我这里有一些代码,这有点草率,有什么办法可以将图像,编码和名称放入单独的文件中,并将它们导入到主代码中使用?我尝试将它们放入一个单独的文件中,然后导入它们,但是它仍然显示未定义的错误?谁能帮助我找出原因或如何解决。
主要代码
import cv2
import numpy as np
# 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)
me_image = face_recognition.load_image_file("me.jpg")
me_face_encoding = face_recognition.face_encodings(me_image)[0]
mom_image = face_recognition.load_image_file("mom.jpg")
mom_face_encoding = face_recognition.face_encodings(mom_image)[0]
mattm_image = face_recognition.load_image_file("mattm.jpg")
mattm_face_encoding = face_recognition.face_encodings(mattm_image)[0]
soph_image = face_recognition.load_image_file("soph.jpg")
soph_face_encoding = face_recognition.face_encodings(soph_image)[0]
known_face_encodings = [
me_face_encoding,
mom_face_encoding,
mattm_face_encoding,
soph_face_encoding
]
known_face_names = [
"Jacob North",
"Shelly North",
"Matt Mersino",
"Sophia North"
]
# 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()
我希望分离的代码
me_image = face_recognition.load_image_file("me.jpg")
me_face_encoding = face_recognition.face_encodings(me_image)[0]
mom_image = face_recognition.load_image_file("mom.jpg")
mom_face_encoding = face_recognition.face_encodings(mom_image)[0]
mattm_image = face_recognition.load_image_file("mattm.jpg")
mattm_face_encoding = face_recognition.face_encodings(mattm_image)[0]
soph_image = face_recognition.load_image_file("soph.jpg")
soph_face_encoding = face_recognition.face_encodings(soph_image)[0]
known_face_encodings = [
me_face_encoding,
mom_face_encoding,
mattm_face_encoding,
soph_face_encoding
]
known_face_names = [
"Jacob North",
"Shelly North",
"Matt Mersino",
"Sophia North"
]
我只想使其更整洁,更容易使用。