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| """CAM-vs-ground-truth localisation quality: pointing game + IoU. | |
| Phase 6's own exit criterion (docs/04_ROADMAP.md): "a table of saliency | |
| methods x (sanity-check pass/fail, pointing-game accuracy, IoU)". This | |
| module computes the pointing-game and IoU halves of that table, against | |
| IDRiD's real per-lesion-type pixel masks -- the sanity-check half already | |
| exists in drdetect.explain.sanity_checks (docs/08_PHASE6_RESULTS.md). | |
| """ | |
| from __future__ import annotations | |
| import cv2 | |
| import numpy as np | |
| __all__ = ["upsample_cam", "pointing_game_hit", "cam_mask_iou"] | |
| def upsample_cam(cam: np.ndarray, target_shape: tuple[int, int]) -> np.ndarray: | |
| """Resize a CAM (native resolution: the model's input size, e.g. 512x512) | |
| up to `target_shape` (height, width) -- typically a lesion mask's own | |
| full resolution. A CAM is already a smooth, interpolated heatmap, so | |
| upsampling it preserves the mask's own pixel precision, rather than | |
| downsampling the mask and losing the smallest lesions -- microaneurysms | |
| are already known, from this project's own Phase 3 findings, to vanish | |
| below ~1024px.""" | |
| h, w = target_shape | |
| return cv2.resize(cam, (w, h), interpolation=cv2.INTER_LINEAR) | |
| def pointing_game_hit(cam: np.ndarray, mask: np.ndarray) -> bool: | |
| """True if the CAM's single point of maximum attention falls on a | |
| positive mask pixel -- the standard pointing-game definition (Zhang et | |
| al. 2018), threshold-free by construction.""" | |
| y, x = np.unravel_index(np.argmax(cam), cam.shape) | |
| return bool(mask[y, x] > 0) | |
| def cam_mask_iou(cam: np.ndarray, mask: np.ndarray, threshold: float = 0.5) -> float: | |
| """IoU between the CAM thresholded at a fixed value and the binary | |
| lesion mask. | |
| Expect this to read low for genuinely sparse lesion types | |
| (haemorrhages and microaneurysms are well under 0.1% of image pixels, | |
| see drdetect.segmentation.metrics) even for a CAM correctly pointing at | |
| the right general region -- Grad-CAM produces a smooth, diffuse | |
| attention region by construction, not a pixel-precise segmentation, so | |
| IoU against a sparse mask is a much harder bar than the pointing game. | |
| NaN, not 0, when the union is empty (both CAM-positive and mask are | |
| empty) -- 0 would misrepresent "nothing to compare" as "total miss". | |
| """ | |
| binary_cam = cam > threshold | |
| binary_mask = mask.astype(bool) | |
| intersection = np.logical_and(binary_cam, binary_mask).sum() | |
| union = np.logical_or(binary_cam, binary_mask).sum() | |
| return float(intersection / union) if union > 0 else float("nan") | |