import face_recognition
from PIL import Image, ImageDraw
import numpy as np
import os


PATH_TO_IMAGE_KNOWN = 'dataset/known_faces'
PATH_TO_IMAGE_UNKNOWN = 'dataset/unknown_faces'

# This is an example of running face recognition on a single image
# and drawing a box around each person that was identified.

# Load a sample picture and learn how to recognize it.
me_image = face_recognition.load_image_file(os.path.join(PATH_TO_IMAGE_KNOWN, 'Nguyễn Ngọc Lâm.jpg'))
va_image = face_recognition.load_image_file(os.path.join(PATH_TO_IMAGE_KNOWN, 'Nguyễn thị Vân Anh.JPG'))
q_image = face_recognition.load_image_file(os.path.join(PATH_TO_IMAGE_KNOWN, 'Lê Duy Quang.JPG'))
g_image = face_recognition.load_image_file(os.path.join(PATH_TO_IMAGE_KNOWN, 'Phạm Hoàng Giang.JPG'))
qn_image = face_recognition.load_image_file(os.path.join(PATH_TO_IMAGE_KNOWN, 'Dương Minh Quân.JPG'))
me_face_encoding = face_recognition.face_encodings(me_image)[0]
va_face_encoding = face_recognition.face_encodings(va_image)[0]
q_face_encoding = face_recognition.face_encodings(q_image)[0]
#g_face_encoding = face_recognition.face_encodings(g_image)[0]
qn_face_encoding = face_recognition.face_encodings(qn_image)[0]

# Load a second sample picture and learn how to recognize it.
# biden_image = face_recognition.load_image_file("biden.jpg")
# biden_face_encoding = face_recognition.face_encodings(biden_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    me_face_encoding,
    va_face_encoding,
    q_face_encoding,
    #g_face_encoding,
    qn_face_encoding
]
known_face_names = [
    "Nguyen Ngoc Lam",
    'Nguyen thi Van Anh',
    'Le Duy Quang',
    #'Pham Hoang Giang',
    'Duong Minh Quan'
]

# Load an image with an unknown face
unknown_image = face_recognition.load_image_file(os.path.join(PATH_TO_IMAGE_UNKNOWN, 'IMG_2200.JPG'))

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

# Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library
# See http://pillow.readthedocs.io/ for more about PIL/Pillow
pil_image = Image.fromarray(unknown_image)
# Create a Pillow ImageDraw Draw instance to draw with
draw = ImageDraw.Draw(pil_image)

# Loop through each face found in the unknown image
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
    # See if the face is a match for the known face(s)
    matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.45)

    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]

    # Draw a box around the face using the Pillow module
    draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))

    # Draw a label with a name below the face
    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))


# Remove the drawing library from memory as per the Pillow docs
del draw

# Display the resulting image
pil_image.show()

# You can also save a copy of the new image to disk if you want by uncommenting this line
# pil_image.save("image_with_boxes.jpg")