fix eval score
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Jethong
parent
2a15d7e505
commit
f67e6e1387
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@ -61,6 +61,7 @@ PostProcess:
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score_thresh: 0.5
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Metric:
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name: E2EMetric
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gt_mat_dir: # the dir of gt_mat
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character_dict_path: ppocr/utils/ic15_dict.txt
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main_indicator: f_score_e2e
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@ -106,7 +107,7 @@ Eval:
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order: 'hwc'
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- ToCHWImage:
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- KeepKeys:
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keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags' ]
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keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags', 'img_id']
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loader:
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shuffle: False
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drop_last: False
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@ -2,7 +2,7 @@
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- [一、简介](#简介)
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- [二、环境配置](#环境配置)
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- [三、快速使用](#快速使用)
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- [四、模型训练、评估、推理](#快速训练)
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- [四、模型训练、评估、推理](#模型训练、评估、推理)
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<a name="简介"></a>
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## 一、简介
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@ -20,7 +20,9 @@ PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.Wang
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输入图像经过特征提取送入四个分支，分别是：文本边缘偏移量预测TBO模块，文本中心线预测TCL模块，文本方向偏移量预测TDO模块，以及文本字符分类图预测TCC模块。
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其中TBO以及TCL的输出经过后处理后可以得到文本的检测结果，TCL、TDO、TCC负责文本识别。
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其检测识别效果图如下：
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@ -61,12 +63,12 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
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可视化文本检测结果默认保存到./inference_results文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
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<a name="快速训练"></a>
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<a name="模型训练、评估、推理"></a>
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## 四、模型训练、评估、推理
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本节以totaltext数据集为例，介绍PaddleOCR中端到端模型的训练、评估与测试。
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### 准备数据
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下载解压[totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md)数据集到PaddleOCR/train_data/目录，数据集组织结构：
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下载解压[totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md) 数据集到PaddleOCR/train_data/目录，数据集组织结构：
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```
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/PaddleOCR/train_data/total_text/train/
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|- rgb/ # total_text数据集的训练数据
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@ -64,9 +64,6 @@ class PGDataSet(Dataset):
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for line in f.readlines():
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poly_str, txt = line.strip().split('\t')
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poly = list(map(float, poly_str.split(',')))
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if self.mode.lower() == "eval":
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while len(poly) < 100:
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poly.append(-1)
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text_polys.append(
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np.array(
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poly, dtype=np.float32).reshape(-1, 2))
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@ -139,23 +136,21 @@ class PGDataSet(Dataset):
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try:
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if self.data_format == 'icdar':
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im_path = os.path.join(data_path, 'rgb', data_line)
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if self.mode.lower() == "eval":
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poly_path = os.path.join(data_path, 'poly_gt',
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data_line.split('.')[0] + '.txt')
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else:
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poly_path = os.path.join(data_path, 'poly',
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data_line.split('.')[0] + '.txt')
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poly_path = os.path.join(data_path, 'poly',
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data_line.split('.')[0] + '.txt')
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text_polys, text_tags, text_strs = self.extract_polys(poly_path)
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else:
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image_dir = os.path.join(os.path.dirname(data_path), 'image')
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im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
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data_line, image_dir)
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img_id = int(data_line.split(".")[0][3:])
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data = {
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'img_path': im_path,
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'polys': text_polys,
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'tags': text_tags,
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'strs': text_strs
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'strs': text_strs,
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'img_id': img_id
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}
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with open(data['img_path'], 'rb') as f:
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img = f.read()
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@ -24,53 +24,24 @@ from ppocr.utils.e2e_utils.extract_textpoint import get_dict
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class E2EMetric(object):
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def __init__(self,
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gt_mat_dir,
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character_dict_path,
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main_indicator='f_score_e2e',
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**kwargs):
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self.gt_mat_dir = gt_mat_dir
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self.label_list = get_dict(character_dict_path)
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self.max_index = len(self.label_list)
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self.main_indicator = main_indicator
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self.reset()
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def __call__(self, preds, batch, **kwargs):
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temp_gt_polyons_batch = batch[2]
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temp_gt_strs_batch = batch[3]
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ignore_tags_batch = batch[4]
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gt_polyons_batch = []
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gt_strs_batch = []
