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