fix config add e2e_ch.md e2e_res_img623_pg
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Jethong
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8452816897
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@ -1,14 +1,17 @@
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Global:
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use_gpu: False
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use_gpu: True
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epoch_num: 600
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log_smooth_window: 20
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print_batch_step: 2
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print_batch_step: 10
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save_model_dir: ./output/pg_r50_vd_tt/
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save_epoch_step: 1
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# evaluation is run every 5000 iterationss after the 4000th iteration
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save_epoch_step: 10
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# evaluation is run every 0 iterationss after the 1000th iteration
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eval_batch_step: [ 0, 1000 ]
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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load_static_weights: False
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# 1. If pretrained_model is saved in static mode, such as classification pretrained model
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# from static branch, load_static_weights must be set as True.
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# 2. If you want to finetune the pretrained models we provide in the docs,
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# you should set load_static_weights as False.
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load_static_weights: True
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cal_metric_during_train: False
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pretrained_model:
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checkpoints:
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@ -19,7 +22,7 @@ Global:
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Architecture:
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model_type: e2e
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algorithm: PG
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algorithm: PGNet
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Transform:
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Backbone:
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name: ResNet
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@ -34,28 +37,16 @@ Architecture:
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Loss:
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name: PGLoss
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#Optimizer:
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# name: Adam
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# beta1: 0.9
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# beta2: 0.999
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# lr:
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# name: Cosine
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# learning_rate: 0.001
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# warmup_epoch: 1
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# regularizer:
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# name: 'L2'
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# factor: 0
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Optimizer:
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name: RMSProp
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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name: Piecewise
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learning_rate: 0.001
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decay_epochs: [ 40, 80, 120, 160, 200 ]
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values: [ 0.001, 0.00033, 0.0001, 0.000033, 0.00001 ]
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regularizer:
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name: 'L2'
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factor: 0.00005
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factor: 0
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PostProcess:
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name: PGPostProcess
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@ -65,45 +56,45 @@ PostProcess:
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Metric:
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name: E2EMetric
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Lexicon_Table: [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' ]
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main_indicator: f_score_e2e
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Train:
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dataset:
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name: PGDateSet
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label_file_list:
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ratio_list:
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data_format: textnet # textnet/partvgg
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Lexicon_Table: [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' ]
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label_file_list: [./train_data/total_text/train/]
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ratio_list: [1.0]
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data_format: icdar
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- PGProcessTrain:
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batch_size: 14
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data_format: icdar
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tcl_len: 64
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min_crop_size: 24
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min_text_size: 4
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max_text_size: 512
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Lexicon_Table: [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' ]
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- KeepKeys:
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keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
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loader:
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shuffle: True
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drop_last: True
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batch_size_per_card: 1
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num_workers: 8
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batch_size_per_card: 14
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num_workers: 16
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Eval:
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dataset:
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name: PGDateSet
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name: PGDataSet
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data_dir: ./train_data/
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label_file_list:
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label_file_list: [./train_data/total_text/test/]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- E2ELabelEncode:
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label_list: [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' ]
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Lexicon_Table: [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' ]
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max_len: 50
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- E2EResizeForTest:
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valid_set: totaltext
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max_side_len: 768
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@ -0,0 +1,120 @@
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# 端到端文字识别
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本节以partvgg/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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## 数据准备
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支持两种不同的数据形式textnet / icdar ，分别为四点标注数据和十四点标注数据，十四点标注数据效果要比四点标注效果好
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###数据形式为textnet
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解压数据集和下载标注文件后，PaddleOCR/train_data/part_vgg_synth/train/ 有一个文件夹和一个文件，分别是：
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```
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/PaddleOCR/train_data/part_vgg_synth/train/
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└─ image/ partvgg数据集的训练数据
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└─ train_annotation_info.txt partvgg数据集的测试标注
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```
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提供的标注文件格式如下，中间用"\t"分隔：
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```
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" 图像文件名 图像标注信息--四点标注 图像标注信息--识别标注
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119_nile_110_31 140.2 222.5 266.0 194.6 278.7 251.8 152.9 279.7 Path: 32.9 133.1 106.0 130.8 106.4 143.8 33.3 146.1 were 21.8 81.9 106.9 80.4 107.7 123.2 22.6 124.7 why
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```
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标注文件txt当中，其中每一行代表一组数据，以第一行为例。第一个代表同级目录image/下面的文件名， 后面每9个代表一组标注信息，前8个代表文本框的四个点坐标（x,y)，从左上角的点开始顺时针排列。
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最后一个代表文字的识别结果，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**
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###数据形式为icdar
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解压数据集和下载标注文件后，PaddleOCR/train_data/total_text/train/ 有两个文件夹，分别是：
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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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└─ poly/ total_text数据集的测试标注
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```
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提供的标注文件格式如下，中间用"\t"分隔：
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```
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" 图像标注信息--十四点标注数据 图像标注信息--识别标注
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1004.0,689.0,1019.0,698.0,1034.0,708.0,1049.0,718.0,1064.0,728.0,1079.0,738.0,1095.0,748.0,1094.0,774.0,1079.0,765.0,1065.0,756.0,1050.0,747.0,1036.0,738.0,1021.0,729.0,1007.0,721.0 EST
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1102.0,755.0,1116.0,764.0,1131.0,773.0,1146.0,783.0,1161.0,792.0,1176.0,801.0,1191.0,811.0,1193.0,837.0,1178.0,828.0,1164.0,819.0,1150.0,810.0,1135.0,801.0,1121.0,792.0,1107.0,784.0 1972
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```
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标注文件当中，其中每一个txt文件代表一组数据，文件名同级目录rgb/下面的文件名。以第一行为例，前面28个代表文本框的十四个点坐标（x,y)，从左上角的点开始顺时针排列。
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最后一个代表文字的识别结果，**当其内容为“###”时，表示该文本框无效，在训练时会跳过。**
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如果您想在其他数据集上训练，可以按照上述形式构建标注文件。
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## 快速启动训练
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首先下载模型backbone的pretrain model，PaddleOCR的检测模型目前支持两种backbone，分别是MobileNetV3、ResNet_vd系列，
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您可以根据需求使用[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/tree/master/ppcls/modeling/architectures)中的模型更换backbone。
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```shell
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cd PaddleOCR/
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下载ResNet50_vd的预训练模型
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wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
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# 解压预训练模型文件，以ResNet50_vd为例
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tar -xf ./pretrain_models/ResNet50_vd_ssld_pretrained.tar ./pretrain_models/
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# 注：正确解压backbone预训练权重文件后，文件夹下包含众多以网络层命名的权重文件，格式如下：
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./pretrain_models/ResNet50_vd_ssld_pretrained/
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└─ conv_last_bn_mean
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└─ conv_last_bn_offset
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└─ conv_last_bn_scale
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└─ conv_last_bn_variance
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└─ ......
