def compare(epoch, batch=0,prefix=""):
x0 = np.load("%s/epoch%s.npz" % (prefix,epoch))
print "loss", x0.items()[0][1]
unchanged_img = x0.items()[-3][1]
changed_img = x0.items()[-2][1]
res_reshape2 = x0.items()[-1][1]
display.draw(unchanged_img[batch])
display.draw(changed_img[batch])
display.draw(res_reshape2[batch])
shapes = np.random.rand(nprims, nbatch, 4)*2 - 2
return np.array(shapes, dtype=config.floatX)
width = 224
height = 224
# Generate initial rays
exfragcoords = gen_fragcoords(width, height)
nprims = 500
print("Compiling Renderer")
render = make_render(nprims, width, height)
shapes = genshapebatch(nprims, 2)
print("Rendering")
img = render(exfragcoords, shapes)
from ig import display
display.draw(img[:,:,0])
# np.save('data/observed_img', img, allow_pickle=True, fix_imports=True)
#
# # print("Drawing Img")
# # draw(img)
#
# print("Doing Pixel Comparison")
# cost = similarity_cost2(img, nprims, width, height)
# sim = cost(exfragcoords, shapes)
#
# print(sim)
#
# img_tiled = np.tile(img,(1,3,1,1)) # Tile because vgg expects RGB but img is depth
# ## Render the image to create an observation
