File size: 3,164 Bytes
52a9452 | 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 | import numpy as np
from concern import Logger, AverageMeter
from concern.config import Configurable
from concern.icdar2015_eval.detection.iou import DetectionIoUEvaluator
class QuadMeasurer(Configurable):
def __init__(self, **kwargs):
self.evaluator = DetectionIoUEvaluator()
def measure(self, batch, output, is_output_polygon=False, box_thresh=0.6):
'''
batch: (image, polygons, ignore_tags
batch: a dict produced by dataloaders.
image: tensor of shape (N, C, H, W).
polygons: tensor of shape (N, K, 4, 2), the polygons of objective regions.
ignore_tags: tensor of shape (N, K), indicates whether a region is ignorable or not.
shape: the original shape of images.
filename: the original filenames of images.
output: (polygons, ...)
'''
results = []
gt_polyons_batch = batch['polygons']
ignore_tags_batch = batch['ignore_tags']
pred_polygons_batch = np.array(output[0])
pred_scores_batch = np.array(output[1])
for polygons, pred_polygons, pred_scores, ignore_tags in\
zip(gt_polyons_batch, pred_polygons_batch, pred_scores_batch, ignore_tags_batch):
gt = [dict(points=polygons[i], ignore=ignore_tags[i])
for i in range(len(polygons))]
if is_output_polygon:
pred = [dict(points=pred_polygons[i])
for i in range(len(pred_polygons))]
else:
pred = []
# print(pred_polygons.shape)
for i in range(pred_polygons.shape[0]):
if pred_scores[i] >= box_thresh:
# print(pred_polygons[i,:,:].tolist())
pred.append(dict(points=pred_polygons[i,:,:].tolist()))
# pred = [dict(points=pred_polygons[i,:,:].tolist()) if pred_scores[i] >= box_thresh for i in range(pred_polygons.shape[0])]
results.append(self.evaluator.evaluate_image(gt, pred))
return results
def validate_measure(self, batch, output, is_output_polygon=False, box_thresh=0.6):
return self.measure(batch, output, is_output_polygon, box_thresh)
def evaluate_measure(self, batch, output):
return self.measure(batch, output),\
np.linspace(0, batch['image'].shape[0]).tolist()
def gather_measure(self, raw_metrics, logger: Logger):
raw_metrics = [image_metrics
for batch_metrics in raw_metrics
for image_metrics in batch_metrics]
result = self.evaluator.combine_results(raw_metrics)
precision = AverageMeter()
recall = AverageMeter()
fmeasure = AverageMeter()
precision.update(result['precision'], n=len(raw_metrics))
recall.update(result['recall'], n=len(raw_metrics))
fmeasure_score = 2 * precision.val * recall.val /\
(precision.val + recall.val + 1e-8)
fmeasure.update(fmeasure_score)
return {
'precision': precision,
'recall': recall,
'fmeasure': fmeasure
}
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