text: #make sure to compile pybullet with PYBULLET_USE_NUMPY enabled
  #otherwise use testrender.py (slower but compatible without numpy)
  #you can also use GUI mode, for faster OpenGL rendering (instead of TinyRender CPU)
  
  import numpy as np
  import matplotlib.pyplot as plt
  import pybullet
  import time
  import pybullet_data
  
  plt.ion()
  
  img = np.random.rand(200, 320)
  #img = [tandard_normal((50,100))
  image = plt.imshow(img, interpolation='none', animated=True, label="blah")
  ax = plt.gca()
  
  #pybullet.connect(pybullet.GUI)
  pybullet.connect(pybullet.DIRECT)
  
  pybullet.setAdditionalSearchPath(pybullet_data.getDataPath())
  pybullet.loadURDF("plane.urdf", [0, 0, -1])
  pybullet.loadURDF("r2d2.urdf")
  
  camTargetPos = [0, 0, 0]
  cameraUp = [0, 0, 1]
  cameraPos = [1, 1, 1]
  pybullet.setGravity(0, 0, -10)
  
  pitch = -10.0
  
  roll = 0
  upAxisIndex = 2
  camDistance = 4
  pixelWidth = 320
  pixelHeight = 200
  nearPlane = 0.01
  farPlane = 100
  
  fov = 60
  
  main_start = time.time()
  while (1):
    for yaw in range(0, 360, 10):
      pybullet.stepSimulation()
      start = time.time()
  
      viewMatrix = pybullet.computeViewMatrixFromYawPitchRoll(camTargetPos, camDistance, yaw, pitch,
                                                              roll, upAxisIndex)
      aspect = pixelWidth / pixelHeight
      projectionMatrix = pybullet.computeProjectionMatrixFOV(fov, aspect, nearPlane, farPlane)
      img_arr = pybullet.getCameraImage(pixelWidth,
                                        pixelHeight,
                                        viewMatrix,
                                        projectionMatrix,
                                        shadow=1,
                                        lightDirection=[1, 1, 1],
                                        renderer=pybullet.ER_BULLET_HARDWARE_OPENGL)
      stop = time.time()
      print("renderImage %f" % (stop - start))
  
      w = img_arr[0]  #width of the image, in pixels
      h = img_arr[1]  #height of the image, in pixels
      rgb = img_arr[2]  #color data RGB
      dep = img_arr[3]  #depth data
  
      print('width = %d height = %d' % (w, h))
  
      #note that sending the data to matplotlib is really slow
  
      #reshape is needed
      np_img_arr = np.reshape(rgb, (h, w, 4))
      np_img_arr = np_img_arr * (1. / 255.)
  
      #show
      #plt.imshow(np_img_arr,interpolation='none',extent=(0,1600,0,1200))
      #image = plt.imshow(np_img_arr,interpolation='none',animated=True,label="blah")
  
      image.set_data(np_img_arr)
      ax.plot([0])
      #plt.draw()
      #plt.show()
      plt.pause(0.01)
      #image.draw()
  
  main_stop = time.time()
  
  print("Total time %f" % (main_stop - main_start))
  
  pybullet.resetSimulation()
  