import face_recognition
import cv2
import numpy as np

# Load an image file
image_path = "principal.jpg"
image = face_recognition.load_image_file(image_path)

# Find all face locations and encodings in the image
face_locations = face_recognition.face_locations(image)
face_encodings = face_recognition.face_encodings(image, face_locations)

# Ensure only one face is found
if len(face_encodings) == 1:
    # Save the encoding for later comparison
    known_face_encoding = face_encodings[0]
else:
    print("Error: Expected exactly one face in the image.")

# Load the image with an unknown face
unknown_image_path = "2_principal.jpg"
unknown_image = face_recognition.load_image_file(unknown_image_path)

# Find all face locations and encodings in the unknown image
unknown_face_locations = face_recognition.face_locations(unknown_image)
unknown_face_encodings = face_recognition.face_encodings(unknown_image, unknown_face_locations)

# Initialize OpenCV window
window_name = "Recognized Person"
cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)

# Draw a red rectangle around the recognized face and count the matches
recognized_count = 0
for (top, right, bottom, left) in unknown_face_locations:
    cv2.rectangle(unknown_image, (left, top), (right, bottom), (0, 0, 255), 2)
    results = face_recognition.compare_faces([known_face_encoding], unknown_face_encodings[recognized_count])
    if results[0]:
        recognized_count += 1
        print(recognized_count)

# Display the image with the rectangle
cv2.imshow(window_name, unknown_image)
cv2.waitKey(0)
cv2.destroyAllWindows()