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```
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#### 启动训练
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*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
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```shell
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# 单机单卡训练 e2e 模型
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python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml \
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-o Global.pretrain_weights=./pretrain_models/ResNet50_vd_ssld_pretrained/ Global.load_static_weights=True
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# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
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python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml \
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-o Global.pretrain_weights=./pretrain_models/ResNet50_vd_ssld_pretrained/ Global.load_static_weights=True
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```
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上述指令中，通过-c 选择训练使用configs/e2e/e2e_r50_vd_pg.yml配置文件。
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有关配置文件的详细解释，请参考[链接](./config.md)。
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您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
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```shell
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python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Optimizer.base_lr=0.0001
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```
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#### 断点训练
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如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
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```shell
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python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints=./your/trained/model
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```
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**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
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## 指标评估
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PaddleOCR计算三个OCR端到端相关的指标，分别是：Precision、Recall、Hmean。
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运行如下代码，根据配置文件`e2e_r50_vd_pg.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
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评估时设置后处理参数`max_side_len=768`，使用不同数据集、不同模型训练，可调整参数进行优化
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训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.checkpoints`指向保存的参数文件。
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```shell
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python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints="{path/to/weights}/best_accuracy"
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```
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## 测试端到端效果
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测试单张图像的端到端识别效果
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```shell
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python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
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```
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测试文件夹下所有图像的端到端识别效果
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```shell
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python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
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```
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@ -34,7 +34,7 @@ import paddle.distributed as dist
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from ppocr.data.imaug import transform, create_operators
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from ppocr.data.simple_dataset import SimpleDataSet
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from ppocr.data.lmdb_dataset import LMDBDataSet
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from ppocr.data.pgnet_dataset import PGDateSet
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from ppocr.data.pgnet_dataset import PGDataSet
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__all__ = ['build_dataloader', 'transform', 'create_operators']
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@ -55,8 +55,7 @@ signal.signal(signal.SIGTERM, term_mp)
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def build_dataloader(config, mode, device, logger, seed=None):
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config = copy.deepcopy(config)
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support_dict = ['SimpleDataSet', 'LMDBDateSet', 'PGDateSet']
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support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet']
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module_name = config[mode]['dataset']['name']
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assert module_name in support_dict, Exception(
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'DataSet only support {}'.format(support_dict))
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@ -35,9 +35,10 @@ class ClsLabelEncode(object):
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class E2ELabelEncode(object):
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def __init__(self, label_list, **kwargs):
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self.label_list = label_list
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self.max_len = 50
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def __init__(self, Lexicon_Table, max_len, **kwargs):
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self.Lexicon_Table = Lexicon_Table
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self.max_len = max_len
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self.pad_num = len(self.Lexicon_Table)
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def __call__(self, data):
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text_label_index_list, temp_text = [], []
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@ -46,9 +47,10 @@ class E2ELabelEncode(object):
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text = text.upper()
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temp_text = []
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for c_ in text:
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if c_ in self.label_list:
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temp_text.append(self.label_list.index(c_))
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temp_text = temp_text + [36] * (self.max_len - len(temp_text))
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if c_ in self.Lexicon_Table:
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temp_text.append(self.Lexicon_Table.index(c_))
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temp_text = temp_text + [self.pad_num] * (self.max_len -
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len(temp_text))
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text_label_index_list.append(temp_text)
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data['strs'] = np.array(text_label_index_list)
