174 lines
6.8 KiB
Python
174 lines
6.8 KiB
Python
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import sys
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import signal
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signal.signal(signal.SIGINT, signal.SIG_IGN)
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import face_recognition
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import os
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import argparse
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import pickle
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import csv
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import numpy as np
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from pathlib import Path
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from datetime import datetime
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date_time = datetime.now().strftime("%Y%m%d_%H%M%S")
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default_log_filename = f"matcher_log_{date_time}.txt"
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default_out_filename = f"result_{date_time}.csv"
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def resolve_path(path, default):
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default_dirname = "output"
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default_filename = default
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if not path:
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resolved_path = Path(default_dirname) / default_filename
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return resolved_path.resolve()
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resolved_path = Path(path).resolve()
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if resolved_path.is_dir() or path.endswith(os.sep) or path.endswith('/') or not resolved_path.suffix:
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resolved_path = resolved_path / default_filename
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return resolved_path.resolve()
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return resolved_path
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def load_encodings(encodings, log):
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encodings_path = Path(encodings).resolve()
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log_path = resolve_path(log, default_log_filename)
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log_path.parent.mkdir(parents=True, exist_ok=True)
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try:
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with open(encodings_path, "rb") as f:
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data = pickle.load(f)
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print(f"Dati caricati correttamente da '{encodings_path}'")
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with open(log_path, "w", encoding="utf-8") as log_f:
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log_f.write(f"--- [INFO] Encodings caricati correttamente da '{encodings_path}' ---\n")
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return data["encodings"], data["filenames"]
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except FileNotFoundError:
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print(f"Errore: Il file '{encodings_path}' non esiste.")
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with open(log_path, "w", encoding="utf-8") as log_f:
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log_f.write(f"--- [ERRORE] Il file '{encodings_path}' non esiste ---\n")
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sys.exit(1)
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def encode_image(image, log):
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image_path = Path(image).resolve()
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log_path = resolve_path(log, default_log_filename)
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log_path.parent.mkdir(parents=True, exist_ok=True)
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if not image_path.is_file():
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print(f"Errore: Il file {image_path} non esiste.")
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with open(log_path, "a", encoding="utf-8") as log_f:
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log_f.write(f"--- [ERRORE] Il file {image_path} non esiste ---\n")
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return None
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print(f"Elaborazione di: {image_path}...")
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with open(log_path, "a", encoding="utf-8") as log_f:
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log_f.write(f"--- [INFO] Match avviato per {image_path} ---\n")
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image = face_recognition.load_image_file(image_path)
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image_encoding = face_recognition.face_encodings(image)
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if image_encoding:
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return image_encoding[0]
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else:
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print(f"Nessun volto trovato in {image_path}")
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with open(log_path, "a", encoding="utf-8") as log_f:
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log_f.write(f"--- [INFO] Nessun volto trovato in {image_path} ---\n")
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sys.exit(1)
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def match_faces(image_encoding, encodings, filenames, log):
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log_path = resolve_path(log, default_log_filename)
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log_path.parent.mkdir(parents=True, exist_ok=True)
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tolerance = 0.5
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results = []
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if not encodings:
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return results
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face_distances = face_recognition.face_distance(encodings, image_encoding)
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with open(log_path, "a", encoding="utf-8") as log_f:
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log_f.write(f"\n============== [INIZIO ELABORAZIONE] ==============\n")
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current_file = None
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face_index = 0
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total_matches = 0
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try:
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for i, distance in enumerate(face_distances):
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filename = filenames[i]
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if filename != current_file:
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current_file = filename
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face_index = 1
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print()
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log_f.write("\n")
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else:
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face_index += 1
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is_match = distance < tolerance
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status = '✓' if is_match else ''
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somiglianza = max(0, 100 - (distance * 100))
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msg = f"Volto {face_index:<2} in {filename} - [Somiglianza: {somiglianza:>5.1f}%] {status}"
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print(msg)
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log_f.write(f"{msg}\n")
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if is_match:
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total_matches += 1
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if filename not in results:
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results.append(filename)
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print("\nMatch completato con successo.")
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log_f.write(f"\n============== [ELABORAZIONE COMPLETATA CON SUCCESSO] ==============\n\n")
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except KeyboardInterrupt:
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print("\nInterruzione manuale dell'elaborazione, salvataggio dei dati finora elaborati...")
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log_f.write("\n============== [ELABORAZIONE INTERROTTA MANUALMENTE] ==============\n")
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finally:
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print(f"Trovati {total_matches} match su {len(encodings)} confronti")
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print(f"Volto trovato in {len(results)} foto")
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log_f.write(f"--- [INFO] Trovati {total_matches} match su {len(encodings)} confronti ---\n")
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log_f.write(f"--- [INFO] Volto trovato in {len(results)} foto ---\n")
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return results
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def save_output(results, output, log):
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output_path = resolve_path(output, default_out_filename)
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log_path = resolve_path(log, default_log_filename)
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log_path.parent.mkdir(parents=True, exist_ok=True)
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try:
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, "w", newline="", encoding="utf-8") as csvfile:
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writer = csv.writer(csvfile)
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formatted_data = [[f] for f in results]
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writer.writerows(formatted_data)
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print(f"Risultati salvati in {output_path.resolve()}")
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with open(log_path, "a", encoding="utf-8") as log_f:
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log_f.write(f"--- [INFO] Risultati salvati in {output_path.resolve()} ---\n")
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except Exception as e:
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print(f"Errore durante il salvataggio: {e}")
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with open(log_path, "a", encoding="utf-8") as log_f:
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log_f.write(f"--- [ERRORE] Errore durante il salvataggio: {e} ---\n")
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def main():
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signal.signal(signal.SIGINT, signal.default_int_handler)
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parser = argparse.ArgumentParser(description="Associa ad un volto noto un set di immagini in cui compare (tramite encodings precalcolati)")
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parser.add_argument("-i", "--image", required=True, help="Percorso dell'immagine del volto da riconoscere")
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parser.add_argument("-e", "--encodings", required=True, help="Percorso del file contenente gli encodings delle foto 'unknown'.")
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parser.add_argument("-o", "--out", help="Percorso del file/cartella di output. Default: './output/result_[datetime].csv'")
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parser.add_argument("-l", "--log", help="Percorso del file di log. Default: './output/matcher_log_[datetime].txt'")
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args = parser.parse_args()
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encodings, filenames = load_encodings(args.encodings, args.log)
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image_encoding = encode_image(args.image, args.log)
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if image_encoding is not None and encodings and filenames:
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matches = match_faces(image_encoding, encodings, filenames, args.log)
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save_output(matches, args.out, args.log)
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if __name__ == "__main__":
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main()
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