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79 lines
3.1 KiB
79 lines
3.1 KiB
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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