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return data
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@ -197,7 +197,6 @@ class DetResizeForTest(object):
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sys.exit(0)
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ratio_h = resize_h / float(h)
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ratio_w = resize_w / float(w)
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# return img, np.array([h, w])
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return img, [ratio_h, ratio_w]
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def resize_image_type2(self, img):
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@ -206,7 +205,6 @@ class DetResizeForTest(object):
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resize_w = w
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resize_h = h
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# Fix the longer side
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if resize_h > resize_w:
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ratio = float(self.resize_long) / resize_h
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else:
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@ -245,10 +243,8 @@ class E2EResizeForTest(object):
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return data
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def resize_image_for_totaltext(self, im, max_side_len=512):
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"""
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"""
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h, w, _ = im.shape
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h, w, _ = im.shape
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resize_w = w
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resize_h = h
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ratio = 1.25
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@ -1,4 +1,4 @@
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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -15,7 +15,6 @@
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import math
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import cv2
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import numpy as np
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import os
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__all__ = ['PGProcessTrain']
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@ -23,15 +22,11 @@ __all__ = ['PGProcessTrain']
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class PGProcessTrain(object):
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def __init__(self,
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batch_size=14,
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data_format='icdar',
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tcl_len=64,
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min_crop_size=24,
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min_text_size=10,
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max_text_size=512,
|
||||
**kwargs):
|
||||
self.batch_size = batch_size
|
||||
self.data_format = data_format
|
||||
self.tcl_len = tcl_len
|
||||
self.min_crop_size = min_crop_size
|
||||
self.min_text_size = min_text_size
|
||||
self.max_text_size = max_text_size
|
||||
|
|
@ -60,24 +55,22 @@ class PGProcessTrain(object):
|
|||
"""
|
||||
point_num = poly.shape[0]
|
||||
min_area_quad = np.zeros((4, 2), dtype=np.float32)
|
||||
if True:
|
||||
rect = cv2.minAreaRect(poly.astype(
|
||||
np.int32)) # (center (x,y), (width, height), angle of rotation)
|
||||
center_point = rect[0]
|
||||
box = np.array(cv2.boxPoints(rect))
|
||||
rect = cv2.minAreaRect(poly.astype(
|
||||
np.int32)) # (center (x,y), (width, height), angle of rotation)
|
||||
box = np.array(cv2.boxPoints(rect))
|
||||
|
||||
first_point_idx = 0
|
||||
min_dist = 1e4
|
||||
for i in range(4):
|
||||
dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \
|
||||
np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \
|
||||
np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \
|
||||
np.linalg.norm(box[(i + 3) % 4] - poly[-1])
|
||||
if dist < min_dist:
|
||||
min_dist = dist
|
||||
first_point_idx = i
|
||||
for i in range(4):
|
||||
min_area_quad[i] = box[(first_point_idx + i) % 4]
|
||||
first_point_idx = 0
|
||||
min_dist = 1e4
|
||||
for i in range(4):
|
||||
dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \
|
||||
np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \
|
||||
np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \
|
||||
np.linalg.norm(box[(i + 3) % 4] - poly[-1])
|
||||
if dist < min_dist:
|
||||
min_dist = dist
|
||||
first_point_idx = i
|
||||
for i in range(4):
|
||||
min_area_quad[i] = box[(first_point_idx + i) % 4]
|
||||
|
||||
return min_area_quad
|
||||
|
||||
|
|
@ -235,8 +228,6 @@ class PGProcessTrain(object):
|
|||
ys, xs = np.where(tmp_image > 0)
|
||||
xy_text = np.array(list(zip(xs, ys)), dtype='float32')
|
||||
|
||||
# left_center_pt = np.array(key_point_xys[0]).reshape(1, 2)
|
||||
# right_center_pt = np.array(key_point_xys[-1]).reshape(1, 2)
|
||||
left_center_pt = (
|
||||
(min_area_quad[0] - min_area_quad[1]) / 2.0).reshape(1, 2)
|
||||
right_center_pt = (
|
||||
|
|
@ -317,16 +308,6 @@ class PGProcessTrain(object):
|
|||
average_height = max(sum(height_list) / len(height_list), 1.0)
|
||||
return average_height
|
||||
|
||||
def encode(self, text):
|
||||
text_list = []
|
||||
for char in text:
|
||||
if char not in self.dict:
|
||||
continue
|
||||
text_list.append([self.dict[char]])
|
||||
if len(text_list) == 0:
|
||||
return None
|
||||
return text_list
|
||||
|
||||
def generate_tcl_ctc_label(self,
|
||||
h,
|
||||
w,
|
||||
|
|
@ -390,8 +371,6 @@ class PGProcessTrain(object):
|
|||
text_label = text_strs[poly_idx]
|
||||
text_label = self.prepare_text_label(text_label,
|
||||
self.Lexicon_Table)
|
||||
# text = text.decode('utf-8')
|
||||
# text_label_index_list = self.encode(text)
|
||||
|
||||
text_label_index_list = [[self.Lexicon_Table.index(c_)]
|
||||
for c_ in text_label
|
||||
|
|
@ -402,22 +381,18 @@ class PGProcessTrain(object):
|
|||
tcl_poly = self.poly2tcl(poly, tcl_ratio)
|
||||
tcl_quads = self.poly2quads(tcl_poly)
|
||||
poly_quads = self.poly2quads(poly)
|
||||
# stcl map
|
||||
|
||||
stcl_quads, quad_index = self.shrink_poly_along_width(
|
||||
tcl_quads,
|
||||
shrink_ratio_of_width=shrink_ratio_of_width,
|
||||
expand_height_ratio=1.0 / tcl_ratio)
|
||||
# generate tcl map
|
||||
|
||||
cv2.fillPoly(score_map,
|
||||
np.round(stcl_quads).astype(np.int32), 1.0)
|
||||
cv2.fillPoly(score_map_big,
|
||||
np.round(stcl_quads / ds_ratio).astype(np.int32),
|
||||
1.0)
|
||||
|
||||
# generate tbo map
|
||||
# tbo_tcl_poly = poly2tcl(poly, 0.5)
|
||||
# tbo_tcl_quads = poly2quads(tbo_tcl_poly)
|
||||
# for idx, quad in enumerate(tbo_tcl_quads):
|
||||
for idx, quad in enumerate(stcl_quads):
|
||||
quad_mask = np.zeros((h, w), dtype=np.float32)
|
||||
quad_mask = cv2.fillPoly(
|
||||
|
|
@ -432,7 +407,6 @@ class PGProcessTrain(object):
|
|||
score_label_map_text_label_list.append(text_pos_list_)
|
||||
|
||||
label_idx += 1
|
||||
# cv2.fillPoly(score_label_map, np.round(poly_quads[np.newaxis, :, :]).astype(np.int32), label_idx)
|
