def demo_draw(feed_dict, result, extra_info):
reconstruct = get_env('demo.is_reconstruct', False)
grid_desc = get_env('demo.draw.grid_desc')
all_outputs = []
for i in range(1000):
name = 'canvas_step{}'.format(i)
if name in result:
all_outputs.append(result[name][0, :, :, 0])
final = image.image_grid(all_outputs, grid_desc)
final = (final * 255).astype('uint8')
if reconstruct:
img = feed_dict['img'][0, :, :, 0]
h = final.shape[0]
w = int(img.shape[1] * h / img.shape[0])
img = (img * 255).astype('uint8')
img = image.resize(img, (h, w))
final = np.hstack((img, final))
final = image.resize_minmax(final, 480, 720)
image.imshow('demo', final)
def resize_state(s):
return image.resize(s,
get_env('a3c.input_shape'),
interpolation='NEAREST')
def _crop_and_resize(self, img):
img = image.imdecode(img)
img = image.resize(img, self._img_shape)
return img
def resize_state(s):
return image.grayscale(
image.resize(s,
get_env('dqn.input_shape'),
interpolation='NEAREST'))
def resize_state(s):
return image.resize(s, (84, 84), interpolation='NEAREST')