text: import cv2
  import numpy as np
  import math
  
  
  cap = cv2.VideoCapture(0)
  while(cap.isOpened()):
      # read image
      ret, img = cap.read()
  
      # get hand data from the rectangle sub window on the screen
      cv2.rectangle(img, (300,300), (100,100), (0,255,0),0)
      crop_img = img[100:300, 100:300]
  
      # convert to grayscale
      grey = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)
  
      # applying gaussian blur
      value = (35, 35)
      blurred = cv2.GaussianBlur(grey, value, 0)
  
      # thresholdin: Otsu's Binarization method
      _, thresh1 = cv2.threshold(blurred, 127, 255,
                                 cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
  
      # show thresholded image
      cv2.imshow('Thresholded', thresh1)
  
      # check OpenCV version to avoid unpacking error
      (version, _, _) = cv2.__version__.split('.')
  
      if version == '3':
          image, contours, hierarchy = cv2.findContours(thresh1.copy(), \
                 cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
      elif version == '4':
          contours, hierarchy = cv2.findContours(thresh1.copy(),cv2.RETR_TREE, \
                 cv2.CHAIN_APPROX_NONE)
  
      # find contour with max area
      cnt = max(contours, key = lambda x: cv2.contourArea(x))
  
      # create bounding rectangle around the contour (can skip below two lines)
      x, y, w, h = cv2.boundingRect(cnt)
      cv2.rectangle(crop_img, (x, y), (x+w, y+h), (0, 0, 255), 0)
  
      # finding convex hull
      hull = cv2.convexHull(cnt)
  
      # drawing contours
      drawing = np.zeros(crop_img.shape,np.uint8)
      cv2.drawContours(drawing, [cnt], 0, (0, 255, 0), 0)
      cv2.drawContours(drawing, [hull], 0,(0, 0, 255), 0)
  
      # finding convex hull
      hull = cv2.convexHull(cnt, returnPoints=False)
  
      # finding convexity defects
      defects = cv2.convexityDefects(cnt, hull)
      count_defects = 0
      cv2.drawContours(thresh1, contours, -1, (0, 255, 0), 3)
  
      # applying Cosine Rule to find angle for all defects (between fingers)
      # with angle > 90 degrees and ignore defects
      for i in range(defects.shape[0]):
          s,e,f,d = defects[i,0]
  
          start = tuple(cnt[s][0])
          end = tuple(cnt[e][0])
          far = tuple(cnt[f][0])
  
          # find length of all sides of triangle
          a = math.sqrt((end[0] - start[0])**2 + (end[1] - start[1])**2)
          b = math.sqrt((far[0] - start[0])**2 + (far[1] - start[1])**2)
          c = math.sqrt((end[0] - far[0])**2 + (end[1] - far[1])**2)
  
          # apply cosine rule here
          angle = math.acos((b**2 + c**2 - a**2)/(2*b*c)) * 57
  
          # ignore angles > 90 and highlight rest with red dots
          if angle <= 90:
              count_defects += 1
              cv2.circle(crop_img, far, 1, [0,0,255], -1)
          #dist = cv2.pointPolygonTest(cnt,far,True)
  
          # draw a line from start to end i.e. the convex points (finger tips)
          # (can skip this part)
          cv2.line(crop_img,start, end, [0,255,0], 2)
          #cv2.circle(crop_img,far,5,[0,0,255],-1)
  
      # define actions required
      if count_defects == 1:
          cv2.putText(img,"first item", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
      elif count_defects == 2:
          str = "second item"
          cv2.putText(img, str, (5, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
      elif count_defects == 3:
          cv2.putText(img,"third item", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
      elif count_defects == 4:
          cv2.putText(img,"fourth item", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
      else:
          cv2.putText(img,"ok", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
  
      # show appropriate images in windows
      cv2.imshow('Gesture', img)
      all_img = np.hstack((drawing, crop_img))
      cv2.imshow('Contours', all_img)
  
      k = cv2.waitKey(10)
      if k == 27:
          break