>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # Prepare primary data (base image and mask)
>>> image = np.zeros((300, 300, 3), dtype=np.uint8)
>>> mask = np.zeros((300, 300), dtype=np.uint8)
>>>
>>> # 1. Create a simple overlay image (a red square)
>>> overlay_image1 = np.zeros((50, 50, 3), dtype=np.uint8)
>>> overlay_image1[:, :, 0] = 255  # Red color
>>>
>>> # 2. Create another overlay with a mask (a blue circle with transparency)
>>> overlay_image2 = np.zeros((80, 80, 3), dtype=np.uint8)
>>> overlay_image2[:, :, 2] = 255  # Blue color
>>> overlay_mask2 = np.zeros((80, 80), dtype=np.uint8)
>>> # Create a circular mask
>>> center = (40, 40)
>>> radius = 30
>>> for i in range(80):
...     for j in range(80):
...         if (i - center[0])**2 + (j - center[1])**2 < radius**2:
...             overlay_mask2[i, j] = 255
>>>
>>> # 3. Create an overlay with both bbox and mask_id
>>> overlay_image3 = np.zeros((60, 120, 3), dtype=np.uint8)
>>> overlay_image3[:, :, 1] = 255  # Green color
>>> # Create a rectangular mask with rounded corners
>>> overlay_mask3 = np.zeros((60, 120), dtype=np.uint8)
>>> cv2.rectangle(overlay_mask3, (10, 10), (110, 50), 255, -1)
>>>
>>> # Create the metadata list - each item is a dictionary with overlay information
>>> overlay_metadata = [
...     {
...         'image': overlay_image1,
...         # No bbox provided - will be placed randomly
...     },
...     {
...         'image': overlay_image2,
...         'bbox': [0.6, 0.1, 0.9, 0.4],  # Normalized coordinates [x_min, y_min, x_max, y_max]
...         'mask': overlay_mask2,
...         'mask_id': 1  # This overlay will update the mask with id 1
...     },
...     {
...         'image': overlay_image3,
...         'bbox': [0.1, 0.7, 0.5, 0.9],  # Bottom left placement
...         'mask': overlay_mask3,
...         'mask_id': 2  # This overlay will update the mask with id 2
...     }
... ]
>>>
>>> # Create the transform
>>> transform = A.Compose([
...     A.OverlayElements(p=1.0),
... ])
>>>
>>> # Apply the transform
>>> result = transform(
...     image=image,
...     mask=mask,
...     overlay_metadata=overlay_metadata  # Pass metadata using the default key
... )
>>>
>>> # Get results with overlays applied
>>> result_image = result['image']  # Image with the three overlays applied
>>> result_mask = result['mask']    # Mask with regions labeled using the mask_id values
>>>
>>> # Let's verify the mask contains the specified mask_id values
>>> has_mask_id_1 = np.any(result_mask == 1)  # Should be True
>>> has_mask_id_2 = np.any(result_mask == 2)  # Should be True
Parameters

Bounding Box Parameters

Keypoint Parameters
