Распознавание лиц и OpenCV с ошибкой DroidCam

Question

Эта программа использует веб-камеру и находит и распознает лица с помощью библиотеки face_recignition с помощью OpenCV. У меня нет подходящей веб-камеры (моя встроенная веб-камера ужасна), поэтому я решил использовать для нее DroidCam Client (https://www.dev47apps.com/). Это работает, вынимая отдельные кадры из веб-камеры и затем распознавая ее.

CODE (Использование веб-камеры по умолчанию)

import face_recognition
import cv2
import numpy as np
import time

#   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.

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.
aarna_image = face_recognition.load_image_file("aarna.jpg")
aarna_face_encoding = face_recognition.face_encodings(aarna_image)[0]

# Load a second sample picture and learn how to recognize it.
aditya_image = face_recognition.load_image_file("aditya.jpg")
aditya_face_encoding = face_recognition.face_encodings(aditya_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    aarna_face_encoding,
    aditya_face_encoding
]
known_face_names = [
    "Aarna Gupta",
    "Aditya"
]

    # Initialize some variables
while True:
    face_locations = []
    face_encodings = []
    face_names = []
    process_this_frame = 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)




# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

Когда код, приведенный выше, выполняется, он работает нормально. и это код для DroidCam

import face_recognition
import cv2
import numpy as np
import time

#   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.

# Load a sample picture and learn how to recognize it.
aarna_image = face_recognition.load_image_file("aarna.jpg")
aarna_face_encoding = face_recognition.face_encodings(aarna_image)[0]

# Load a second sample picture and learn how to recognize it.
aditya_image = face_recognition.load_image_file("aditya.jpg")
aditya_face_encoding = face_recognition.face_encodings(aditya_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    aarna_face_encoding,
    aditya_face_encoding
]
known_face_names = [
    "Aarna Gupta",
    "Aditya"
]

    # Initialize some variables
while True:
    face_locations = []
    face_encodings = []
    face_names = []
    process_this_frame = True


    # Grab a single frame of video

    cap = cv2.VideoCapture(http://192.168.0.0:4747/video)
    #THE link redirects to the WEBCAM =====^^^^^^^===================================================
    ret, frame = cap.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)




# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

При запуске он показывает только один обработанный кадр видео, а не несколько обработанных кадров, и возвращает следующий вывод:

Traceback (most recent call last):
  File "F:/Webcam Face/main.py", line 49, in <module>
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\imgproc\src\resize.cpp:4045: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'

Пожалуйста, помогите с кодом.