simple_facerec.py

import face_recognition
import cv2
import os
import glob
import numpy as np

class SimpleFacerec:
    def __init__(self):
        self.known_face_encodings = []
        self.known_face_names = []

        # Resize frame for a faster speed
        self.frame_resizing = 0.25

    def load_encoding_images(self, images_path):
        """
        Load encoding images from path
        :param images_path: Path to directory containing images
        :return: None
        """
        # Ensure the path exists
        if not os.path.exists(images_path):
            print(f"Error: Path {images_path} does not exist.")
            return

        # Load Images with error handling
        images_path = glob.glob(os.path.join(images_path, "*.*"))

        print(f"{len(images_path)} images found.")

        # Store image encoding and names
        for img_path in images_path:
            try:
                # Read image with error handling
                img = cv2.imread(img_path)
                
                # Check if image is successfully read
                if img is None:
                    print(f"Error: Could not read image {img_path}")
                    continue

                # Convert to RGB
                rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

                # Get the filename only from the initial file path
                basename = os.path.basename(img_path)
                (filename, ext) = os.path.splitext(basename)

                # Get face encodings
                face_encodings = face_recognition.face_encodings(rgb_img)
                
                # Check if any faces found in the image
                if not face_encodings:
                    print(f"No face found in {img_path}")
                    continue

                # Get encoding of the first face
                img_encoding = face_encodings[0]

                # Store file name and file encoding
                self.known_face_encodings.append(img_encoding)
                self.known_face_names.append(filename)

            except Exception as e:
                print(f"Error processing {img_path}: {e}")

        print(f"{len(self.known_face_encodings)} encoding images loaded successfully")

    def detect_known_faces(self, frame):
        # Check if frame is empty
        if frame is None:
            return [], []

        # Resize frame
        small_frame = cv2.resize(frame, (0, 0), fx=self.frame_resizing, fy=self.frame_resizing)
        
        # Convert the image from BGR color to RGB color
        rgb_small_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
        
        # Find faces and encodings
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in face_encodings:
            # Check if we have any known face encodings
            if not self.known_face_encodings:
                face_names.append("Unknown")
                continue

            # Compare faces
            matches = face_recognition.compare_faces(self.known_face_encodings, face_encoding)
            name = "Unknown"

            # Use the known face with the smallest distance
            face_distances = face_recognition.face_distance(self.known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            
            if matches[best_match_index]:
                name = self.known_face_names[best_match_index]
            
            face_names.append(name)

        # Adjust face locations
        face_locations = np.array(face_locations)
        face_locations = face_locations / self.frame_resizing
        return face_locations.astype(int), face_names