OpenCV(4.0.1) error: (-215:Assertion failed) !dsize.empty() in function 'cv::hal::resize'

Question

I am new to machine learning, I am trying to apply masked face recognition on a model I trained with vgg. I have three image folders (mask, no mask, mask at the bottom (does not cover completely)), I couldn't solve the following error.

error: OpenCV(4.0.1) C:\ci\opencv-suite_1573470242804\work\modules\imgproc\src\resize.cpp:3427: error: (-215:Assertion failed) !dsize.empty() in function 'cv::hal::resize'

from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import numpy as np
import argparse
import imutils
import time
import cv2
import os
import pickle


def detect_and_predict_mask(frame, faceNet, maskNet):

    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
        (104.0, 177.0, 123.0))

    faceNet.setInput(blob)
    detections = faceNet.forward()

    faces = []
    locs = []
    preds = []

    for i in range(0, detections.shape[2]):


        confidence = detections[0, 0, i, 2]

        if confidence > args["confidence"]:

            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            (startX, startY) = (max(0, startX), max(0, startY))
            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

            face = frame[startY:endY, startX:endX]
            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
            face = cv2.resize(face, (64,64))
            face = img_to_array(face)
            face = preprocess_input(face)

            faces.append(face)
            locs.append((startX, startY, endX, endY))

    if len(faces) > 0:

        faces = np.array(faces, dtype="float32")
        preds = maskNet.predict(faces, batch_size=32)

    return (locs, preds)

ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face", type=str,
    default="face_detector",
    help="path to face detector model directory")
ap.add_argument("-m", "--model", type=str,
    default="output/sonn.model",
    help="path to trained face mask detector model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
    help="minimum probability to filter weak detections")
args = vars(ap.parse_args())


print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
weightsPath = os.path.sep.join([args["face"],
    "res10_300x300_ssd_iter_140000.caffemodel"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

print("[INFO] loading face mask detector model...")
maskNet = load_model(args["model"])

lb = pickle.loads(open("output/simple_nn_lb.pickle", "rb").read())

print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)

while True:

    frame = vs.read()
    frame = imutils.resize(frame, width=400)
    
    if -1 > 0:
        frame = frame.flatten()
        frame = frame.reshape((1, frame.shape[0]))
    else:
        frame = frame.reshape((1, frame.shape[0], frame.shape[1],
            frame.shape[2]))


    (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)


    for (box, pred) in zip(locs, preds):
        (startX, startY, endX, endY)= box
        print(pred)
        i = pred.argmax(axis=1)[0]
        label = lb.classes_[i]
        text = "{}: {:.2f}%".format(label, preds[0][i] * 100)
        cv2.putText(frame, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
            (0, 0, 255), 2)
        cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 0, 255), 2)

    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    if key == ord("q"):
        break

cv2.destroyAllWindows()
vs.stop()