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https://github.com/PaddlePaddle/PaddleOCR.git
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99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
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import numpy as np
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from .detection.iou import DetectionIoUEvaluator
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class AverageMeter(object):
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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return self
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class QuadMetric():
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def __init__(self, is_output_polygon=False):
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self.is_output_polygon = is_output_polygon
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self.evaluator = DetectionIoUEvaluator(
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is_output_polygon=is_output_polygon)
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def measure(self, batch, output, box_thresh=0.6):
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'''
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batch: (image, polygons, ignore_tags
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batch: a dict produced by dataloaders.
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image: tensor of shape (N, C, H, W).
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polygons: tensor of shape (N, K, 4, 2), the polygons of objective regions.
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ignore_tags: tensor of shape (N, K), indicates whether a region is ignorable or not.
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shape: the original shape of images.
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filename: the original filenames of images.
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output: (polygons, ...)
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'''
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results = []
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gt_polyons_batch = batch['text_polys']
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ignore_tags_batch = batch['ignore_tags']
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pred_polygons_batch = np.array(output[0])
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pred_scores_batch = np.array(output[1])
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for polygons, pred_polygons, pred_scores, ignore_tags in zip(
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gt_polyons_batch, pred_polygons_batch, pred_scores_batch,
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ignore_tags_batch):
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gt = [
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dict(
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points=np.int64(polygons[i]), ignore=ignore_tags[i])
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for i in range(len(polygons))
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]
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if self.is_output_polygon:
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pred = [
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dict(points=pred_polygons[i])
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for i in range(len(pred_polygons))
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]
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else:
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pred = []
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# print(pred_polygons.shape)
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for i in range(pred_polygons.shape[0]):
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if pred_scores[i] >= box_thresh:
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# print(pred_polygons[i,:,:].tolist())
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pred.append(
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dict(points=pred_polygons[i, :, :].astype(np.int)))
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# pred = [dict(points=pred_polygons[i,:,:].tolist()) if pred_scores[i] >= box_thresh for i in range(pred_polygons.shape[0])]
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results.append(self.evaluator.evaluate_image(gt, pred))
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return results
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def validate_measure(self, batch, output, box_thresh=0.6):
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return self.measure(batch, output, box_thresh)
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def evaluate_measure(self, batch, output):
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return self.measure(batch, output), np.linspace(
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0, batch['image'].shape[0]).tolist()
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def gather_measure(self, raw_metrics):
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raw_metrics = [
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image_metrics
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for batch_metrics in raw_metrics for image_metrics in batch_metrics
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]
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result = self.evaluator.combine_results(raw_metrics)
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precision = AverageMeter()
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recall = AverageMeter()
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fmeasure = AverageMeter()
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precision.update(result['precision'], n=len(raw_metrics))
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recall.update(result['recall'], n=len(raw_metrics))
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fmeasure_score = 2 * precision.val * recall.val / (
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precision.val + recall.val + 1e-8)
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fmeasure.update(fmeasure_score)
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return {'precision': precision, 'recall': recall, 'fmeasure': fmeasure}
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