        'target_image': target_img,
+                        'reference_image': ref_img,
+                        'distance': distance
+                    })
+    
+    # Sort matches by the distance (smallest distances indicate best matches)
+    best_matches = sorted(best_matches, key=lambda x: x['distance'])
+    
+    return best_matches
+
+# Example usage
+reference_images = [
+    "path_to_reference_image1.jpg",
+    "path_to_reference_image2.jpg"
+]
+
+target_images = [
+    "path_to_target_image1.jpg",
+    "path_to_target_image2.jpg",
+    # Add more target images here...
+]
+
+# Find the best matches
+matches = find_best_matches(reference_images, target_images, model_name='VGG-Face', metric='cosine')
+
+# Display the best match
+for match in matches[:5]:  # Display top 5 matches
+    print(f"Reference Image: {match['reference_image']}, Target Image: {match['target_image']}, Distance: {match['distance']}")
+