import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy.ndimage import rotate
# Parameters
frames = 100
noise_size = (200, 200)
box_size = 60
edge_thickness = 2
center = np.array(noise_size) // 2
gif_path = "rotating_inverted_box.gif"
# Static noise for left panel
static_noise = np.random.rand(*noise_size)
# Box edge mask
box_outline = np.zeros((box_size, box_size))
box_outline[:edge_thickness, :] = 1
box_outline[-edge_thickness:, :] = 1
box_outline[:, :edge_thickness] = 1
box_outline[:, -edge_thickness:] = 1
# Setup figure
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
img1 = axes[0].imshow(static_noise.copy(), cmap='gray', vmin=0, vmax=1, animated=True)
img2 = axes[1].imshow(np.random.rand(*noise_size), cmap='gray', vmin=0, vmax=1, animated=True)
for ax in axes:
ax.axis('off')
axes[0].set_title("Static Noise")
axes[1].set_title("Alternating Noise")
def insert_inverted_contour(background, mask, angle):
rotated = rotate(mask, angle, reshape=False, order=1)
rotated_mask = rotated > 0.5
temp = background.copy()
y0, x0 = center[0] - box_size // 2, center[1] - box_size // 2
region = temp[y0:y0 + box_size, x0:x0 + box_size]
region[rotated_mask] = 1.0 - region[rotated_mask]
temp[y0:y0 + box_size, x0:x0 + box_size] = region
return temp
def update(frame):
angle = (frame * 3.6) % 360
frame1 = insert_inverted_contour(static_noise, box_outline, angle)
dynamic_noise = np.random.rand(*noise_size)
frame2 = insert_inverted_contour(dynamic_noise, box_outline, angle)
img1.set_array(frame1)
img2.set_array(frame2)
return img1, img2
ani = animation.FuncAnimation(fig, update, frames=frames, blit=True, interval=50)
# Save as GIF
ani.save(gif_path, writer='pillow', fps=20)
print(f"GIF saved as: {gif_path}")
plt.tight_layout()
plt.show()
