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1413876 98d0fef 1413876 98d0fef 0793f25 1413876 98d0fef 0793f25 1413876 0793f25 1413876 | 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 | # evaluation/vizwiz_localization_eval.py
"""
VizWiz Object Localization Evaluation Module
Adapted from EvalAI vqaEval.py for VizWiz Object Localization Challenge
"""
import json
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
class Localization:
"""
Original Localization class from EvalAI
"""
def __init__(self, eval_bbox=True, eval_segm=True):
self.eval_bbox = eval_bbox
self.eval_segm = eval_segm
print("Created Localization Object")
def compute_coco_metrics(self, gt_annotations, sub_annotations):
"""
Compute COCO metrics for object detection/segmentation
Args:
gt_annotations (str): Path to ground truth annotations JSON
sub_annotations (str): Path to submission annotations JSON
Returns:
tuple: (bbox_mAP, bbox_AP50, segm_mAP, segm_AP50)
"""
print("In compute_coco_metrics")
# Load ground truth and submission annotations
coco_gt = COCO(gt_annotations)
coco_sub = coco_gt.loadRes(sub_annotations)
bbox_mAP = 0.0
bbox_AP50 = 0.0
segm_mAP = 0.0
segm_AP50 = 0.0
if self.eval_bbox:
# Object Detection Evaluation
coco_eval_bbox = COCOeval(coco_gt, coco_sub, iouType='bbox')
coco_eval_bbox.evaluate()
coco_eval_bbox.accumulate()
coco_eval_bbox.summarize()
# CORRECTED: stats[0] is mAP, stats[1] is AP50
bbox_mAP = coco_eval_bbox.stats[0]
bbox_AP50 = coco_eval_bbox.stats[1]
if self.eval_segm:
# Instance Segmentation Evaluation
coco_eval_mask = COCOeval(coco_gt, coco_sub, iouType='segm')
coco_eval_mask.evaluate()
coco_eval_mask.accumulate()
coco_eval_mask.summarize()
segm_mAP = coco_eval_mask.stats[0]
segm_AP50 = coco_eval_mask.stats[1]
return bbox_mAP, bbox_AP50, segm_mAP, segm_AP50
def evaluate_submission(ground_truth_path, predictions_path):
"""
Evaluate a submission and return score compatible with UI code
Args:
ground_truth_path (str): Path to ground truth JSON
predictions_path (str): Path to predictions JSON
Returns:
float or None: Score as percentage (0-100), or None if error
"""
try:
# Basic validation
with open(predictions_path, 'r') as f:
predictions = json.load(f)
print("Loaded Predictions file")
if not isinstance(predictions, list) or len(predictions) == 0:
return None
# Check required fields
required_fields = ['image_id', 'category_id', 'bbox', 'score']
if not all(field in predictions[0] for field in required_fields):
return None
print(f"Required Fields Checked")
# Run evaluation
evaluator = Localization()
print("Evaluation Object Instantiated")
bbox_mAP, bbox_AP50, segm_mAP, segm_AP50 = evaluator.compute_coco_metrics(
ground_truth_path,
predictions_path
)
print(f"bbox score calculated : {bbox_mAP}")
return float(bbox_mAP), float(bbox_AP50), segm_mAP, segm_AP50
except Exception as e:
print(f"Evaluation error: {e}")
return None |