def test_plot_canvas(self):
image = np.ones((128, 128, 3), dtype=np.uint8)
image_stack = np.stack([image * i for i in range(25)], axis=0)
canvas = imageutils.batch_to_canvas(image_stack)
fig, ax = smplt.plot_canvas(canvas, 128, 128)
return fig
def test_plot_canvas(self):
image = np.ones((128, 128, 3), dtype=np.uint8)
image_stack = np.stack([image * i for i in range(25)], axis=0)
from supermariopy import imageutils
canvas = imageutils.batch_to_canvas(image_stack)
from supermariopy import plotting
fig, ax = plotting.plot_canvas(canvas, 128, 128)
def test_batch_to_canvas(self):
from supermariopy.imageutils import batch_to_canvas
x = np.ones((9, 100, 100, 3))
canvas = batch_to_canvas(x)
assert canvas.shape == (300, 300, 3)
canvas = batch_to_canvas(x, cols=5)
assert canvas.shape == (200, 500, 3)
canvas = batch_to_canvas(x, cols=1)
assert canvas.shape == (900, 100, 3)
canvas = batch_to_canvas(x, cols=0)
assert canvas.shape == (900, 100, 3)
canvas = batch_to_canvas(x, cols=None)
assert canvas.shape == (300, 300, 3)
def test_crop(self):
kps = stickman.VUNetStickman.get_example_valid_keypoints_deepfashion()
joint_img = stickman.VUNetStickman.make_joint_img(
(128, 128),
stickman.VUNET_JOINT_ORDER_DEEPFASHION,
kps * 128,
)
joint_order = stickman.VUNET_JOINT_ORDER_DEEPFASHION
crops = [
stickman.VUNetStickman.normalize(joint_img, kps * 128, joint_img,
joint_order, 1)
]
crops = [c[0] for c in crops]
crops = np.stack(crops, axis=0)
crops = np.split(crops, 8, axis=-1)
n_crops = len(crops)
cols = math.ceil(math.sqrt(n_crops))
crops = np.concatenate(crops, axis=0)
crops = imageutils.batch_to_canvas(crops, cols)
fig, ax = plt.subplots(1, 1)
ax.imshow(crops)
plt.savefig("test_crop.png")
return fig