def show_grid_gradient(env, matrix_vals, colormap='Reds', tile_size=TILE_PIXELS, scale=(0, 10), alpha=0.75, title="My Visualization"):
if not matrix_vals.shape == (env.width, env.height):
raise ValueError("Expected matrix_vals shape {} but got {}".format((env.width, env.height), matrix_vals.shape))
if scale:
low, high = scale
else:
low, high = np.min(matrix_vals[1:-1, 1:-1]), np.max(matrix_vals[1:-1, 1:-1])
img = env.grid.render(tile_size=TILE_PIXELS)
matrix_vals = (matrix_vals - low) / (high - low)
my_cmap = color.get_cmap(colormap)
color_array = np.zeros((env.width, env.height, 4))
color_array[1:-1, 1:-1, :] = my_cmap(matrix_vals[1:-1, 1:-1])
for i in range(1, env.width-1):
for j in range(1, env.height-1):
ymin = j * tile_size
ymax = (j+1) * tile_size
xmin = i * tile_size
xmax = (i+1) * tile_size
tile = img[ymin:ymax, xmin:xmax, :]
highligh_color = color_array[i, j, :-1] * 255
highlight_img(tile, highligh_color, alpha=alpha)
window = gym_minigrid.window.Window(title)
window.show_img(img)
window.show()
return img
def render_tile(cls, obj, highlight=False, tile_size=TILE_PIXELS, subdivs=3):
if obj is None:
key = (
tile_size,
highlight,
)
else:
key = (tile_size, highlight, *obj.encode())
if key in cls.tile_cache:
img = cls.tile_cache[key]
else:
img = np.zeros(
shape=(tile_size * subdivs, tile_size * subdivs, 3), dtype=np.uint8
)
# Draw the grid lines (top and left edges)
fill_coords(img, point_in_rect(0, 0.031, 0, 1), (100, 100, 100))
fill_coords(img, point_in_rect(0, 1, 0, 0.031), (100, 100, 100))
if obj != None:
obj.render(img)
if highlight:
highlight_img(img)
img = downsample(img, subdivs)
cls.tile_cache[key] = img
return img
def render_tile(
cls,
obj,
highlight=None,
tile_size=minigrid.TILE_PIXELS,
subdivs=3,
cell_type=None,
):
"""Render a tile and cache the result."""
# Hash map lookup key for the cache
if isinstance(highlight, list):
key = (tuple(highlight), tile_size)
else:
key = (highlight, tile_size)
key = obj.encode() + key if obj else key
if key in cls.tile_cache:
return cls.tile_cache[key]
img = np.zeros(shape=(tile_size * subdivs, tile_size * subdivs, 3),
dtype=np.uint8)
# Draw the grid lines (top and left edges)
rendering.fill_coords(img, rendering.point_in_rect(0, 0.031, 0, 1),
(100, 100, 100))
rendering.fill_coords(img, rendering.point_in_rect(0, 1, 0, 0.031),
(100, 100, 100))
if obj is not None and obj.type != "agent":
obj.render(img)
# Highlight the cell if needed (do not highlight walls)
if highlight and not (cell_type is not None and cell_type == "wall"):
if isinstance(highlight, list):
for a, agent_highlight in enumerate(highlight):
if agent_highlight:
rendering.highlight_img(img, color=AGENT_COLOURS[a])
else:
# Default highlighting for agent's partially observed views
rendering.highlight_img(img)
# Render agents after highlight to avoid highlighting agent triangle (the
# combination of colours makes it difficult to ID agent)
if obj is not None and obj.type == "agent":
obj.render(img)
# Downsample the image to perform supersampling/anti-aliasing
img = rendering.downsample(img, subdivs)
# Cache the rendered tile
cls.tile_cache[key] = img
return img