>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # Prepare primary data
>>> primary_image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> primary_mask = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
>>> primary_bboxes = np.array([[10, 10, 40, 40], [50, 50, 90, 90]], dtype=np.float32)
>>> primary_labels = [1, 2]
>>>
>>> # Prepare additional images for mosaic
>>> additional_image1 = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> additional_mask1 = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
>>> additional_bboxes1 = np.array([[20, 20, 60, 60]], dtype=np.float32)
>>> additional_labels1 = [3]
>>>
>>> additional_image2 = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> additional_mask2 = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
>>> additional_bboxes2 = np.array([[30, 30, 70, 70]], dtype=np.float32)
>>> additional_labels2 = [4]
>>>
>>> additional_image3 = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> additional_mask3 = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
>>> additional_bboxes3 = np.array([[5, 5, 45, 45]], dtype=np.float32)
>>> additional_labels3 = [5]
>>>
>>> # Create metadata for additional images - structured as a list of dicts
>>> mosaic_metadata = [
...     {
...         'image': additional_image1,
...         'mask': additional_mask1,
...         'bboxes': additional_bboxes1,
...         'labels': additional_labels1
...     },
...     {
...         'image': additional_image2,
...         'mask': additional_mask2,
...         'bboxes': additional_bboxes2,
...         'labels': additional_labels2
...     },
...     {
...         'image': additional_image3,
...         'mask': additional_mask3,
...         'bboxes': additional_bboxes3,
...         'labels': additional_labels3
...     }
... ]
>>>
>>> # Create the transform with Mosaic
>>> transform = A.Compose([
...     A.Mosaic(
...         grid_yx=(2, 2),
...         target_size=(200, 200),
...         cell_shape=(120, 120),
...         center_range=(0.4, 0.6),
...         fit_mode="cover",
...         p=1.0
...     ),
... ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['labels']))
>>>
>>> # Apply the transform
>>> transformed = transform(
...     image=primary_image,
...     mask=primary_mask,
...     bboxes=primary_bboxes,
...     labels=primary_labels,
...     mosaic_metadata=mosaic_metadata  # Pass the metadata using the default key
... )
>>>
>>> # Access the transformed data
>>> mosaic_image = transformed['image']        # Combined mosaic image
>>> mosaic_mask = transformed['mask']          # Combined mosaic mask
>>> mosaic_bboxes = transformed['bboxes']      # Combined and repositioned bboxes
>>> mosaic_labels = transformed['labels']      # Combined labels from all images
Bounding Box Parameters

Keypoint Parameters

Original

Transformed
