import face_recognition
import cv2
import os
def file_name(dir):
names = os.listdir(dir)
i=0
for name in names:
index = name.rfind('.')
name = name[:index]
names[i]=name
i=i+1
return names
def file_list(dir):
list_name=os.listdir(dir)
return list_name
video_capture = cv2.VideoCapture(0)
face_dir="E:\\face"
names1=file_name(face_dir)
root=file_list(face_dir)
for name1 in names1:
image = face_recognition.load_image_file("E:\\face\\"+name1+".jpg")
name1 = face_recognition.face_encodings(image)[0]
# name1 = name1.astype('float64')
# Create arrays of known face encodings and their names
known_face_encodings = names1
known_face_names = names1
print(known_face_encodings)
# 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)
#face_encoding = face_encoding.astype('float64')
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
print(matches)
# 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]
print(first_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()
总是提示： return np.linalg.norm(face_encodings - face_to_compare, axis=1)
TypeError: ufunc 'subtract' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
这是什么鬼，转换了数据类型也没有用？？？