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temp_gt_polyons_batch = temp_gt_polyons_batch[0].tolist()
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for temp_list in temp_gt_polyons_batch:
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t = []
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for index in temp_list:
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if index[0] != -1 and index[1] != -1:
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t.append(index)
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gt_polyons_batch.append(t)
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temp_gt_strs_batch = temp_gt_strs_batch[0].tolist()
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for temp_list in temp_gt_strs_batch:
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t = ""
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for index in temp_list:
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if index < self.max_index:
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t += self.label_list[index]
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gt_strs_batch.append(t)
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for pred, gt_polyons, gt_strs, ignore_tags in zip(
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[preds], [gt_polyons_batch], [gt_strs_batch], ignore_tags_batch):
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# prepare gt
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gt_info_list = [{
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'points': gt_polyon,
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'text': gt_str,
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'ignore': ignore_tag
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} for gt_polyon, gt_str, ignore_tag in
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zip(gt_polyons, gt_strs, ignore_tags)]
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# prepare det
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e2e_info_list = [{
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'points': det_polyon,
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'text': pred_str
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} for det_polyon, pred_str in zip(pred['points'], pred['strs'])]
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result = get_socre(gt_info_list, e2e_info_list)
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self.results.append(result)
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img_id = batch[5][0]
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e2e_info_list = [{
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'points': det_polyon,
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'text': pred_str
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} for det_polyon, pred_str in zip(preds['points'], preds['strs'])]
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result = get_socre(self.gt_mat_dir, img_id, e2e_info_list)
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self.results.append(result)
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def get_metric(self):
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metircs = combine_results(self.results)
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@ -138,6 +138,7 @@ class PGPostProcess(object):
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continue
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keep_str_list.append(keep_str)
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detected_poly = np.round(detected_poly).astype('int32')
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if self.valid_set == 'partvgg':
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middle_point = len(detected_poly) // 2
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detected_poly = detected_poly[
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@ -13,10 +13,11 @@
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# limitations under the License.
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import numpy as np
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import scipy.io as io
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from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
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def get_socre(gt_dict, pred_dict):
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def get_socre(gt_dir, img_id, pred_dict):
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allInputs = 1
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def input_reading_mod(pred_dict):
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@ -30,31 +31,9 @@ def get_socre(gt_dict, pred_dict):
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det.append([point, text])
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return det
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def gt_reading_mod(gt_dict):
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"""This helper reads groundtruths from mat files"""
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gt = []
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n = len(gt_dict)
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for i in range(n):
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points = gt_dict[i]['points']
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h = len(points)
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text = gt_dict[i]['text']
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xx = [
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np.array(
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['x:'], dtype='<U2'), 0, np.array(
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['y:'], dtype='<U2'), 0, np.array(
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['#'], dtype='<U1'), np.array(
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['#'], dtype='<U1')
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]
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t_x, t_y = [], []
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for j in range(h):
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t_x.append(points[j][0])
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t_y.append(points[j][1])
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xx[1] = np.array([t_x], dtype='int16')
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xx[3] = np.array([t_y], dtype='int16')
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if text != "" and "#" not in text:
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xx[4] = np.array([text], dtype='U{}'.format(len(text)))
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xx[5] = np.array(['c'], dtype='<U1')
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gt.append(xx)
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def gt_reading_mod(gt_dir, gt_id):
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gt = io.loadmat('%s/poly_gt_img%s.mat' % (gt_dir, gt_id))
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gt = gt['polygt']
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return gt
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def detection_filtering(detections, groundtruths, threshold=0.5):
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@ -101,7 +80,7 @@ def get_socre(gt_dict, pred_dict):
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input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
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and (input_id != 'Deteval_result_non_curved.txt'):
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detections = input_reading_mod(pred_dict)
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groundtruths = gt_reading_mod(gt_dict)
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groundtruths = gt_reading_mod(gt_dir, img_id).tolist()
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detections = detection_filtering(
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detections,
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groundtruths) # filters detections overlapping with DC area
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