wehub-resource-sync
129 lines
4.6 KiB
Python
129 lines
4.6 KiB
Python
import os
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import json
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import copy
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import itertools
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from collections import OrderedDict
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import detectron2.utils.comm as comm
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from detectron2.evaluation import COCOEvaluator
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from .concern.icdar2015_eval.detection.iou import DetectionIoUEvaluator
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class FUNSDEvaluator(COCOEvaluator):
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def evaluate(self, img_ids=None):
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"""
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Args:
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img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
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"""
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if self._distributed:
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comm.synchronize()
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predictions = comm.gather(self._predictions, dst=0)
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predictions = list(itertools.chain(*predictions))
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if not comm.is_main_process():
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return {}
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else:
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predictions = self._predictions
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if len(predictions) == 0:
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self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
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return {}
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self._logger.warning("[evaluating...]The evaluator may take long time")
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id2img = {}
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gt = {}
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with open('data/instances_test.json', 'r',
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encoding='utf-8') as fr:
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data = json.load(fr)
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for img in data['images']:
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id = img['id']
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name = os.path.basename(img['file_name'])[:-len('.jpg')]
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assert id not in id2img.keys()
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id2img[id] = name
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assert len(id2img) == len(data['images'])
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img2id, id2bbox = {}, {}
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for i in range(len(data['images'])):
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key = os.path.basename(data['images'][i]['file_name'][:-len('.png')])
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assert key not in img2id.keys()
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img2id[key] = data['images'][i]['id']
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for i in range(len(data['annotations'])):
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img_id = data['annotations'][i]['image_id']
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if img_id not in id2bbox.keys():
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id2bbox[img_id] = []
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x0, y0, w, h = data['annotations'][i]['bbox']
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x1, y1 = x0 + w, y0 + h
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line = [(x0, y0), (x1, y0), (x1, y1), (x0, y1)]
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id2bbox[img_id].append(
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{
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'points': line,
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'text': 1234,
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'ignore': False,
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}
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)
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for key, val in img2id.items():
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assert key not in gt.keys()
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gt[key] = id2bbox[val]
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self._results = OrderedDict()
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evaluator = DetectionIoUEvaluator()
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for iter in range(3, 10):
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thr = iter * 0.1
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self._results[thr] = {}
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total_prediction = {}
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for cur_pred in predictions:
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assert cur_pred['image_id'] in id2img.keys()
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id = id2img[cur_pred['image_id']]
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if id not in total_prediction.keys(): total_prediction[id] = []
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for cur_inst in cur_pred['instances']:
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x0, y0, w, h = cur_inst['bbox']
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cur_score = cur_inst['score']
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if cur_score < thr:
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continue
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x1, y1 = x0 + w, y0 + h
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x0, x1 = int(x0 + 0.5), int(x1 + 0.5)
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y0, y1 = int(y0 + 0.5), int(y1 + 0.5)
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min_x, max_x = min([x0, x1]), max([x0, x1])
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min_y, max_y = min([y0, y1]), max([y0, y1])
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pred_line = [min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]
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pred_line_str = ','.join(list(map(str, pred_line)))
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total_prediction[id].append(pred_line_str)
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final_gt = []
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final_res = []
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for key, _ in gt.items():
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final_gt.append(copy.deepcopy(gt[key]))
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cur_res = []
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pred = total_prediction[key]
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for i in range(len(pred)):
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line = list(map(int, pred[i].split(',')))
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line = [(line[0], line[1]), (line[2], line[3]), (line[4], line[5]), (line[6], line[7])]
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cur_res.append(
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{
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'points': line,
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'text': 1234,
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'ignore': False,
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}
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)
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final_res.append(cur_res)
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results = []
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for cur_gt, pred in zip(final_gt, final_res):
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results.append(evaluator.evaluate_image(cur_gt, pred))
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metrics = evaluator.combine_results(results)
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for key, val in metrics.items():
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self._results["{:.1f}_{}".format(thr, key)] = val
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return copy.deepcopy(self._results)
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