105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
import face_recognition
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
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# other example, but it includes some basic performance tweaks to make things run a lot faster:
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# 1. Process each video frame at 1/4 resolution (though still display it at full resolution)
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# 2. Only detect faces in every other frame of video.
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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(0)
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# Load a sample picture and learn how to recognize it.
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PUN_image = face_recognition.load_image_file("PUN.jpg")
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PUN_face_encoding = face_recognition.face_encodings(PUN_image)[0]
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# Load a second sample picture and learn how to recognize it.
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TAT_image = face_recognition.load_image_file("tat.jpg")
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TAT_face_encoding = face_recognition.face_encodings(TAT_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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PUN_face_encoding,
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TAT_face_encoding
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]
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known_face_names = [
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"PUN",
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"TAT"
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]
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# Initialize some variables
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face_locations = []
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face_encodings = []
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face_names = []
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process_this_frame = True
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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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# Only process every other frame of video to save time
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if process_this_frame:
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# Resize frame of video to 1/4 size for faster face recognition processing
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small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
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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_small_frame = np.ascontiguousarray(small_frame[:, :, ::-1])
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# Find all the faces and face encodings in the current frame of video
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face_locations = face_recognition.face_locations(rgb_small_frame)
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face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
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face_names = []
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for face_encoding in 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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face_names.append(name)
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process_this_frame = not process_this_frame
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# Display the results
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for (top, right, bottom, left), name in zip(face_locations, face_names):
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# Scale back up face locations since the frame we detected in was scaled to 1/4 size
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top *= 4
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right *= 4
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bottom *= 4
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left *= 4
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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() |