encode_student_data.py

from imutils import paths
import face_recognition
import pickle
import cv2 
import os
def encode_student_data(student_id):
    # Grabbing the input images in our dataset 
	#print("[INFO] quantifying faces..")
    student_image_path = "dataset/"+student_id
    imagePaths = list(paths.list_images(student_image_path))
    # initialise the list of known encodings and known names
    knownEncodings = []
    knownNames = []

	# loop over the image paths
    for (i, imagePath) in enumerate(imagePaths):
		#Extract person name from the image path
		#print("[INFO] processing image {}/{}".format(i+1,len(imagePaths)))
        name = imagePath.split(os.path.sep)[-2]

		# load the image and convert it frim RGM (OpenCV ordering)
		# to dlib ordering (RGB)
        image = cv2.imread(imagePath)
        rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
		#detect the coordinates of face
        box = face_recognition.face_locations(rgb, model = 'hog')
		# Now as we have face coordinates now compute the encodings
        encodings = face_recognition.face_encodings(rgb, box)
		# Loop over the encodings :
        for encoding in encodings:
            # add the values and names in the empty list
            knownEncodings.append(encoding)
            knownNames.append(name)
                
    if(os.path.isfile('encodings.pickle')):
        with open('encodings.pickle', 'rb') as f:
            old_data = pickle.load(f)
        old_data['encodings'] += knownEncodings
        old_data['names'] 	  += knownNames
    else:
        old_data = {"encodings": knownEncodings, "names": knownNames} 
    f = open("encodings.pickle","wb")
    f.write(pickle.dumps(old_data))
    f.close()