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79 lines
2.6 KiB
79 lines
2.6 KiB
# USAGE
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# python recognize_faces_image.py --encodings encodings.pickle --image examples/example_01.png
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# import the necessary packages
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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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# construct the argument parser and parse the arguments
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ap = argparse.ArgumentParser()
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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("-i", "--image", required=True,
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help="path to input image")
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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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# load the known faces and embeddings
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print("[INFO] loading encodings...")
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data = pickle.loads(open(args["encodings"], "rb").read())
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# load the input image and convert it from BGR to RGB
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image = cv2.imread(args["image"])
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rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# detect the (x, y)-coordinates of the bounding boxes corresponding
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# to each face in the input image, then compute the facial embeddings
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# for each face
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print("[INFO] recognizing faces...")
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boxes = face_recognition.face_locations(rgb,
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model=args["detection_method"])
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encodings = face_recognition.face_encodings(rgb, boxes)
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# initialize the list of names for each face detected
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names = []
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# loop over the facial embeddings
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for encoding in encodings:
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# attempt to match each face in the input image to our known
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# encodings
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matches = face_recognition.compare_faces(data["encodings"],
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encoding)
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name = "Unknown"
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# check to see if we have found a match
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if True in matches:
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# find the indexes of all matched faces then initialize a
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# dictionary to count the total number of times each face
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# was matched
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matchedIdxs = [i for (i, b) in enumerate(matches) if b]
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counts = {}
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# loop over the matched indexes and maintain a count for
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# each recognized face face
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for i in matchedIdxs:
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name = data["names"][i]
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counts[name] = counts.get(name, 0) + 1
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# determine the recognized face with the largest number of
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# votes (note: in the event of an unlikely tie Python will
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# select first entry in the dictionary)
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name = max(counts, key=counts.get)
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# update the list of names
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names.append(name)
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# loop over the recognized faces
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for ((top, right, bottom, left), name) in zip(boxes, names):
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# draw the predicted face name on the image
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cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
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y = top - 15 if top - 15 > 15 else top + 15
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cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
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0.75, (0, 255, 0), 2)
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# show the output image
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cv2.imshow("Image", image)
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cv2.waitKey(0)
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