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encode_faces.py
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encode_faces.py
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"""
USAGE
python encode_faces.py --dataset data/photos/ -e data/encodings/test.pkl -d 'dnn'
"""
# import the necessary packages
import sys
from imutils import paths
import face_recognition
import argparse
import pickle
import cv2
import os
# construct the argument parser and parse the arguments
from face_detection import HaarFaceDetector, front_cascPath, DlibFaceDetector, DnnDetector
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", type=str, default="dnn",
help="face detection model to use: `haar`, `hog` or `dnn`")
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"]))
# initialize the list of known encodings and known names
knownEncodings = []
knownNames = []
# detector
if args["detection"] == "dnn":
detector = DnnDetector(model_path="face_detection/detection_models", model_type="TF")
elif args["detection"] == "hog":
detector = DlibFaceDetector(model_type="hog")
elif args["detection"] == "haar":
detector = HaarFaceDetector(casc_path="face_detection/detection_models/haar_cascades",
casc_list=[front_cascPath])
else:
sys.exit()
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# extract the person name from the image path
name = imagePath.split(os.path.sep)[-2]
print("[INFO] processing image {}/{} ({})".format(i + 1,
len(imagePaths), name))
# 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
boxes = detector.get_face_bbox(image)
if len(boxes) < 1:
continue
# 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()