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Running on Zero
Running on Zero
| """ICDR evidence table + templated natural-language rationale, and outline-based | |
| lesion-overlay rendering for the final report (docs/04_ROADMAP.md, Phase 6). | |
| Reuses `drdetect.fusion.features`'s model-running internals (the same tiled | |
| segmentation inference, OD/fovea localisation, and microaneurysm candidate | |
| scoring already built and evaluated for Phase 5's fusion head) rather than | |
| re-implementing lesion detection a second time -- this module only adds the | |
| per-instance/per-quadrant structure a human-readable report needs, on top of | |
| the aggregate scalar features Phase 5's fusion head consumes. | |
| """ | |
| from __future__ import annotations | |
| from pathlib import Path | |
| import cv2 | |
| import numpy as np | |
| from drdetect.fusion.features import ( | |
| LOCALIZATION_SIZE, | |
| LesionModels, | |
| _predict_od_fovea, | |
| _tile_predict, | |
| lesion_centroids, | |
| ) | |
| from drdetect.segmentation.microaneurysms import score_candidates | |
| from drdetect.segmentation.quadrants import assign_quadrant | |
| __all__ = [ | |
| "extract_lesion_evidence", | |
| "render_lesion_overlay", | |
| "build_icdr_rationale", | |
| "LESION_COLORS_RGB", | |
| ] | |
| _SEGMENTED_LESIONS = ["hard_exudates", "soft_exudates", "haemorrhages"] | |
| # RGB, since render_lesion_overlay draws on an RGB array throughout this module. | |
| LESION_COLORS_RGB = { | |
| "hard_exudates": (255, 215, 0), # amber | |
| "soft_exudates": (0, 191, 255), # light blue | |
| "haemorrhages": (255, 0, 0), # red | |
| "microaneurysms": (255, 0, 255), # magenta | |
| } | |
| def extract_lesion_evidence(image_path: str | Path, models: LesionModels) -> dict: | |
| """Per-lesion-type instance list (quadrant + size) and binary masks for | |
| rendering, from the same models `drdetect.fusion.features` uses for the | |
| fusion head's aggregate scalars.""" | |
| image_bgr = cv2.imread(str(image_path), cv2.IMREAD_COLOR) | |
| image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) | |
| od_xy, fovea_xy, _diameter = _predict_od_fovea(image_rgb, models) | |
| scale_x = LOCALIZATION_SIZE[1] / image_rgb.shape[1] | |
| scale_y = LOCALIZATION_SIZE[0] / image_rgb.shape[0] | |
| lesions: dict[str, dict] = {} | |
| masks: dict[str, np.ndarray] = {} | |
| for lesion_type in _SEGMENTED_LESIONS: | |
| module = getattr(models, lesion_type) | |
| prob_map = _tile_predict(module, image_rgb, models.device) | |
| binary_mask = prob_map > 0.5 | |
| masks[lesion_type] = binary_mask | |
| centroids = lesion_centroids(prob_map, threshold=0.5) | |
| instances = [ | |
| {"quadrant": assign_quadrant((cx * scale_x, cy * scale_y), od_xy, fovea_xy)} | |
| for cx, cy in centroids | |
| ] | |
| lesions[lesion_type] = { | |
| "count": len(instances), | |
| "area_pct": float(binary_mask.mean() * 100.0), | |
| "instances": instances, | |
| } | |
| dummy_mask = np.zeros(image_bgr.shape[:2], dtype=np.uint8) | |
| ma_result = score_candidates( | |
| image_bgr, dummy_mask, models.microaneurysm_classifier.model, device=str(models.device) | |
| ) | |
| accepted = ma_result["scores"] > 0.95 | |
| ma_instances = [ | |
| { | |
| "quadrant": assign_quadrant((c.cx * scale_x, c.cy * scale_y), od_xy, fovea_xy), | |
| "xy": (float(c.cx), float(c.cy)), | |
| } | |
| for c, keep in zip(ma_result["candidates"], accepted, strict=True) | |
| if keep | |
| ] | |
| lesions["microaneurysms"] = {"count": len(ma_instances), "instances": ma_instances} | |
| return {"od_xy": od_xy, "fovea_xy": fovea_xy, "lesions": lesions, "masks": masks} | |
