Revert crop order changes in - #330
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📝 WalkthroughWalkthroughMask processing now preserves threshold behavior when resizing standard masks. It fills outside-box logits with the threshold and handles infinite thresholds with finite dtype limits. RF-DETR mask processing no longer accepts or applies bounding boxes. ChangesMask processing
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: 🔵 Low · up to Out-of-range mask confidence values can still produce invalid segmentation-mask output during resizing. Validate confidence inputs or handle all values at and beyond the threshold endpoints before merging. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 inconclusive)
✅ Passed checks (4 passed)
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## main #330 +/- ##
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- Coverage 48.84% 48.82% -0.03%
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Files 112 112
Lines 6823 6827 +4
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Hits 3333 3333
- Misses 3490 3494 +4 ☔ View full report in Codecov by Harness. |
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In `@depthai_nodes/node/parsers/utils/masks_utils.py`:
- Line 66: Update the mask_logits path around crop_mask so excluded pixels are
filled with -np.inf before the threshold comparison, preventing them from
becoming foreground when the logit threshold is negative. Preserve zero as the
default fill value for already-thresholded binary masks.
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depthai_nodes/node/parsers/utils/masks_utils.py
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| scaled_bbox = bbox * np.array([mask_w, mask_h, mask_w, mask_h]) | ||
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| mask_logits = np.sum(protos * mask_coeff[..., np.newaxis, np.newaxis], axis=0) | ||
| mask_logits = crop_mask(mask_logits, scaled_bbox) |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
Preserve excluded logits below the threshold.
crop_mask replaces pixels outside the bounding box with 0. For any mask_conf < 0.5, probability_to_logit_threshold(mask_conf) is negative, so those zero-valued pixels pass the mask_logits > logit_threshold check. The output mask then marks the area outside the bounding box as foreground.
Use -np.inf for excluded logits in this path. Keep zero as the default fill value for already-thresholded binary masks.
Proposed fix
-def crop_mask(mask: np.ndarray, bbox: np.ndarray) -> np.ndarray:
+def crop_mask(mask: np.ndarray, bbox: np.ndarray, fill_value=0) -> np.ndarray:
...
- return mask * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
+ inside = (r >= x1) & (r < x2) & (c >= y1) & (c < y2)
+ return np.where(inside, mask, fill_value)
...
- mask_logits = crop_mask(mask_logits, scaled_bbox)
+ mask_logits = crop_mask(mask_logits, scaled_bbox, fill_value=-np.inf)📝 Committable suggestion
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| mask_logits = crop_mask(mask_logits, scaled_bbox) | |
| mask_logits = crop_mask(mask_logits, scaled_bbox, fill_value=-np.inf) |
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In `@depthai_nodes/node/parsers/utils/masks_utils.py` at line 66, Update the
mask_logits path around crop_mask so excluded pixels are filled with -np.inf
before the threshold comparison, preventing them from becoming foreground when
the logit threshold is negative. Preserve zero as the default fill value for
already-thresholded binary masks.
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✅ Addressed in commit e1d99fd
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I see, this is a valid point and I have been testing with mask_conf=0.5 which is probably why I haven't run into this problem yet. However, feeding -inf through OpenCV bilinear interpolation can produce NaN, I addressed this instead in e1d99fd by filling with the logit_threshold, which can never trigger positively since we use the exclusionary >
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@dtronmans, correct. Using logit_threshold avoids non-finite values during OpenCV interpolation. The strict > comparison excludes pixels with logits equal to the threshold.
✏️ Learnings added
Learnt from: dtronmans
Repo: luxonis/depthai-nodes PR: 330
File: depthai_nodes/node/parsers/utils/masks_utils.py:0-0
Timestamp: 2026-09-04T14:04:32.036Z
Learning: In `depthai_nodes/node/parsers/utils/masks_utils.py`, `process_single_mask` must fill mask logits outside the bounding box with `logit_threshold`, not `-np.inf`, because OpenCV bilinear interpolation can produce `NaN` from infinite inputs. The subsequent strict `mask_logits > logit_threshold` comparison excludes pixels with the fill value.
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The solution with logit_threshold also affects resize. I pushed a different solution in d310cc3 which essentially matches your original one but adds one more crop at the very end. This should ensure that there is no leakage. But please test this out with luxonis-eval to see if metrics agree.
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Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!
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@klemen1999 unfortunately the tests fail with this extra crop:
FAILED tests/test_rvc4_nnarchive_regression.py::test_rvc4_nnarchive_regression[instance_segmentation_multiclass_squares_ldf_native_empty_task] - AssertionError: MaskMeanAveragePrecision.AP mismatch: actual=0.813644, expected=0.715013, rel_error=13.79%, allowed=2.00%.
Just to verify I went back to e1d99fd to test with and the tests pass. I see that your commit message is about semantically matching luxonis-train, but is this last additional crop in the repo itself? For reference here is the order of operations in luxonis-train for postprocessing masks
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Inline comments:
In `@depthai_nodes/node/parsers/utils/masks_utils.py`:
- Line 68: Update the mask processing around crop_mask so logits pass
logit_threshold as fill_value before resizing, while binary masks continue using
the default 0; add a regression test covering below-threshold logits near the
bounding-box boundary.
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Inline comments:
In `@depthai_nodes/node/parsers/utils/masks_utils.py`:
- Around line 73-76: Update process_single_mask() to handle all out-of-range
probability thresholds: use mask_conf <= 0.0 and mask_conf >= 1.0 when assigning
the dtype-specific minimum and maximum logit thresholds, respectively. Preserve
the existing behavior for values strictly between zero and one.
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