# USAGE
# When encoding on laptop, desktop, or GPU (slower, more accurate):
# python encode_faces.py --dataset dataset --encodings encodings.pickle --detection-method cnn
# When encoding on Raspberry Pi (faster, more accurate):
# python encode_faces.py --dataset dataset --encodings encodings.pickle --detection-method hog

# import the necessary packages
from imutils import paths
import face_recognition
import argparse
import pickle
import cv2
import os

# construct the argument parser and parse the arguments
parser  = argparse.ArgumentParser()
ap = parser.add_argument_group("required group")
ap.add_argument("-i", "--dataset",required = True,
    help="path to input directory of faces + images")
ap.add_argument("-e", "--encodings",required = True,
    help="path to serialized db of facial encodings")
ap.add_argument("-d", "--detection-method", type=str, default="hog",
    help="face detection model to use: either `hog` or `cnn`")
args = vars(parser.parse_args([]))

# grab the paths to the input images in our dataset
print("[INFO] quantifying faces...")
imagePaths = list(paths.list_images(args["dataset"]))

# initialize the list of known encodings and known names
knownEncodings = []
knownNames = []

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

    # load the input image and convert it from RGB (OpenCV ordering)
    # to dlib ordering (RGB)
    image = cv2.imread(imagePath)
    rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # detect the (x, y)-coordinates of the bounding boxes
    # corresponding to each face in the input image
    boxes = face_recognition.face_locations(rgb,
        model=args["detection_method"])

    # compute the facial embedding for the face
    encodings = face_recognition.face_encodings(rgb, boxes)

    # loop over the encodings
    for encoding in encodings:
        # add each encoding + name to our set of known names and
        # encodings
        knownEncodings.append(encoding)
        knownNames.append(name)

# dump the facial encodings + names to disk
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "names": knownNames}
f = open(args["encodings"], "wb")
f.write(pickle.dumps(data))
f.close()