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Revert crop order changes in #330

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Merged
dtronmans merged 6 commits into from
Sep 7, 2026
+25 −21
Merged

Revert crop order changes in #330

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3125949
fix crop order
dtronmans Sep 4, 2026
e1d99fd
remove rf-detr cropping, fix for mask_conf under 0.5
dtronmans Sep 4, 2026
d310cc3
attempt to sematically match luxonis-train
klemen1999 Sep 4, 2026
f3cc6c5
Revert "attempt to sematically match luxonis-train"
dtronmans Sep 7, 2026
c5d3756
explicit conditions for mask_conf being 0 or 1
dtronmans Sep 7, 2026
f04a715
handle thresholds past 0 and 1
dtronmans Sep 7, 2026
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23 changes: 10 additions & 13 deletions depthai_nodes/node/parsers/utils/masks_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,19 +59,25 @@ def process_single_mask(
@return: Processed binary mask resized to `output_shape`.
@rtype: np.ndarray
"""
_, mask_h, mask_w = protos.shape # CHW
scaled_bbox = bbox * np.array([mask_w, mask_h, mask_w, mask_h])
logit_threshold = probability_to_logit_threshold(mask_conf)

mask_logits = np.sum(protos * mask_coeff[..., np.newaxis, np.newaxis], axis=0)
# Replace logits outside the bounding box with zero before interpolation.
mask_logits = crop_mask(mask_logits, scaled_bbox)

@coderabbitai coderabbitai Bot Sep 4, 2026 •
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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)
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Suggested change
mask_logits = crop_mask(mask_logits, scaled_bbox)
mask_logits = crop_mask(mask_logits, scaled_bbox, fill_value=-np.inf)
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only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.

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.

After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli.

✅ Addressed in commit e1d99fd

@dtronmans dtronmans Sep 4, 2026

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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.

Note: Learnings are effective only in the context of similar code segments. To apply general coding guidelines, please configure review instructions. You can manage existing learnings in the app.

@dtronmans Thanks for confirming the fix. I couldn't resolve this review thread on the repository platform, so it remains open. Please retry or resolve it manually.

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@klemen1999 klemen1999 Sep 4, 2026

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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!

@dtronmans dtronmans Sep 7, 2026

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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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Outdated
mask_logits = cv2.resize(
mask_logits,
(output_shape[1], output_shape[0]),
interpolation=cv2.INTER_LINEAR,
)
logit_threshold = probability_to_logit_threshold(mask_conf)
mask = (mask_logits > logit_threshold).astype(np.uint8)

scaled_bbox = bbox * np.array(
# Enforce the bounding box because zero-filled pixels can pass thresholding.
scaled_output_bbox = bbox * np.array(
[output_shape[1], output_shape[0], output_shape[1], output_shape[0]]
)
return crop_mask(mask, scaled_bbox)
return crop_mask(mask, scaled_output_bbox)


def get_segmentation_outputs(
Expand Down Expand Up @@ -101,7 +107,6 @@ def get_segmentation_outputs(
def process_single_mask_rfdetr(
mask_logits: np.ndarray,
mask_conf: float,
bbox: np.ndarray,
input_shape: tuple[int, int],
) -> np.ndarray:
"""Process a single RF-DETR instance segmentation mask.
Expand All @@ -110,9 +115,6 @@ def process_single_mask_rfdetr(
@type mask_logits: np.ndarray
@param mask_conf: Mask confidence threshold.
@type mask_conf: float
@param bbox: A numpy array of bbox coordinates in (x_center, y_center, width,
height) normalized format.
@type bbox: np.ndarray
@param input_shape: Target output mask shape as (height, width).
@type input_shape: tuple[int, int]
@return: Processed mask resized to the model input shape.
Expand All @@ -129,9 +131,4 @@ def process_single_mask_rfdetr(
interpolation=cv2.INTER_LINEAR,
)
logit_threshold = probability_to_logit_threshold(mask_conf)
mask = (resized_mask_logits > logit_threshold).astype(np.uint8)

scaled_bbox = bbox * np.array(
[input_shape[1], input_shape[0], input_shape[1], input_shape[0]]
)
return crop_mask(mask, scaled_bbox)
return (resized_mask_logits > logit_threshold).astype(np.uint8)
3 changes: 1 addition & 2 deletions depthai_nodes/node/parsers/utils/rf_detr.py
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Expand Up @@ -66,11 +66,10 @@ def compute_rfdetr_detections(
)

final_mask = np.full(input_shape, 255, dtype=np.uint8)
for i, (mask_logits, bbox) in enumerate(zip(masks, boxes_cxcywh)):
for i, mask_logits in enumerate(masks):
resized_mask = process_single_mask_rfdetr(
mask_logits=mask_logits,
mask_conf=mask_conf,
bbox=bbox,
input_shape=input_shape,
)
foreground = resized_mask > 0
Expand Down
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