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- import face_recognition
- import cv2
- import numpy as np
- # This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
- # other example, but it includes some basic performance tweaks to make things run a lot faster:
- # 1. Process each video frame at 1/4 resolution (though still display it at full resolution)
- # 2. Only detect faces in every other frame of video.
- # PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
- # OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
- # specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
- # Get a reference to webcam #0 (the default one)
- video_capture = cv2.VideoCapture(0)
- # Load a sample picture and learn how to recognize it.
- obama_image = face_recognition.load_image_file("obama.jpg")
- obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
- # Load a second sample picture and learn how to recognize it.
- biden_image = face_recognition.load_image_file("biden.jpg")
- biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
- # Create arrays of known face encodings and their names
- known_face_encodings = [
- obama_face_encoding,
- biden_face_encoding
- ]
- known_face_names = [
- "Barack Obama",
- "Joe Biden"
- ]
- # Initialize some variables
- face_locations = []
- face_encodings = []
- face_names = []
- process_this_frame = True
- while True:
- # Grab a single frame of video
- ret, frame = video_capture.read()
- # Resize frame of video to 1/4 size for faster face recognition processing
- small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
- # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
- rgb_small_frame = small_frame[:, :, ::-1]
- # Only process every other frame of video to save time
- if process_this_frame:
- # Find all the faces and face encodings in the current frame of video
- face_locations = face_recognition.face_locations(rgb_small_frame)
- face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
- face_names = []
- for face_encoding in face_encodings:
- # See if the face is a match for the known face(s)
- matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
- name = "Unknown"
- # # If a match was found in known_face_encodings, just use the first one.
- # if True in matches:
- # first_match_index = matches.index(True)
- # name = known_face_names[first_match_index]
- # Or instead, use the known face with the smallest distance to the new face
- face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
- best_match_index = np.argmin(face_distances)
- if matches[best_match_index]:
- name = known_face_names[best_match_index]
- face_names.append(name)
- process_this_frame = not process_this_frame
- # Display the results
- for (top, right, bottom, left), name in zip(face_locations, face_names):
- # Scale back up face locations since the frame we detected in was scaled to 1/4 size
- top *= 4
- right *= 4
- bottom *= 4
- left *= 4
- # Draw a box around the face
- cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
- # Draw a label with a name below the face
- cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
- font = cv2.FONT_HERSHEY_DUPLEX
- cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
- # Display the resulting image
- cv2.imshow('Video', frame)
- # Hit 'q' on the keyboard to quit!
- if cv2.waitKey(1) & 0xFF == ord('q'):
- break
- # Release handle to the webcam
- video_capture.release()
- cv2.destroyAllWindows()
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