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from face_engine import FaceEngine
from PIL import Image, ImageDraw
import matplotlib.pylab as plt

engine = FaceEngine()
imgFileName = ''
imgFileName = ''
try:
    boxes, extra  = engine.find_faces(imgFileName)
    print( boxes )
    
    img = Image.open( imgFileName )
    drawing = ImageDraw.Draw( img )
    for i in range( len( boxes ) ):
        shape = [(boxes[i][0], boxes[i][1]),
                 (boxes[i][2], boxes[i][3])]
        drawing.rectangle( shape,
                          outline= 'red',
                          width= 2)
    plt.imshow(img)
    plt.show()
except:
    print('No face found')

from face_engine import FaceEngine
from PIL import Image, ImageDraw
import matplotlib.pylab as plt

engine = FaceEngine()
imgList = []
imgLabel = []
engine.fit( imgList, imgLabel)

testImage = ''
boxes, rames = engine.make_prediction( testImage )
print( rames, boxes )
img = Image.open(testImage)
crawing = ImageDraw.Draw( img ) 
for i in range( len( boxes ) ):
    shape = [(boxes[i][0], boxes[i][1]),
             (boxes[i][2], boxes[i][3])]
    drawing.rectangle( shape,
                      outline= 'red',
                      width= 2)
plt.imshow(img)
plt.show()

import face_recognition
from PIL import Image, ImageDraw
import matplotlib.pylab as plt

imgX = ''

image = face_recognition.load_image_file(imgX)
landmarks = face_recognition.face_landmarks(image)
print(landmarks)
print(f'nose: {landmarks[0]["nose_bridge"]}')

img = Image.open(imgX)
drawing = ImageDraw.Draw(img)
for landmark in landmarks:
    for feature in landmark.keys():
        drawing.line( landmark[feature], width= 5)

plt.imshow(img)
plt.show()