||||
cv2.fillPoly(score_label_map,
|
||||
np.round(poly_quads).astype(np.int32), label_idx)
|
||||
score_label_map_text_label_list.append(text_label_index_list)
|
||||
|
|
@ -641,8 +615,6 @@ class PGProcessTrain(object):
|
|||
d = a1 * b2 - a2 * b1
|
||||
|
||||
if d == 0:
|
||||
# print("line1", line1)
|
||||
# print("line2", line2)
|
||||
print('Cross point does not exist')
|
||||
return np.array([0, 0], dtype=np.float32)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
||||
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -18,9 +18,9 @@ from .imaug import transform, create_operators
|
|||
import random
|
||||
|
||||
|
||||
class PGDateSet(Dataset):
|
||||
class PGDataSet(Dataset):
|
||||
def __init__(self, config, mode, logger, seed=None):
|
||||
super(PGDateSet, self).__init__()
|
||||
super(PGDataSet, self).__init__()
|
||||
|
||||
self.logger = logger
|
||||
self.seed = seed
|
||||
|
|
@ -81,7 +81,9 @@ class PGDateSet(Dataset):
|
|||
"""
|
||||
info_list = im_fn.split('\t')
|
||||
img_path = ''
|
||||
for ext in ['.jpg', '.png', '.jpeg', '.JPG']:
|
||||
for ext in [
|
||||
'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'JPG'
|
||||
]:
|
||||
if os.path.exists(os.path.join(img_dir, info_list[0] + ext)):
|
||||
img_path = os.path.join(img_dir, info_list[0] + ext)
|
||||
break
|
||||
|
|
@ -111,11 +113,12 @@ class PGDateSet(Dataset):
|
|||
for idx, data_source in enumerate(file_list):
|
||||
image_files = []
|
||||
if data_format == 'icdar':
|
||||
image_files = [
|
||||
(data_source, x)
|
||||
for x in os.listdir(os.path.join(data_source, 'rgb'))
|
||||
if x.split('.')[-1] in ['jpg', 'png', 'jpeg', 'JPG']
|
||||
]
|
||||
image_files = [(data_source, x) for x in
|
||||
os.listdir(os.path.join(data_source, 'rgb'))
|
||||
if x.split('.')[-1] in [
|
||||
'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif',
|
||||
'tiff', 'gif', 'JPG'
|
||||
]]
|
||||
elif data_format == 'textnet':
|
||||
with open(data_source) as f:
|
||||
image_files = [(data_source, x.strip())
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
|
||||
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -21,20 +21,12 @@ import paddle
|
|||
import numpy as np
|
||||
import copy
|
||||
|
||||
from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
|
||||
from .det_basic_loss import DiceLoss
|
||||
|
||||
|
||||
class PGLoss(nn.Layer):
|
||||
"""
|
||||
Differentiable Binarization (DB) Loss Function
|
||||
args:
|
||||
param (dict): the super paramter for DB Loss
|
||||
"""
|
||||
|
||||
def __init__(self, alpha=5, beta=10, eps=1e-6, **kwargs):
|
||||
def __init__(self, eps=1e-6, **kwargs):
|
||||
super(PGLoss, self).__init__()
|
||||
self.alpha = alpha
|
||||
self.beta = beta
|
||||
self.dice_loss = DiceLoss(eps=eps)
|
||||
|
||||
def org_tcl_rois(self, batch_size, pos_lists, pos_masks, label_lists):
|
||||
|
|
@ -86,27 +78,30 @@ class PGLoss(nn.Layer):
|
|||
return pos_lists_, pos_masks_, label_lists_
|
||||
|
||||
def pre_process(self, label_list, pos_list, pos_mask):
|
||||
max_len = 30 # the max texts in a single image
|
||||
max_str_len = 50 # the max len in a single text
|
||||
pad_num = 36 # padding num
|
||||
label_list = label_list.numpy()
|
||||
b, h, w, c = label_list.shape
|
||||
batch, _, _, _ = label_list.shape
|
||||
pos_list = pos_list.numpy()
|
||||
pos_mask = pos_mask.numpy()
|
||||
pos_list_t = []
|
||||
pos_mask_t = []
|
||||
label_list_t = []
|
||||
for i in range(b):
|
||||
for j in range(30):
|
||||
for i in range(batch):
|
||||
for j in range(max_len):
|
||||
if pos_mask[i, j].any():
|
||||
pos_list_t.append(pos_list[i][j])
|
||||
pos_mask_t.append(pos_mask[i][j])
|
||||
label_list_t.append(label_list[i][j])
|
||||
pos_list, pos_mask, label_list = self.org_tcl_rois(
|
||||
b, pos_list_t, pos_mask_t, label_list_t)
|
||||
batch, pos_list_t, pos_mask_t, label_list_t)
|
||||
label = []
|
||||
tt = [l.tolist() for l in label_list]
|
||||
for i in range(64):
|
||||
for i in range(batch):
|
||||
k = 0
|
||||
for j in range(50):
|
||||
if tt[i][j][0] != 36:
|
||||
for j in range(max_str_len):
|
||||
if tt[i][j][0] != pad_num:
|
||||
k += 1
|
||||
else:
|
||||
break
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -22,12 +22,9 @@ from ppocr.utils.e2e_metric.Deteval import *
|
|||
|
||||
|
||||
class E2EMetric(object):
|
||||
def __init__(self, main_indicator='f_score_e2e', **kwargs):
|
||||
self.label_list = [
|
||||
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C',
|
||||
'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',
|
||||
'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
|
||||
]
|
||||
def __init__(self, Lexicon_Table, main_indicator='f_score_e2e', **kwargs):
|
||||
self.label_list = Lexicon_Table
|
||||
self.max_index = len(self.label_list)
|
||||
self.main_indicator = main_indicator
|
||||
self.reset()
|
||||
|
||||
|
|
@ -40,12 +37,12 @@ class E2EMetric(object):
|
|||
for temp_list in temp_gt_strs_batch:
|
||||
t = ""
|
||||
for index in temp_list:
|
||||
if index < 36:
|
||||
if index < self.max_index:
|
||||
t += self.label_list[index]
|
||||
gt_strs_batch.append(t)
|
||||
|
||||
for pred, gt_polyons, gt_strs, ignore_tags in zip(
|
||||
preds, gt_polyons_batch, gt_strs_batch, ignore_tags_batch):
|
||||
[preds], gt_polyons_batch, [gt_strs_batch], ignore_tags_batch):
|
||||
# prepare gt
|
||||
gt_info_list = [{
|
||||
'points': gt_polyon,
|
||||
|
|
@ -57,7 +54,7 @@ class E2EMetric(object):
|
|||
e2e_info_list = [{
|
||||
'points': det_polyon,
|
||||
'text': pred_str
|
||||
} for det_polyon, pred_str in zip(pred['points'], preds['strs'])]
|
||||
} for det_polyon, pred_str in zip(pred['points'], pred['strs'])]
|
||||
result = get_socre(gt_info_list, e2e_info_list)
|
||||
self.results.append(result)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
||||
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -62,8 +62,6 @@ class ConvBNLayer(nn.Layer):
|
|||
moving_variance_name=bn_name + '_variance')
|
||||
|
||||
def forward(self, inputs):
|
||||
# if self.is_vd_mode:
|
||||
# inputs = self._pool2d_avg(inputs)
|
||||
y = self._conv(inputs)
|
||||
y = self._batch_norm(y)
|
||||
return y
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
|
||||
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -179,7 +179,7 @@ class PGHead(nn.Layer):
|
|||
name="conv_f_char{}".format(5))
|
||||
self.conv3 = nn.Conv2D(
|
||||
in_channels=256,
|
||||
out_channels=6625,
|
||||
out_channels=37,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
|
||||
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -60,8 +60,6 @@ class ConvBNLayer(nn.Layer):
|
|||
use_global_stats=False)
|
||||
|
||||
def forward(self, inputs):
|
||||
# if self.is_vd_mode:
|
||||
# inputs = self._pool2d_avg(inputs)
|
||||
y = self._conv(inputs)
|
||||
y = self._batch_norm(y)
|
||||
return y
|
||||
|
|
@ -112,7 +110,6 @@ class PGFPN(nn.Layer):
|
|||
num_inputs = [2048, 2048, 1024, 512, 256]
|
||||
num_outputs = [256, 256, 192, 192, 128]
|
||||
self.out_channels = 128
|
||||
# print(in_channels)
|
||||
self.conv_bn_layer_1 = ConvBNLayer(
|
||||
in_channels=3,
|
||||
out_channels=32,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -23,14 +23,9 @@ __dir__ = os.path.dirname(__file__)
|
|||
sys.path.append(__dir__)
|
||||
sys.path.append(os.path.join(__dir__, '..'))
