Example of generating random shapes with particular properties.

Out:

```
(128, 128, 1) [('rectangle', ((104, 127), (3, 128)))]
```

```
import matplotlib.pyplot as plt
from skimage.draw import random_shapes
# Let's start simple and generate a 128x128 image
# with a single grayscale rectangle.
result = random_shapes((128, 128), max_shapes=1, shape='rectangle', gray=True)
# We get back a tuple consisting of (1) the image with the generated shapes
# and (2) a list of label tuples with the kind of shape (e.g. circle, rectangle)
# and ((r0, r1), (c0, c1)) coordinates.
image, labels = result
print(image.shape, labels)
# We can visualize the images.
fig, axis = plt.subplots()
axis.imshow(image.squeeze(), cmap='gray')
axis.set_axis_off()
# The generated images can be much more complex. For example, let's try many
# shapes of any color. If we want the colors to be particularly light, we can
# set the min_pixel_intensity to a high value from the range [0,255].
image1, _ = random_shapes((128, 128), max_shapes=10, min_pixel_intensity=100)
# Moar :)
image2, _ = random_shapes((128, 128), max_shapes=10, min_pixel_intensity=200)
image3, _ = random_shapes((128, 128), max_shapes=10, min_pixel_intensity=50)
image4, _ = random_shapes((128, 128), max_shapes=10, min_pixel_intensity=0)
fig, axes = plt.subplots(2, 2, figsize=(10, 10))
for ax, image in zip(axes.ravel(), [image1, image2, image3, image4]):
ax.imshow(image)
ax.set_axis_off()
# These shapes are well suited to test segmentation algorithms. Often, we want
# shapes to overlap to test the algorithm. This is also possible:
image, _ = random_shapes(
(128, 128), min_shapes=5, max_shapes=10, min_size=20, allow_overlap=True
)
fig, axis = plt.subplots()
axis.imshow(image)
axis.set_axis_off()
plt.show()
```

**Total running time of the script:** ( 0 minutes 0.383 seconds)