#
# 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']}")