sample.py

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

matplotlib.use('MACOSX')

img_h, img_w = 250, 500

img = np.zeros((img_h, img_w))

bounding_box_sizes = 25, 50
thumbnail_top_left_x, thumbnail_top_left_y = 100, 320
img[thumbnail_top_left_x:thumbnail_top_left_x + bounding_box_sizes[0],
thumbnail_top_left_y:thumbnail_top_left_y + bounding_box_sizes[1]] = 1
small_mask = np.array(img)

plt.ion()

img_sample_sizes = 20, 40

# plt.show()


for s in range(1000):
    range_x = max(bounding_box_sizes[0] - img_sample_sizes[0], 0)  # 10 -> 24.
    range_y = max(bounding_box_sizes[1] - img_sample_sizes[1], 0)  # 10 -> 24.
    x0, x1 = thumbnail_top_left_x + range_x, thumbnail_top_left_x + bounding_box_sizes[0] - range_x
    y0, y1 = thumbnail_top_left_y + range_y, thumbnail_top_left_y + bounding_box_sizes[1] - range_y
    assert x0 < x1
    assert y0 < y1

    plt.imshow(img, cmap='jet')
    plt.imsave(f'plt_{str(s).zfill(4)}.png', img)
    plt.pause(0.01)

    x = np.random.choice(range(x0, x1))
    y = np.random.choice(range(y0, y1))

    bigger_mask = np.zeros_like(img)
    bigger_mask[x - img_sample_sizes[0]:x + img_sample_sizes[0], y - img_sample_sizes[1]: y + img_sample_sizes[1]] = 1
    # masks should match.
    assert np.all(np.logical_and(bigger_mask, small_mask).astype(np.int) == small_mask.astype(np.int))
    img[x - img_sample_sizes[0]:x + img_sample_sizes[0], y - img_sample_sizes[1]: y + img_sample_sizes[1]] += 1
    # plt.show()
    # big_size += 5  # just for fun.