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import numpy as np |
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from concern import Logger, AverageMeter |
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from concern.config import Configurable |
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from concern.icdar2015_eval.detection.iou import DetectionIoUEvaluator |
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class QuadMeasurer(Configurable): |
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def __init__(self, **kwargs): |
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self.evaluator = DetectionIoUEvaluator() |
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def measure(self, batch, output, is_output_polygon=False, 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['polygons'] |
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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\ |
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zip(gt_polyons_batch, pred_polygons_batch, pred_scores_batch, ignore_tags_batch): |
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gt = [dict(points=polygons[i], ignore=ignore_tags[i]) |
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for i in range(len(polygons))] |
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if is_output_polygon: |
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pred = [dict(points=pred_polygons[i]) |
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for i in range(len(pred_polygons))] |
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else: |
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pred = [] |
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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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pred.append(dict(points=pred_polygons[i,:,:].tolist())) |
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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, is_output_polygon=False, box_thresh=0.6): |
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return self.measure(batch, output, is_output_polygon, box_thresh) |
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def evaluate_measure(self, batch, output): |
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return self.measure(batch, output),\ |
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np.linspace(0, batch['image'].shape[0]).tolist() |
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def gather_measure(self, raw_metrics, logger: Logger): |
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raw_metrics = [image_metrics |
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for batch_metrics in raw_metrics |
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for image_metrics in batch_metrics] |
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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 { |
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'precision': precision, |
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'recall': recall, |
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'fmeasure': fmeasure |
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} |
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