add mask_path to Detection label

[korbit-ai]

What changes are proposed in this pull request?

Fix #4486

How is this patch tested? If it is not, please explain why.

I genuinely couldn't figure out how to run the tests, I followed the instructions but kept getting errors when using pytest, so I don't know if I have inadvertently broken something.
I wrote a quick snippet to test the feature instead:

import tempfile
import numpy as np
from PIL import Image
import fiftyone as fo

MASK_SIZE = 256
BBOX = [0, 0, 1, 1]

# create a "mask"
mask = (np.random.rand(MASK_SIZE, MASK_SIZE) * 255).astype(np.uint8)
mask_image = Image.fromarray(mask)
mask_path = tempfile.mktemp(".png")
mask_image.save(mask_path)
print(f"Saved mask to {mask_path}")

# check mask are the same, even though they are stored differently
detection1 = fo.Detection(label="detection1", mask=mask, bounding_box=BBOX)
detection2 = fo.Detection(label="detection2", mask_path=mask_path, bounding_box=BBOX)
assert np.allclose(detection1.get_mask(), detection2.get_mask()), "detection masks are different"  # type: ignore
print(detection1, detection2)

# check polyline conversion
polyline1 = detection1.to_polyline()
polyline2 = detection2.to_polyline()
assert np.allclose(polyline1.points, polyline2.points), "polyline points are different" # type: ignore

# check mask export
mask2_path = tempfile.mktemp(".png")
detection1.export_mask(mask2_path, update=True)
print(f"Exported mask to {mask2_path}")
mask2_image = Image.open(mask2_path)
mask2 = np.array(mask2_image)
assert np.allclose(mask, mask2), "exported mask is different"
print(detection1, detection2)

# check mask import
detection2.import_mask(update=True)
assert np.allclose(detection1.get_mask(), detection2.get_mask()), "imported mask is different"  # type: ignore
print(detection1, detection2)

Release Notes

Is this a user-facing change that should be mentioned in the release notes?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for FiftyOne users.

Allows Detections to store masks on disk, this allows to offload some disk usage from the mongo database to somewhere else. See also #4486

What areas of FiftyOne does this PR affect?

• App: FiftyOne application changes
• Build: Build and test infrastructure changes
• Core: Core fiftyone Python library changes
• Documentation: FiftyOne documentation changes
• Other

Summary by CodeRabbit

• New Features

  • Introduced new capabilities for managing instance segmentation masks in the Detection class, including import/export functionality and transformations.
  • Enhanced mask retrieval processes across various modules for improved maintainability.

• Bug Fixes

  • Updated checks for mask presence to utilize the new mask retrieval methods, ensuring consistent access and functionality.

Description by Korbit AI

What change is being made?

Add a mask_path attribute to the Detection label class to support handling instance segmentation masks stored on disk, and update related methods to utilize this new attribute.

Why are these changes being made?

These changes are being made to enhance the flexibility of the Detection label class by allowing it to reference segmentation masks stored as files on disk, which can be more efficient for large datasets. This approach provides an alternative to storing masks directly in memory, thus optimizing resource usage and potentially improving performance when dealing with large-scale image datasets.

[korbit-ai]

Clone of the PR voxel51/fiftyone#4693

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Commits

Files that changed from the base of the PR and between 991c1d0 and e0718b9.

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Review Summary by Korbit AI

Code Execution Comments

• Handle potential AttributeError in detection.get_mask() with try/except to ensure robust execution.

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[korbit-ai]

The code is accessing the get_mask() method of the detection object without handling the potential AttributeError exception that could be raised if the method is not defined. To make this code more robust, consider wrapping the detection.get_mask() call in a try/except block that catches AttributeError and handles it appropriately, such as setting mask to None or providing a default mask value if the method is not available.

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