encode_faces.py

# USAGE
# python encode_faces.py --dataset dataset --encodings encodings.pickle

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

# construct the argument parser and parse the arguments
#ap = argparse.ArgumentParser()
#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="cnn",
#	help="face detection model to use: either `hog` or `cnn`")
#args = vars(ap.parse_args())

# grab the paths to the input images in our dataset
print("[INFO] quantifying faces...")
#imagePaths = list(paths.list_images(args["dataset"]))
imagePaths = list(paths.list_images('./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('/')[-1].split('\\')[0]

	# load the input image and convert it from RGB (OpenCV ordering)
	# to dlib ordering (RGB)
    image = cv2.imread(imagePath)
    image=cv2.resize(image,(355,355))
    rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    boxes = face_recognition.face_locations(rgb,model="cnn")
	# 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}
print("[INFO] writing encodings...")
#f = open(args["encodings"], "wb")
#f = open()
hkl.dump(data,'encodings_new.h5', "w")
print("[INFO] successful")