!git clone https://github.com/davisking/dlib.git

%cd dlib
!mkdir build; cd build; cmake ..; cmake --build .

!git clone https://github.com/ageitgey/face_recognition.git
%cd  /content/face_recognition
!python setup.py install

%cd ..
!pip install git+https://github.com/ageitgey/face_recognition_models
!pip install face_recognition

!mkdir images
!wget https://www.unlockmen.com/wp-content/uploads/2021/07/hideo-kojima-tribeca-games-festival.jpg -O /content/images/Kojima.jpg
!wget https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Norman_Reedus_2016_%28cropped%29.jpg/220px-Norman_Reedus_2016_%28cropped%29.jpg -O /content/images/Norman.jpg
!wget https://mpics.mgronline.com/pics/Images/564000008778201.JPEG -O /content/images/two_people.jpg

import matplotlib.pyplot as plt
import os
filenames = [os.path.join('/content/images',x) for x in os.listdir("/content/images/")]
print(filenames)
for filename in filenames:
    img = plt.imread(filename)
    plt.imshow(img)
    plt.axis('off')
    plt.show()

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

# 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.
kojima_image = face_recognition.load_image_file(os.path.join('/content/images',"Kojima.jpg"))
kojima_face_encoding = face_recognition.face_encodings(kojima_image)[0]

# Load a second sample picture and learn how to recognize it.
norman_image = face_recognition.load_image_file(os.path.join('/content/images',"Norman.jpg"))
norman_face_encoding = face_recognition.face_encodings(norman_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    kojima_face_encoding,
    norman_face_encoding
]
known_face_names = [
    "Hideo Kojima",
    "Norman Reedus"
]

# Load an image with an unknown face
unknown_image = face_recognition.load_image_file(os.path.join('/content/images',"two_people.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)

    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

from IPython.display import Image as IImage
from PIL import Image

# Save the PIL image
pil_image.save("result3.jpg")

# Display the saved image
IImage(filename='result3.jpg', width=800)