|
||||
|
||||
import numpy as np
|
||||
from .locality_aware_nms import nms_locality
|
||||
from ppocr.utils.e2e_utils.extract_textpoint import *
|
||||
from ppocr.utils.e2e_utils.ski_thin import *
|
||||
from ppocr.utils.e2e_utils.visual import *
|
||||
import paddle
|
||||
import cv2
|
||||
import time
|
||||
|
||||
|
||||
class PGPostProcess(object):
|
||||
|
|
@ -115,7 +110,6 @@ class PGPostProcess(object):
|
|||
if len(yx_center_line) == 1:
|
||||
yx_center_line.append(yx_center_line[-1])
|
||||
|
||||
# expand corresponding offset for total-text.
|
||||
offset_expand = 1.0
|
||||
if self.valid_set == 'totaltext':
|
||||
offset_expand = 1.2
|
||||
|
|
@ -137,7 +131,6 @@ class PGPostProcess(object):
|
|||
[ratio_w, ratio_h]).reshape(-1, 2)
|
||||
point_pair_list.append(point_pair)
|
||||
|
||||
# for visualization
|
||||
all_point_list.append([
|
||||
int(round(x * 4.0 / ratio_w)),
|
||||
int(round(y * 4.0 / ratio_h))
|
||||
|
|
@ -145,7 +138,6 @@ class PGPostProcess(object):
|
|||
all_point_pair_list.append(point_pair.round().astype(np.int32)
|
||||
.tolist())
|
||||
|
||||
# ndarry: (x, 2)
|
||||
detected_poly, pair_length_info = point_pair2poly(point_pair_list)
|
||||
detected_poly = expand_poly_along_width(
|
||||
detected_poly, shrink_ratio_of_width=0.2)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -11,42 +11,27 @@
|
|||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import numpy as np
|
||||
from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
|
||||
|
||||
try: # python2
|
||||
range = xrange
|
||||
except Exception:
|
||||
# python3
|
||||
range = range
|
||||
"""
|
||||
Input format: y0,x0, ..... yn,xn. Each detection is separated by the end of line token ('\n')'
|
||||
"""
|
||||
|
||||
# if len(sys.argv) != 4:
|
||||
# print('\n usage: test.py pred_dir gt_dir savefile')
|
||||
# sys.exit()
|
||||
|
||||
|
||||
def get_socre(gt_dict, pred_dict):
|
||||
# allInputs = listdir(input_dir)
|
||||
allInputs = 1
|
||||
|
||||
def input_reading_mod(pred_dict, input):
|
||||
def input_reading_mod(pred_dict):
|
||||
"""This helper reads input from txt files"""
|
||||
det = []
|
||||
n = len(pred_dict)
|
||||
for i in range(n):
|
||||
points = pred_dict[i]['points']
|
||||
text = pred_dict[i]['text']
|
||||
# for i in range(len(points)):
|
||||
point = ",".join(map(str, points.reshape(-1, )))
|
||||
det.append([point, text])
|
||||
return det
|
||||
|
||||
def gt_reading_mod(gt_dict, gt_id):
|
||||
def gt_reading_mod(gt_dict):
|
||||
"""This helper reads groundtruths from mat files"""
|
||||
# gt_id = gt_id.split('.')[0]
|
||||
gt = []
|
||||
n = len(gt_dict)
|
||||
for i in range(n):
|
||||
|
|
@ -74,23 +59,12 @@ def get_socre(gt_dict, pred_dict):
|
|||
|
||||
def detection_filtering(detections, groundtruths, threshold=0.5):
|
||||
for gt_id, gt in enumerate(groundtruths):
|
||||
print
|
||||
"liushanshan gt[1] = {}".format(gt[1])
|
||||
print
|
||||
"liushanshan gt[2] = {}".format(gt[2])
|
||||
print
|
||||
"liushanshan gt[3] = {}".format(gt[3])
|
||||
print
|
||||
"liushanshan gt[4] = {}".format(gt[4])
|
||||
print
|
||||
"liushanshan gt[5] = {}".format(gt[5])
|
||||
if (gt[5] == '#') and (gt[1].shape[1] > 1):
|
||||
gt_x = list(map(int, np.squeeze(gt[1])))
|
||||
gt_y = list(map(int, np.squeeze(gt[3])))
|
||||
for det_id, detection in enumerate(detections):
|
||||
detection_orig = detection
|
||||
detection = [float(x) for x in detection[0].split(',')]
|
||||
# detection = detection.split(',')
|
||||
detection = list(map(int, detection))
|
||||
det_x = detection[0::2]
|
||||
det_y = detection[1::2]
|
||||
|
|
@ -105,18 +79,10 @@ def get_socre(gt_dict, pred_dict):
|
|||
"""
|
||||
sigma = inter_area / gt_area
|
||||
"""
|
||||
# print(area_of_intersection(det_x, det_y, gt_x, gt_y))
|
||||
return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
|
||||
area(gt_x, gt_y)), 2)
|
||||
|
||||
def tau_calculation(det_x, det_y, gt_x, gt_y):
|
||||
"""
|
||||
tau = inter_area / det_area
|
||||
"""
|
||||
# print "liushanshan det_x {}".format(det_x)
|
||||
# print "liushanshan det_y {}".format(det_y)
|
||||
# print "liushanshan area {}".format(area(det_x, det_y))
|
||||
# print "liushanshan tau = {}".format(np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) / area(det_x, det_y)), 2))
|
||||
if area(det_x, det_y) == 0.0:
|
||||
return 0
|
||||
return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
|
||||
|
|
@ -141,10 +107,8 @@ def get_socre(gt_dict, pred_dict):
|
|||
input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
|
||||
input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
|
||||
and (input_id != 'Deteval_result_non_curved.txt'):
|
||||
print(input_id)
|
||||
detections = input_reading_mod(pred_dict, input_id)
|
||||
# print "liushanshan detections = {}".format(detections)
|
||||
groundtruths = gt_reading_mod(gt_dict, input_id)
|
||||
detections = input_reading_mod(pred_dict)
|
||||
groundtruths = gt_reading_mod(gt_dict)
|
||||
detections = detection_filtering(
|
||||
detections,
|
||||
groundtruths) # filters detections overlapping with DC area
|
||||
|
|
@ -187,10 +151,6 @@ def get_socre(gt_dict, pred_dict):
|
|||
global_tau.append(local_tau_table)
|
||||
global_pred_str.append(local_pred_str)
|
||||
global_gt_str.append(local_gt_str)
|
||||
print
|
||||
"liushanshan global_pred_str = {}".format(global_pred_str)
|
||||
print
|
||||
"liushanshan global_gt_str = {}".format(global_gt_str)
|
||||
|
||||
global_accumulative_recall = 0
|
||||
global_accumulative_precision = 0
|
||||
|
|
@ -236,17 +196,11 @@ def get_socre(gt_dict, pred_dict):
|
|||
gt_flag[0, gt_id] = 1
|
||||
matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
