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62 lines
2.0 KiB
62 lines
2.0 KiB
# USAGE
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# python encode_faces.py --dataset dataset --encodings encodings.pickle
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# import the necessary packages
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from imutils import paths
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import face_recognition
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import argparse
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import pickle
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import cv2
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import os
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# construct the argument parser and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-i", "--dataset", required=True,
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help="path to input directory of faces + images")
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ap.add_argument("-e", "--encodings", required=True,
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help="path to serialized db of facial encodings")
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ap.add_argument("-d", "--detection-method", type=str, default="cnn",
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help="face detection model to use: either `hog` or `cnn`")
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args = vars(ap.parse_args())
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# grab the paths to the input images in our dataset
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print("[INFO] quantifying faces...")
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imagePaths = list(paths.list_images(args["dataset"]))
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# initialize the list of known encodings and known names
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knownEncodings = []
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knownNames = []
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# loop over the image paths
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for (i, imagePath) in enumerate(imagePaths):
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# extract the person name from the image path
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print("[INFO] processing image {}/{}".format(i + 1,
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len(imagePaths)))
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name = imagePath.split(os.path.sep)[-2]
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# load the input image and convert it from RGB (OpenCV ordering)
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# to dlib ordering (RGB)
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image = cv2.imread(imagePath)
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rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# detect the (x, y)-coordinates of the bounding boxes
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# corresponding to each face in the input image
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boxes = face_recognition.face_locations(rgb,
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model=args["detection_method"])
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# compute the facial embedding for the face
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encodings = face_recognition.face_encodings(rgb, boxes)
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# loop over the encodings
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for encoding in encodings:
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# add each encoding + name to our set of known names and
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# encodings
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knownEncodings.append(encoding)
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knownNames.append(name)
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# dump the facial encodings + names to disk
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print("[INFO] serializing encodings...")
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data = {"encodings": knownEncodings, "names": knownNames}
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f = open(args["encodings"], "wb")
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f.write(pickle.dumps(data))
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f.close()
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