| def render_lesion_overlay(image_rgb: np.ndarray, evidence: dict) -> np.ndarray: | |
| """Outline contours, not filled blobs -- the roadmap's own stated reason: | |
| outlines beat blobs for clinical legibility (a filled overlay hides the | |
| tissue underneath it; an outline points at it without obscuring it). | |
| Microaneurysms are marked with small circles rather than outlines -- | |
| they're candidate *points* from the classifier (drdetect.segmentation. | |
| microaneurysms), not a segmented region, so there's no contour to draw. | |
| """ | |
| overlay = image_rgb.copy() | |
| for lesion_type, mask in evidence["masks"].items(): | |
| color = LESION_COLORS_RGB[lesion_type] | |
| contours, _ = cv2.findContours( | |
| mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE | |
| ) | |
| cv2.drawContours(overlay, contours, -1, color, thickness=3) | |
| ma_color = LESION_COLORS_RGB["microaneurysms"] | |
| for inst in evidence["lesions"].get("microaneurysms", {}).get("instances", []): | |
| x, y = inst["xy"] | |
| cv2.circle(overlay, (int(round(x)), int(round(y))), radius=12, color=ma_color, thickness=2) | |
| return overlay | |
| _ICDR_GRADE_NAMES = { | |
| 0: "No apparent DR", | |
| 1: "Mild NPDR", | |
| 2: "Moderate NPDR", | |
| 3: "Severe NPDR", | |
| 4: "Proliferative DR", | |
| } | |
| def build_icdr_rationale(grade: int, evidence: dict) -> str: | |
| """A templated sentence grounding the predicted ICDR grade in the actual | |
| detected evidence -- not a restatement of the grade, an explanation of | |
| what supports it (or, honestly, what doesn't fully support it). | |
| Neovascularisation (the feature that actually defines grade 4, PDR) has | |
| no segmentation model in this project (docs/01's own scope-honesty | |
| note: "neovascularisation has no public pixel masks... document as | |
| future work, do not fabricate it") -- a grade-4 rationale says so | |
| explicitly rather than inventing NV evidence this pipeline cannot see. | |
| """ | |
| lesions = evidence["lesions"] | |
| ma_n = lesions.get("microaneurysms", {}).get("count", 0) | |
| he_n = lesions.get("haemorrhages", {}).get("count", 0) | |
| ex_pct = lesions.get("hard_exudates", {}).get("area_pct", 0.0) | |
| se_pct = lesions.get("soft_exudates", {}).get("area_pct", 0.0) | |
| quadrants_with_he = { | |
| inst["quadrant"] for inst in lesions.get("haemorrhages", {}).get("instances", []) | |
| } | |
| evidence_parts = [] | |
| if ma_n: | |
| evidence_parts.append(f"{ma_n} microaneurysm candidate(s)") | |
| if he_n: | |
| evidence_parts.append( | |
| f"{he_n} haemorrhage region(s) across {len(quadrants_with_he)} quadrant(s)" | |
| ) | |
| if ex_pct > 0.01: | |
| evidence_parts.append(f"hard exudates covering {ex_pct:.2f}% of the image") | |
| if se_pct > 0.01: | |
| evidence_parts.append( | |
| f"soft exudates (cotton-wool spots) covering {se_pct:.2f}% of the image" | |
| ) | |
| grade_name = _ICDR_GRADE_NAMES.get(grade, f"grade {grade}") | |
| if not evidence_parts: | |
| return f"ICDR grade {grade} ({grade_name}): no significant lesion evidence detected." | |
| body = "; ".join(evidence_parts) | |
| note = "" | |
| if grade == 4: | |
| note = ( | |
| " Note: proliferative DR (grade 4) is defined by neovascularisation, which this " | |
| "pipeline does not detect (no public pixel masks exist to train against) -- this " | |
| "grade is the classifier's own image-level prediction, not evidence-backed for the " | |
| "specific feature that defines it." | |
| ) | |
| return f"ICDR grade {grade} ({grade_name}): {body}.{note}" | |