|
||||
# recg start
|
||||
print
|
||||
"liushanshan one to one det_id = {}".format(matched_det_id)
|
||||
print
|
||||
"liushanshan one to one gt_id = {}".format(gt_id)
|
||||
|
||||
gt_str_cur = global_gt_str[idy][gt_id]
|
||||
pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
|
||||
0]]
|
||||
print
|
||||
"liushanshan one to one gt_str_cur = {}".format(gt_str_cur)
|
||||
print
|
||||
"liushanshan one to one pred_str_cur = {}".format(pred_str_cur)
|
||||
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
else:
|
||||
|
|
@ -290,20 +244,10 @@ def get_socre(gt_dict, pred_dict):
|
|||
gt_flag[0, gt_id] = 1
|
||||
det_flag[0, qualified_tau_candidates] = 1
|
||||
# recg start
|
||||
print
|
||||
"liushanshan one to many det_id = {}".format(
|
||||
qualified_tau_candidates)
|
||||
print
|
||||
"liushanshan one to many gt_id = {}".format(gt_id)
|
||||
gt_str_cur = global_gt_str[idy][gt_id]
|
||||
pred_str_cur = global_pred_str[idy][
|
||||
qualified_tau_candidates[0].tolist()[0]]
|
||||
print
|
||||
"liushanshan one to many gt_str_cur = {}".format(
|
||||
gt_str_cur)
|
||||
print
|
||||
"liushanshan one to many pred_str_cur = {}".format(
|
||||
pred_str_cur)
|
||||
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
else:
|
||||
|
|
@ -315,19 +259,11 @@ def get_socre(gt_dict, pred_dict):
|
|||
gt_flag[0, gt_id] = 1
|
||||
det_flag[0, qualified_tau_candidates] = 1
|
||||
# recg start
|
||||
print
|
||||
"liushanshan one to many det_id = {}".format(
|
||||
qualified_tau_candidates)
|
||||
print
|
||||
"liushanshan one to many gt_id = {}".format(gt_id)
|
||||
|
||||
gt_str_cur = global_gt_str[idy][gt_id]
|
||||
pred_str_cur = global_pred_str[idy][
|
||||
qualified_tau_candidates[0].tolist()[0]]
|
||||
print
|
||||
"liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
|
||||
print
|
||||
"liushanshan one to many pred_str_cur = {}".format(
|
||||
pred_str_cur)
|
||||
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
else:
|
||||
|
|
@ -377,25 +313,14 @@ def get_socre(gt_dict, pred_dict):
|
|||
gt_flag[0, qualified_sigma_candidates] = 1
|
||||
det_flag[0, det_id] = 1
|
||||
# recg start
|
||||
print
|
||||
"liushanshan many to one det_id = {}".format(det_id)
|
||||
print
|
||||
"liushanshan many to one gt_id = {}".format(
|
||||
qualified_sigma_candidates)
|
||||
pred_str_cur = global_pred_str[idy][det_id]
|
||||
gt_len = len(qualified_sigma_candidates[0])
|
||||
for idx in range(gt_len):
|
||||
ele_gt_id = qualified_sigma_candidates[0].tolist()[
|
||||
idx]
|
||||
if not global_gt_str[idy].has_key(ele_gt_id):
|
||||
if ele_gt_id not in global_gt_str[idy]:
|
||||
continue
|
||||
gt_str_cur = global_gt_str[idy][ele_gt_id]
|
||||
print
|
||||
"liushanshan many to one gt_str_cur = {}".format(
|
||||
gt_str_cur)
|
||||
print
|
||||
"liushanshan many to one pred_str_cur = {}".format(
|
||||
pred_str_cur)
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
break
|
||||
|
|
@ -409,24 +334,14 @@ def get_socre(gt_dict, pred_dict):
|
|||
det_flag[0, det_id] = 1
|
||||
gt_flag[0, qualified_sigma_candidates] = 1
|
||||
# recg start
|
||||
print
|
||||
"liushanshan many to one det_id = {}".format(det_id)
|
||||
print
|
||||
"liushanshan many to one gt_id = {}".format(
|
||||
qualified_sigma_candidates)
|
||||
|
||||
pred_str_cur = global_pred_str[idy][det_id]
|
||||
gt_len = len(qualified_sigma_candidates[0])
|
||||
for idx in range(gt_len):
|
||||
ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
|
||||
if not global_gt_str[idy].has_key(ele_gt_id):
|
||||
if ele_gt_id not in global_gt_str[idy]:
|
||||
continue
|
||||
gt_str_cur = global_gt_str[idy][ele_gt_id]
|
||||
print
|
||||
"liushanshan many to one gt_str_cur = {}".format(
|
||||
gt_str_cur)
|
||||
print
|
||||
"liushanshan many to one pred_str_cur = {}".format(
|
||||
pred_str_cur)
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
break
|
||||
|
|
@ -434,9 +349,6 @@ def get_socre(gt_dict, pred_dict):
|
|||
if pred_str_cur.lower() == gt_str_cur.lower():
|
||||
hit_str_num += 1
|
||||
break
|
||||
else:
|
||||
print
|
||||
'no match'
|
||||
# recg end
|
||||
|
||||
global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
|
||||
|
|
@ -448,7 +360,6 @@ def get_socre(gt_dict, pred_dict):
|
|||
|
||||
single_data = {}
|
||||
for idx in range(len(global_sigma)):
|
||||
# print(allInputs[idx])
|
||||
local_sigma_table = global_sigma[idx]
|
||||
local_tau_table = global_tau[idx]
|
||||
|
||||
|
|
@ -504,8 +415,6 @@ def get_socre(gt_dict, pred_dict):
|
|||
except ZeroDivisionError:
|
||||
local_f_score = 0
|
||||
|
||||
# temp = ('%s: Recall=%.4f, Precision=%.4f, f_score=%.4f\n' % (
|
||||
# allInputs[idx], local_recall, local_precision, local_f_score))
|
||||
single_data['sigma'] = global_sigma
|
||||
single_data['global_tau'] = global_tau
|
||||
single_data['global_pred_str'] = global_pred_str
|
||||
|
|
@ -575,17 +484,9 @@ def combine_results(all_data):
|
|||
gt_flag[0, gt_id] = 1
|
||||
matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
|
||||
# recg start
|
||||
print
|
||||
"liushanshan one to one det_id = {}".format(matched_det_id)
|
||||
print
|
||||
"liushanshan one to one gt_id = {}".format(gt_id)
|
||||
gt_str_cur = global_gt_str[idy][gt_id]
|
||||
pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
|
||||
0]]
|
||||
print
|
||||
"liushanshan one to one gt_str_cur = {}".format(gt_str_cur)
|
||||
print
|
||||
"liushanshan one to one pred_str_cur = {}".format(pred_str_cur)
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
else:
|
||||
|
|
@ -629,20 +530,9 @@ def combine_results(all_data):
|
|||
gt_flag[0, gt_id] = 1
|
||||
det_flag[0, qualified_tau_candidates] = 1
|
