Not working correctly, not too sure whats wrong

[Jack123135]

• face_recognition version:
• Python version:
• Operating System:

Description

I ran the command, making all the adjustments need for it to work for me, but in some instances the command would run once with a frozen image of my face and then would just quit with no error. Other instances, the terminal has given me the error bellow.

What I Did

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.

jackson_image = face_recognition.load_image_file("Jackson.jpg")
jackson_face_encoding = face_recognition.face_encodings(jackson_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 = [
jackson_face_encoding,
]
known_face_names = [
"Jackson Woosnam"
]

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()

# Only process every other frame of video to save time
if process_this_frame:
    # 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]
    
    # 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()

  File "c:\Users\darle\Friday AI Assistant\facial recognition.py", line 53, in <module>
    face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\darle\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\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]
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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 0x00000199B9E41AB0>, array([[[149, 165, 162],
        [151, 167, 167],
        [149, 158, 156],
        ...,
        [130, 132, 130],
        [126, 130, 130],
        [131, 131, 130]],

       [[150, 163, 160],
        [150, 161, 172],
        [151, 160, 168],
        ...,
        [129, 133, 132],
        [131, 136, 133],
        [130, 134, 136]],

       [[153, 162, 163],
        [155, 162, 159],
        [153, 158, 158],
        ...,
        [129, 135, 128],
        [128, 137, 130],
        [127, 133, 130]],

       ...,

       [[  6,  12,   8],
        [  2,  10,   8],
        [  1,  11,   8],
        ...,
        [104, 116, 113],
        [105, 115, 113],
        [105, 115, 116]],

       [[  6,  11,   9],
        [  8,  13,  11],
        [  2,  10,   8],
        ...,
        [102, 114, 110],
        [107, 114, 113],
        [105, 111, 110]],

       [[  5,  11,  11],
        [  2,   8,   8],
        [  3,   9,   8],
        ...,
        [102, 115, 113],
        [104, 116, 113],

[Klu5ure]

#1537 (comment)
Replace the line:

rgb_frame = frame[:, :, ::-1]

with:

rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)