File size: 6,922 Bytes
505117b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import cv2
import numpy as np
import pandas as pd
import torch
from detectron2 import model_zoo
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog, build_detection_test_loader
from detectron2.data.datasets import register_coco_instances
from detectron2.engine import DefaultPredictor
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.modeling import build_model
from pycocotools.coco import COCO
def main() -> None:
parser = argparse.ArgumentParser(
description="Evaluate BubbleID-Flow Mask R-CNN weights on a COCO-format holdout set."
)
parser.add_argument("--val-json", required=True)
parser.add_argument("--weights", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--image-root", default="")
parser.add_argument("--score-threshold", type=float, default=0.05)
parser.add_argument(
"--coverage-score-threshold",
type=float,
default=0.30,
help="Operational score threshold used for combined-mask coverage metrics.",
)
parser.add_argument("--detections-per-image", type=int, default=300)
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
args = parser.parse_args()
if args.detections_per_image <= 0:
raise ValueError("detections-per-image must be positive")
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
dataset_name = "bubbleid_flow_eval"
register_coco_instances(dataset_name, {}, args.val_json, args.image_root)
MetadataCatalog.get(dataset_name).thing_classes = ["bubble"]
cfg = build_cfg(
weights=args.weights,
score_threshold=args.score_threshold,
detections_per_image=args.detections_per_image,
device=args.device,
)
model = build_model(cfg)
DetectionCheckpointer(model).load(args.weights)
evaluator = COCOEvaluator(
dataset_name,
tasks=("bbox", "segm"),
distributed=False,
output_dir=str(output_dir),
)
loader = build_detection_test_loader(cfg, dataset_name)
results = inference_on_dataset(model, loader, evaluator)
coverage_cfg = build_cfg(
weights=args.weights,
score_threshold=args.coverage_score_threshold,
detections_per_image=args.detections_per_image,
device=args.device,
)
coverage_rows, coverage_summary = evaluate_union_coverage(
coco_json=Path(args.val_json),
predictor=DefaultPredictor(coverage_cfg),
)
coverage_rows.to_csv(output_dir / "coverage_metrics_by_image.csv", index=False)
results["coverage"] = coverage_summary
result_path = output_dir / "coco_evaluation_results.json"
result_path.write_text(json.dumps(results, indent=2, sort_keys=True), encoding="utf-8")
(output_dir / "evaluation_config.yaml").write_text(cfg.dump(), encoding="utf-8")
print(json.dumps(results, indent=2, sort_keys=True))
print(f"Wrote {result_path}")
def build_cfg(
*,
weights: str,
score_threshold: float,
detections_per_image: int,
device: str,
):
cfg = get_cfg()
cfg.merge_from_file(
model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
)
cfg.DATASETS.TEST = ("bubbleid_flow_eval",)
cfg.DATALOADER.NUM_WORKERS = 0
cfg.MODEL.WEIGHTS = weights
cfg.MODEL.DEVICE = device
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = score_threshold
cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[8], [16], [32], [64], [128]]
cfg.INPUT.MIN_SIZE_TEST = 640
cfg.INPUT.MAX_SIZE_TEST = 900
cfg.TEST.DETECTIONS_PER_IMAGE = detections_per_image
return cfg
def evaluate_union_coverage(
*,
coco_json: Path,
predictor: DefaultPredictor,
) -> tuple[pd.DataFrame, dict[str, float | int]]:
coco = COCO(str(coco_json))
rows = []
for image_id in sorted(coco.getImgIds()):
record = coco.loadImgs([image_id])[0]
image_path = Path(record["file_name"])
image = cv2.imread(str(image_path), cv2.IMREAD_COLOR)
if image is None:
raise ValueError(f"Could not read image: {image_path}")
ground_truth = np.zeros(image.shape[:2], dtype=bool)
for annotation in coco.loadAnns(coco.getAnnIds(imgIds=[image_id])):
ground_truth |= coco.annToMask(annotation).astype(bool)
instances = predictor(image)["instances"].to("cpu")
masks = (
instances.pred_masks.numpy()
if instances.has("pred_masks")
else np.empty((0, *image.shape[:2]), dtype=bool)
)
predicted = np.any(masks, axis=0) if len(masks) else np.zeros(image.shape[:2], dtype=bool)
intersection = int(np.count_nonzero(ground_truth & predicted))
union = int(np.count_nonzero(ground_truth | predicted))
ground_truth_area = int(np.count_nonzero(ground_truth))
predicted_area = int(np.count_nonzero(predicted))
pixel_count = ground_truth.size
iou = intersection / union if union else 1.0
denominator = ground_truth_area + predicted_area
dice = 2 * intersection / denominator if denominator else 1.0
ground_truth_fraction = ground_truth_area / pixel_count
predicted_fraction = predicted_area / pixel_count
rows.append(
{
"image_id": image_id,
"file_name": image_path.name,
"ground_truth_vapor_area_fraction": ground_truth_fraction,
"predicted_vapor_area_fraction": predicted_fraction,
"signed_area_fraction_error": predicted_fraction - ground_truth_fraction,
"absolute_area_fraction_error": abs(predicted_fraction - ground_truth_fraction),
"union_mask_iou": iou,
"union_mask_dice": dice,
"predicted_instances": int(len(instances)),
}
)
frame = pd.DataFrame(rows)
summary: dict[str, float | int] = {
"images": int(len(frame)),
"mean_union_mask_iou": float(frame["union_mask_iou"].mean()),
"median_union_mask_iou": float(frame["union_mask_iou"].median()),
"mean_union_mask_dice": float(frame["union_mask_dice"].mean()),
"mean_absolute_area_fraction_error": float(
frame["absolute_area_fraction_error"].mean()
),
"mean_signed_area_fraction_error": float(frame["signed_area_fraction_error"].mean()),
"max_absolute_area_fraction_error": float(frame["absolute_area_fraction_error"].max()),
"max_predicted_instances": int(frame["predicted_instances"].max()),
}
return frame, summary
if __name__ == "__main__":
main()
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