||||
# recg start
|
||||
print
|
||||
"liushanshan one to many det_id = {}".format(
|
||||
qualified_tau_candidates)
|
||||
print
|
||||
"liushanshan one to many gt_id = {}".format(gt_id)
|
||||
gt_str_cur = global_gt_str[idy][gt_id]
|
||||
pred_str_cur = global_pred_str[idy][
|
||||
qualified_tau_candidates[0].tolist()[0]]
|
||||
print
|
||||
"liushanshan one to many gt_str_cur = {}".format(
|
||||
gt_str_cur)
|
||||
print
|
||||
"liushanshan one to many pred_str_cur = {}".format(
|
||||
pred_str_cur)
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
else:
|
||||
|
|
@ -654,19 +544,9 @@ def combine_results(all_data):
|
|||
gt_flag[0, gt_id] = 1
|
||||
det_flag[0, qualified_tau_candidates] = 1
|
||||
# recg start
|
||||
print
|
||||
"liushanshan one to many det_id = {}".format(
|
||||
qualified_tau_candidates)
|
||||
print
|
||||
"liushanshan one to many gt_id = {}".format(gt_id)
|
||||
gt_str_cur = global_gt_str[idy][gt_id]
|
||||
pred_str_cur = global_pred_str[idy][
|
||||
qualified_tau_candidates[0].tolist()[0]]
|
||||
print
|
||||
"liushanshan one to many gt_str_cur = {}".format(gt_str_cur)
|
||||
print
|
||||
"liushanshan one to many pred_str_cur = {}".format(
|
||||
pred_str_cur)
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
else:
|
||||
|
|
@ -716,11 +596,6 @@ def combine_results(all_data):
|
|||
gt_flag[0, qualified_sigma_candidates] = 1
|
||||
det_flag[0, det_id] = 1
|
||||
# recg start
|
||||
print
|
||||
"liushanshan many to one det_id = {}".format(det_id)
|
||||
print
|
||||
"liushanshan many to one gt_id = {}".format(
|
||||
qualified_sigma_candidates)
|
||||
pred_str_cur = global_pred_str[idy][det_id]
|
||||
gt_len = len(qualified_sigma_candidates[0])
|
||||
for idx in range(gt_len):
|
||||
|
|
@ -729,12 +604,6 @@ def combine_results(all_data):
|
|||
if ele_gt_id not in global_gt_str[idy]:
|
||||
continue
|
||||
gt_str_cur = global_gt_str[idy][ele_gt_id]
|
||||
print
|
||||
"liushanshan many to one gt_str_cur = {}".format(
|
||||
gt_str_cur)
|
||||
print
|
||||
"liushanshan many to one pred_str_cur = {}".format(
|
||||
pred_str_cur)
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
break
|
||||
|
|
@ -748,24 +617,13 @@ def combine_results(all_data):
|
|||
det_flag[0, det_id] = 1
|
||||
gt_flag[0, qualified_sigma_candidates] = 1
|
||||
# recg start
|
||||
print
|
||||
"liushanshan many to one det_id = {}".format(det_id)
|
||||
print
|
||||
"liushanshan many to one gt_id = {}".format(
|
||||
qualified_sigma_candidates)
|
||||
pred_str_cur = global_pred_str[idy][det_id]
|
||||
gt_len = len(qualified_sigma_candidates[0])
|
||||
for idx in range(gt_len):
|
||||
ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
|
||||
if not global_gt_str[idy].has_key(ele_gt_id):
|
||||
if ele_gt_id not in global_gt_str[idy]:
|
||||
continue
|
||||
gt_str_cur = global_gt_str[idy][ele_gt_id]
|
||||
print
|
||||
"liushanshan many to one gt_str_cur = {}".format(
|
||||
gt_str_cur)
|
||||
print
|
||||
"liushanshan many to one pred_str_cur = {}".format(
|
||||
pred_str_cur)
|
||||
if pred_str_cur == gt_str_cur:
|
||||
hit_str_num += 1
|
||||
break
|
||||
|
|
@ -773,9 +631,6 @@ def combine_results(all_data):
|
|||
if pred_str_cur.lower() == gt_str_cur.lower():
|
||||
hit_str_num += 1
|
||||
break
|
||||
else:
|
||||
print
|
||||
'no match'
|
||||
# recg end
|
||||
|
||||
global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -16,14 +16,12 @@ from __future__ import absolute_import
|
|||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import cv2
|
||||
import time
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
from itertools import groupby
|
||||
from ppocr.utils.e2e_utils.ski_thin import thin
|
||||
from skimage.morphology._skeletonize import thin
|
||||
|
||||
|
||||
def softmax(logits):
|
||||
|
|
@ -518,28 +516,6 @@ def generate_pivot_list_tt_inference(p_score,
|
|||
continue
|
||||
pos_list_sorted = sort_and_expand_with_direction_v2(
|
||||
pos_list, f_direction, p_tcl_map)
|
||||
# pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
|
||||
pos_list_sorted_with_id = add_id(pos_list_sorted, image_id=image_id)
|
||||
all_pos_yxs.append(pos_list_sorted_with_id)
|
||||
return all_pos_yxs
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
np.random.seed(0)
|
||||
import time
|
||||
|
||||
logits_map = np.random.random([10, 20, 33])
|
||||
# a list of [x, y]
|
||||
instance_gather_info_1 = [(2, 3), (2, 4), (3, 5)]
|
||||
instance_gather_info_2 = [(15, 6), (15, 7), (18, 8)]
|
||||
instance_gather_info_3 = [(8, 8), (8, 8), (8, 8)]
|
||||
gather_info_list = [
|
||||
instance_gather_info_1, instance_gather_info_2, instance_gather_info_3
|
||||
]
|
||||
|
||||
time0 = time.time()
|
||||
res = ctc_decoder_for_image(
|
||||
gather_info_list, logits_map, keep_blank_in_idxs=True)
|
||||
print(res)
|
||||
print('cost {}'.format(time.time() - time0))
|
||||
print('--' * 20)
|
||||
|
|
|
|||
|
|
@ -1,136 +0,0 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import numpy as np
|
||||
from scipy import ndimage as ndi
|
||||
|
||||
G123_LUT = np.array(
|
||||
[
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,
|
||||
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
|
||||
1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
|
||||
1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0
|
||||
],
|
||||
dtype=np.bool)
|
||||
|
||||
G123P_LUT = np.array(
|
||||
[
|
||||
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1,
|
||||
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
|
||||
],
|
||||
dtype=np.bool)
|
||||
|
||||
|
||||
def thin(image, max_iter=None):
|
||||
"""
|
||||
Perform morphological thinning of a binary image.
