增加几何校验
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Ukawka
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#!/usr/bin/env python3
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"""Alpha constraints and deterministic seed candidates, never semantic masks."""
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import argparse
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import json
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import math
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from pathlib import Path
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import cv2
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import numpy as np
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from PIL import Image
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from normalize_reference import prepare_rgba
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def pixel(point, width, height):
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# Normalized points map to their containing pixel; 1 maps to the last pixel.
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return min(width - 1, int(point[0] * width)), min(height - 1, int(point[1] * height))
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def ground_parts(image, plan):
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alpha = np.array(image.convert('RGBA'))[:, :, 3]
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foreground = alpha > 0
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height, width = alpha.shape
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usable_alpha = bool(foreground.any() and not foreground.all())
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silhouette_distance = cv2.distanceTransform(np.pad(foreground.astype(np.uint8), 1), cv2.DIST_L2, cv2.DIST_MASK_PRECISE)[1:-1, 1:-1]
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diagnostics, prompts = [], []
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for part in plan['parts']:
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box = part['bbox_norm_xyxy']
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entry = {
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'id': part['id'], 'status': 'hidden' if part['visibility'] == 'hidden' else 'blocked',
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'bbox_px_xyxy': None, 'bbox_foreground_ratio': None,
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'foreground_bbox_px_xyxy': None, 'foreground_components': 0,
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'model_positive_points': [], 'candidate_points': [], 'warnings': [],
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'semantic_verified': False,
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}
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for point in part.get('positive_points_norm', []):
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x, y = pixel(point, width, height)
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in_box = bool(box and box[0] <= point[0] <= box[2] and box[1] <= point[1] <= box[3])
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on_foreground = bool(foreground[y, x])
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entry['model_positive_points'].append({
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'point_norm': point, 'point_px': [x, y], 'alpha': int(alpha[y, x]),
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'inside_bbox': in_box, 'on_foreground': on_foreground,
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'status': 'UNTRUSTED_SEED' if usable_alpha and on_foreground and in_box else 'INVALID_SEED',
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})
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if part['visibility'] != 'hidden' and box:
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x0, y0 = math.floor(box[0] * width), math.floor(box[1] * height)
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x1, y1 = math.ceil(box[2] * width), math.ceil(box[3] * height)
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entry['bbox_px_xyxy'] = [x0, y0, x1, y1]
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roi = foreground[y0:y1, x0:x1]
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entry['bbox_foreground_ratio'] = float(roi.mean()) if roi.size else 0
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if not usable_alpha:
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entry['warnings'].append('NO_USABLE_ALPHA')
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elif not roi.any():
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entry['warnings'].append('EMPTY_FOREGROUND_ROI')
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else:
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ys, xs = np.nonzero(roi)
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entry['foreground_bbox_px_xyxy'] = [int(xs.min()) + x0, int(ys.min()) + y0, int(xs.max()) + x0 + 1, int(ys.max()) + y0 + 1]
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count, _ = cv2.connectedComponents(roi.astype(np.uint8), connectivity=8)
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entry['foreground_components'] = count - 1
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# Pad the ROI with background so an opaque ROI has a finite
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# distance, and candidates stay away from both silhouette/box edges.
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distance = cv2.distanceTransform(np.pad(roi.astype(np.uint8), 1), cv2.DIST_L2, cv2.DIST_MASK_PRECISE)[1:-1, 1:-1]
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# Only centers strictly inside the original continuous ROI qualify.
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eligible_x = ((np.arange(x0, x1) + .5) / width >= box[0]) & ((np.arange(x0, x1) + .5) / width <= box[2])
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eligible_y = ((np.arange(y0, y1) + .5) / height >= box[1]) & ((np.arange(y0, y1) + .5) / height <= box[3])
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eligible = roi & eligible_y[:, None] & eligible_x[None, :]
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if eligible.any():
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scores = np.where(eligible, distance, -1)
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cy, cx = np.unravel_index(np.argmax(scores), scores.shape)
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x, y = int(cx) + x0, int(cy) + y0
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entry['candidate_points'] = [{
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'point_px': [x, y], 'point_norm': [(x + .5) / width, (y + .5) / height],
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'distance_to_roi_boundary_px': float(distance[cy, cx]),
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'distance_to_silhouette_px': float(silhouette_distance[y, x]),
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'source': 'alpha_roi_distance_transform',
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}]
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entry['status'] = 'candidate'
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else:
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entry['warnings'].append('NO_PIXEL_CENTER_IN_ROI')
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if entry['bbox_foreground_ratio'] < .5:
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entry['warnings'].append('LOW_FOREGROUND_RATIO')
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if count > 2:
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entry['warnings'].append('MULTIPLE_FOREGROUND_COMPONENTS')
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entry['warnings'].append('SEMANTIC_REVIEW_REQUIRED')
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diagnostics.append(entry)
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prompts.append({
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'id': part['id'], 'text_prompt': part['semantic'], 'status': entry['status'],
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'bbox_px_xyxy': entry['bbox_px_xyxy'],
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'positive_points_px': [p['point_px'] for p in entry['candidate_points']],
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'positive_points_norm': [p['point_norm'] for p in entry['candidate_points']],
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'negative_points_px': [], 'seed_source': 'alpha_roi_distance_transform',
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'semantic_verified': False, 'requires_mask_qa': True,
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})
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points = [p for entry in diagnostics for p in entry['model_positive_points']]
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return {
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'schema_version': 1, 'width': width, 'height': height,
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'alpha_threshold': 0, 'coordinate_mapping': 'floor(norm * size), clamped at size - 1; boxes are half-open',
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'summary': {'model_points': len(points), 'invalid_model_points': sum(p['status'] == 'INVALID_SEED' for p in points),
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'background_model_points': sum(not p['on_foreground'] for p in points),
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'candidate_parts': sum(p['status'] == 'candidate' for p in diagnostics),
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'blocked_parts': sum(p['status'] == 'blocked' for p in diagnostics)},
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'parts': diagnostics,
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}, {'schema_version': 1, 'width': width, 'height': height, 'quality_status': 'needs_review', 'parts': prompts}
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--input', required=True)
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parser.add_argument('--output', required=True)
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parser.add_argument('--prepare', action='store_true')
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parser.add_argument('--parts')
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parser.add_argument('--prompts')
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args = parser.parse_args()
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with Image.open(args.input) as image:
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if args.prepare:
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prepared, metadata = prepare_rgba(image)
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prepared.save(args.output, format='PNG')
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print(json.dumps(metadata))
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else:
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if not args.parts or not args.prompts:
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parser.error('--parts and --prompts are required for grounding')
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plan = json.loads(Path(args.parts).read_text(encoding='utf-8'))
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diagnostics, prompts = ground_parts(image, plan)
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Path(args.output).write_text(json.dumps(diagnostics, indent=2), encoding='utf-8')
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Path(args.prompts).write_text(json.dumps(prompts, indent=2), encoding='utf-8')
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if __name__ == '__main__':
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main()
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