add pybullet example for eglRendererPlugin, will use EGL in a plugin on Linux, Win32OpenGLWindow in a plugin on Windows

examples/pybullet/examples/testrender_egl.py

@@ -0,0 +1,86 @@
+#using the eglRendererPlugin (hardware OpenGL acceleration)
+#using EGL on Linux and default OpenGL window on Win32.
+
+#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
+
+
+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.DIRECT)
+
+pybullet.loadPlugin("eglRendererPlugin")
+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 not 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(rgb)#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()