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"""
Computes COCO-style evaluation metrics (Average Precision & Average Recall)
"""
# --- Configure here ---
GROUND_TRUTH_JSON = "ground_truth.json"
PREDICTIONS_JSON = "predictions.json"
IOU_TYPE = "segm" # "segm", "bbox", or "keypoints"
OUTPUT_PATH = "results_ap.json" # set to None to skip saving
# ----------------------
import json
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def _load_predictions_for_coco(gt_coco: COCO, predictions_json_path: str):
"""
Loads predictions into COCO's result format.
Args:
gt_coco (COCO): COCO object initialized with ground truth annotations.
predictions_json_path (str): Path to predictions JSON file.
Returns:
COCO: A COCO results object that can be passed into COCOeval.
"""
with open(predictions_json_path, "r") as f:
data = json.load(f)
# Normalize predictions into a list of annotations
if isinstance(data, list):
anns = data
elif isinstance(data, dict) and "annotations" in data:
anns = data["annotations"]
else:
raise ValueError("Predictions must be a list or a dict with an 'annotations' key.")
# Ensure every annotation has a 'score' field (required for COCOeval)
for ann in anns:
if "score" not in ann:
ann["score"] = 1.0 # Assign default score if missing
# Load predictions into COCO format
return gt_coco.loadRes(anns)
def compute_ap_map(ground_truth_json: str, predictions_json: str, iou_type: str = "segm"):
"""
Computes COCO-style AP/mAP and AR metrics.
Args:
ground_truth_json (str): Path to COCO-format ground truth file.
predictions_json (str): Path to predictions file.
iou_type (str): Type of evaluation ("segm", "bbox", or "keypoints").
Returns:
dict: Dictionary containing AP and AR values across IoU thresholds,
object sizes, and max detections.
"""
# Load ground truth
gt_coco = COCO(ground_truth_json)
# Load predictions into COCO result format
pred_coco = _load_predictions_for_coco(gt_coco, predictions_json)
# Run COCO evaluation
coco_eval = COCOeval(gt_coco, pred_coco, iou_type)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
# Collect results from coco_eval.stats (12 values for bbox/segm)
stats = coco_eval.stats
results = {
"AP[0.50:0.95]": float(stats[0]), # mean AP over IoU thresholds .50:.95
"AP@0.50": float(stats[1]), # AP at IoU=0.50
"AP@0.75": float(stats[2]), # AP at IoU=0.75
"AP_small": float(stats[3]), # AP for small objects
"AP_medium": float(stats[4]), # AP for medium objects
"AP_large": float(stats[5]), # AP for large objects
"AR@1": float(stats[6]), # AR given max 1 detection per image
"AR@10": float(stats[7]), # AR given max 10 detections per image
"AR@100": float(stats[8]), # AR given max 100 detections per image
"AR_small": float(stats[9]), # AR for small objects
"AR_medium": float(stats[10]), # AR for medium objects
"AR_large": float(stats[11]), # AR for large objects
}
return results
if __name__ == "__main__":
scores = compute_ap_map(GROUND_TRUTH_JSON, PREDICTIONS_JSON, IOU_TYPE)
# Optionally save results to JSON
if OUTPUT_PATH:
with open(OUTPUT_PATH, "w") as f:
json.dump(scores, f, indent=2)
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