|
||||
Parameters
|
||||
----------
|
||||
image : binary (M, N) ndarray
|
||||
The image to be thinned.
|
||||
max_iter : int, number of iterations, optional
|
||||
Regardless of the value of this parameter, the thinned image
|
||||
is returned immediately if an iteration produces no change.
|
||||
If this parameter is specified it thus sets an upper bound on
|
||||
the number of iterations performed.
|
||||
Returns
|
||||
-------
|
||||
out : ndarray of bool
|
||||
Thinned image.
|
||||
See also
|
||||
--------
|
||||
skeletonize, medial_axis
|
||||
Notes
|
||||
-----
|
||||
This algorithm [1]_ works by making multiple passes over the image,
|
||||
removing pixels matching a set of criteria designed to thin
|
||||
connected regions while preserving eight-connected components and
|
||||
2 x 2 squares [2]_. In each of the two sub-iterations the algorithm
|
||||
correlates the intermediate skeleton image with a neighborhood mask,
|
||||
then looks up each neighborhood in a lookup table indicating whether
|
||||
the central pixel should be deleted in that sub-iteration.
|
||||
References
|
||||
----------
|
||||
.. [1] Z. Guo and R. W. Hall, "Parallel thinning with
|
||||
two-subiteration algorithms," Comm. ACM, vol. 32, no. 3,
|
||||
pp. 359-373, 1989. :DOI:`10.1145/62065.62074`
|
||||
.. [2] Lam, L., Seong-Whan Lee, and Ching Y. Suen, "Thinning
|
||||
Methodologies-A Comprehensive Survey," IEEE Transactions on
|
||||
Pattern Analysis and Machine Intelligence, Vol 14, No. 9,
|
||||
p. 879, 1992. :DOI:`10.1109/34.161346`
|
||||
Examples
|
||||
--------
|
||||
>>> square = np.zeros((7, 7), dtype=np.uint8)
|
||||
>>> square[1:-1, 2:-2] = 1
|
||||
>>> square[0, 1] = 1
|
||||
>>> square
|
||||
array([[0, 1, 0, 0, 0, 0, 0],
|
||||
[0, 0, 1, 1, 1, 0, 0],
|
||||
[0, 0, 1, 1, 1, 0, 0],
|
||||
[0, 0, 1, 1, 1, 0, 0],
|
||||
[0, 0, 1, 1, 1, 0, 0],
|
||||
[0, 0, 1, 1, 1, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
|
||||
>>> skel = thin(square)
|
||||
>>> skel.astype(np.uint8)
|
||||
array([[0, 1, 0, 0, 0, 0, 0],
|
||||
[0, 0, 1, 0, 0, 0, 0],
|
||||
[0, 0, 0, 1, 0, 0, 0],
|
||||
[0, 0, 0, 1, 0, 0, 0],
|
||||
[0, 0, 0, 1, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
|
||||
"""
|
||||
# convert image to uint8 with values in {0, 1}
|
||||
skel = np.asanyarray(image, dtype=bool).astype(np.uint8)
|
||||
|
||||
# neighborhood mask
|
||||
mask = np.array([[8, 4, 2], [16, 0, 1], [32, 64, 128]], dtype=np.uint8)
|
||||
|
||||
# iterate until convergence, up to the iteration limit
|
||||
max_iter = max_iter or np.inf
|
||||
n_iter = 0
|
||||
n_pts_old, n_pts_new = np.inf, np.sum(skel)
|
||||
while n_pts_old != n_pts_new and n_iter < max_iter:
|
||||
n_pts_old = n_pts_new
|
||||
|
||||
# perform the two "subiterations" described in the paper
|
||||
for lut in [G123_LUT, G123P_LUT]:
|
||||
# correlate image with neighborhood mask
|
||||
N = ndi.correlate(skel, mask, mode='constant')
|
||||
# take deletion decision from this subiteration's LUT
|
||||
D = np.take(lut, N)
|
||||
# perform deletion
|
||||
skel[D] = 0
|
||||
|
||||
n_pts_new = np.sum(skel) # count points after thinning
|
||||
n_iter += 1
|
||||
|
||||
return skel.astype(np.bool)
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
|
|
@ -50,7 +50,7 @@ def resize_image(im, max_side_len=512):
|
|||
def resize_image_min(im, max_side_len=512):
|
||||
"""
|
||||
"""
|
||||
print('--> Using resize_image_min')
|
||||
# print('--> Using resize_image_min')
|
||||
h, w, _ = im.shape
|
||||
|
||||
resize_w = w
|
||||
|
|
|
|||
|
|
@ -45,8 +45,14 @@ def draw_e2e_res(dt_boxes, strs, config, img, img_name):
|
|||
for box, str in zip(dt_boxes, strs):
|
||||
box = box.astype(np.int32).reshape((-1, 1, 2))
|
||||
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
|
||||
cv2.putText(src_im, str, org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
|
||||
fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.7, color=(0, 255, 0), thickness=1)
|
||||
cv2.putText(
|
||||
src_im,
|
||||
str,
|
||||
org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
|
||||
fontFace=cv2.FONT_HERSHEY_COMPLEX,
|
||||
fontScale=0.7,
|
||||
color=(0, 255, 0),
|
||||
thickness=1)
|
||||
save_det_path = os.path.dirname(config['Global'][
|
||||
'save_res_path']) + "/e2e_results/"
|
||||
if not os.path.exists(save_det_path):
|
||||
|
|
@ -55,6 +61,7 @@ def draw_e2e_res(dt_boxes, strs, config, img, img_name):
|
|||
cv2.imwrite(save_path, src_im)
|
||||
logger.info("The e2e Image saved in {}".format(save_path))
|
||||
|
||||
|
||||
def main():
|
||||
global_config = config['Global']
|
||||
|
||||
|
|
@ -111,4 +118,4 @@ def main():
|
|||
|
||||
if __name__ == '__main__':
|
||||
config, device, logger, vdl_writer = program.preprocess()
|
||||
main()
|
||||
main()
|
||||
|
|
|
|||
|
|
@ -375,7 +375,7 @@ def preprocess(is_train=False):
|
|||
alg = config['Architecture']['algorithm']
|
||||
assert alg in [
|
||||
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
|
||||
'CLS', 'PG'
|
||||
'CLS', 'PGNet'
|
||||
]
|
||||
|
||||
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
|
||||
|
|
|
|||
Loading…
Reference in New Issue