Database Integration Development Tutorials, Guides & Insights

Use the face_recognition library to encode faces from the dataset. This step creates unique numerical representations for each face.

import os
import face_recognition
import cv2
import pickle

# Path to dataset
DATASET_PATH = "dataset"
ENCODINGS_FILE = "encodings.pickle"

def encode_faces():
    known_encodings = []
    known_names = []

    # Iterate through each person's folder
    for person in os.listdir(DATASET_PATH):
        person_path = os.path.join(DATASET_PATH, person)
        if not os.path.isdir(person_path):
            continue

        # Process each image
        for img_file in os.listdir(person_path):
            img_path = os.path.join(person_path, img_file)
            image = face_recognition.load_image_file(img_path)
            face_encodings = face_recognition.face_encodings(image)

            if face_encodings:
                known_encodings.append(face_encodings[0])
                known_names.append(person)

    # Save encodings to a file
    with open(ENCODINGS_FILE, "wb") as f:
        pickle.dump({"encodings": known_encodings, "names": known_names}, f)

    print("Encodings saved!")

encode_faces()

• Use environment variables or configuration files to manage sensitive information instead of hardcoding them in your source code.

• Regularly back up your database to prevent data loss.

public function up()
{
    Schema::create('posts', function (Blueprint $table) {
        $table->id();
        $table->string('title');
        $table->text('body');
        $table->timestamps();
    });
}

Run the migration to create the table:

  source venv/bin/activate

Create a file named app.py and add: