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FaceRecognitionDetecFacialFeaturesAndFaceRecog.py
import face_recognition
import cv2
import numpy as np
def LoadFaces():
bruno_image = face_recognition.load_image_file("d:\Faces\Bruno1.jpg")
bruno_face_encoding = face_recognition.face_encodings(bruno_image)[0]
valentino_image = face_recognition.load_image_file("d:\Faces\Valen1.jpg")
valentino_face_encoding = face_recognition.face_encodings(valentino_image)[0]
known_face_encodings = [
bruno_face_encoding,
valentino_face_encoding
]
known_face_names = [
"Bruno",
"Valentino"
]
return known_face_encodings, known_face_names;
video_capture = cv2.VideoCapture(0)
known_face_encodings, known_face_names = LoadFaces()
while True:
ret, frame = video_capture.read()
rgb_frame = frame[:, :, ::-1]
face_landmarks_list = face_recognition.face_landmarks(rgb_frame)
for face_landmarks in face_landmarks_list:
for facial_feature in face_landmarks.keys():
pts = np.array([face_landmarks[facial_feature]], np.int32)
pts = pts.reshape((-1,1,2))
cv2.polylines(frame, [pts], False, (0,255,0))
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
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]
#cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.rectangle(frame, (left, bottom), (right, bottom), (0, 255, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.7, (255, 255, 255), 1)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
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