# On importe les librairies necessaires 
import face_recognition
import numpy as np
from PIL import Image, ImageDraw
from IPython.display import display

# Voici un exemple d'exécution de la reconnaissance faciale sur une seule image
# et dessiner une boîte autour de chaque personne identifiée.

# Charger un exemple d'image et apprenez à le reconnaître.
#lounis_OUMOHAND 
Lounis_OUMOHAND = face_recognition.load_image_file(r"C:\Users\oumoh\Downloads\Lounis.jpg")
Lounis_face_encoding = face_recognition.face_encodings(Lounis_OUMOHAND)[0]

#Nabil_BENSAFIA
Nabil_BENSAFIA = face_recognition.load_image_file(r"C:\Users\oumoh\Downloads\nabil.png")
Nabil_face_encoding = face_recognition.face_encodings(Nabil_BENSAFIA)[0]



# Créer des tableaux d'encodages de visage connus et leurs noms
known_face_encodings = [
    Lounis_face_encoding,
    Nabil_face_encoding 
    
 
]
known_face_names = [
     "Lounis OUMOHAND",
      "Nabil BENSAFIA"

]
print('Learned encoding for', len(known_face_encodings), 'images.')


# Charger une image avec un visage inconnu

unknown_image = face_recognition.load_image_file(r"C:\Users\oumoh\Downloads\lounes.jpg")
# Trouver tous les visages et encodages de visage dans l'image inconnue
face_locations = face_recognition.face_locations(unknown_image)
face_encodings = face_recognition.face_encodings(unknown_image, face_locations)

# Convertir l'image en une image au format PIL afin que nous puissions dessiner dessus avec la bibliothèque Pillow
# See http://pillow.readthedocs.io/ for more about PIL/Pillow
pil_image = Image.fromarray(unknown_image)
# Créer une instance Pillow ImageDraw Draw avec laquelle on dessine
draw = ImageDraw.Draw(pil_image)

# Boucle for sur chaque visage trouvé dans l'image inconnue
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
    # Voir si le visage correspond au(x) visage(s) connu(s)
    matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

    name = "Unknown"

    # Ou plutôt utiliser un visage connu avec la plus petite distance par rapport au nouveau visage
    face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
    best_match_index = np.argmin(face_distances)
    if matches[best_match_index]:
        name = known_face_names[best_match_index]

    # Dessinez une boîte autour du visage à l'aide de la bibliothèque Pillow
    draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))

    # Dessinez une étiquette avec un nom sous le visage
    text_width, text_height = draw.textsize(name)
    draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))
    draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))


# Supprimer la bibliothèque de dessins de la mémoire selon les documents Pillow
del draw

# Afficher l'image résultante
display(pil_image)

import face_recognition
import cv2
import numpy as np

video_capture = cv2.VideoCapture(0)

Lounis_OUMOHAND= face_recognition.load_image_file(r"C:\Users\oumoh\Downloads\lounis.jpg")
lounis_face_encoding = face_recognition.face_encodings(Lounis_OUMOHAND)[0]
Lounis_OUMOHAND
lounis_face_encoding

# Nabil bensafia
Nabil_BENSAFIA= face_recognition.load_image_file(r"C:\Users\oumoh\Downloads\nabil.png")
Nabil_face_encoding = face_recognition.face_encodings(Nabil_BENSAFIA)[0]


# Créer des tableaux d'encodages de visage connus et leurs noms
known_face_encodings = [
    lounis_face_encoding,
    Nabil_face_encoding
]

known_face_names = [
    "Lounis_OUMOHAND",
    "Nabil_BENSAFIA"   
]

# Initialisation

face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        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:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"

            # # If a match was found in known_face_encodings, just use the first one.
            # if True in matches:
            #     first_match_index = matches.index(True)
            #     name = known_face_names[first_match_index]

            # Or instead, use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]

            face_names.append(name)

    process_this_frame = not process_this_frame


    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()