#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()
