i was making motion detection for detecting alphabets using opencv mnp models(SOLVED)

edit

i copied this code from github but its working fine for him but in my case cemera suddenly stops when i remove blue objects from frame

File "C:/Users/Deepak/PycharmProjects/Alphabet_Recognition_Gestures-master/Alphabet_Recognition_Gestures-master/alphabet_recognition.py", line 84, in <module> cnt = sorted(blackboard_cnts, key = cv2.contourArea, reverse = True)[0] cv2.error: OpenCV(4.0.0) C:\projects\opencv-python\opencv\modules\imgproc\src\shapedescr.cpp:272: error: (-215:Assertion failed) npoints >= 0 && (depth == CV_32F || depth == CV_32S) in function 'cv::contourArea'

[ WARN:1] terminating async callback

from keras.models import load_model
from collections import deque
import numpy as np
import cv2

# Load the models built in the previous steps
mlp_model = load_model('emnist_mlp_model.h5')
cnn_model = load_model('emnist_cnn_model.h5')

# Letters lookup
letters = { 1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j',
11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't',
21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 27: '-'}

# Define the upper and lower boundaries for a color to be considered "Blue"
blueLower = np.array([100, 60, 60])
blueUpper = np.array([140, 255, 255])

# Define a 5x5 kernel for erosion and dilation
kernel = np.ones((5, 5), np.uint8)

# Define Black Board
blackboard = np.zeros((480,640,3), dtype=np.uint8)
alphabet = np.zeros((200, 200, 3), dtype=np.uint8)

# Setup deques to store alphabet drawn on screen
points = deque(maxlen=512)

# Define prediction variables
prediction1 = 26
prediction2 = 26

index = 0
# Load the video
camera = cv2.VideoCapture(0)

# Keep looping
while True:
    # Grab the current paintWindow
    (grabbed, frame) = camera.read()
    frame = cv2.flip(frame, 1)
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Determine which pixels fall within the blue boundaries and then blur the binary image
    blueMask = cv2.inRange(hsv, blueLower, blueUpper)
    blueMask = cv2.erode(blueMask, kernel, iterations=2)
    blueMask = cv2.morphologyEx(blueMask, cv2.MORPH_OPEN, kernel)
    blueMask = cv2.dilate(blueMask, kernel, iterations=1)

    # Find contours (bottle cap in my case) in the image
    (cnts, _) = cv2.findContours(blueMask.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)
    center = None

    # Check to see if any contours were found
    if len(cnts) > 0:
        # Sort the contours and find the largest one -- we
        # will assume this contour correspondes to the area of the bottle cap
        cnt = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
        # Get the radius of the enclosing circle around the found contour
        ((x, y), radius) = cv2.minEnclosingCircle(cnt)
        # Draw the circle around the contour

        cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)
        # Get the moments to calculate the center of the contour (in this case Circle)
        M = cv2.moments(cnt)
        center = (int(M['m10'] / M['m00']), int(M['m01'] / M['m00']))

        points.appendleft(center)

    elif len(cnts) == 0:
        cv2.putText(frame, "Multilayer Perceptron : " + str(letters[int(prediction1) + 1]), (10, 410),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
        if len(points) != 0:
            blackboard_gray = cv2.cvtColor(blackboard, cv2.COLOR_BGR2GRAY)
            blur1 = cv2 ...