80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
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import face_recognition
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import cv2
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import numpy as np
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# This is a super simple (but slow) example of running face recognition on live video from your webcam.
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# There's a second example that's a little more complicated but runs faster.
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# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
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# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
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# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
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# Get a reference to webcam #0 (the default one)
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video_capture = cv2.VideoCapture(2)
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# Load a sample picture and learn how to recognize it.
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Ruben_image = face_recognition.load_image_file("Ruben.jpg")
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Ruben_face_encoding = face_recognition.face_encodings(Ruben_image)[0]
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# Load a second samp=le picture and learn how to recognize it.
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Marjo_image = face_recognition.load_image_file("Marjo2.jpg")
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Marjo_face_encoding = face_recognition.face_encodings(Marjo_image)[0]
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# Create arrays of known face encodings and their names
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known_face_encodings = [
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Ruben_face_encoding,
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Marjo_face_encoding
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]
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known_face_names = [
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"Ruben",
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"Marjo"
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]
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while True:
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# Grab a single frame of video
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ret, frame = video_capture.read()
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# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
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rgb_frame = frame[:, :, ::-1]
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# Find all the faces and face enqcodings in the frame of video
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face_locations = face_recognition.face_locations(rgb_frame)
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face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
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# Loop through each face in this frame of video
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for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
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# See if the face is a match for the known face(s)
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matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
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name = "Unknown"
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# If a match was found in known_face_encodings, just use the first one.
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# if True in matches:
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# first_match_index = matches.index(True)
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# name = known_face_names[first_match_index]
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# Or instead, use the known face with the smallest distance to the new face
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face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
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best_match_index = np.argmin(face_distances)
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if matches[best_match_index]:
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name = known_face_names[best_match_index]
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# Draw a box around the face
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cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
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# Draw a label with a name below the face
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cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
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font = cv2.FONT_HERSHEY_DUPLEX
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cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
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# Display the resulting image
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cv2.imshow('Video', frame)
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# Hit 'q' on the keyboard to quit!
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# Release handle to the webcam
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video_capture.release()
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cv2.destroyAllWindows()
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