尝试在我的网络摄像头上实时使用人脸识别，但是 opencv 在打开摄像头后就崩溃了。这里是代码：
import face_recognition
import cv2
import os
import re
fileIndex = {
'#png_files'
}
video_capture = cv2.VideoCapture(0)
known_face_encodings = []
known_face_names = []
for n in os.listdir('#path'):
sp = os.path.splitext(n)
if re.match(r'^\.(png|jpe?g|gif)$', sp[1]):
if fileIndex.get(n):
img = face_recognition.load_image_file("#path" + n)
encoding = face_recognition.face_encodings(img)[0]
known_face_encodings.append(encoding)
known_face_names.append(n)
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]
name = fileIndex.get(name, 'Unknown')
face_names.append(os.path.splitext(name)[0])
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), (3, 168, 124), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (3, 168, 124), 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('user-cam', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
这里是 Python 错误：
Traceback (most recent call last):
File "/home/s3idani/Scripts/security/face_recognition/py/facerec_from_webcam_more_faster.py", line 60, in <module>
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
File "/home/s3idani/.local/lib/python3.8/site-packages/face_recognition/api.py", line 214, in face_encodings
return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
File "/home/s3idani/.local/lib/python3.8/site-packages/face_recognition/api.py", line 214, in <listcomp>
return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
TypeError: compute_face_descriptor(): incompatible function arguments. The following argument types are supported:
1. (self: _dlib_pybind11.face_recognition_model_v1, img: numpy.ndarray[(rows,cols,3),numpy.uint8], face: _dlib_pybind11.full_object_detection, num_jitters: int = 0, padding: float = 0.25) -> _dlib_pybind11.vector
2. (self: _dlib_pybind11.face_recognition_model_v1, img: numpy.ndarray[(rows,cols,3),numpy.uint8], num_jitters: int = 0) -> _dlib_pybind11.vector
3. (self: _dlib_pybind11.face_recognition_model_v1, img: numpy.ndarray[(rows,cols,3),numpy.uint8], faces: _dlib_pybind11.full_object_detections, num_jitters: int = 0, padding: float = 0.25) -> _dlib_pybind11.vectors
4. (self: _dlib_pybind11.face_recognition_model_v1, batch_img: List[numpy.ndarray[(rows,cols,3),numpy.uint8]], batch_faces: List[_dlib_pybind11.full_object_detections], num_jitters: int = 0, padding: float = 0.25) -> _dlib_pybind11.vectorss
5. (self: _dlib_pybind11.face_recognition_model_v1, batch_img: List[numpy.ndarray[(rows,cols,3),numpy.uint8]], num_jitters: int = 0) -> _dlib_pybind11.vectors
Invoked with: <_dlib_pybind11.face_recognition_model_v1 object at 0x7f906a226830>, array([[[252, 248, 200],
[255, 252, 205],
[252, 248, 203],
...,
[188, 210, 189],
[181, 201, 177],
[163, 186, 170]],
[[252, 251, 205],
[253, 255, 208],
[252, 254, 205],
...,
[189, 211, 187],
[187, 207, 183],
[171, 187, 172]],
[[251, 250, 206],
[253, 255, 216],
[253, 255, 217],
...,
[184, 207, 189],
[202, 223, 197],
[166, 192, 166]],
...,
[[182, 162, 117],
[201, 189, 146],
[181, 166, 125],
...,
[211, 240, 227],
[209, 237, 225],
[203, 237, 220]],
[[166, 150, 119],
[174, 156, 122],
[162, 148, 112],
...,
[206, 242, 223],
[207, 237, 219],
[205, 232, 216]],
[[136, 128, 110],
[150, 143, 112],
[155, 148, 113],
...,
[208, 239, 222],
[207, 236, 220],
[205, 231, 211]]], dtype=uint8), <_dlib_pybind11.full_object_detection object at 0x7f906a226370>, 1
蟒蛇3.8
opencv 4.7
dlib 19.24
如何解决这个问题？
答案1
我遇到了同样的问题，我发现更换
rgb_frame = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
和
rgb_frame = small_frame[:, :, ::-1]
这个问题可能更有帮助https://github.com/ageitgey/face_recognition/issues/1497