text: import cv2
  import numpy as np
  import numpy as cv
  #import cv2 as cv
  from irCamera_SeekMosaic import irCamera_SeekMosaic
  #from PIL import Image
  
  vlcamera = cv2.VideoCapture(0)
  ircamera = irCamera_SeekMosaic(54339)
  
  dsize = (1, 1) #default is no resizing
  vlret = False
  irret = False
  
  while 1:
      if vlcamera is not None:
          vlret, visible_image = vlcamera.read()
      
      if ircamera is not None:
          irret, thermal_image_data_fixedpoint = ircamera.read()
  
          #resize the image data so we can see it later
          height, width = thermal_image_data_fixedpoint.shape[:2]
          if height < 320:
              # make this a little bit larger to view it clearly
              dsize = (width * 4, height * 4)
          else:
              dsize = (width * 2, height * 2)
     
          thermal_image_data_fixedpoint_enlarged = cv2.resize(thermal_image_data_fixedpoint, dsize)
          #find min and max pixel values so we can optimize the image contrast
          [minVal, maxVal, minLoc, maxLoc] = cv2.minMaxLoc(thermal_image_data_fixedpoint_enlarged)
          
          #Now create a version of the data to show on the screen or run through an inferencing model
          imageDelta = maxVal - minVal
          offsetImg = thermal_image_data_fixedpoint_enlarged - minVal
          scaledImg = offsetImg / imageDelta
  
          #thermal_img_normalized = cv2.normalize(thermal_image_data_fixedpoint_enlarged, dst=None, alpha=0, beta=65535, norm_type=cv2.NORM_MINMAX)
  
      # then we'll just show the images in OpenCV windows
      if vlret:
          cv2.imshow('Visible', visible_image)
      if irret:
          #cv2.imshow('Thermal IR', thermal_img_normalized)
          cv2.imshow('Presentation IR', scaledImg)
      k=cv2.waitKey(1)
  
  #
  # And finally cleanup
  #
  
  if vlcamera is not None:
      vlcamera.release()
      
  if ircamera is not None:
      ircamera.release()
  
  cv.destroyAllWindows()
  
  
  